system

Abstract

The system according to this embodiment aims to support working professionals in effectively performing reskilling. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a support unit, and an automation unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a direction for reskilling based on the analysis results obtained by the analysis unit. The support unit supports the reskilling program proposed by the proposal unit. The automation unit automates the application procedure for the qualification exam based on the reskilling program supported by the support unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for working people to find an appropriate direction when performing reskilling or to receive support for reskilling programs.

[0005] The system according to the embodiment aims to assist working people in effectively performing reskilling.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a support unit, and an automation unit. The data collection unit collects user information. The analysis unit analyzes the information collected by the data collection unit. The proposal unit proposes a direction for reskilling based on the analysis results obtained by the analysis unit. The support unit supports the reskilling program proposed by the proposal unit. The automation unit automates the application process for the certification exam based on the reskilling program supported by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment can support working professionals in effectively performing reskilling. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The reskilling navigation system according to an embodiment of the present invention is an agent service specifically designed to provide reskilling navigation for working professionals. This reskilling navigation system conducts a detailed interview with the user regarding their wishes and current skills, and proposes a direction for reskilling based on that information. The proposed content includes the direction of skills, difficulty level, acquisition period, future potential of the skills, and costs. Next, it provides support for the reskilling program. Specifically, it proposes highly-rated textbooks, online courses, seminars, etc., and provides a detailed cost estimate for the proposed program. It also provides support for progress through the creation of learning plans, progress management, and regular feedback. Furthermore, it automates the application process for certification exams, managing the schedule for certification exams specified by the user and submitting applications at the appropriate time. For example, the reskilling navigation system caters to a wide range of targets, including working professionals considering a career change, mid-level and managerial employees aiming for career advancement, housewives and parents raising children considering returning to work, young employees and new graduates who want to improve their skills, people who want to deepen their knowledge as a hobby or side job, and seniors starting new activities after retirement. The challenges faced by the target audience include not knowing the latest skill sets, not knowing how to choose skills and qualifications, finding it difficult to acquire new knowledge due to a long break from learning, not knowing how to estimate time and cost, being unsure about the effectiveness of the program, difficulty balancing work and family life, and lack of confidence in re-employment. To address these challenges, AI provides an optimal learning plan for each user, enabling efficient learning. Furthermore, the plan is updated and provided in real time according to learning progress and changes in circumstances. In addition, it provides opportunities to speak directly with top industry experts to support real-time problem solving. By offering customized programs for seniors and enabling users with similar goals to support each other, it is also possible to improve the quality of learning and motivation. In this way, the reskilling navigation system can provide efficient reskilling navigation for working adults.

[0029] The reskilling navigation system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a support unit, and an automation unit. The collection unit collects user information. User information includes, but is not limited to, personal information, learning history, and skill sets. The collection unit collects user information using, for example, questionnaires. The collection unit can also collect user information using sensors. Furthermore, the collection unit can also collect user information using log data. For example, the collection unit collects information about the user's wishes and current skills through questionnaires. When using sensors, the collection unit can collect user behavior data. When using log data, the collection unit can collect the user's past learning history. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, data mining techniques, but is not limited to such examples. For example, the analysis unit analyzes user information using data mining techniques. The analysis unit can also analyze user information using statistical analysis techniques. Furthermore, the analysis unit can also analyze user information using machine learning techniques. For example, the analysis unit analyzes the user's skill set using data mining techniques. When using statistical analysis techniques, the analysis unit can analyze the user's learning history. When using machine learning techniques, the analysis unit can analyze the user's skill set and learning history. The proposal unit proposes a direction for reskilling based on the analysis results obtained by the analysis unit. Proposals are made based on, for example, industry trends or individual career goals, but are not limited to these examples. For example, the proposal unit proposes a direction for reskilling based on industry trends. The proposal unit can also propose a direction for reskilling based on individual career goals. Furthermore, the proposal unit can also propose a direction for reskilling based on the user's interests and concerns. For example, the proposal unit analyzes industry trends and proposes a skill direction suitable for the user. When based on individual career goals, the proposal unit can propose a skill direction that matches the user's career goals. When based on the user's interests and concerns, the proposal unit can propose a skill direction that matches the user's interests and concerns.The Support Department supports the reskilling program proposed by the Proposal Department. Support includes, but is not limited to, mentoring, provision of learning materials, and progress management. For example, the Support Department can support users through mentoring. The Support Department can also support users by providing learning materials. Furthermore, the Support Department can support users through progress management. For example, the Support Department supports users' learning through mentoring. In the case of providing learning materials, the Support Department can provide materials suitable for the user. In the case of progress management, the Support Department can manage the user's learning progress and provide appropriate feedback. The Automation Department automates the application process for certification exams based on the reskilling program supported by the Support Department. Automation includes, but is not limited to, automatic online form completion and schedule management. For example, the Automation Department automatically completes online forms to submit certification exam applications. The Automation Department can also manage the certification exam schedule and submit applications at the appropriate time. Furthermore, the Automation Department can adjust the timing of applications according to the user's readiness. For example, the Automation Department automatically completes online forms to submit certification exam applications. In the case of schedule management, the automation unit can check the application deadline for qualification exams and submit the application at the appropriate time. In the case of responding to the user's preparation status, the automation unit can check the user's preparation status and submit the application at the optimal time. As a result, the reskilling navigation system according to this embodiment enables efficient reskilling navigation by collecting, analyzing, proposing, supporting, and automating user information.

[0030] The data collection unit collects user information. User information includes, but is not limited to, personal information, learning history, and skill sets. The data collection unit collects user information using, for example, questionnaires. Questionnaires are provided through online forms or mobile apps, and users can easily answer them. Questionnaire questions include the user's current occupation, past work experience, educational background, qualifications, areas of interest, and desired career path. This allows the data collection unit to understand the user's detailed profile. The data collection unit can also collect user information using sensors. Sensors are installed in, for example, wearable devices or smartphones and collect user behavior data and biometric data. This allows the data collection unit to obtain information such as the user's activity level, stress level, and sleep patterns. Furthermore, the data collection unit can also collect user information using log data. Log data includes the user's operation history and learning history when using online learning platforms or educational apps. For example, the data collection unit collects information such as which courses the user has taken, what level of progress they have made, and what problems they have worked on. This allows the data collection unit to understand the user's learning patterns and proficiency level. The data collection unit centrally manages this information and stores it in a database. The database is secure and uses encryption technology to protect user privacy. The data collection unit regularly updates the data to maintain the most up-to-date information. This allows the data collection unit to accurately and efficiently collect user information and provide it to the analysis and proposal units.

[0031] The analysis unit analyzes the information collected by the data collection unit. Analysis is performed using, for example, data mining techniques, but is not limited to such examples. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, enabling detailed analysis of user information. For example, the analysis unit can use data mining techniques to analyze a user's skill set. Specifically, it analyzes data such as the user's past learning history, work history, and qualifications to identify their current skill level and any skills they lack. The analysis unit can also analyze user information using statistical analysis techniques. Statistical analysis techniques are methods for understanding data distribution and trends, and are effective when analyzing a user's learning history and behavioral data. For example, the analysis unit can use statistical analysis techniques to analyze a user's learning history and evaluate their learning progress and results. Furthermore, the analysis unit can also analyze user information using machine learning techniques. Machine learning techniques are methods for learning from data and performing predictions and classifications, and are effective when analyzing a user's skill set and learning history. For example, the analysis department can use machine learning techniques to analyze a user's skill set and learning history, predicting the skills they will need in the future and the direction of their learning. Based on these analysis results, the analysis department creates a detailed report on the user's reskilling. The report includes the user's current skill level, areas where they are lacking skills, and recommended learning courses and materials. The analysis department provides the report to the proposal department, which uses it as foundational data to propose directions for reskilling. This allows the analysis department to accurately and efficiently analyze user information and clearly define directions for reskilling.

