system

Abstract

The system according to this embodiment aims to provide users with optimal learning programs and career paths based on future trends in the occupational market. [Solution] The system according to the embodiment comprises a trend analysis unit, a customized learning unit, a practical training unit, and a career advice unit. The trend analysis unit analyzes future occupational market trends. The customized learning unit provides a learning program tailored to the user's interests and skill level based on the trends analyzed by the trend analysis unit. The practical training unit helps the user acquire occupational skills through simulations and practical projects based on the learning program provided by the customized learning unit. The career advice unit proposes the optimal career path to the user based on the skills acquired by the practical training unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the provision of learning programs based on future trends in the job market has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal learning program and career path for a user based on future trends in the job market.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a trend analysis unit, a customized learning unit, a practical training unit, and a career advice unit. The trend analysis unit analyzes future occupational market trends. The customized learning unit provides a learning program tailored to the user's interests and skill level based on the trends analyzed by the trend analysis unit. The practical training unit helps the user acquire occupational skills through simulations and practical projects based on the learning program provided by the customized learning unit. The career advice unit proposes the optimal career path for the user based on the skills acquired by the practical training unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with an optimal learning program and career path based on future occupational market trends. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by the contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 future vocational training system according to an embodiment of the present invention is a system that provides training programs related to future occupations and skills. This system analyzes trends in the future occupation market and provides customized learning programs according to the user's interests and skill level. Furthermore, it enables users to acquire actual occupational skills through simulations and practical projects and proposes an optimal career path. For example, in the future vocational training system, the trend analysis unit first analyzes trends in the future occupation market. In this process, AI analyzes past data and predicts future occupational market trends. For example, the AI ​​analyzes past occupational market data and identifies fields where future demand will be high. Next, the customized learning unit provides customized learning programs according to the user's interests and skill level. For example, if the user is interested in AI, learning content from basic to advanced levels of AI is provided. This allows the user to proceed with learning according to their interests and skill level. Furthermore, the practical training unit enables users to acquire actual occupational skills through simulations and practical projects. For example, through robotics simulations, users can acquire skills in actual robot operation. This enables users to acquire actual occupational skills. Finally, the career advice unit proposes an optimal career path for the user. For example, it suggests optimal occupations and career paths based on the user's skill set. This allows users to find the occupation or career path that is best suited to them. Thus, future career training systems enable users to acquire skills that prepare them for the future and broaden their career options. For instance, acquiring skills in fields such as AI, robotics, and sustainable energy technologies can enhance their competitiveness in the future job market. Furthermore, through customized learning programs and practical training, users can acquire real-world occupational skills. Additionally, receiving career advice from AI helps users find the occupation or career path that is best suited to them.This means that future vocational training systems will enable users to acquire skills that prepare them for the future and broaden their career options.

[0029] The futuristic vocational training system according to this embodiment comprises a trend analysis unit, a customized learning unit, a practical training unit, and a career advice unit. The trend analysis unit analyzes future vocational market trends. For example, the trend analysis unit analyzes past data and predicts future vocational market trends. The trend analysis unit uses AI to analyze past vocational market data and identify areas where future demand will increase. For example, the trend analysis unit analyzes past employment statistics, industry trends, and technological innovation data to predict future vocational market trends. The customized learning unit provides learning programs tailored to the user's interests and skill level. For example, the customized learning unit evaluates the user's interests and skill level and provides a customized learning program. For example, the customized learning unit uses AI to evaluate the user's interests and skill level and provides the optimal learning program. For example, the customized learning unit provides the optimal learning program to the user based on questionnaires, past learning history, and skill test results. The practical training unit helps users acquire vocational skills through simulations and practical projects. For example, the practical training unit conducts simulations to acquire actual vocational skills. The practical training unit uses AI to conduct simulations and assist users in acquiring actual occupational skills. For example, the practical training unit assists users in acquiring skills in actual robot operation through robotics simulations. The career advice unit proposes the optimal career path for the user. For example, the career advice unit proposes the optimal occupation and career path based on the user's skill set. The career advice unit uses AI to evaluate the user's skill set and proposes the optimal career path. For example, the career advice unit proposes the optimal occupation and career path based on the user's skill test results, past experience, and self-assessment results. As a result, the future occupational training system according to this embodiment allows users to acquire skills to prepare for the future and broaden their career options.

[0030] The Trend Analysis Department analyzes future trends in the occupational market. For example, it analyzes historical data to predict future occupational market trends. Specifically, the Trend Analysis Department uses AI to analyze historical occupational market data and identify sectors where future demand will increase. The AI ​​uses machine learning algorithms to analyze large amounts of employment statistics data, industry trend data, and technological innovation data to predict future occupational market trends. For example, based on employment statistics data from the past 10 years, it calculates the growth rate and decline rate of specific occupational sectors and identifies occupations where future demand will increase. Furthermore, by analyzing industry trend data, it is possible to predict the demand for occupations that respond to new technologies and market changes. In addition, by analyzing technological innovation data, it is possible to predict the demand for new occupations and skills that will arise from the introduction of new technologies. As a result, the Trend Analysis Department can accurately predict future occupational market trends and provide users with information that will be useful for future career choices. Furthermore, the Trend Analysis Department can regularly update data and make predictions based on the latest market trends. For example, by collecting monthly employment statistics data and quarterly industry trend data and analyzing them using AI, it is possible to grasp the latest occupational market trends. This allows the trend analysis department to consistently provide forecasts based on the latest information, helping users make optimal decisions regarding their future career choices.

[0031] The Customized Learning Department provides learning programs tailored to users' interests and skill levels. Specifically, it evaluates users' interests and skill levels and provides customized learning programs. It uses AI to evaluate users' interests and skill levels and provide the optimal learning program. For example, the Customized Learning Department provides the optimal learning program to users based on questionnaires, past learning history, and skill test results. The AI ​​analyzes the user's answered questionnaire data and past learning history to identify the user's interests and areas of expertise. It also evaluates the user's current skill level based on skill test results and provides a learning program of appropriate difficulty. For example, a user interested in programming is provided with a step-by-step learning program from basic to advanced levels, and opportunities to hone skills through actual projects. A user interested in design is provided with a program that covers everything from the basic principles of design to how to use the latest design tools. Furthermore, the Customized Learning Department can monitor users' learning progress in real time and adjust the learning program as needed. For example, if a user is struggling with a particular task, it can provide additional resources and support to maximize the effectiveness of their learning. Also, if a user achieves outstanding results in a particular area, it can provide a more advanced learning program to support further skill improvement. This allows the customized learning unit to provide flexible learning programs tailored to the individual needs of users, supporting effective skill acquisition.

