system

Abstract

The system according to this embodiment aims to propose an optimal career path and provide a learning plan based on the user's skills and desired career path. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input from the user regarding their skills and desired career path. The analysis unit analyzes a vast amount of job data based on the information received by the reception unit. The proposal unit proposes the optimal career path based on the analysis results obtained by the analysis unit. The provision unit provides a learning plan based on the career path proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is common for a user to search for job offers based on their current skills in job hunting activities, and there is a problem that the optimization of the career path has not been sufficiently carried out.

[0005] The system according to the embodiment aims to propose an optimal career path and provide a learning plan based on the skills of the user and the desired career path.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input from the user regarding their skills and desired career path. The analysis unit analyzes a vast amount of job data based on the information received by the reception unit. The proposal unit proposes the optimal career path based on the analysis results obtained by the analysis unit. The provision unit provides a learning plan based on the career path proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal career path and provide a learning plan based on the user's skills and desired career path. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that fundamentally changes the way job search websites are used. This AI agent system helps users find high-market-value jobs and systematically acquire the necessary skills, rather than searching for jobs based on their current skills. The AI ​​agent system analyzes a vast amount of job data and proposes the optimal career path considering the individual's learning ability. The AI ​​agent system can expand career possibilities by working backward from the future. When changing jobs, one should think about their career based on what they "can do," rather than compromising on what they "can do." The AI ​​agent system aims to maximize the user's potential by providing the optimal combination of skills and learning plans. For example, a user accesses a job search website and inputs their current skills and desired career path. Next, the AI ​​agent system analyzes a vast amount of job data and proposes the optimal career path considering the user's learning ability and background. At this time, the AI ​​agent system also handles the arrangement of a learning plan and learning materials, or the negotiation of compensation and schedule with engineers who will act as freelance tutors. Furthermore, the AI ​​agent system proposes the user's career path by working backward from the future. It prepares a learning plan that can be acquired only on weekends and negotiates tutoring jobs with active engineers from cloudworker sites. In this way, the AI ​​agent system maximizes the user's potential. The AI ​​agent system analyzes millions of job postings in real time and understands the correlations between thousands of skills. It also predicts success rates based on extensive job placement data and suggests the optimal combination of learning materials from tens of thousands. Furthermore, it tracks market value fluctuations in real time. This allows users to think about their careers based on what they *can* do, rather than compromising on what they *can* do. Based on this, the AI ​​agent system can suggest the optimal career path and provide a learning plan tailored to the user's skills and desired career path.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input from the user regarding their skills and desired career path. For example, the reception unit can receive input from the user when they access a job search website and input their current skills and desired career path. The reception unit stores the user's input information in a database and uses it for subsequent processing. The analysis unit analyzes a vast amount of job data based on the information received by the reception unit. For example, the analysis unit analyzes millions of job data points in real time and extracts the most suitable job information based on the user's skills and desired career path. The analysis unit uses data mining and statistical analysis techniques to identify job postings suitable for the user from the job data. The proposal unit proposes the most suitable career path based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a career path that allows the user to find a job with high market value in the future and systematically acquire the necessary skills, based on the user's skills and desired career path. The proposal unit can propose the most suitable career path considering the user's learning ability and background. The provision unit provides a learning plan based on the career path proposed by the proposal unit. The service provider, for example, provides learning plans and learning materials to help users systematically acquire the skills they need. The service provider can also coordinate compensation and schedules with engineers who act as freelance tutors. As a result, the AI ​​agent system according to this embodiment can propose the optimal career path and provide a learning plan based on the user's skills and desired career path.

[0030] The reception desk accepts user input regarding their skills and desired career path. Specifically, it provides an interface for users to access the job search site and input their current skills and desired career path. Users can input detailed skill information, such as programming languages, project management experience, past work history, and acquired qualifications. They can also input specific conditions for their desired career path, such as job title, industry, work location, and salary range. The reception desk stores this input information in a database and uses it for subsequent processing. The input information is stored in a secure database and designed to protect user privacy. Furthermore, the reception desk also has functions to verify the accuracy of the information entered by users. For example, it checks for errors in the input content and notifies the user if there is any missing information, prompting them to complete it. It also provides a history management function so that users can reuse information they have entered in the past. This allows users to input information efficiently and improves the accuracy and reliability of the entire system.