[0032] The proposal department proposes reskilling directions based on the analysis results obtained by the analysis department. These proposals are based on, for example, industry trends or individual career goals, but are not limited to these examples. The proposal department first analyzes industry trends to identify skills and job roles that are currently and will be in high demand. This utilizes industry reports, market research data, and job postings. For example, the proposal department analyzes the latest technological trends in the IT industry or the adoption of new treatments in the medical industry to propose skill directions suitable for the user. The proposal department can also propose reskilling directions based on individual career goals. Users' career goals are collected through questionnaires and interviews and analyzed by the analysis department. Based on this information, the proposal department proposes skill directions tailored to the user's career goals. For example, if a user aims for a management position, the proposal department proposes learning programs to strengthen leadership and project management skills. Furthermore, the proposal department can also propose reskilling directions based on the user's interests and preferences. These interests and preferences are collected from questionnaires and behavioral data and analyzed by the analysis department. Based on this information, the proposal department suggests skill development directions tailored to the user's interests and preferences. For example, if a user is interested in data science, the proposal department will suggest learning programs to enhance their skills in data analysis and machine learning. The proposal department provides these suggestions to the user to help them choose a direction for reskilling. The suggestions are flexibly adjusted according to the user's needs and circumstances, enabling the provision of an optimal reskilling plan. This allows the proposal department to suggest reskilling directions that align with the user's career goals and interests, supporting efficient skill development.

[0033] The Support Department supports the reskilling program proposed by the Proposal Department. Support includes, but is not limited to, mentoring, provision of learning materials, and progress management. The Support Department first supports users through mentoring. Mentoring involves experienced experts and industry professionals providing advice and guidance to users, effectively supporting their learning. Mentors regularly check users' learning progress and provide feedback as needed. Mentors also provide appropriate advice on users' questions and challenges, and support them in maintaining their motivation to learn. Furthermore, the Support Department can support users by providing learning materials. These materials are provided in various formats, including online courses, video lectures, ebooks, and practical exercises. The Support Department selects and provides the most suitable materials according to the user's skill level and learning style. For example, they can provide materials tailored to user needs, from basic courses for beginners to specialized courses for advanced learners. The Support Department constantly checks the quality of the materials and strives to provide content based on the latest information and technology. In the case of progress management, the Support Department manages users' learning progress and provides appropriate feedback. The progress management system monitors users' learning progress in real time, visualizing their progress and achievements. This allows users to understand their own learning status and plan towards achieving their goals. The support department provides timely feedback to users through the progress management system, supporting them in maintaining their motivation to learn. In this way, the support department can provide multifaceted support for users' learning and achieve efficient reskilling.

[0034] The Automation Unit automates the application process for certification exams based on a reskilling program supported by the Support Unit. Automation includes, but is not limited to, automatic online form completion and schedule management. The Automation Unit first automatically completes the online form to submit the certification exam application. Necessary information such as the user's personal information, learning history, and exam type is provided by the Collection and Analysis Units and entered into the online form by the Automation Unit. This allows the user to complete the certification exam application without any effort. The Automation Unit can also manage the certification exam schedule and submit applications at the appropriate time. The schedule management system keeps track of the application deadline and exam date, and submits applications at the optimal time according to the user's learning progress and preparation status. For example, it automatically submits the exam application when the user has completed sufficient study and notifies them of the exam date. Furthermore, the Automation Unit can adjust the timing of the application according to the user's preparation status. By submitting exam applications at the optimal time based on the user's learning progress and mock exam results, it supports the user in taking the exam most effectively. The automation department reduces the burden on users and supports efficient qualification acquisition through these automated processes. This allows the automation department to support users in smoothly progressing through reskilling programs and taking qualification exams without difficulty.

[0035] The proposal department can propose skill direction, difficulty level, acquisition period, skill potential, and costs. For example, the proposal department can propose skill direction. For example, the proposal department can propose skill direction based on industry demand and individual interests. The proposal department can also propose skill difficulty level. For example, the proposal department can propose skill difficulty level based on learning time and required prerequisite knowledge. Furthermore, the proposal department can propose skill acquisition period. For example, the proposal department can propose skill acquisition period based on learning pace and curriculum content. The proposal department can also propose skill potential. For example, the proposal department can propose skill potential based on industry growth forecasts and technological advancements. The proposal department can also propose the costs associated with reskilling. For example, the proposal department can propose costs associated with reskilling based on material costs, tuition fees, examination fees, etc. In this way, the proposal department can provide users with an effective learning plan by proposing specific reskilling directions.

[0036] The support department can suggest highly-rated textbooks, online courses, and seminars. For example, the support department can suggest highly-rated textbooks. For example, the support department can suggest highly-rated textbooks tailored to the user's learning goals. The support department can also suggest online courses. For example, the support department can suggest highly-rated online courses tailored to the user's learning style. Furthermore, the support department can suggest seminars. For example, the support department can suggest highly-rated seminars tailored to the user's interests and concerns. In this way, the support department can improve the quality of learning by providing users with highly-rated learning resources.

[0037] The support department can provide a detailed cost estimate for the proposed program. For example, the support department can provide a detailed cost estimate for the proposed program. For example, the support department can provide a cost breakdown by item. The support department can also provide the basis for the estimate. For example, the support department can provide a detailed estimate for textbook fees, tuition fees, examination fees, etc. In this way, the support department can help users develop a learning plan by providing them with a detailed cost estimate.

[0038] The support department can assist with learning plan development, progress management, and regular feedback to support progress. For example, the support department can develop learning plans, such as setting learning goals and creating schedules. It can also manage progress, such as monitoring progress and providing feedback. Furthermore, it can provide regular feedback, such as providing regular feedback based on evaluation criteria. In this way, the support department can facilitate efficient learning by assisting users in developing and managing their learning plans.

[0039] The automated unit can manage the schedule for user-specified certification exams and submit applications on behalf of the user at the appropriate time. For example, the automated unit can manage the schedule of certification exams. For instance, it can check exam dates and set reminders. The automated unit can also submit applications at the appropriate time. For example, it can submit applications based on the exam application deadline and the user's preparation status. In this way, the automated unit can reduce the burden on the user by automating the certification exam application process.

[0040] The support department uses AI to provide optimal learning plans for each user, enabling efficient learning. For example, the support department can provide learning plans that take into account individual learning goals and styles. Furthermore, the support department can provide support to ensure efficient learning. For example, the support department can monitor learning progress and provide appropriate feedback. In this way, the support department can achieve efficient learning by providing optimal learning plans for each user.

[0041] The support department can update and provide plans in real time according to learning progress and changes in the environment. For example, the support department can update plans according to learning progress. For example, the support department can monitor learning achievement and progress and update plans accordingly. The support department can also update plans in response to changes in the environment. For example, the support department can update plans in response to changes in the user's living situation or learning environment. As a result, the support department can provide flexible learning support by updating plans according to learning progress and changes in the environment.

[0042] The support department can provide opportunities to speak directly with industry-leading experts and assist with real-time problem solving. For example, the support department can provide users with opportunities to speak directly with experts through online meetings and seminars. Furthermore, the support department can assist with real-time problem solving. For example, the support department can quickly resolve user issues through dialogue with experts. In this way, the support department can assist with real-time problem solving by providing opportunities to speak directly with industry-leading experts.

[0043] The support department can provide customized programs for seniors. For example, the support department can provide curricula and support tailored to the needs of seniors. In this way, the support department can support the learning of seniors by providing customized programs.

[0044] The support department can provide assistance to help users with the same goals support each other. For example, the support department can provide an environment where users can support each other through group discussions and peer support. In this way, the support department can improve the quality of learning and motivation by enabling users with the same goals to support each other.

[0045] The data collection unit can analyze the user's past learning history and select the optimal information collection method. For example, the data collection unit can prioritize collecting learning resources that the user has preferred to use in the past. For example, the data collection unit can analyze the user's past learning history and prioritize collecting learning resources that the user has preferred to use. The data collection unit can also collect information based on learning methods that the user has succeeded with in the past. For example, the data collection unit can analyze the user's past learning history and collect information based on successful learning methods. Furthermore, the data collection unit can also collect information that complements areas where the user has struggled in the past. For example, the data collection unit can analyze the user's past learning history and collect information that complements areas where the user has struggled. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past learning history.

[0046] The data collection unit can filter information based on the user's current job responsibilities and areas of interest. For example, the data collection unit can prioritize collecting skill information related to the user's current job responsibilities. For instance, it can analyze the user's job responsibilities and prioritize collecting relevant skill information. The data collection unit can also collect the latest information related to the user's areas of interest. For example, it can analyze the user's areas of interest and collect the latest relevant information. Furthermore, the data collection unit can filter and collect specialized information according to the user's job responsibilities. For example, it can analyze the user's job responsibilities and filter and collect specialized information. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's job responsibilities and areas of interest.

[0047] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of seminars and events held in the user's region. For example, the data collection unit can prioritize the collection of seminars and events held in the region by considering the user's geographical location. The data collection unit can also collect industry information related to the user's region. For example, the data collection unit can collect industry information related to the region by considering the user's geographical location. Furthermore, the data collection unit can prioritize the collection of learning resources available in the user's region. For example, the data collection unit can prioritize the collection of learning resources available in the region by considering the user's geographical location. In this way, the data collection unit can collect highly relevant information by considering the user's geographical location.