[0032] The Practical Training Department helps trainees acquire professional skills through simulations and practical projects. Specifically, the Practical Training Department uses simulations to help trainees acquire actual professional skills. AI is used in the simulations to support users in acquiring actual professional skills. For example, the Practical Training Department helps users acquire skills in actual robot operation through robotics simulations. AI provides a realistic simulation environment, allowing users to experience challenges they will face in a real work environment. For example, in robotics simulations, users can program and operate robots, developing problem-solving abilities in a real work environment. In medical simulations, users can acquire actual medical skills through simulations of surgery and diagnosis. Furthermore, the Practical Training Department provides opportunities to hone professional skills through actual projects. For example, by working on projects in teams, trainees can improve their communication and teamwork skills. In addition, by collaborating with actual companies and organizations and providing internship and practical training opportunities, trainees can gain experience in a real work environment. In this way, the Practical Training Department enables trainees to effectively acquire actual professional skills and prepare for their future careers. Furthermore, the practical training department supports continuous skill improvement by evaluating users' training outcomes and providing feedback. For example, based on simulation results and project outcomes, it proposes specific areas for improvement and next steps to users. In this way, the practical training department can provide support to users in effectively acquiring skills and succeeding in real-world work environments.

[0033] The Career Advice Department proposes the most suitable career path for each user. Specifically, it suggests the most suitable occupation and career path based on the user's skill set. It uses AI to evaluate the user's skill set and propose the optimal career path. For example, the Career Advice Department suggests the most suitable occupation and career path based on the user's skill test results, past experience, and self-assessment. The AI ​​analyzes the user's skill set and interests, and compares them with future occupational market trends to identify the most suitable career path. For example, if a user has programming skills, the AI ​​identifies programming languages ​​and technologies that will be in high demand in the future and proposes a career path based on them. Similarly, if a user has design skills, the AI ​​analyzes the latest trends and demand in the design field and proposes a career path. Furthermore, the Career Advice Department can propose flexible career paths tailored to the user's career goals and lifestyle. For example, if a user desires remote work, it suggests occupations and companies that allow remote work, providing a career path that suits the user's lifestyle. The Career Advice Department can also provide resources and learning programs for skill development aligned with the user's career path. For example, it can suggest learning programs for obtaining qualifications required for specific occupations or specialized training for career advancement. This allows the career advice department to support users in effectively preparing for their future careers. Furthermore, the career advice department can regularly monitor users' career progress and revise or suggest career paths as needed. In this way, the career advice department can support users in always following the optimal career path and contribute to their future success.

[0034] The Trend Analysis Department can analyze historical data and predict future trends in the occupational market. For example, it analyzes past employment statistics, industry reports, and historical market data. The Trend Analysis Department uses AI to analyze historical data and predict future occupational market trends. For instance, it uses statistical models and machine learning algorithms to predict future occupational market trends. This allows for prediction of future occupational market trends by analyzing historical data.

[0035] The customized learning unit can assess users' interests and skill levels and provide customized learning programs. For example, it assesses users' interests and skill levels based on surveys, past learning history, and skill test results. The customized learning unit uses AI to assess users' interests and skill levels and provide optimal learning programs. For instance, if a user is interested in AI, the customized learning unit provides learning content covering AI from basic to advanced levels. This allows for the provision of learning programs tailored to users' interests and skill levels.

[0036] The practical training department allows users to acquire real-world professional skills through simulations. For example, the practical training department assists users in acquiring real-world professional skills through training in virtual environments and participation in actual projects. The practical training department uses AI to conduct simulations and assists users in acquiring real-world professional skills. For example, the practical training department assists users in acquiring actual robot operation skills through robotics simulations. This allows users to acquire real-world professional skills through simulations.

[0037] The career advice department can suggest optimal occupations and career paths based on the user's skill set. For example, it suggests optimal occupations and career paths based on the user's skill test results, past experience, and self-assessment. The career advice department uses AI to evaluate the user's skill set and suggest optimal career paths. For instance, it suggests technical, creative, or leadership career paths based on the user's skill set. This allows it to suggest the most suitable career path based on the user's skill set.

[0038] The Trend Analysis Department can incorporate and analyze not only historical data but also real-time market data during trend analysis. For example, the Trend Analysis Department can incorporate real-time stock price data to analyze future occupational market trends. The Trend Analysis Department can incorporate real-time job postings to identify occupations with increasing demand. The Trend Analysis Department can incorporate real-time economic indicators to predict occupational market trends. The Trend Analysis Department can use AI to analyze real-time market data and predict future occupational market trends. For example, the Trend Analysis Department can obtain real-time market data from online databases, APIs, and news feeds and use it for analysis. This allows for more accurate trend analysis by incorporating real-time market data.

[0039] The Trend Analysis Department can discover new trends by cross-referencing data from different industries during trend analysis. For example, it can cross-reference data from the IT and healthcare industries to discover trends in healthcare technology. It can cross-reference data from the energy and automotive industries to discover trends in electric vehicles. It can cross-reference data from the education and technology industries to discover trends in EdTech. The Trend Analysis Department uses AI to analyze data from different industries and discover new trends. For example, it can cross-reference industry reports, industry news, and statistical data to identify new trends. In this way, new trends can be discovered by cross-referencing data from different industries.

[0040] The trend analysis unit can analyze regional trends by taking into account the user's geographical location information during trend analysis. For example, if the user lives in an urban area, the trend analysis unit will prioritize analyzing occupational trends in urban areas. If the user lives in a rural area, the trend analysis unit will prioritize analyzing occupational trends in rural areas. If the user lives overseas, the trend analysis unit will prioritize analyzing occupational trends in that country. The trend analysis unit uses AI to analyze the user's geographical location information and identify regional trends. For example, the trend analysis unit obtains the user's geographical location information using GPS data, IP addresses, and regional codes, and uses this for analysis. This allows for the analysis of regional trends by taking the user's geographical location information into consideration.

[0041] The Trend Analysis Department can incorporate social media data into its trend analysis. For example, it can analyze hashtags on social media to identify popular occupational trends. It can analyze user posts on social media to identify skills that are in high demand. It can analyze posts by influencers on social media to predict future occupational trends. The Trend Analysis Department uses AI to analyze social media data and predict future occupational market trends. For example, it can analyze social media post content, user followers, and engagement data to identify trends. By incorporating social media data, it enables more accurate trend analysis.

[0042] The customized learning unit can select the most suitable program by referring to the user's past learning history when providing learning programs. For example, the customized learning unit can provide a learning program that allows the user to move on to the next step based on what the user has learned in the past. The customized learning unit can provide a learning program that reinforces areas where the user has struggled in the past. The customized learning unit can provide a learning program that delves deeper into areas where the user has been interested in the past. The customized learning unit uses AI to analyze the user's past learning history and provide the most suitable learning program. For example, the customized learning unit can refer to the learning management system, course records, and grade data to provide the most suitable learning program for the user. In this way, the optimal learning program can be provided by referring to the user's past learning history.

[0043] The Customized Learning Department can customize learning programs based on the user's current occupation and skill set. For example, if the user is an engineer, the Customized Learning Department will provide a learning program to enhance their technical skills. If the user is a marketing professional, the Customized Learning Department will provide a learning program to enhance their digital marketing skills. If the user is a manager, the Customized Learning Department will provide a learning program to enhance their leadership skills. The Customized Learning Department uses AI to analyze the user's current occupation and skill set and provide the optimal learning program. For example, the Customized Learning Department will provide the optimal learning program to the user based on their resume, skills test results, and self-assessment results. This allows the learning program to be customized based on the user's current occupation and skill set.