[0031] The analytics department analyzes a vast amount of job data based on information received by the reception department. Specifically, it analyzes millions of job postings in real time and extracts the most suitable job information based on the user's skills and desired career path. The analytics department uses data mining and statistical analysis techniques to identify job postings that are suitable for the user from the job data. For example, it uses data mining techniques to extract job postings that match the user's skill set from the job database, and statistical analysis techniques to analyze job trends and market demand. Furthermore, the analytics department utilizes AI to analyze job data. The AI ​​uses natural language processing techniques to analyze the text data of job postings and extracts keywords related to the user's skills and desired career path. This allows for the rapid and accurate provision of the most suitable job information to the user. In addition, the analytics department can perform more accurate job matching by considering the user's past application history and evaluation data. This allows users to efficiently find the most suitable job information and proceed smoothly with their job search.

[0032] The Proposal Department proposes the optimal career path based on the analysis results obtained by the Analysis Department. Specifically, it proposes a career path that helps users find high-market-value jobs in the future and systematically acquire the necessary skills, based on their skills and desired career path. The Proposal Department can propose the optimal career path by considering the user's learning ability and background. For example, it analyzes the user's current skill set and market demand to identify skills and job types that are predicted to be in high demand in the future. Then, it proposes a learning plan to help users acquire the necessary skills and experience step by step to get those jobs. The Proposal Department uses AI to generate the optimal career path based on the user's skills and desired career path. The AI ​​analyzes past data and market trends to propose the most effective career path for the user. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. As a result, users can find the optimal career path for themselves and systematically acquire skills for future career success.

[0033] The Service Provider provides learning plans based on the career paths proposed by the Proposal Provider. Specifically, it provides learning plans and learning materials to help users systematically acquire the skills they need. For example, the Service Provider provides a list of online courses and materials, allowing users to learn at their own pace. The Service Provider can also coordinate compensation and schedules with engineers who act as freelance tutors. This allows users to acquire skills while receiving expert guidance. The Service Provider uses AI to monitor users' learning progress and adjust the learning plan as needed. For example, if a user is struggling to acquire a particular skill, the AI ​​will provide additional materials and practice problems related to that skill. It will also suggest the next skills and courses to learn based on the user's learning progress. This allows users to acquire skills efficiently and grow systematically along the proposed career path. Furthermore, the Service Provider has a function to evaluate users' learning outcomes and visualize their skill acquisition status. This allows users to learn while feeling a sense of their own growth.

[0034] The service provider can provide learning plans and learning materials. For example, the service provider can provide learning plans that allow users to systematically acquire the skills they need. The learning plan may include learning content, learning period, and learning methods. For example, the service provider can provide a learning plan that a user can acquire in just a weekend. The service provider can also provide learning materials. These materials may include textbooks, online courses, and video materials. For example, the service provider can provide courses that users can learn online. By providing learning plans and learning materials, users can systematically acquire the skills they need. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's learning history into a generating AI and have the generating AI suggest an optimal learning plan.

[0035] The service provider can coordinate compensation and schedules with engineers who will act as freelance tutors. For example, the service provider can enable users to hire freelance engineers as tutors to learn necessary skills. The service provider adjusts the tutor's compensation and schedule to enable users to learn efficiently. For example, the service provider can set the tutor's compensation on an hourly basis and coordinate the user's and tutor's schedules using an online calendar. The service provider can also set the tutor's compensation as a fixed fee or performance-based fee. This allows users to learn efficiently by coordinating compensation and schedules with tutors. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the tutor's compensation and schedule into a generating AI and have the generating AI suggest the optimal compensation and schedule.

[0036] The analysis unit can propose the optimal career path by considering the user's learning ability and background. For example, the analysis unit can evaluate the user's learning ability and propose the optimal career path based on the evaluation results. The analysis unit can evaluate the user's learning ability from test results, past learning history, etc. The analysis unit can also propose a career path by considering the user's background. Background includes educational background, work experience, and specialized knowledge. For example, the analysis unit can propose the optimal career path by considering the user's educational background and work experience. This allows for the proposal of a more appropriate career path by considering the user's learning ability and background. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning ability and background into a generating AI and have the generating AI propose the optimal career path.