[0048] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information on experts that a user follows. For example, the data collection unit can analyze a user's social media activity and collect information on experts that a user follows. The data collection unit can also collect information on groups and communities that a user participates in. For example, the data collection unit can analyze a user's social media activity and collect information on groups and communities that a user participates in. Furthermore, the data collection unit can collect information related to articles and posts that a user shares. For example, the data collection unit can analyze a user's social media activity and collect information related to articles and posts that a user shares. In this way, the data collection unit can collect relevant information by analyzing a user's social media activity.

[0049] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can evaluate the importance of the collected information and perform a detailed analysis on the information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. For example, the analysis unit can evaluate the importance of the collected information and perform a simplified analysis on the information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to its importance. For example, the analysis unit can evaluate the importance of the collected information and determine the priority of the analysis according to its importance. As a result, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the collected information.

[0050] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical information. For instance, the analysis unit can evaluate the category of information and then apply a specialized analysis algorithm to technical information. Furthermore, the analysis unit can apply an economic analysis algorithm to market information. For example, the analysis unit can evaluate the category of information and then apply an economic analysis algorithm to market information. In addition, the analysis unit can apply a statistical analysis algorithm to trend information. For example, the analysis unit can evaluate the category of information and then apply a statistical analysis algorithm to trend information. This allows the analysis unit to apply the most appropriate analysis algorithm according to the category of information, thereby improving the accuracy of the analysis.

[0051] The analysis unit can determine the priority of the analysis based on the timing of information collection. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can evaluate the timing of information collection and prioritize the analysis of the most recent information. The analysis unit can also perform a simplified analysis of older information. For example, the analysis unit can evaluate the timing of information collection and perform a simplified analysis of older information. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the timing of information collection. For example, the analysis unit can evaluate the timing of information collection and adjust the level of detail of the analysis according to the timing of collection. As a result, the analysis unit can prioritize the analysis of the most recent information by determining the priority of the analysis based on the timing of information collection.

[0052] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can evaluate the relevance of the information and prioritize the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. For example, the analysis unit can evaluate the relevance of the information and postpone the analysis of less relevant information. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the information. For example, the analysis unit can evaluate the relevance of the information and determine the order of analysis according to the relevance. As a result, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0053] The proposal department can adjust the level of detail in its proposals based on the importance of the skills. For example, it can provide detailed proposals for highly important skills. For example, it can evaluate the importance of skills and provide detailed proposals for those that are most important. It can also provide simplified proposals for less important skills. For example, it can evaluate the importance of skills and provide simplified proposals for those that are less important. Furthermore, the proposal department can prioritize proposals according to their importance. For example, it can evaluate the importance of skills and prioritize proposals according to their importance. This allows the proposal department to make more efficient proposals by adjusting the level of detail based on the importance of the skills.

[0054] The proposal department can apply different proposal algorithms depending on the skill category when making a proposal. For example, the proposal department can apply a specialized proposal algorithm to technical skills. For example, the proposal department can evaluate the skill category and then apply a specialized proposal algorithm to technical skills. The proposal department can also apply a psychological proposal algorithm to soft skills. For example, the proposal department can evaluate the skill category and then apply a psychological proposal algorithm to soft skills. Furthermore, the proposal department can apply a management-based proposal algorithm to management skills. For example, the proposal department can evaluate the skill category and then apply a management-based proposal algorithm to management skills. This allows the proposal department to improve the accuracy of its proposals by applying the most appropriate proposal algorithm for each skill category.

[0055] The proposal department can prioritize proposals based on the timing of skill acquisition. For example, the proposal department can prioritize proposals for skills that need to be acquired urgently. For example, the proposal department can evaluate the timing of skill acquisition and prioritize proposals for skills that need to be acquired urgently. The proposal department can also postpone proposals for skills that need to be acquired over the long term. For example, the proposal department can evaluate the timing of skill acquisition and postpone proposals for skills that need to be acquired over the long term. Furthermore, the proposal department can adjust the level of detail of proposals according to the timing of skill acquisition. For example, the proposal department can evaluate the timing of skill acquisition and adjust the level of detail of proposals accordingly. This allows the proposal department to make efficient proposals by prioritizing proposals based on the timing of skill acquisition.

[0056] The proposal department can adjust the order of proposals based on the relevance of skills during the proposal process. For example, the proposal department can prioritize proposing highly relevant skills. For example, the proposal department can evaluate the relevance of skills and prioritize proposing highly relevant skills. The proposal department can also postpone proposing less relevant skills. For example, the proposal department can evaluate the relevance of skills and postpone proposing less relevant skills. Furthermore, the proposal department can determine the order of proposals according to the relevance of skills. For example, the proposal department can evaluate the relevance of skills and determine the order of proposals according to their relevance. This allows the proposal department to make efficient proposals by adjusting the order of proposals based on the relevance of skills.

[0057] The support department can analyze a user's past learning behavior and select the optimal support method during support sessions. For example, the support department can provide support based on learning methods that have been successful for the user in the past. For example, the support department can analyze a user's past learning behavior and provide support based on those successful methods. The support department can also provide support to address areas where the user has struggled in the past. For example, the support department can analyze a user's past learning behavior and provide support to address areas where the user has struggled. Furthermore, the support department can prioritize support based on the user's past learning behavior. For example, the support department can analyze a user's past learning behavior and prioritize support based on that behavior. In this way, the support department can select the optimal support method by analyzing a user's past learning behavior.

[0058] The support department can customize the means of support based on the user's current living situation. For example, if the user is busy, the support department can provide effective support in a short amount of time. For example, the support department can assess the user's living situation and provide effective support in a short amount of time if the user is busy. The support department can also provide detailed support if the user has time. For example, the support department can assess the user's living situation and provide detailed support if the user has time. Furthermore, the support department can adjust the means of support according to the user's living situation. For example, the support department can assess the user's living situation and adjust the means of support according to the situation. In this way, the support department can provide efficient support by customizing the means of support based on the user's current living situation.

[0059] The support department can select the optimal support method by considering the user's geographical location during support. For example, the support department can provide information on seminars and events held in the user's region. For example, the support department can provide information on seminars and events held in the user's region by considering the user's geographical location. Furthermore, the support department can provide learning resources relevant to the user's region. For example, the support department can provide learning resources relevant to the region by considering the user's geographical location. In addition, the support department can provide support services available in the user's region. For example, the support department can provide support services available in the region by considering the user's geographical location. This allows the support department to select the optimal support method by considering the user's geographical location.

[0060] The support department can analyze a user's social media activity and suggest appropriate support methods during support sessions. For example, the support department can provide information on experts the user follows. Furthermore, the support department can provide information on groups and communities the user participates in. Additionally, the support department can provide support related to articles and posts the user has shared. This allows the support department to suggest the most suitable support methods by analyzing the user's social media activity.

[0061] The automation unit can analyze the user's past behavior history to select the optimal automation method during automation. For example, the automation unit can perform automation based on actions the user has successfully performed in the past. For example, the automation unit can analyze the user's past behavior history and perform automation based on successful actions. The automation unit can also perform automation that complements actions the user has struggled with in the past. For example, the automation unit can analyze the user's past behavior history and perform automation that complements actions the user has struggled with. Furthermore, the automation unit can determine the priority of automation according to the user's past behavior history. For example, the automation unit can analyze the user's past behavior history and determine the priority of automation according to that behavior history. In this way, the automation unit can select the optimal automation method by analyzing the user's past behavior history.

[0062] The automation unit can customize the automation methods based on the user's current lifestyle during automation. For example, if the user is busy, the automation unit can perform efficient automation in a short amount of time. For example, the automation unit can assess the user's lifestyle and perform efficient automation in a short amount of time if the user is busy. The automation unit can also perform detailed automation if the user has time. For example, the automation unit can assess the user's lifestyle and perform detailed automation if the user has time. Furthermore, the automation unit can adjust the automation methods according to the user's lifestyle. For example, the automation unit can assess the user's lifestyle and adjust the automation methods according to the situation. As a result, the automation unit can perform efficient automation by customizing the automation methods based on the user's current lifestyle.