[0044] The customized learning unit can provide learning programs in the most optimal format, taking into account the user's device information. For example, if the user is using a smartphone, the customized learning unit will provide a mobile-optimized learning program. If the user is using a tablet, the customized learning unit will provide a large-screen-optimized learning program. If the user is using a personal computer, the customized learning unit will provide a desktop-optimized learning program. The customized learning unit uses AI to analyze the user's device information and provide the learning program in the most optimal format. For example, the customized learning unit acquires and uses information such as device type, OS, and browser information for analysis. This allows the learning program to be provided in the most optimal format by taking the user's device information into consideration.

[0045] The Customized Learning Department can provide relevant learning content by analyzing users' social media activity when delivering learning programs. For example, it can provide relevant learning content based on articles shared by users on social media. It can provide relevant learning content based on posts from influencers followed by users. It can provide relevant learning content based on topics in online communities that users participate in. The Customized Learning Department uses AI to analyze users' social media activity and provide optimal learning content. For example, it can analyze social media posts, followers, and engagement data to provide the most suitable learning content for users. In this way, it can provide relevant learning content by analyzing users' social media activity.

[0046] The Practical Training Department can select the most suitable training content by referring to the user's past training history during practical training sessions. For example, the Practical Training Department can provide training to help the user move to the next step based on the training content the user has previously completed. The Practical Training Department can provide training to reinforce areas where the user has struggled in the past. The Practical Training Department can provide training that delves deeper into areas where the user has previously shown interest. The Practical Training Department uses AI to analyze the user's past training history and provide the most suitable training content. For example, the Practical Training Department can refer to the training management system, course completion records, and performance data to provide the most suitable training content for the user. In this way, it can provide the most suitable training content by referring to the user's past training history.

[0047] The Practical Training Department can customize training content based on the user's current skill set during practical training sessions. For example, if a user has programming skills, the Practical Training Department will provide advanced programming training. If a user has design skills, the Practical Training Department will provide training that includes design projects. If a user has management skills, the Practical Training Department will provide leadership training. The Practical Training Department uses AI to analyze the user's current skill set and provide optimal training content. For example, the Practical Training Department will provide the most suitable training content for the user based on their resume, skills test results, and self-assessment results. This allows for the customization of training content based on the user's current skill set.

[0048] The Practical Training Department can provide an optimal training environment during practical training sessions by considering the user's geographical location. For example, if the user lives in an urban area, the Practical Training Department will provide an urban training environment. If the user lives in a rural area, the Practical Training Department will provide a rural training environment. If the user lives overseas, the Practical Training Department will provide a training environment for that country. The Practical Training Department uses AI to analyze the user's geographical location and provide an optimal training environment. For example, the Practical Training Department obtains the user's geographical location using GPS data, IP addresses, and regional codes, and uses this information for analysis. This allows the department to provide an optimal training environment by considering the user's geographical location.

[0049] The Practical Training Department can analyze users' social media activity during practical training sessions and provide relevant training content. For example, the Practical Training Department can provide relevant training content based on articles shared by users on social media. The Practical Training Department can provide relevant training content based on posts from influencers followed by users. The Practical Training Department can provide relevant training content based on topics in online communities that users participate in. The Practical Training Department can use AI to analyze users' social media activity and provide optimal training content. For example, the Practical Training Department can analyze social media posts, followers, and engagement data to provide the most suitable training content for each user. In this way, by analyzing users' social media activity, it is possible to provide relevant training content.

[0050] The career advice department can provide optimal advice by referring to the user's past career history when offering career advice. For example, the career advice department can provide career advice to help the user take the next step based on the occupations the user has experienced in the past. The career advice department can provide career advice to help the user avoid occupations they have had difficulty with in the past. The career advice department can provide career advice that delves deeper into areas the user has been interested in in the past. The career advice department uses AI to analyze the user's past career history and provide optimal career advice. For example, the career advice department can refer to resumes, CVs, and past projects to provide the user with the most suitable career advice. In this way, by referring to the user's past career history, it can provide optimal career advice.

[0051] The Career Advice Department can customize the advice it provides based on the user's current skill set. For example, if a user has programming skills, it will suggest a technical career path. If a user has design skills, it will suggest a creative career path. If a user has management skills, it will suggest a leadership career path. The Career Advice Department uses AI to analyze the user's current skill set and suggest the optimal career path. For example, it will suggest the best career path for the user based on their resume, skills test results, and self-assessment results. This allows for customized career advice based on the user's current skill set.

[0052] The Career Advice Department can propose the optimal career path when providing career advice, taking into account the user's geographical location. For example, if the user lives in an urban area, the Career Advice Department will propose a career path in an urban area. If the user lives in a rural area, the Career Advice Department will propose a career path in a rural area. If the user lives overseas, the Career Advice Department will propose a career path in that country. The Career Advice Department uses AI to analyze the user's geographical location and propose the optimal career path. For example, the Career Advice Department obtains the user's geographical location using GPS data, IP addresses, and regional codes, and uses this information for analysis. This allows the department to propose the optimal career path by taking the user's geographical location into consideration.

[0053] The Career Advice Department can analyze a user's social media activity and propose relevant career paths when providing career advice. For example, the Career Advice Department can propose relevant career paths based on articles the user has shared on social media. The Career Advice Department can propose relevant career paths based on posts from influencers the user follows. The Career Advice Department can propose relevant career paths based on topics in online communities the user participates in. The Career Advice Department uses AI to analyze a user's social media activity and propose the optimal career path. For example, the Career Advice Department analyzes social media posts, followers, and engagement data to propose the most suitable career path for the user. In this way, by analyzing a user's social media activity, it can propose relevant career paths.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Future vocational training systems can analyze regional trends by considering the user's geographical location. For example, if a user lives in an urban area, the system can prioritize analyzing urban vocational trends. If a user lives in a rural area, it can prioritize analyzing rural vocational trends. If a user lives abroad, it can prioritize analyzing vocational trends in that country. The trend analysis unit uses AI to analyze the user's geographical location and identify regional trends. This allows for the analysis of regional trends by considering the user's geographical location, enabling the provision of more appropriate vocational training.

[0056] Future career training systems can analyze users' social media activity and provide relevant learning content. For example, they can provide relevant learning content based on articles users share on social media, posts from influencers users follow, and topics from online communities users participate in. The customized learning section can use AI to analyze users' social media activity and provide optimal learning content. This allows for the provision of relevant learning content and a more engaging learning experience by analyzing users' social media activity.

[0057] Future vocational training systems can select the most suitable training content by referencing a user's past training history. For example, they can provide training to help users advance to the next step based on their past training. They can also provide training to reinforce areas where users previously struggled. Furthermore, they can provide training to delve deeper into areas where users have shown interest in the past. The practical training department can use AI to analyze a user's past training history and provide the most suitable training content. This allows for the provision of optimal training content and a more effective training experience by referencing the user's past training history.