[0037] The proposal unit can propose a user's career path by working backward from the future. For example, the proposal unit can set a career stage that the user aims for in the future and then work backward to propose the skills and experience necessary to reach that career stage. The proposal unit can set the user's goals and work backward from their career stage to propose the optimal career path. For example, if the user aims to work as a cloud architect in the future, the proposal unit will work backward to propose the skills and qualifications necessary for that. In this way, by proposing a career path by working backward from the future, the user's career possibilities can be expanded. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's goal setting and career stage calculation into a generating AI and have the generating AI propose the optimal career path.

[0038] The analytics department can analyze millions of job postings in real time. For example, the analytics department analyzes millions of job postings using data streaming and real-time data processing technologies. The analytics department can extract the latest job information from the data and provide it to users. For example, the analytics department can analyze job postings for a specific industry or region in real time to provide users with the most suitable job information. This allows for the provision of the latest job information by analyzing millions of job postings in real time. Some or all of the above-described processes in the analytics department may be performed using AI, or not. For example, the analytics department can input millions of job postings into a generating AI and have the generating AI perform real-time analysis.

[0039] The analysis unit can understand the correlations between thousands of skills. For example, the analysis unit understands the correlations between thousands of skills using methods such as correlation coefficients and co-occurrence network analysis. By understanding the correlations between skills, the analysis unit can suggest the optimal skill combination to the user. For example, the analysis unit extracts skills related to a specific skill set and suggests them to the user. This allows the analysis unit to suggest the optimal skill combination by understanding the correlations between thousands of skills. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on thousands of skills into a generating AI and have the generating AI perform the correlation analysis.

[0040] The analysis department can predict the probability of success from a vast amount of job change data. For example, the analysis department can predict the probability of success based on job change data from the past 10 years or job change data for a specific industry. The analysis department can use statistical analysis and machine learning models to predict the probability of success for the optimal career path for a user. For example, the analysis department can predict the probability of success for a specific career path by considering the user's skill set and work history. In this way, by predicting the probability of success from a vast amount of job change data, it can propose the optimal career path for the user. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input a vast amount of job change data into a generating AI and have the generating AI perform the prediction of the probability of success.

[0041] The analysis department can track market value fluctuations in real time. For example, the analysis department tracks stock price fluctuations and increases / decreases in job postings using data streaming technology and real-time data processing technology. By tracking market value fluctuations in real time, the analysis department can provide users with the latest market information. For example, the analysis department can track and provide users with real-time market value fluctuations for specific industries or regions. This allows the analysis department to provide the latest market information by tracking market value fluctuations in real time. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input market value fluctuation data into a generating AI and have the generating AI perform real-time tracking.

[0042] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, the reception desk can automatically display skills and career paths that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest skills and career paths that the user will use at a particular time based on their past input history. In this way, by analyzing past input history, the reception desk can provide the optimal input interface for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform the task of providing the optimal input interface.

[0043] The reception desk can customize input fields based on the user's current job responsibilities and areas of interest when users input skills and career paths. For example, the reception desk can prioritize displaying skills related to the user's current job responsibilities. The reception desk can also suggest relevant career paths based on the user's areas of interest. Furthermore, the reception desk can combine the user's job responsibilities and areas of interest to customize the most suitable input fields. This allows for the provision of more appropriate input fields by customizing them based on the user's current job responsibilities and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's job responsibilities and areas of interest into a generating AI and have the generating AI perform the customization of input fields.

[0044] The reception desk can prioritize displaying highly relevant input fields when users input skills and career paths, taking into account their geographical location. For example, the reception desk can prioritize displaying skills and career paths relevant to a region based on the user's current location. The reception desk can also suggest input fields relevant to the local job market, taking into account the user's geographical location. Furthermore, the reception desk can display input fields that align with local trends based on the user's geographical location. This allows for the priority display of skills and career paths relevant to a region by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI display highly relevant input fields.