[0063] The automation unit can select the optimal automation method by considering the user's geographical location during automation. For example, the automation unit can automatically register users for seminars and events held in their region. For example, the automation unit can automatically register users for seminars and events held in their region by considering the user's geographical location. The automation unit can also automatically acquire learning resources related to the user's region. For example, the automation unit can automatically acquire learning resources related to the region by considering the user's geographical location. Furthermore, the automation unit can automatically register users for support services available in their region. For example, the automation unit can automatically register users for support services available in their region by considering the user's geographical location. In this way, the automation unit can select the optimal automation method by considering the user's geographical location.

[0064] The automation unit can analyze the user's social media activity and propose automation methods during the automation process. For example, the automation unit can automatically acquire information about experts the user follows. For example, the automation unit can analyze the user's social media activity and automatically acquire information about the experts the user follows. The automation unit can also automatically acquire information about groups and communities the user participates in. For example, the automation unit can analyze the user's social media activity and automatically acquire information about groups and communities the user participates in. Furthermore, the automation unit can automatically acquire information related to articles and posts the user shares. For example, the automation unit can analyze the user's social media activity and automatically acquire information related to articles and posts the user shares. As a result, the automation unit can analyze the user's social media activity and propose the most suitable automation method.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The analysis unit can analyze the user's past learning history and select the optimal analysis method. For example, it can prioritize the analysis of learning resources that the user has preferred to use in the past. It can also perform analysis based on learning methods that the user has been successful with in the past. Furthermore, it can perform analysis that complements areas where the user has struggled in the past. In this way, the analysis unit can select the optimal analysis method by analyzing the user's past learning history.

[0067] The suggestion department can adjust the content of suggestions based on the user's current job responsibilities and areas of interest. For example, it can prioritize suggesting skill information relevant to the user's current job. It can also suggest the latest information related to the user's areas of interest. Furthermore, it can suggest specialized information tailored to the user's job responsibilities. In this way, the suggestion department can provide highly relevant suggestions by adjusting the content of suggestions based on the user's job responsibilities and areas of interest.

[0068] The support department can select the most appropriate support method by considering the user's geographical location. For example, it can provide information on seminars and events held in the user's region. It can also provide learning resources relevant to the user's region. Furthermore, it can provide support services available in the user's region. In this way, the support department can select the most appropriate support method by considering the user's geographical location.

[0069] The automation unit can analyze users' social media activity and propose automation methods. For example, it can automatically retrieve information about experts that users follow. It can also automatically retrieve information about groups and communities that users participate in. Furthermore, it can automatically retrieve information related to articles and posts that users have shared. In this way, the automation unit can analyze users' social media activity and propose the most suitable automation method.

[0070] The information gathering unit can customize its methods of information collection based on the user's current lifestyle. For example, if the user is busy, it can collect information quickly and effectively. If the user has more time, it can collect detailed information. Furthermore, it can adjust the methods of information collection according to the user's lifestyle. As a result, the information gathering unit can efficiently collect information by customizing its methods of information collection based on the user's current lifestyle.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The collection unit collects user information. User information includes, for example, personal information, learning history, and skill set. The collection unit collects information using questionnaires, sensors, and log data. For example, it can collect information about the user's wishes and current skills through questionnaires, collect user behavior data using sensors, and collect the user's past learning history using log data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data mining techniques, statistical analysis techniques, and machine learning techniques. For example, data mining techniques can be used to analyze the user's skill set, statistical analysis techniques can be used to analyze the user's learning history, and machine learning techniques can be used to analyze the user's skill set and learning history. Step 3: The proposal team proposes a direction for reskilling based on the analysis results obtained by the analysis team. The proposal is based on industry trends, individual career goals, and user interests. For example, it can analyze industry trends and propose a skill direction suitable for the user, propose a skill direction aligned with individual career goals, and propose a skill direction aligned with user interests. Step 4: The support team supports the reskilling program proposed by the proposal team. Support includes mentoring, provision of learning materials, and progress management. For example, they can support user learning through mentoring, provide learning materials suitable for the user, manage the user's learning progress, and provide appropriate feedback. Step 5: The automation unit automates the certification exam application process based on the reskilling program supported by the support unit. Automation includes automatic online form completion and schedule management. For example, it can automatically complete online forms to apply for certification exams, manage the exam schedule, submit applications at the appropriate time, and adjust the application timing according to the user's readiness.

[0073] (Example of form 2) The reskilling navigation system according to an embodiment of the present invention is an agent service specifically designed to provide reskilling navigation for working professionals. This reskilling navigation system conducts a detailed interview with the user regarding their wishes and current skills, and proposes a direction for reskilling based on that information. The proposed content includes the direction of skills, difficulty level, acquisition period, future potential of the skills, and costs. Next, it provides support for the reskilling program. Specifically, it proposes highly-rated textbooks, online courses, seminars, etc., and provides a detailed cost estimate for the proposed program. It also provides support for progress through the creation of learning plans, progress management, and regular feedback. Furthermore, it automates the application process for certification exams, managing the schedule for certification exams specified by the user and submitting applications at the appropriate time. For example, the reskilling navigation system caters to a wide range of targets, including working professionals considering a career change, mid-level and managerial employees aiming for career advancement, housewives and parents raising children considering returning to work, young employees and new graduates who want to improve their skills, people who want to deepen their knowledge as a hobby or side job, and seniors starting new activities after retirement. The challenges faced by the target audience include not knowing the latest skill sets, not knowing how to choose skills and qualifications, finding it difficult to acquire new knowledge due to a long break from learning, not knowing how to estimate time and cost, being unsure about the effectiveness of the program, difficulty balancing work and family life, and lack of confidence in re-employment. To address these challenges, AI provides an optimal learning plan for each user, enabling efficient learning. Furthermore, the plan is updated and provided in real time according to learning progress and changes in circumstances. In addition, it provides opportunities to speak directly with top industry experts to support real-time problem solving. By offering customized programs for seniors and enabling users with similar goals to support each other, it is also possible to improve the quality of learning and motivation. In this way, the reskilling navigation system can provide efficient reskilling navigation for working adults.

[0074] The reskilling navigation system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a support unit, and an automation unit. The collection unit collects user information. User information includes, but is not limited to, personal information, learning history, and skill sets. The collection unit collects user information using, for example, questionnaires. The collection unit can also collect user information using sensors. Furthermore, the collection unit can also collect user information using log data. For example, the collection unit collects information about the user's wishes and current skills through questionnaires. When using sensors, the collection unit can collect user behavior data. When using log data, the collection unit can collect the user's past learning history. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, data mining techniques, but is not limited to such examples. For example, the analysis unit analyzes user information using data mining techniques. The analysis unit can also analyze user information using statistical analysis techniques. Furthermore, the analysis unit can also analyze user information using machine learning techniques. For example, the analysis unit analyzes the user's skill set using data mining techniques. When using statistical analysis techniques, the analysis unit can analyze the user's learning history. When using machine learning techniques, the analysis unit can analyze the user's skill set and learning history. The proposal unit proposes a direction for reskilling based on the analysis results obtained by the analysis unit. Proposals are made based on, for example, industry trends or individual career goals, but are not limited to these examples. For example, the proposal unit proposes a direction for reskilling based on industry trends. The proposal unit can also propose a direction for reskilling based on individual career goals. Furthermore, the proposal unit can also propose a direction for reskilling based on the user's interests and concerns. For example, the proposal unit analyzes industry trends and proposes a skill direction suitable for the user. When based on individual career goals, the proposal unit can propose a skill direction that matches the user's career goals. When based on the user's interests and concerns, the proposal unit can propose a skill direction that matches the user's interests and concerns.The Support Department supports the reskilling program proposed by the Proposal Department. Support includes, but is not limited to, mentoring, provision of learning materials, and progress management. For example, the Support Department can support users through mentoring. The Support Department can also support users by providing learning materials. Furthermore, the Support Department can support users through progress management. For example, the Support Department supports users' learning through mentoring. In the case of providing learning materials, the Support Department can provide materials suitable for the user. In the case of progress management, the Support Department can manage the user's learning progress and provide appropriate feedback. The Automation Department automates the application process for certification exams based on the reskilling program supported by the Support Department. Automation includes, but is not limited to, automatic online form completion and schedule management. For example, the Automation Department automatically completes online forms to submit certification exam applications. The Automation Department can also manage the certification exam schedule and submit applications at the appropriate time. Furthermore, the Automation Department can adjust the timing of applications according to the user's readiness. For example, the Automation Department automatically completes online forms to submit certification exam applications. In the case of schedule management, the automation unit can check the application deadline for qualification exams and submit the application at the appropriate time. In the case of responding to the user's preparation status, the automation unit can check the user's preparation status and submit the application at the optimal time. As a result, the reskilling navigation system according to this embodiment enables efficient reskilling navigation by collecting, analyzing, proposing, supporting, and automating user information.