[0058] Future career training systems can customize learning programs based on a user's current occupation and skill set. For example, if a user is an engineer, a learning program can be provided to enhance their technical skills. If a user is a marketing professional, a learning program can be provided to enhance their digital marketing skills. If a user is a manager, a learning program can be provided to enhance their leadership skills. The customized learning unit can use AI to analyze the user's current occupation and skill set and provide the optimal learning program. This allows for a more effective learning experience by customizing learning programs based on the user's current occupation and skill set.

[0059] Future career training systems can provide optimal career advice by referencing a user's past career history. For example, they can provide career advice to help users take their next steps based on their past work experience. They can also provide career advice to help users avoid occupations they previously struggled with. Furthermore, they can provide career advice to help users delve deeper into areas they were previously interested in. The career advice unit can use AI to analyze a user's past career history and provide optimal career advice. This allows the system to provide optimal career advice and suggest more appropriate career paths by referencing the user's past career history.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The Trend Analysis Department analyzes future trends in the occupational market. For example, it analyzes historical data to predict future occupational market trends. The Trend Analysis Department uses AI to analyze historical occupational market data and identify sectors where future demand will increase. Specifically, it analyzes historical employment statistics, industry trends, and technological innovation data. Step 2: The customized learning unit provides learning programs tailored to the user's interests and skill level. For example, it evaluates the user's interests and skill level and provides a customized learning program. The customized learning unit uses AI to evaluate the user's interests and skill level and provide the optimal learning program. Specifically, it uses questionnaires, past learning history, and skill test results as its basis. Step 3: The Practical Training Department helps trainees acquire professional skills through simulations and practical projects. For example, they conduct simulations to learn actual professional skills. The Practical Training Department uses AI to conduct simulations and assists users in acquiring actual professional skills. Specifically, it assists users in acquiring skills in actual robot operation through robotics simulations. Step 4: The Career Advice Department proposes the optimal career path for the user. For example, it suggests the best occupation and career path based on the user's skill set. The Career Advice Department uses AI to evaluate the user's skill set and propose the optimal career path. Specifically, it uses the user's skill test results, past experience, and self-assessment results.

[0062] (Example of form 2) The future vocational training system according to an embodiment of the present invention is a system that provides training programs related to future occupations and skills. This system analyzes trends in the future occupation market and provides customized learning programs according to the user's interests and skill level. Furthermore, it enables users to acquire actual occupational skills through simulations and practical projects and proposes an optimal career path. For example, in the future vocational training system, the trend analysis unit first analyzes trends in the future occupation market. In this process, AI analyzes past data and predicts future occupational market trends. For example, the AI ​​analyzes past occupational market data and identifies fields where future demand will be high. Next, the customized learning unit provides customized learning programs according to the user's interests and skill level. For example, if the user is interested in AI, learning content from basic to advanced levels of AI is provided. This allows the user to proceed with learning according to their interests and skill level. Furthermore, the practical training unit enables users to acquire actual occupational skills through simulations and practical projects. For example, through robotics simulations, users can acquire skills in actual robot operation. This enables users to acquire actual occupational skills. Finally, the career advice unit proposes an optimal career path for the user. For example, it suggests optimal occupations and career paths based on the user's skill set. This allows users to find the occupation or career path that is best suited to them. Thus, future career training systems enable users to acquire skills that prepare them for the future and broaden their career options. For instance, acquiring skills in fields such as AI, robotics, and sustainable energy technologies can enhance their competitiveness in the future job market. Furthermore, through customized learning programs and practical training, users can acquire real-world occupational skills. Additionally, receiving career advice from AI helps users find the occupation or career path that is best suited to them.This means that future vocational training systems will enable users to acquire skills that prepare them for the future and broaden their career options.

[0063] The futuristic vocational training system according to this embodiment comprises a trend analysis unit, a customized learning unit, a practical training unit, and a career advice unit. The trend analysis unit analyzes future vocational market trends. For example, the trend analysis unit analyzes past data and predicts future vocational market trends. The trend analysis unit uses AI to analyze past vocational market data and identify areas where future demand will increase. For example, the trend analysis unit analyzes past employment statistics, industry trends, and technological innovation data to predict future vocational market trends. The customized learning unit provides learning programs tailored to the user's interests and skill level. For example, the customized learning unit evaluates the user's interests and skill level and provides a customized learning program. For example, the customized learning unit uses AI to evaluate the user's interests and skill level and provides the optimal learning program. For example, the customized learning unit provides the optimal learning program to the user based on questionnaires, past learning history, and skill test results. The practical training unit helps users acquire vocational skills through simulations and practical projects. For example, the practical training unit conducts simulations to acquire actual vocational skills. The practical training unit uses AI to conduct simulations and assist users in acquiring actual occupational skills. For example, the practical training unit assists users in acquiring skills in actual robot operation through robotics simulations. The career advice unit proposes the optimal career path for the user. For example, the career advice unit proposes the optimal occupation and career path based on the user's skill set. The career advice unit uses AI to evaluate the user's skill set and proposes the optimal career path. For example, the career advice unit proposes the optimal occupation and career path based on the user's skill test results, past experience, and self-assessment results. As a result, the future occupational training system according to this embodiment allows users to acquire skills to prepare for the future and broaden their career options.

[0064] The Trend Analysis Department analyzes future trends in the occupational market. For example, it analyzes historical data to predict future occupational market trends. Specifically, the Trend Analysis Department uses AI to analyze historical occupational market data and identify sectors where future demand will increase. The AI ​​uses machine learning algorithms to analyze large amounts of employment statistics data, industry trend data, and technological innovation data to predict future occupational market trends. For example, based on employment statistics data from the past 10 years, it calculates the growth rate and decline rate of specific occupational sectors and identifies occupations where future demand will increase. Furthermore, by analyzing industry trend data, it is possible to predict the demand for occupations that respond to new technologies and market changes. In addition, by analyzing technological innovation data, it is possible to predict the demand for new occupations and skills that will arise from the introduction of new technologies. As a result, the Trend Analysis Department can accurately predict future occupational market trends and provide users with information that will be useful for future career choices. Furthermore, the Trend Analysis Department can regularly update data and make predictions based on the latest market trends. For example, by collecting monthly employment statistics data and quarterly industry trend data and analyzing them using AI, it is possible to grasp the latest occupational market trends. This allows the trend analysis department to consistently provide forecasts based on the latest information, helping users make optimal decisions regarding their future career choices.