[0045] The reception desk can analyze the user's social media activity when they input skills and career paths and suggest relevant input fields. For example, the reception desk can suggest relevant skills and career paths based on the user's social media activity. The reception desk can display the most suitable input fields by considering the user's social media followers and areas of interest. The reception desk can also analyze the user's social media posts and suggest relevant career paths. In this way, relevant skills and career paths can be suggested by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input fields.

[0046] The analysis unit can improve the accuracy of its analysis by considering the user's past work history. For example, the analysis unit can prioritize the analysis of relevant job postings based on the user's past work history. The analysis unit can extract job postings related to specific skill sets from the user's work history. The analysis unit can also filter the most suitable job postings by considering the user's work history. This improves the accuracy of the analysis by considering past work history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's past work history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0047] The analysis unit can filter job postings based on the user's current skill set during analysis. For example, the analysis unit can prioritize displaying job postings relevant to the user's current skill set. The analysis unit can filter the most suitable job postings based on the user's skill set. The analysis unit can also extract relevant job postings based on the user's skill set. This allows for the provision of more appropriate job information by filtering job postings based on the user's current skill set. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skill set data into a generating AI and have the generating AI perform the filtering of job postings.

[0048] The analysis unit can analyze job data while considering the geographical distribution of users. For example, the analysis unit can prioritize the analysis of job data relevant to a region based on the geographical distribution of users. The analysis unit can extract data relevant to the regional job market while considering the geographical distribution of users. Furthermore, the analysis unit can analyze job data tailored to regional trends based on the geographical distribution of users. This allows for the priority analysis of regionally relevant job data by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the analysis of job data.

[0049] The analysis unit can evaluate the relevance of job posting data by referring to the user's social media activity during analysis. For example, the analysis unit can evaluate relevant job posting data based on the user's social media activity. The analysis unit can evaluate the relevance of job posting data by considering the user's social media followers and areas of interest. The analysis unit can also analyze the content of the user's social media posts and evaluate the relevance of job posting data. In this way, highly relevant job posting data can be evaluated by referring to social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the relevance evaluation of job posting data.

[0050] The suggestion unit can make optimal suggestions by considering the user's past career path. For example, the suggestion unit can suggest relevant career paths based on the user's past career path. The suggestion unit can extract career paths related to specific skill sets from the user's career path. The suggestion unit can also filter the optimal career path by considering the user's career path. This allows for the suggestion of more appropriate career paths by considering the past career path. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past career path data into a generating AI and have the generating AI execute the optimal suggestion.

[0051] The suggestion unit can filter career paths based on the user's current skill set when making suggestions. For example, the suggestion unit can prioritize displaying career paths related to the user's current skill set. The suggestion unit can filter the optimal career path based on the user's skill set. The suggestion unit can also extract relevant career paths based on the user's skill set. This allows for the suggestion of more appropriate career paths by filtering career paths based on the current skill set. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's skill set data into a generating AI and have the generating AI perform the career path filtering.

[0052] The suggestion unit can propose career paths while considering the user's geographical distribution. For example, the suggestion unit can prioritize suggesting region-related career paths based on the user's geographical distribution. The suggestion unit can also propose career paths related to the local job market, taking into account the user's geographical distribution. Furthermore, the suggestion unit can propose career paths aligned with local trends based on the user's geographical distribution. In this way, by considering geographical distribution, it is possible to propose region-related career paths. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's geographical distribution data into a generating AI and have the generating AI execute the career path proposal.

[0053] The proposal unit can evaluate the relevance of career paths by referring to the user's social media activity when making a proposal. For example, the proposal unit can evaluate relevant career paths based on the content of the user's social media activities. The proposal unit can evaluate the relevance of career paths by considering the user's social media followers and areas of interest. The proposal unit can also analyze the content of the user's social media posts and evaluate the relevance of career paths. In this way, by referring to social media activity, it is possible to evaluate highly relevant career paths. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI perform the career path relevance evaluation.

[0054] The service provider can provide the optimal learning plan by considering the user's past learning history when providing a learning plan. For example, the service provider can prioritize providing relevant learning plans based on the user's past learning history. The service provider can extract learning plans related to a specific skill set from the user's learning history. Furthermore, the service provider can filter the optimal learning plan by considering the user's learning history. This allows for the provision of a more appropriate learning plan by considering past learning history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing the optimal plan.