[0075] The data collection unit collects user information. User information includes, but is not limited to, personal information, learning history, and skill sets. The data collection unit collects user information using, for example, questionnaires. Questionnaires are provided through online forms or mobile apps, and users can easily answer them. Questionnaire questions include the user's current occupation, past work experience, educational background, qualifications, areas of interest, and desired career path. This allows the data collection unit to understand the user's detailed profile. The data collection unit can also collect user information using sensors. Sensors are installed in, for example, wearable devices or smartphones and collect user behavior data and biometric data. This allows the data collection unit to obtain information such as the user's activity level, stress level, and sleep patterns. Furthermore, the data collection unit can also collect user information using log data. Log data includes the user's operation history and learning history when using online learning platforms or educational apps. For example, the data collection unit collects information such as which courses the user has taken, what level of progress they have made, and what problems they have worked on. This allows the data collection unit to understand the user's learning patterns and proficiency level. The data collection unit centrally manages this information and stores it in a database. The database is secure and uses encryption technology to protect user privacy. The data collection unit regularly updates the data to maintain the most up-to-date information. This allows the data collection unit to accurately and efficiently collect user information and provide it to the analysis and proposal units.

[0076] The analysis unit analyzes the information collected by the data collection unit. Analysis is performed using, for example, data mining techniques, but is not limited to such examples. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, enabling detailed analysis of user information. For example, the analysis unit can use data mining techniques to analyze a user's skill set. Specifically, it analyzes data such as the user's past learning history, work history, and qualifications to identify their current skill level and any skills they lack. The analysis unit can also analyze user information using statistical analysis techniques. Statistical analysis techniques are methods for understanding data distribution and trends, and are effective when analyzing a user's learning history and behavioral data. For example, the analysis unit can use statistical analysis techniques to analyze a user's learning history and evaluate their learning progress and results. Furthermore, the analysis unit can also analyze user information using machine learning techniques. Machine learning techniques are methods for learning from data and performing predictions and classifications, and are effective when analyzing a user's skill set and learning history. For example, the analysis department can use machine learning techniques to analyze a user's skill set and learning history, predicting the skills they will need in the future and the direction of their learning. Based on these analysis results, the analysis department creates a detailed report on the user's reskilling. The report includes the user's current skill level, areas where they are lacking skills, and recommended learning courses and materials. The analysis department provides the report to the proposal department, which uses it as foundational data to propose directions for reskilling. This allows the analysis department to accurately and efficiently analyze user information and clearly define directions for reskilling.

[0077] The proposal department proposes reskilling directions based on the analysis results obtained by the analysis department. These proposals are based on, for example, industry trends or individual career goals, but are not limited to these examples. The proposal department first analyzes industry trends to identify skills and job roles that are currently and will be in high demand. This utilizes industry reports, market research data, and job postings. For example, the proposal department analyzes the latest technological trends in the IT industry or the adoption of new treatments in the medical industry to propose skill directions suitable for the user. The proposal department can also propose reskilling directions based on individual career goals. Users' career goals are collected through questionnaires and interviews and analyzed by the analysis department. Based on this information, the proposal department proposes skill directions tailored to the user's career goals. For example, if a user aims for a management position, the proposal department proposes learning programs to strengthen leadership and project management skills. Furthermore, the proposal department can also propose reskilling directions based on the user's interests and preferences. These interests and preferences are collected from questionnaires and behavioral data and analyzed by the analysis department. Based on this information, the proposal department suggests skill development directions tailored to the user's interests and preferences. For example, if a user is interested in data science, the proposal department will suggest learning programs to enhance their skills in data analysis and machine learning. The proposal department provides these suggestions to the user to help them choose a direction for reskilling. The suggestions are flexibly adjusted according to the user's needs and circumstances, enabling the provision of an optimal reskilling plan. This allows the proposal department to suggest reskilling directions that align with the user's career goals and interests, supporting efficient skill development.

[0078] The Support Department supports the reskilling program proposed by the Proposal Department. Support includes, but is not limited to, mentoring, provision of learning materials, and progress management. The Support Department first supports users through mentoring. Mentoring involves experienced experts and industry professionals providing advice and guidance to users, effectively supporting their learning. Mentors regularly check users' learning progress and provide feedback as needed. Mentors also provide appropriate advice on users' questions and challenges, and support them in maintaining their motivation to learn. Furthermore, the Support Department can support users by providing learning materials. These materials are provided in various formats, including online courses, video lectures, ebooks, and practical exercises. The Support Department selects and provides the most suitable materials according to the user's skill level and learning style. For example, they can provide materials tailored to user needs, from basic courses for beginners to specialized courses for advanced learners. The Support Department constantly checks the quality of the materials and strives to provide content based on the latest information and technology. In the case of progress management, the Support Department manages users' learning progress and provides appropriate feedback. The progress management system monitors users' learning progress in real time, visualizing their progress and achievements. This allows users to understand their own learning status and plan towards achieving their goals. The support department provides timely feedback to users through the progress management system, supporting them in maintaining their motivation to learn. In this way, the support department can provide multifaceted support for users' learning and achieve efficient reskilling.

[0079] The Automation Unit automates the application process for certification exams based on a reskilling program supported by the Support Unit. Automation includes, but is not limited to, automatic online form completion and schedule management. The Automation Unit first automatically completes the online form to submit the certification exam application. Necessary information such as the user's personal information, learning history, and exam type is provided by the Collection and Analysis Units and entered into the online form by the Automation Unit. This allows the user to complete the certification exam application without any effort. The Automation Unit can also manage the certification exam schedule and submit applications at the appropriate time. The schedule management system keeps track of the application deadline and exam date, and submits applications at the optimal time according to the user's learning progress and preparation status. For example, it automatically submits the exam application when the user has completed sufficient study and notifies them of the exam date. Furthermore, the Automation Unit can adjust the timing of the application according to the user's preparation status. By submitting exam applications at the optimal time based on the user's learning progress and mock exam results, it supports the user in taking the exam most effectively. The automation department reduces the burden on users and supports efficient qualification acquisition through these automated processes. This allows the automation department to support users in smoothly progressing through reskilling programs and taking qualification exams without difficulty.

[0080] The proposal department can propose skill direction, difficulty level, acquisition period, skill potential, and costs. For example, the proposal department can propose skill direction. For example, the proposal department can propose skill direction based on industry demand and individual interests. The proposal department can also propose skill difficulty level. For example, the proposal department can propose skill difficulty level based on learning time and required prerequisite knowledge. Furthermore, the proposal department can propose skill acquisition period. For example, the proposal department can propose skill acquisition period based on learning pace and curriculum content. The proposal department can also propose skill potential. For example, the proposal department can propose skill potential based on industry growth forecasts and technological advancements. The proposal department can also propose the costs associated with reskilling. For example, the proposal department can propose costs associated with reskilling based on material costs, tuition fees, examination fees, etc. In this way, the proposal department can provide users with an effective learning plan by proposing specific reskilling directions.

[0081] The support department can suggest highly-rated textbooks, online courses, and seminars. For example, the support department can suggest highly-rated textbooks. For example, the support department can suggest highly-rated textbooks tailored to the user's learning goals. The support department can also suggest online courses. For example, the support department can suggest highly-rated online courses tailored to the user's learning style. Furthermore, the support department can suggest seminars. For example, the support department can suggest highly-rated seminars tailored to the user's interests and concerns. In this way, the support department can improve the quality of learning by providing users with highly-rated learning resources.

[0082] The support department can provide a detailed cost estimate for the proposed program. For example, the support department can provide a detailed cost estimate for the proposed program. For example, the support department can provide a cost breakdown by item. The support department can also provide the basis for the estimate. For example, the support department can provide a detailed estimate for textbook fees, tuition fees, examination fees, etc. In this way, the support department can help users develop a learning plan by providing them with a detailed cost estimate.

[0083] The support department can assist with learning plan development, progress management, and regular feedback to support progress. For example, the support department can develop learning plans, such as setting learning goals and creating schedules. It can also manage progress, such as monitoring progress and providing feedback. Furthermore, it can provide regular feedback, such as providing regular feedback based on evaluation criteria. In this way, the support department can facilitate efficient learning by assisting users in developing and managing their learning plans.