[0065] The Customized Learning Department provides learning programs tailored to users' interests and skill levels. Specifically, it evaluates users' interests and skill levels and provides customized learning programs. It uses AI to evaluate users' interests and skill levels and provide the optimal learning program. For example, the Customized Learning Department provides the optimal learning program to users based on questionnaires, past learning history, and skill test results. The AI ​​analyzes the user's answered questionnaire data and past learning history to identify the user's interests and areas of expertise. It also evaluates the user's current skill level based on skill test results and provides a learning program of appropriate difficulty. For example, a user interested in programming is provided with a step-by-step learning program from basic to advanced levels, and opportunities to hone skills through actual projects. A user interested in design is provided with a program that covers everything from the basic principles of design to how to use the latest design tools. Furthermore, the Customized Learning Department can monitor users' learning progress in real time and adjust the learning program as needed. For example, if a user is struggling with a particular task, it can provide additional resources and support to maximize the effectiveness of their learning. Also, if a user achieves outstanding results in a particular area, it can provide a more advanced learning program to support further skill improvement. This allows the customized learning unit to provide flexible learning programs tailored to the individual needs of users, supporting effective skill acquisition.

[0066] The Practical Training Department helps trainees acquire professional skills through simulations and practical projects. Specifically, the Practical Training Department uses simulations to help trainees acquire actual professional skills. AI is used in the simulations to support users in acquiring actual professional skills. For example, the Practical Training Department helps users acquire skills in actual robot operation through robotics simulations. AI provides a realistic simulation environment, allowing users to experience challenges they will face in a real work environment. For example, in robotics simulations, users can program and operate robots, developing problem-solving abilities in a real work environment. In medical simulations, users can acquire actual medical skills through simulations of surgery and diagnosis. Furthermore, the Practical Training Department provides opportunities to hone professional skills through actual projects. For example, by working on projects in teams, trainees can improve their communication and teamwork skills. In addition, by collaborating with actual companies and organizations and providing internship and practical training opportunities, trainees can gain experience in a real work environment. In this way, the Practical Training Department enables trainees to effectively acquire actual professional skills and prepare for their future careers. Furthermore, the practical training department supports continuous skill improvement by evaluating users' training outcomes and providing feedback. For example, based on simulation results and project outcomes, it proposes specific areas for improvement and next steps to users. In this way, the practical training department can provide support to users in effectively acquiring skills and succeeding in real-world work environments.

[0067] The Career Advice Department proposes the most suitable career path for each user. Specifically, it suggests the most suitable occupation and career path based on the user's skill set. It uses AI to evaluate the user's skill set and propose the optimal career path. For example, the Career Advice Department suggests the most suitable occupation and career path based on the user's skill test results, past experience, and self-assessment. The AI ​​analyzes the user's skill set and interests, and compares them with future occupational market trends to identify the most suitable career path. For example, if a user has programming skills, the AI ​​identifies programming languages ​​and technologies that will be in high demand in the future and proposes a career path based on them. Similarly, if a user has design skills, the AI ​​analyzes the latest trends and demand in the design field and proposes a career path. Furthermore, the Career Advice Department can propose flexible career paths tailored to the user's career goals and lifestyle. For example, if a user desires remote work, it suggests occupations and companies that allow remote work, providing a career path that suits the user's lifestyle. The Career Advice Department can also provide resources and learning programs for skill development aligned with the user's career path. For example, it can suggest learning programs for obtaining qualifications required for specific occupations or specialized training for career advancement. This allows the career advice department to support users in effectively preparing for their future careers. Furthermore, the career advice department can regularly monitor users' career progress and revise or suggest career paths as needed. In this way, the career advice department can support users in always following the optimal career path and contribute to their future success.

[0068] The Trend Analysis Department can analyze historical data and predict future trends in the occupational market. For example, it analyzes past employment statistics, industry reports, and historical market data. The Trend Analysis Department uses AI to analyze historical data and predict future occupational market trends. For instance, it uses statistical models and machine learning algorithms to predict future occupational market trends. This allows for prediction of future occupational market trends by analyzing historical data.

[0069] The customized learning unit can assess users' interests and skill levels and provide customized learning programs. For example, it assesses users' interests and skill levels based on surveys, past learning history, and skill test results. The customized learning unit uses AI to assess users' interests and skill levels and provide optimal learning programs. For instance, if a user is interested in AI, the customized learning unit provides learning content covering AI from basic to advanced levels. This allows for the provision of learning programs tailored to users' interests and skill levels.

[0070] The practical training department allows users to acquire real-world professional skills through simulations. For example, the practical training department assists users in acquiring real-world professional skills through training in virtual environments and participation in actual projects. The practical training department uses AI to conduct simulations and assists users in acquiring real-world professional skills. For example, the practical training department assists users in acquiring actual robot operation skills through robotics simulations. This allows users to acquire real-world professional skills through simulations.

[0071] The career advice department can suggest optimal occupations and career paths based on the user's skill set. For example, it suggests optimal occupations and career paths based on the user's skill test results, past experience, and self-assessment. The career advice department uses AI to evaluate the user's skill set and suggest optimal career paths. For instance, it suggests technical, creative, or leadership career paths based on the user's skill set. This allows it to suggest the most suitable career path based on the user's skill set.

[0072] The trend analysis unit can estimate the user's emotions and adjust the trend analysis results based on those estimated emotions. For example, if the user is optimistic, the trend analysis unit will provide analysis results that emphasize positive trends. If the user is anxious, the trend analysis unit will provide analysis results that emphasize trends that minimize risk. If the user has neutral emotions, the trend analysis unit will provide balanced trend analysis results. 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. This allows the trend analysis results to be adjusted according to the user's emotions.

[0073] The Trend Analysis Department can incorporate and analyze not only historical data but also real-time market data during trend analysis. For example, the Trend Analysis Department can incorporate real-time stock price data to analyze future occupational market trends. The Trend Analysis Department can incorporate real-time job postings to identify occupations with increasing demand. The Trend Analysis Department can incorporate real-time economic indicators to predict occupational market trends. The Trend Analysis Department can use AI to analyze real-time market data and predict future occupational market trends. For example, the Trend Analysis Department can obtain real-time market data from online databases, APIs, and news feeds and use it for analysis. This allows for more accurate trend analysis by incorporating real-time market data.

[0074] The Trend Analysis Department can discover new trends by cross-referencing data from different industries during trend analysis. For example, it can cross-reference data from the IT and healthcare industries to discover trends in healthcare technology. It can cross-reference data from the energy and automotive industries to discover trends in electric vehicles. It can cross-reference data from the education and technology industries to discover trends in EdTech. The Trend Analysis Department uses AI to analyze data from different industries and discover new trends. For example, it can cross-reference industry reports, industry news, and statistical data to identify new trends. In this way, new trends can be discovered by cross-referencing data from different industries.

[0075] The trend analysis unit can estimate the user's emotions and determine the priority of trend analysis based on the estimated emotions. For example, if the user is excited, the trend analysis unit will prioritize analyzing rapidly growing trends. If the user is feeling anxious, the trend analysis unit will prioritize analyzing stable trends. If the user has neutral emotions, the trend analysis unit will prioritize analyzing balanced trends. 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. This allows the priority of trend analysis to be determined according to the user's emotions.