[0055] The service provider can customize learning content based on the user's current skill set when providing a learning plan. For example, the service provider can prioritize providing learning content related to the user's current skill set. The service provider can customize the optimal learning content based on the user's skill set. The service provider can also extract relevant learning content based on the user's skill set. This allows for the provision of a more appropriate learning plan by customizing the learning content based on the current skill set. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's skill set data into a generating AI and have the generating AI perform the customization of the learning content.

[0056] The service provider can provide optimal learning plans by considering the geographical distribution of users. For example, the service provider can prioritize providing learning plans relevant to a region based on the geographical distribution of users. The service provider can also provide learning plans relevant to the local job market by considering the geographical distribution of users. Furthermore, the service provider can provide learning plans tailored to local trends based on the geographical distribution of users. In this way, by considering geographical distribution, it is possible to provide learning plans relevant to a region. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the geographical distribution data of users into a generating AI and have the generating AI perform the task of providing optimal learning plans.

[0057] The service provider can suggest learning content by referring to the user's social media activity when providing a learning plan. For example, the service provider can suggest relevant learning content based on the user's social media activity. The service provider can suggest learning content by considering the user's social media followers and areas of interest. The service provider can also analyze the user's social media posts and suggest relevant learning content. In this way, relevant learning content can be suggested by referring to social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the suggestion of learning content.

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

[0059] The analytics department can improve the accuracy of its analysis by considering the user's past work history. For example, it can prioritize the analysis of relevant job postings based on the user's past work history. It can extract job postings related to specific skill sets from the user's work history. It can also filter the most suitable job postings by considering the user's work history. In this way, the accuracy of the analysis can be improved by considering past work history.

[0060] The proposal function can provide optimal suggestions by considering the user's past career path. For example, it can suggest relevant career paths based on the user's past career path. It can extract career paths related to specific skill sets from the user's career path. It can also filter the optimal career path by considering the user's career path. In this way, by considering past career paths, it can propose more appropriate career paths.

[0061] The system can provide optimal learning plans by considering the user's past learning history. For example, it can prioritize providing relevant learning plans based on the user's past learning history. It can extract learning plans related to specific skill sets from the user's learning history. It can also filter optimal learning plans by considering the user's learning history. This allows for the provision of more appropriate learning plans by taking past learning history into account.

[0062] The analytics department can filter job postings based on the user's current skill set. For example, it can prioritize displaying job postings relevant to the user's current skill set. It can filter the most relevant job postings based on the user's skill set. It can also extract relevant job postings based on the user's skill set. This allows for more appropriate job information to be provided by filtering job postings based on the user's current skill set.

[0063] The learning platform can customize learning content based on the user's current skill set. For example, it can prioritize providing learning content relevant to the user's current skill set. It can customize the optimal learning content based on the user's skill set. It can also extract relevant learning content based on the user's skill set. This allows for the provision of a more appropriate learning plan by customizing learning content based on the user's current skill set.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives input from users regarding their skills and desired career path. For example, a user can access a job search website and enter their current skills and desired career path. The reception desk stores the user's input information in a database and uses it for subsequent processing. Step 2: The analysis department analyzes the vast amount of job data based on the information received by the reception department. For example, it analyzes millions of job postings in real time and extracts the most suitable job information based on the user's skills and desired career path. The analysis department uses data mining and statistical analysis techniques to identify job postings that are suitable for the user from the job data. Step 3: The proposal department proposes the optimal career path based on the analysis results obtained by the analysis department. For example, based on the user's skills and desired career path, it proposes a career path that will help them find a job with high market value in the future and systematically acquire the necessary skills. The proposal department can propose the optimal career path by taking into account the user's learning ability and background. Step 4: The provisioning department provides a learning plan based on the career path proposed by the proposaling department. For example, they provide a learning plan and learning materials to help the user systematically acquire the necessary skills. The provisioning department can also coordinate compensation and schedules with engineers who will act as freelance tutors.