[0084] The automated unit can manage the schedule for user-specified certification exams and submit applications on behalf of the user at the appropriate time. For example, the automated unit can manage the schedule of certification exams. For instance, it can check exam dates and set reminders. The automated unit can also submit applications at the appropriate time. For example, it can submit applications based on the exam application deadline and the user's preparation status. In this way, the automated unit can reduce the burden on the user by automating the certification exam application process.

[0085] The support department uses AI to provide optimal learning plans for each user, enabling efficient learning. For example, the support department can provide learning plans that take into account individual learning goals and styles. Furthermore, the support department can provide support to ensure efficient learning. For example, the support department can monitor learning progress and provide appropriate feedback. In this way, the support department can achieve efficient learning by providing optimal learning plans for each user.

[0086] The support department can update and provide plans in real time according to learning progress and changes in the environment. For example, the support department can update plans according to learning progress. For example, the support department can monitor learning achievement and progress and update plans accordingly. The support department can also update plans in response to changes in the environment. For example, the support department can update plans in response to changes in the user's living situation or learning environment. As a result, the support department can provide flexible learning support by updating plans according to learning progress and changes in the environment.

[0087] The support department can provide opportunities to speak directly with industry-leading experts and assist with real-time problem solving. For example, the support department can provide users with opportunities to speak directly with experts through online meetings and seminars. Furthermore, the support department can assist with real-time problem solving. For example, the support department can quickly resolve user issues through dialogue with experts. In this way, the support department can assist with real-time problem solving by providing opportunities to speak directly with industry-leading experts.

[0088] The support department can provide customized programs for seniors. For example, the support department can provide curricula and support tailored to the needs of seniors. In this way, the support department can support the learning of seniors by providing customized programs.

[0089] The support department can provide assistance to help users with the same goals support each other. For example, the support department can provide an environment where users can support each other through group discussions and peer support. In this way, the support department can improve the quality of learning and motivation by enabling users with the same goals to support each other.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit can collect information during times when the user is relaxed. For example, the data collection unit can estimate the user's emotions and collect information during times when the user is relaxed. The data collection unit can also collect detailed information when the user is concentrating. For example, the data collection unit can estimate the user's emotions and collect detailed information when the user is concentrating. Furthermore, if the user is tired, the data collection unit can collect information after they have rested. For example, the data collection unit can estimate the user's emotions and collect information after they have rested. In this way, the data collection unit can efficiently collect information by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The data collection unit can analyze the user's past learning history and select the optimal information collection method. For example, the data collection unit can prioritize collecting learning resources that the user has preferred to use in the past. For example, the data collection unit can analyze the user's past learning history and prioritize collecting learning resources that the user has preferred to use. The data collection unit can also collect information based on learning methods that the user has succeeded with in the past. For example, the data collection unit can analyze the user's past learning history and collect information based on successful learning methods. Furthermore, the data collection unit can also collect information that complements areas where the user has struggled in the past. For example, the data collection unit can analyze the user's past learning history and collect information that complements areas where the user has struggled. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past learning history.

[0092] The data collection unit can filter information based on the user's current job responsibilities and areas of interest. For example, the data collection unit can prioritize collecting skill information related to the user's current job responsibilities. For instance, it can analyze the user's job responsibilities and prioritize collecting relevant skill information. The data collection unit can also collect the latest information related to the user's areas of interest. For example, it can analyze the user's areas of interest and collect the latest relevant information. Furthermore, the data collection unit can filter and collect specialized information according to the user's job responsibilities. For example, it can analyze the user's job responsibilities and filter and collect specialized information. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's job responsibilities and areas of interest.

[0093] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting information that provides a sense of security. For example, if the user is feeling anxious, the data collection unit can prioritize collecting information that provides a sense of security. The data collection unit can also prioritize collecting challenging information if the user is excited. For example, if the user is excited, the data collection unit can prioritize collecting challenging information. Furthermore, if the user is calm, the data collection unit can prioritize collecting detailed information. For example, if the user is calm, the data collection unit can prioritize collecting detailed information. This allows the data collection unit to efficiently collect information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of seminars and events held in the user's region. For example, the data collection unit can prioritize the collection of seminars and events held in the region by considering the user's geographical location. The data collection unit can also collect industry information related to the user's region. For example, the data collection unit can collect industry information related to the region by considering the user's geographical location. Furthermore, the data collection unit can prioritize the collection of learning resources available in the user's region. For example, the data collection unit can prioritize the collection of learning resources available in the region by considering the user's geographical location. In this way, the data collection unit can collect highly relevant information by considering the user's geographical location.

[0095] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information on experts that a user follows. For example, the data collection unit can analyze a user's social media activity and collect information on experts that a user follows. The data collection unit can also collect information on groups and communities that a user participates in. For example, the data collection unit can analyze a user's social media activity and collect information on groups and communities that a user participates in. Furthermore, the data collection unit can collect information related to articles and posts that a user shares. For example, the data collection unit can analyze a user's social media activity and collect information related to articles and posts that a user shares. In this way, the data collection unit can collect relevant information by analyzing a user's social media activity.

[0096] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is nervous, the analysis unit can estimate the user's emotions and provide simple and easy-to-understand analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the analysis unit estimates the user's emotions and provides detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the analysis unit estimates the user's emotions and provides concise analysis results if the user is in a hurry. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can evaluate the importance of the collected information and perform a detailed analysis on the information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. For example, the analysis unit can evaluate the importance of the collected information and perform a simplified analysis on the information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to its importance. For example, the analysis unit can evaluate the importance of the collected information and determine the priority of the analysis according to its importance. As a result, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the collected information.

[0098] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical information. For instance, the analysis unit can evaluate the category of information and then apply a specialized analysis algorithm to technical information. Furthermore, the analysis unit can apply an economic analysis algorithm to market information. For example, the analysis unit can evaluate the category of information and then apply an economic analysis algorithm to market information. In addition, the analysis unit can apply a statistical analysis algorithm to trend information. For example, the analysis unit can evaluate the category of information and then apply a statistical analysis algorithm to trend information. This allows the analysis unit to apply the most appropriate analysis algorithm according to the category of information, thereby improving the accuracy of the analysis.

[0099] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, the analysis unit can estimate the user's emotions and provide a short, concise analysis if the user is in a hurry. The analysis unit can also provide a detailed analysis if the user is relaxed. For example, the analysis unit can estimate the user's emotions and provide a detailed analysis if the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis if the user is excited. For example, the analysis unit can estimate the user's emotions and provide a visually stimulating analysis if the user is excited. In this way, the analysis unit can provide the user with the optimal analysis result by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The analysis unit can determine the priority of the analysis based on the timing of information collection. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can evaluate the timing of information collection and prioritize the analysis of the most recent information. The analysis unit can also perform a simplified analysis of older information. For example, the analysis unit can evaluate the timing of information collection and perform a simplified analysis of older information. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the timing of information collection. For example, the analysis unit can evaluate the timing of information collection and adjust the level of detail of the analysis according to the timing of collection. As a result, the analysis unit can prioritize the analysis of the most recent information by determining the priority of the analysis based on the timing of information collection.

[0101] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can evaluate the relevance of the information and prioritize the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. For example, the analysis unit can evaluate the relevance of the information and postpone the analysis of less relevant information. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the information. For example, the analysis unit can evaluate the relevance of the information and determine the order of analysis according to the relevance. As a result, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0102] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion function can provide simple and easily understandable suggestions. For example, if the user is nervous, the suggestion function can provide simple and easily understandable suggestions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. For example, if the user is in a hurry, the suggestion function can provide concise suggestions. For example, if the suggestion function can estimate the user's emotions and provide concise suggestions. In this way, the suggestion function can provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] The proposal department can adjust the level of detail in its proposals based on the importance of the skills. For example, it can provide detailed proposals for highly important skills. For example, it can evaluate the importance of skills and provide detailed proposals for those that are most important. It can also provide simplified proposals for less important skills. For example, it can evaluate the importance of skills and provide simplified proposals for those that are less important. Furthermore, the proposal department can prioritize proposals according to their importance. For example, it can evaluate the importance of skills and prioritize proposals according to their importance. This allows the proposal department to make more efficient proposals by adjusting the level of detail based on the importance of the skills.

[0104] The proposal department can apply different proposal algorithms depending on the skill category when making a proposal. For example, the proposal department can apply a specialized proposal algorithm to technical skills. For example, the proposal department can evaluate the skill category and then apply a specialized proposal algorithm to technical skills. The proposal department can also apply a psychological proposal algorithm to soft skills. For example, the proposal department can evaluate the skill category and then apply a psychological proposal algorithm to soft skills. Furthermore, the proposal department can apply a management-based proposal algorithm to management skills. For example, the proposal department can evaluate the skill category and then apply a management-based proposal algorithm to management skills. This allows the proposal department to improve the accuracy of its proposals by applying the most appropriate proposal algorithm for each skill category.