[0076] The trend analysis unit can analyze regional trends by taking into account the user's geographical location information during trend analysis. For example, if the user lives in an urban area, the trend analysis unit will prioritize analyzing occupational trends in urban areas. If the user lives in a rural area, the trend analysis unit will prioritize analyzing occupational trends in rural areas. If the user lives overseas, the trend analysis unit will prioritize analyzing occupational trends in that country. The trend analysis unit uses AI to analyze the user's geographical location information and identify regional trends. For example, the trend analysis unit obtains the user's geographical location information using GPS data, IP addresses, and regional codes, and uses this for analysis. This allows for the analysis of regional trends by taking the user's geographical location information into consideration.

[0077] The Trend Analysis Department can incorporate social media data into its trend analysis. For example, it can analyze hashtags on social media to identify popular occupational trends. It can analyze user posts on social media to identify skills that are in high demand. It can analyze posts by influencers on social media to predict future occupational trends. The Trend Analysis Department uses AI to analyze social media data and predict future occupational market trends. For example, it can analyze social media post content, user followers, and engagement data to identify trends. By incorporating social media data, it enables more accurate trend analysis.

[0078] The customized learning unit can estimate the user's emotions and adjust the content of the learning program based on those emotions. For example, if the user is excited, the customized learning unit will provide a learning program that includes challenging tasks. If the user is feeling anxious, the customized learning unit will provide a learning program that starts with basic content. If the user is relaxed, the customized learning unit will provide a learning program that includes advanced content. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the content of the learning program to be adjusted according to the user's emotions.

[0079] The customized learning unit can select the most suitable program by referring to the user's past learning history when providing learning programs. For example, the customized learning unit can provide a learning program that allows the user to move on to the next step based on what the user has learned in the past. The customized learning unit can provide a learning program that reinforces areas where the user has struggled in the past. The customized learning unit can provide a learning program that delves deeper into areas where the user has been interested in the past. The customized learning unit uses AI to analyze the user's past learning history and provide the most suitable learning program. For example, the customized learning unit can refer to the learning management system, course records, and grade data to provide the most suitable learning program for the user. In this way, the optimal learning program can be provided by referring to the user's past learning history.

[0080] The Customized Learning Department can customize learning programs based on the user's current occupation and skill set. For example, if the user is an engineer, the Customized Learning Department will provide a learning program to enhance their technical skills. If the user is a marketing professional, the Customized Learning Department will provide a learning program to enhance their digital marketing skills. If the user is a manager, the Customized Learning Department will provide a learning program to enhance their leadership skills. The Customized Learning Department uses AI to analyze the user's current occupation and skill set and provide the optimal learning program. For example, the Customized Learning Department will provide the optimal learning program to the user based on their resume, skills test results, and self-assessment results. This allows the learning program to be customized based on the user's current occupation and skill set.

[0081] The customized learning unit can estimate the user's emotions and adjust the pace of the learning program based on those emotions. For example, if the user is in a hurry, the customized learning unit can provide a learning program that can be completed in a short period of time. If the user is relaxed, the customized learning unit can provide a learning program that progresses slowly. If the user is feeling anxious, the customized learning unit can provide a learning program that progresses in stages. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the pace of the learning program to be adjusted according to the user's emotions.

[0082] The customized learning unit can provide learning programs in the most optimal format, taking into account the user's device information. For example, if the user is using a smartphone, the customized learning unit will provide a mobile-optimized learning program. If the user is using a tablet, the customized learning unit will provide a large-screen-optimized learning program. If the user is using a personal computer, the customized learning unit will provide a desktop-optimized learning program. The customized learning unit uses AI to analyze the user's device information and provide the learning program in the most optimal format. For example, the customized learning unit acquires and uses information such as device type, OS, and browser information for analysis. This allows the learning program to be provided in the most optimal format by taking the user's device information into consideration.

[0083] The Customized Learning Department can provide relevant learning content by analyzing users' social media activity when delivering learning programs. For example, it can provide relevant learning content based on articles shared by users on social media. It can provide relevant learning content based on posts from influencers followed by users. It can provide relevant learning content based on topics in online communities that users participate in. The Customized Learning Department uses AI to analyze users' social media activity and provide optimal learning content. For example, it can analyze social media posts, followers, and engagement data to provide the most suitable learning content for users. In this way, it can provide relevant learning content by analyzing users' social media activity.

[0084] The practical training unit can estimate the user's emotions and adjust the difficulty of the training based on the estimated emotions. For example, if the user is excited, the practical training unit will provide a high-difficulty training. If the user is feeling anxious, the practical training unit will provide a low-difficulty training. If the user is relaxed, the practical training unit will provide training of a moderate difficulty. 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. This allows the difficulty of the training to be adjusted according to the user's emotions.

[0085] The Practical Training Department can select the most suitable training content by referring to the user's past training history during practical training sessions. For example, the Practical Training Department can provide training to help the user move to the next step based on the training content the user has previously completed. The Practical Training Department can provide training to reinforce areas where the user has struggled in the past. The Practical Training Department can provide training that delves deeper into areas where the user has previously shown interest. The Practical Training Department uses AI to analyze the user's past training history and provide the most suitable training content. For example, the Practical Training Department can refer to the training management system, course completion records, and performance data to provide the most suitable training content for the user. In this way, it can provide the most suitable training content by referring to the user's past training history.

[0086] The Practical Training Department can customize training content based on the user's current skill set during practical training sessions. For example, if a user has programming skills, the Practical Training Department will provide advanced programming training. If a user has design skills, the Practical Training Department will provide training that includes design projects. If a user has management skills, the Practical Training Department will provide leadership training. The Practical Training Department uses AI to analyze the user's current skill set and provide optimal training content. For example, the Practical Training Department will provide the most suitable training content for the user based on their resume, skills test results, and self-assessment results. This allows for the customization of training content based on the user's current skill set.

[0087] The practical training section can estimate the user's emotions and adjust the training pace based on those emotions. For example, if the user is in a hurry, the practical training section will provide training that can be completed in a short time. If the user is relaxed, the practical training section will provide training that progresses slowly. If the user is feeling anxious, the practical training section will provide training that progresses step by step. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the training pace to be adjusted according to the user's emotions.

[0088] The Practical Training Department can provide an optimal training environment during practical training sessions by considering the user's geographical location. For example, if the user lives in an urban area, the Practical Training Department will provide an urban training environment. If the user lives in a rural area, the Practical Training Department will provide a rural training environment. If the user lives overseas, the Practical Training Department will provide a training environment for that country. The Practical Training Department uses AI to analyze the user's geographical location and provide an optimal training environment. For example, the Practical Training Department obtains the user's geographical location using GPS data, IP addresses, and regional codes, and uses this information for analysis. This allows the department to provide an optimal training environment by considering the user's geographical location.

[0089] The Practical Training Department can analyze users' social media activity during practical training sessions and provide relevant training content. For example, the Practical Training Department can provide relevant training content based on articles shared by users on social media. The Practical Training Department can provide relevant training content based on posts from influencers followed by users. The Practical Training Department can provide relevant training content based on topics in online communities that users participate in. The Practical Training Department can use AI to analyze users' social media activity and provide optimal training content. For example, the Practical Training Department can analyze social media posts, followers, and engagement data to provide the most suitable training content for each user. In this way, by analyzing users' social media activity, it is possible to provide relevant training content.