[0066] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that fundamentally changes the way job search websites are used. This AI agent system helps users find high-market-value jobs and systematically acquire the necessary skills, rather than searching for jobs based on their current skills. The AI ​​agent system analyzes a vast amount of job data and proposes the optimal career path considering the individual's learning ability. The AI ​​agent system can expand career possibilities by working backward from the future. When changing jobs, one should think about their career based on what they "can do," rather than compromising on what they "can do." The AI ​​agent system aims to maximize the user's potential by providing the optimal combination of skills and learning plans. For example, a user accesses a job search website and inputs their current skills and desired career path. Next, the AI ​​agent system analyzes a vast amount of job data and proposes the optimal career path considering the user's learning ability and background. At this time, the AI ​​agent system also handles the arrangement of a learning plan and learning materials, or the negotiation of compensation and schedule with engineers who will act as freelance tutors. Furthermore, the AI ​​agent system proposes the user's career path by working backward from the future. It prepares a learning plan that can be acquired only on weekends and negotiates tutoring jobs with active engineers from cloudworker sites. In this way, the AI ​​agent system maximizes the user's potential. The AI ​​agent system analyzes millions of job postings in real time and understands the correlations between thousands of skills. It also predicts success rates based on extensive job placement data and suggests the optimal combination of learning materials from tens of thousands. Furthermore, it tracks market value fluctuations in real time. This allows users to think about their careers based on what they *can* do, rather than compromising on what they *can* do. Based on this, the AI ​​agent system can suggest the optimal career path and provide a learning plan tailored to the user's skills and desired career path.

[0067] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input from the user regarding their skills and desired career path. For example, the reception unit can receive input from the user when they access a job search website and input their current skills and desired career path. The reception unit stores the user's input information in a database and uses it for subsequent processing. The analysis unit analyzes a vast amount of job data based on the information received by the reception unit. For example, the analysis unit analyzes millions of job data points in real time and extracts the most suitable job information based on the user's skills and desired career path. The analysis unit uses data mining and statistical analysis techniques to identify job postings suitable for the user from the job data. The proposal unit proposes the most suitable career path based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a career path that allows the user to find a job with high market value in the future and systematically acquire the necessary skills, based on the user's skills and desired career path. The proposal unit can propose the most suitable career path considering the user's learning ability and background. The provision unit provides a learning plan based on the career path proposed by the proposal unit. The service provider, for example, provides learning plans and learning materials to help users systematically acquire the skills they need. The service provider can also coordinate compensation and schedules with engineers who act as freelance tutors. As a result, the AI ​​agent system according to this embodiment can propose the optimal career path and provide a learning plan based on the user's skills and desired career path.

[0068] The reception desk accepts user input regarding their skills and desired career path. Specifically, it provides an interface for users to access the job search site and input their current skills and desired career path. Users can input detailed skill information, such as programming languages, project management experience, past work history, and acquired qualifications. They can also input specific conditions for their desired career path, such as job title, industry, work location, and salary range. The reception desk stores this input information in a database and uses it for subsequent processing. The input information is stored in a secure database and designed to protect user privacy. Furthermore, the reception desk also has functions to verify the accuracy of the information entered by users. For example, it checks for errors in the input content and notifies the user if there is any missing information, prompting them to complete it. It also provides a history management function so that users can reuse information they have entered in the past. This allows users to input information efficiently and improves the accuracy and reliability of the entire system.

[0069] The analytics department analyzes a vast amount of job data based on information received by the reception department. Specifically, it analyzes millions of job postings in real time and extracts the most suitable job information based on the user's skills and desired career path. The analytics department uses data mining and statistical analysis techniques to identify job postings that are suitable for the user from the job data. For example, it uses data mining techniques to extract job postings that match the user's skill set from the job database, and statistical analysis techniques to analyze job trends and market demand. Furthermore, the analytics department utilizes AI to analyze job data. The AI ​​uses natural language processing techniques to analyze the text data of job postings and extracts keywords related to the user's skills and desired career path. This allows for the rapid and accurate provision of the most suitable job information to the user. In addition, the analytics department can perform more accurate job matching by considering the user's past application history and evaluation data. This allows users to efficiently find the most suitable job information and proceed smoothly with their job search.