[0105] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. It can also provide detailed suggestions if the user is relaxed. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows the suggestion unit to provide the most suitable suggestions for the user by adjusting the length of its suggestions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The proposal department can prioritize proposals based on the timing of skill acquisition. For example, the proposal department can prioritize proposals for skills that need to be acquired urgently. For example, the proposal department can evaluate the timing of skill acquisition and prioritize proposals for skills that need to be acquired urgently. The proposal department can also postpone proposals for skills that need to be acquired over the long term. For example, the proposal department can evaluate the timing of skill acquisition and postpone proposals for skills that need to be acquired over the long term. Furthermore, the proposal department can adjust the level of detail of proposals according to the timing of skill acquisition. For example, the proposal department can evaluate the timing of skill acquisition and adjust the level of detail of proposals accordingly. This allows the proposal department to make efficient proposals by prioritizing proposals based on the timing of skill acquisition.

[0107] The proposal department can adjust the order of proposals based on the relevance of skills during the proposal process. For example, the proposal department can prioritize proposing highly relevant skills. For example, the proposal department can evaluate the relevance of skills and prioritize proposing highly relevant skills. The proposal department can also postpone proposing less relevant skills. For example, the proposal department can evaluate the relevance of skills and postpone proposing less relevant skills. Furthermore, the proposal department can determine the order of proposals according to the relevance of skills. For example, the proposal department can evaluate the relevance of skills and determine the order of proposals according to their relevance. This allows the proposal department to make efficient proposals by adjusting the order of proposals based on the relevance of skills.

[0108] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is feeling anxious, the support unit can provide a relaxing support method. For example, the support unit can estimate the user's emotions and provide a relaxing support method if the user is feeling anxious. The support unit can also provide detailed support if the user is relaxed. For example, the support unit can estimate the user's emotions and provide detailed support if the user is relaxed. Furthermore, the support unit can provide rapid support if the user is in a hurry. For example, the support unit can estimate the user's emotions and provide rapid support if the user is in a hurry. In this way, the support unit can provide optimal support to the user by adjusting its support methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The support department can analyze a user's past learning behavior and select the optimal support method during support sessions. For example, the support department can provide support based on learning methods that have been successful for the user in the past. For example, the support department can analyze a user's past learning behavior and provide support based on those successful methods. The support department can also provide support to address areas where the user has struggled in the past. For example, the support department can analyze a user's past learning behavior and provide support to address areas where the user has struggled. Furthermore, the support department can prioritize support based on the user's past learning behavior. For example, the support department can analyze a user's past learning behavior and prioritize support based on that behavior. In this way, the support department can select the optimal support method by analyzing a user's past learning behavior.

[0110] The support department can customize the means of support based on the user's current living situation. For example, if the user is busy, the support department can provide effective support in a short amount of time. For example, the support department can assess the user's living situation and provide effective support in a short amount of time if the user is busy. The support department can also provide detailed support if the user has time. For example, the support department can assess the user's living situation and provide detailed support if the user has time. Furthermore, the support department can adjust the means of support according to the user's living situation. For example, the support department can assess the user's living situation and adjust the means of support according to the situation. In this way, the support department can provide efficient support by customizing the means of support based on the user's current living situation.

[0111] The support unit can estimate the user's emotions and prioritize support based on those emotions. For example, if the user is feeling anxious, the support unit can prioritize providing reassuring support. For example, if the user is feeling anxious, the support unit can prioritize providing reassuring support. For example, if the user is feeling excited, the support unit can prioritize providing challenging support. For example, if the user is feeling excited, the support unit can prioritize providing challenging support. Furthermore, if the user is calm, the support unit can prioritize providing detailed support. For example, if the user is feeling calm, the support unit can prioritize providing detailed support. This allows the support unit to provide efficient support by prioritizing support according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The support department can select the optimal support method by considering the user's geographical location during support. For example, the support department can provide information on seminars and events held in the user's region. For example, the support department can provide information on seminars and events held in the user's region by considering the user's geographical location. Furthermore, the support department can provide learning resources relevant to the user's region. For example, the support department can provide learning resources relevant to the region by considering the user's geographical location. In addition, the support department can provide support services available in the user's region. For example, the support department can provide support services available in the region by considering the user's geographical location. This allows the support department to select the optimal support method by considering the user's geographical location.

[0113] The support department can analyze a user's social media activity and suggest appropriate support methods during support sessions. For example, the support department can provide information on experts the user follows. Furthermore, the support department can provide information on groups and communities the user participates in. Additionally, the support department can provide support related to articles and posts the user has shared. This allows the support department to suggest the most suitable support methods by analyzing the user's social media activity.

[0114] The automation unit can estimate the user's emotions and adjust the timing of automation based on the estimated emotions. For example, if the user is feeling stressed, the automation unit can perform automation during a time when the user can relax. For example, the automation unit can estimate the user's emotions and perform automation during a time when the user is feeling stressed. The automation unit can also perform automation when the user is concentrating. For example, the automation unit can estimate the user's emotions and perform automation when the user is concentrating. Furthermore, if the user is tired, the automation unit can perform automation after the user has rested. For example, the automation unit can estimate the user's emotions and perform automation after the user has rested when the user is tired. In this way, the automation unit can perform efficient automation by adjusting the timing of automation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] The automation unit can analyze the user's past behavior history to select the optimal automation method during automation. For example, the automation unit can perform automation based on actions the user has successfully performed in the past. For example, the automation unit can analyze the user's past behavior history and perform automation based on successful actions. The automation unit can also perform automation that complements actions the user has struggled with in the past. For example, the automation unit can analyze the user's past behavior history and perform automation that complements actions the user has struggled with. Furthermore, the automation unit can determine the priority of automation according to the user's past behavior history. For example, the automation unit can analyze the user's past behavior history and determine the priority of automation according to that behavior history. In this way, the automation unit can select the optimal automation method by analyzing the user's past behavior history.

[0116] The automation unit can customize the automation methods based on the user's current lifestyle during automation. For example, if the user is busy, the automation unit can perform efficient automation in a short amount of time. For example, the automation unit can assess the user's lifestyle and perform efficient automation in a short amount of time if the user is busy. The automation unit can also perform detailed automation if the user has time. For example, the automation unit can assess the user's lifestyle and perform detailed automation if the user has time. Furthermore, the automation unit can adjust the automation methods according to the user's lifestyle. For example, the automation unit can assess the user's lifestyle and adjust the automation methods according to the situation. As a result, the automation unit can perform efficient automation by customizing the automation methods based on the user's current lifestyle.

[0117] The automation unit can estimate the user's emotions and determine the priority of automation based on the estimated emotions. For example, if the user is feeling anxious, the automation unit will prioritize automation that provides a sense of security. For example, the automation unit can estimate the user's emotions and prioritize automation that provides a sense of security if the user is feeling anxious. The automation unit can also prioritize challenging automation if the user is excited. For example, the automation unit can estimate the user's emotions and prioritize challenging automation if the user is excited. Furthermore, the automation unit can prioritize detailed automation if the user is calm. For example, the automation unit can estimate the user's emotions and prioritize detailed automation if the user is calm. In this way, the automation unit can efficiently perform automation by determining the priority of automation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0118] The automation unit can select the optimal automation method by considering the user's geographical location during automation. For example, the automation unit can automatically register users for seminars and events held in their region. For example, the automation unit can automatically register users for seminars and events held in their region by considering the user's geographical location. The automation unit can also automatically acquire learning resources related to the user's region. For example, the automation unit can automatically acquire learning resources related to the region by considering the user's geographical location. Furthermore, the automation unit can automatically register users for support services available in their region. For example, the automation unit can automatically register users for support services available in their region by considering the user's geographical location. In this way, the automation unit can select the optimal automation method by considering the user's geographical location.

[0119] The automation unit can analyze the user's social media activity and propose automation methods during the automation process. For example, the automation unit can automatically acquire information about experts the user follows. For example, the automation unit can analyze the user's social media activity and automatically acquire information about the experts the user follows. The automation unit can also automatically acquire information about groups and communities the user participates in. For example, the automation unit can analyze the user's social media activity and automatically acquire information about groups and communities the user participates in. Furthermore, the automation unit can automatically acquire information related to articles and posts the user shares. For example, the automation unit can analyze the user's social media activity and automatically acquire information related to articles and posts the user shares. As a result, the automation unit can analyze the user's social media activity and propose the most suitable automation method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those estimated emotions. For example, if the user is feeling anxious, the analysis unit can prioritize analyzing information that provides a sense of security. If the user is excited, it can prioritize analyzing challenging information. Furthermore, if the user is calm, it can prioritize analyzing detailed information. In this way, the analysis unit can perform efficient analysis by determining the priority of analysis according to the user's emotions.