[0090] The career advice unit can estimate the user's emotions and adjust the content of the career advice based on those emotions. For example, if the user is optimistic, the career advice unit will suggest a challenging career path. If the user is feeling anxious, the career advice unit will suggest a stable career path. If the user has neutral emotions, the career advice unit will suggest a balanced career path. 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. This allows the content of the career advice to be adjusted according to the user's emotions.

[0091] The career advice department can provide optimal advice by referring to the user's past career history when offering career advice. For example, the career advice department can provide career advice to help the user take the next step based on the occupations the user has experienced in the past. The career advice department can provide career advice to help the user avoid occupations they have had difficulty with in the past. The career advice department can provide career advice that delves deeper into areas the user has been interested in in the past. The career advice department uses AI to analyze the user's past career history and provide optimal career advice. For example, the career advice department can refer to resumes, CVs, and past projects to provide the user with the most suitable career advice. In this way, by referring to the user's past career history, it can provide optimal career advice.

[0092] The Career Advice Department can customize the advice it provides based on the user's current skill set. For example, if a user has programming skills, it will suggest a technical career path. If a user has design skills, it will suggest a creative career path. If a user has management skills, it will suggest a leadership career path. The Career Advice Department uses AI to analyze the user's current skill set and suggest the optimal career path. For example, it will suggest the best career path for the user based on their resume, skills test results, and self-assessment results. This allows for customized career advice based on the user's current skill set.

[0093] The career advice unit can estimate the user's emotions and prioritize career advice based on those emotions. For example, if the user is excited, the career advice unit will prioritize suggesting challenging career paths. If the user is feeling anxious, the career advice unit will prioritize suggesting stable career paths. If the user has neutral emotions, the career advice unit will prioritize suggesting balanced career paths. 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. This allows the priority of career advice to be determined according to the user's emotions.

[0094] The Career Advice Department can propose the optimal career path when providing career advice, taking into account the user's geographical location. For example, if the user lives in an urban area, the Career Advice Department will propose a career path in an urban area. If the user lives in a rural area, the Career Advice Department will propose a career path in a rural area. If the user lives overseas, the Career Advice Department will propose a career path in that country. The Career Advice Department uses AI to analyze the user's geographical location and propose the optimal career path. For example, the Career Advice Department obtains the user's geographical location using GPS data, IP addresses, and regional codes, and uses this information for analysis. This allows the department to propose the optimal career path by taking the user's geographical location into consideration.

[0095] The Career Advice Department can analyze a user's social media activity and propose relevant career paths when providing career advice. For example, the Career Advice Department can propose relevant career paths based on articles the user has shared on social media. The Career Advice Department can propose relevant career paths based on posts from influencers the user follows. The Career Advice Department can propose relevant career paths based on topics in online communities the user participates in. The Career Advice Department uses AI to analyze a user's social media activity and propose the optimal career path. For example, the Career Advice Department analyzes social media posts, followers, and engagement data to propose the most suitable career path for the user. In this way, by analyzing a user's social media activity, it can propose relevant career paths.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] Future vocational training systems can estimate a user's emotions and adjust the difficulty level of the learning program based on those emotions. For example, if a user is excited, they can be given more challenging tasks. If a user is anxious, they can be offered a learning program that starts with basic content. If a user is relaxed, they can be offered a learning program that includes more advanced content. Emotion estimation is achieved using an emotion engine or generative AI. This allows the difficulty level of the learning program to be adjusted according to the user's emotions, providing a more effective learning experience.

[0098] Future vocational training systems can analyze regional trends by considering the user's geographical location. For example, if a user lives in an urban area, the system can prioritize analyzing urban vocational trends. If a user lives in a rural area, it can prioritize analyzing rural vocational trends. If a user lives abroad, it can prioritize analyzing vocational trends in that country. The trend analysis unit uses AI to analyze the user's geographical location and identify regional trends. This allows for the analysis of regional trends by considering the user's geographical location, enabling the provision of more appropriate vocational training.

[0099] Future career training systems can analyze users' social media activity and provide relevant learning content. For example, they can provide relevant learning content based on articles users share on social media, posts from influencers users follow, and topics from online communities users participate in. The customized learning section can use AI to analyze users' social media activity and provide optimal learning content. This allows for the provision of relevant learning content and a more engaging learning experience by analyzing users' social media activity.

[0100] Future vocational training systems can estimate a user's emotions and adjust the training pace based on those emotions. For example, if a user is in a hurry, they can be offered training that can be completed in a short time. If a user is relaxed, they can be offered training that progresses slowly. If a user is feeling anxious, they can be offered training that progresses in stages. Emotion estimation is achieved using an emotion engine or generative AI. This allows the training pace to be adjusted according to the user's emotions, providing a more effective training experience.

[0101] Future vocational training systems can select the most suitable training content by referencing a user's past training history. For example, they can provide training to help users advance to the next step based on their past training. They can also provide training to reinforce areas where users previously struggled. Furthermore, they can provide training to delve deeper into areas where users have shown interest in the past. The practical training department can use AI to analyze a user's past training history and provide the most suitable training content. This allows for the provision of optimal training content and a more effective training experience by referencing the user's past training history.

[0102] Future career training systems can estimate a user's emotions and adjust career advice based on those emotions. For example, if a user is optimistic, a challenging career path can be suggested. If a user is anxious, a stable career path can be suggested. If a user has neutral emotions, a balanced career path can be suggested. Emotion estimation is achieved using an emotion engine or generative AI. This allows career advice to be adjusted according to the user's emotions, resulting in the suggestion of a more appropriate career path.

[0103] Future career training systems can customize learning programs based on a user's current occupation and skill set. For example, if a user is an engineer, a learning program can be provided to enhance their technical skills. If a user is a marketing professional, a learning program can be provided to enhance their digital marketing skills. If a user is a manager, a learning program can be provided to enhance their leadership skills. The customized learning unit can use AI to analyze the user's current occupation and skill set and provide the optimal learning program. This allows for a more effective learning experience by customizing learning programs based on the user's current occupation and skill set.

[0104] Future vocational training systems can estimate a user's emotions and adjust the training difficulty based on those emotions. For example, if a user is excited, a more difficult training session can be provided. If a user is anxious, an easier training session can be provided. If a user is relaxed, a training session of moderate difficulty can be provided. Emotion estimation is achieved using an emotion engine or generative AI. This allows for adjustment of training difficulty according to the user's emotions, providing a more effective training experience.

[0105] Future career training systems can provide optimal career advice by referencing a user's past career history. For example, they can provide career advice to help users take their next steps based on their past work experience. They can also provide career advice to help users avoid occupations they previously struggled with. Furthermore, they can provide career advice to help users delve deeper into areas they were previously interested in. The career advice unit can use AI to analyze a user's past career history and provide optimal career advice. This allows the system to provide optimal career advice and suggest more appropriate career paths by referencing the user's past career history.