[0070] The Proposal Department proposes the optimal career path based on the analysis results obtained by the Analysis Department. Specifically, it proposes a career path that helps users find high-market-value jobs in the future and systematically acquire the necessary skills, based on their skills and desired career path. The Proposal Department can propose the optimal career path by considering the user's learning ability and background. For example, it analyzes the user's current skill set and market demand to identify skills and job types that are predicted to be in high demand in the future. Then, it proposes a learning plan to help users acquire the necessary skills and experience step by step to get those jobs. The Proposal Department uses AI to generate the optimal career path based on the user's skills and desired career path. The AI ​​analyzes past data and market trends to propose the most effective career path for the user. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. As a result, users can find the optimal career path for themselves and systematically acquire skills for future career success.

[0071] The Service Provider provides learning plans based on the career paths proposed by the Proposal Provider. Specifically, it provides learning plans and learning materials to help users systematically acquire the skills they need. For example, the Service Provider provides a list of online courses and materials, allowing users to learn at their own pace. The Service Provider can also coordinate compensation and schedules with engineers who act as freelance tutors. This allows users to acquire skills while receiving expert guidance. The Service Provider uses AI to monitor users' learning progress and adjust the learning plan as needed. For example, if a user is struggling to acquire a particular skill, the AI ​​will provide additional materials and practice problems related to that skill. It will also suggest the next skills and courses to learn based on the user's learning progress. This allows users to acquire skills efficiently and grow systematically along the proposed career path. Furthermore, the Service Provider has a function to evaluate users' learning outcomes and visualize their skill acquisition status. This allows users to learn while feeling a sense of their own growth.

[0072] The service provider can provide learning plans and learning materials. For example, the service provider can provide learning plans that allow users to systematically acquire the skills they need. The learning plan may include learning content, learning period, and learning methods. For example, the service provider can provide a learning plan that a user can acquire in just a weekend. The service provider can also provide learning materials. These materials may include textbooks, online courses, and video materials. For example, the service provider can provide courses that users can learn online. By providing learning plans and learning materials, users can systematically acquire the skills they need. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's learning history into a generating AI and have the generating AI suggest an optimal learning plan.

[0073] The service provider can coordinate compensation and schedules with engineers who will act as freelance tutors. For example, the service provider can enable users to hire freelance engineers as tutors to learn necessary skills. The service provider adjusts the tutor's compensation and schedule to enable users to learn efficiently. For example, the service provider can set the tutor's compensation on an hourly basis and coordinate the user's and tutor's schedules using an online calendar. The service provider can also set the tutor's compensation as a fixed fee or performance-based fee. This allows users to learn efficiently by coordinating compensation and schedules with tutors. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the tutor's compensation and schedule into a generating AI and have the generating AI suggest the optimal compensation and schedule.

[0074] The analysis unit can propose the optimal career path by considering the user's learning ability and background. For example, the analysis unit can evaluate the user's learning ability and propose the optimal career path based on the evaluation results. The analysis unit can evaluate the user's learning ability from test results, past learning history, etc. The analysis unit can also propose a career path by considering the user's background. Background includes educational background, work experience, and specialized knowledge. For example, the analysis unit can propose the optimal career path by considering the user's educational background and work experience. This allows for the proposal of a more appropriate career path by considering the user's learning ability and background. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning ability and background into a generating AI and have the generating AI propose the optimal career path.

[0075] The proposal unit can propose a user's career path by working backward from the future. For example, the proposal unit can set a career stage that the user aims for in the future and then work backward to propose the skills and experience necessary to reach that career stage. The proposal unit can set the user's goals and work backward from their career stage to propose the optimal career path. For example, if the user aims to work as a cloud architect in the future, the proposal unit will work backward to propose the skills and qualifications necessary for that. In this way, by proposing a career path by working backward from the future, the user's career possibilities can be expanded. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's goal setting and career stage calculation into a generating AI and have the generating AI propose the optimal career path.

[0076] The analytics department can analyze millions of job postings in real time. For example, the analytics department analyzes millions of job postings using data streaming and real-time data processing technologies. The analytics department can extract the latest job information from the data and provide it to users. For example, the analytics department can analyze job postings for a specific industry or region in real time to provide users with the most suitable job information. This allows for the provision of the latest job information by analyzing millions of job postings in real time. Some or all of the above-described processes in the analytics department may be performed using AI, or not. For example, the analytics department can input millions of job postings into a generating AI and have the generating AI perform real-time analysis.