[0122] The suggestion function can estimate the user's emotions and adjust the content of suggestions based on those emotions. For example, if the user is feeling stressed, it can offer suggestions that help them relax. If the user is concentrating, it can offer detailed suggestions at that time. Furthermore, if the user is tired, it can offer suggestions after they have rested. This allows the suggestion function to efficiently provide suggestions by adjusting the content according to the user's emotions.

[0123] The support department can estimate the user's emotions and adjust the support content based on those estimates. For example, if the user is feeling anxious, it can provide reassuring support. If the user is excited, it can provide challenging support. Furthermore, if the user is calm, it can provide detailed support. In this way, the support department can provide efficient support by adjusting the support content according to the user's emotions.

[0124] The automation unit can estimate the user's emotions and adjust the automated process based on those emotions. For example, if the user is stressed, it can perform relaxing automation. If the user is focused, it can perform detailed automation at that time. Furthermore, if the user is tired, it can perform automation after they have rested. This allows the automation unit to adjust the automated process according to the user's emotions, enabling efficient automation.

[0125] The data collection unit can estimate the user's emotions and adjust the content of information collection based on those emotions. For example, if the user is feeling anxious, it can prioritize collecting information that provides a sense of security. If the user is excited, it can prioritize collecting challenging information. Furthermore, if the user is calm, it can prioritize collecting detailed information. In this way, the data collection unit can efficiently collect information by adjusting the content of information collection according to the user's emotions.

[0126] The analysis unit can analyze the user's past learning history and select the optimal analysis method. For example, it can prioritize the analysis of learning resources that the user has preferred to use in the past. It can also perform analysis based on learning methods that the user has been successful with in the past. Furthermore, it can perform analysis that complements areas where the user has struggled in the past. In this way, the analysis unit can select the optimal analysis method by analyzing the user's past learning history.

[0127] The suggestion department can adjust the content of suggestions based on the user's current job responsibilities and areas of interest. For example, it can prioritize suggesting skill information relevant to the user's current job. It can also suggest the latest information related to the user's areas of interest. Furthermore, it can suggest specialized information tailored to the user's job responsibilities. In this way, the suggestion department can provide highly relevant suggestions by adjusting the content of suggestions based on the user's job responsibilities and areas of interest.

[0128] The support department can select the most appropriate support method by considering the user's geographical location. For example, it can provide information on seminars and events held in the user's region. It can also provide learning resources relevant to the user's region. Furthermore, it can provide support services available in the user's region. In this way, the support department can select the most appropriate support method by considering the user's geographical location.

[0129] The automation unit can analyze users' social media activity and propose automation methods. For example, it can automatically retrieve information about experts that users follow. It can also automatically retrieve information about groups and communities that users participate in. Furthermore, it can automatically retrieve information related to articles and posts that users have shared. In this way, the automation unit can analyze users' social media activity and propose the most suitable automation method.

[0130] The information gathering unit can customize its methods of information collection based on the user's current lifestyle. For example, if the user is busy, it can collect information quickly and effectively. If the user has more time, it can collect detailed information. Furthermore, it can adjust the methods of information collection according to the user's lifestyle. As a result, the information gathering unit can efficiently collect information by customizing its methods of information collection based on the user's current lifestyle.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The collection unit collects user information. User information includes, for example, personal information, learning history, and skill set. The collection unit collects information using questionnaires, sensors, and log data. For example, it can collect information about the user's wishes and current skills through questionnaires, collect user behavior data using sensors, and collect the user's past learning history using log data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data mining techniques, statistical analysis techniques, and machine learning techniques. For example, data mining techniques can be used to analyze the user's skill set, statistical analysis techniques can be used to analyze the user's learning history, and machine learning techniques can be used to analyze the user's skill set and learning history. Step 3: The proposal team proposes a direction for reskilling based on the analysis results obtained by the analysis team. The proposal is based on industry trends, individual career goals, and user interests. For example, it can analyze industry trends and propose a skill direction suitable for the user, propose a skill direction aligned with individual career goals, and propose a skill direction aligned with user interests. Step 4: The support team supports the reskilling program proposed by the proposal team. Support includes mentoring, provision of learning materials, and progress management. For example, they can support user learning through mentoring, provide learning materials suitable for the user, manage the user's learning progress, and provide appropriate feedback. Step 5: The automation unit automates the certification exam application process based on the reskilling program supported by the support unit. Automation includes automatic online form completion and schedule management. For example, it can automatically complete online forms to apply for certification exams, manage the exam schedule, submit applications at the appropriate time, and adjust the application timing according to the user's readiness.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, support unit, and automation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user information using the sensors and survey functions of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a direction for reskilling based on the analysis results. The support unit is implemented by the control unit 46A of the smart device 14 and supports the proposed reskilling program. The automation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automates the application process for the qualification examination. 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.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, support unit, and automation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user information using the sensors and survey functions of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a direction for reskilling based on the analysis results. The support unit is implemented by the control unit 46A of the smart glasses 214 and supports the proposed reskilling program. The automation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automates the application process for the qualification exam. 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.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, support unit, and automation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user information using the sensors and questionnaire functions of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a direction for reskilling based on the analysis results. The support unit is implemented by the control unit 46A of the headset terminal 314 and supports the proposed reskilling program. The automation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automates the application process for the qualification examination. 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.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, support unit, and automation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user information using the sensors and questionnaire functions of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a direction for reskilling based on the analysis results. The support unit is implemented by the control unit 46A of the robot 414 and supports the proposed reskilling program. The automation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automates the application process for the qualification examination. 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A collection unit that collects user 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 proposal unit proposes a direction for reskilling, A support unit that supports the reskilling program proposed by the aforementioned proposal unit, The system includes an automation unit that automates the application process for qualification exams based on a reskilling program supported by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose the direction, difficulty level, acquisition period, future prospects of the skills, and costs involved. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is We propose highly-rated textbooks, online courses, and seminars in the market. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is We will provide a detailed cost estimate for the proposed program. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is We create learning plans, manage progress, and provide regular feedback to support progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned automation unit, We manage the schedule for the certification exam specified by the user and handle the application process at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit is Using AI, we provide an optimal learning plan for each user, enabling them to learn efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned support unit is The plan is updated and provided in real time according to learning progress and changes in the environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned support unit is We provide opportunities to speak directly with top industry experts and support real-time problem solving. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned support unit is We offer customized programs for seniors. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned support unit is We provide support to help users with the same goals support each other. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze the user's past learning history and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current job responsibilities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the skills. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the skill category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making proposals, prioritize them based on when the skills were acquired. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the skills. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is During support, we analyze the user's past learning behavior to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned automation unit, It estimates the user's emotions and adjusts the timing of automation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned automation unit, During automation, the system analyzes the user's past behavior history to select the optimal automation method. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned automation unit, During automation, the automation methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned automation unit, It estimates user emotions and determines automation priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned automation unit, When automating processes, the system selects the optimal automation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned automation unit, During automation, we analyze users' social media activity and suggest automation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 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 unit that collects user 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 proposal unit proposes a direction for reskilling, A support unit that supports the reskilling program proposed by the aforementioned proposal unit, The system includes an automation unit that automates the application process for qualification exams based on a reskilling program supported by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned proposal section is, We propose the direction, difficulty level, acquisition period, future prospects of the skills, and costs involved. The system according to feature 1.

3. The aforementioned support unit is We propose highly-rated textbooks, online courses, and seminars in the market. The system according to feature 1.

4. The aforementioned support unit is We will provide a detailed cost estimate for the proposed program. The system according to feature 1.

5. The aforementioned support unit is We create learning plans, manage progress, and provide regular feedback to support progress. The system according to feature 1.

6. The aforementioned automation unit, We manage the schedule for the certification exam specified by the user and handle the application process at the appropriate time. The system according to feature 1.

7. The aforementioned support unit is Using AI, we provide an optimal learning plan for each user, enabling efficient learning. The system according to feature 1.

8. The aforementioned support unit is The plan is updated and provided in real time according to learning progress and changes in the environment. The system according to feature 1.

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