[0106] Future career training systems can estimate a user's emotions and prioritize career advice based on those emotions. For example, if a user is excited, challenging career paths can be prioritized. If a user is anxious, stable career paths can be prioritized. If a user has neutral emotions, balanced career paths can be prioritized. Emotion estimation is achieved using an emotion engine or generative AI. This allows for prioritizing career advice according to the user's emotions, leading to the suggestion of more appropriate career paths.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The Trend Analysis Department analyzes future trends in the occupational market. For example, it analyzes historical data to predict future occupational market trends. The Trend Analysis Department uses AI to analyze historical occupational market data and identify sectors where future demand will increase. Specifically, it analyzes historical employment statistics, industry trends, and technological innovation data. Step 2: The customized learning unit provides learning programs tailored to the user's interests and skill level. For example, it evaluates the user's interests and skill level and provides a customized learning program. The customized learning unit uses AI to evaluate the user's interests and skill level and provide the optimal learning program. Specifically, it uses questionnaires, past learning history, and skill test results as its basis. Step 3: The Practical Training Department helps trainees acquire professional skills through simulations and practical projects. For example, they conduct simulations to learn actual professional skills. The Practical Training Department uses AI to conduct simulations and assists users in acquiring actual professional skills. Specifically, it assists users in acquiring skills in actual robot operation through robotics simulations. Step 4: The Career Advice Department proposes the optimal career path for the user. For example, it suggests the best occupation and career path based on the user's skill set. The Career Advice Department uses AI to evaluate the user's skill set and propose the optimal career path. Specifically, it uses the user's skill test results, past experience, and self-assessment results.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the trend analysis unit, customized learning unit, practical training unit, and career advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the trend analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes past data and predicts future trends in the occupational market. The customized learning unit is implemented by the control unit 46A of the smart device 14, which provides a learning program tailored to the user's interests and skill level. The practical training unit is implemented by the control unit 46A of the smart device 14, which helps users acquire occupational skills through simulations and practical projects. The career advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal career path for the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the trend analysis unit, customized learning unit, practical training unit, and career advice unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the trend analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes past data and predicts future trends in the occupational market. The customized learning unit is implemented by the control unit 46A of the smart glasses 214, which provides a learning program tailored to the user's interests and skill level. The practical training unit is implemented by the control unit 46A of the smart glasses 214, which helps users acquire occupational skills through simulations and practical projects. The career advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal career path for the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the trend analysis unit, customized learning unit, practical training unit, and career advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the trend analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes past data and predicts future trends in the occupational market. The customized learning unit is implemented by the control unit 46A of the headset terminal 314, which provides a learning program tailored to the user's interests and skill level. The practical training unit is implemented by the control unit 46A of the headset terminal 314, which helps users acquire occupational skills through simulations and practical projects. The career advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal career path for the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the trend analysis unit, customized learning unit, practical training unit, and career advice unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the trend analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes past data and predicts future trends in the occupational market. The customized learning unit is implemented by the control unit 46A of the robot 414, which provides a learning program tailored to the user's interests and skill level. The practical training unit is implemented by the control unit 46A of the robot 414, which helps users acquire occupational skills through simulations and practical projects. The career advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal career path for the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The Trend Analysis Department analyzes future trends in the job market, Based on the trends analyzed by the aforementioned trend analysis unit, the customized learning unit provides learning programs tailored to the user's interests and skill level. Based on the learning programs provided by the aforementioned customized learning department, the practical training department provides opportunities to acquire professional skills through simulations and hands-on projects. The system includes a career advice unit that proposes the optimal career path to the user based on the skills acquired through the practical training unit. A system characterized by the following features. (Note 2) The aforementioned trend analysis department, Analyzing past data to predict future trends in the job market. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned customized learning unit is We assess users' interests and skill levels and provide customized learning programs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned practical training department, Conduct simulations to acquire actual professional skills. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned career advice department, Based on the user's skill set, we suggest the most suitable occupation and career path. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned trend analysis department, It estimates user sentiment and adjusts the trend analysis results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned trend analysis department, When analyzing trends, we incorporate not only historical data but also real-time market data into our analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned trend analysis department, When analyzing trends, cross-reference data from different industries to discover new trends. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned trend analysis department, We estimate user sentiment and prioritize trend analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned trend analysis department, When analyzing trends, the system analyzes regional trends by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned trend analysis department, When analyzing trends, we incorporate social media data to analyze those trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned customized learning unit is It estimates the user's emotions and adjusts the content of the learning program based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned customized learning unit is When providing a learning program, the system selects the most suitable program by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned customized learning unit is When providing learning programs, customize them based on the user's current occupation and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned customized learning unit is It estimates the user's emotions and adjusts the learning program's progress based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned customized learning unit is When providing the learning program, we will provide it in the most optimal format, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned customized learning unit is When providing learning programs, we analyze users' social media activity and provide relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned practical training department, It estimates the user's emotions and adjusts the training difficulty based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned practical training department, During practical training, the system selects the most suitable training content by referring to the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned practical training department, During practical training, the training content is customized based on the user's current skill set. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned practical training department, It estimates the user's emotions and adjusts the training progress speed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned practical training department, During practical training, the system provides an optimal training environment that takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned practical training department, During practical training, we analyze users' social media activity and provide relevant training content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned career advice department, The system estimates the user's emotions and adjusts the content of career advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned career advice department, When providing career advice, we refer to the user's past career history to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned career advice department, When providing career advice, customize the advice based on the user's current skill set. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned career advice department, It estimates the user's emotions and prioritizes career advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned career advice department, When providing career advice, we propose the optimal career path considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned career advice department, When providing career advice, we analyze the user's social media activity and suggest relevant career paths. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Trend Analysis Department analyzes future trends in the job market, Based on the trends analyzed by the aforementioned trend analysis unit, the customized learning unit provides learning programs tailored to the user's interests and skill level. Based on the learning programs provided by the aforementioned customized learning department, the practical training department provides opportunities to acquire professional skills through simulations and hands-on projects. The system includes a career advice unit that proposes the optimal career path to the user based on the skills acquired through the practical training unit. A system characterized by the following features.

2. The aforementioned trend analysis department, Analyzing past data to predict future trends in the job market. The system according to feature 1.

3. The aforementioned customized learning unit is We assess users' interests and skill levels and provide customized learning programs. The system according to feature 1.

4. The aforementioned practical training department, Conduct simulations to acquire actual professional skills. The system according to feature 1.

5. The aforementioned career advice department, Based on the user's skill set, we suggest the most suitable occupation and career path. The system according to feature 1.

6. The aforementioned trend analysis department, It estimates user sentiment and adjusts the trend analysis results based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned trend analysis department, When analyzing trends, we incorporate not only historical data but also real-time market data into our analysis. The system according to feature 1.

8. The aforementioned trend analysis department, When analyzing trends, cross-reference data from different industries to discover new trends. The system according to feature 1.

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