[0077] The analysis unit can understand the correlations between thousands of skills. For example, the analysis unit understands the correlations between thousands of skills using methods such as correlation coefficients and co-occurrence network analysis. By understanding the correlations between skills, the analysis unit can suggest the optimal skill combination to the user. For example, the analysis unit extracts skills related to a specific skill set and suggests them to the user. This allows the analysis unit to suggest the optimal skill combination by understanding the correlations between thousands of skills. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on thousands of skills into a generating AI and have the generating AI perform the correlation analysis.

[0078] The analysis department can predict the probability of success from a vast amount of job change data. For example, the analysis department can predict the probability of success based on job change data from the past 10 years or job change data for a specific industry. The analysis department can use statistical analysis and machine learning models to predict the probability of success for the optimal career path for a user. For example, the analysis department can predict the probability of success for a specific career path by considering the user's skill set and work history. In this way, by predicting the probability of success from a vast amount of job change data, it can propose the optimal career path for the user. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input a vast amount of job change data into a generating AI and have the generating AI perform the prediction of the probability of success.

[0079] The analysis department can track market value fluctuations in real time. For example, the analysis department tracks stock price fluctuations and increases / decreases in job postings using data streaming technology and real-time data processing technology. By tracking market value fluctuations in real time, the analysis department can provide users with the latest market information. For example, the analysis department can track and provide users with real-time market value fluctuations for specific industries or regions. This allows the analysis department to provide the latest market information by tracking market value fluctuations in real time. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input market value fluctuation data into a generating AI and have the generating AI perform real-time tracking.

[0080] The reception desk can estimate the user's emotions and adjust the input method for skills and career paths based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of skills and career paths. This allows for a more appropriate input interface by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI adjust the input method.

[0081] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, the reception desk can automatically display skills and career paths that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest skills and career paths that the user will use at a particular time based on their past input history. In this way, by analyzing past input history, the reception desk can provide the optimal input interface for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform the task of providing the optimal input interface.

[0082] The reception desk can customize input fields based on the user's current job responsibilities and areas of interest when users input skills and career paths. For example, the reception desk can prioritize displaying skills related to the user's current job responsibilities. The reception desk can also suggest relevant career paths based on the user's areas of interest. Furthermore, the reception desk can combine the user's job responsibilities and areas of interest to customize the most suitable input fields. This allows for the provision of more appropriate input fields by customizing them based on the user's current job responsibilities and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's job responsibilities and areas of interest into a generating AI and have the generating AI perform the customization of input fields.

[0083] The reception desk can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize displaying important input items and postpone other items. If the user is relaxed, the reception desk can display all input items equally. Furthermore, if the user is in a hurry, the reception desk can display only the most important input items to allow for quick input. This provides a more appropriate input interface by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of inputs.

[0084] The reception desk can prioritize displaying highly relevant input fields when users input skills and career paths, taking into account their geographical location. For example, the reception desk can prioritize displaying skills and career paths relevant to a region based on the user's current location. The reception desk can also suggest input fields relevant to the local job market, taking into account the user's geographical location. Furthermore, the reception desk can display input fields that align with local trends based on the user's geographical location. This allows for the priority display of skills and career paths relevant to a region by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI display highly relevant input fields.

[0085] The reception desk can analyze the user's social media activity when they input skills and career paths and suggest relevant input fields. For example, the reception desk can suggest relevant skills and career paths based on the user's social media activity. The reception desk can display the most suitable input fields by considering the user's social media followers and areas of interest. The reception desk can also analyze the user's social media posts and suggest relevant career paths. In this way, relevant skills and career paths can be suggested by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input fields.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis method of job data based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis result and display only the important information. If the user is relaxed, the analysis unit can provide a detailed analysis result and display all the information. Also, if the user is in a hurry, the analysis unit can prioritize displaying only the most important job data. In this way, by adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis method.

[0087] The analysis unit can improve the accuracy of its analysis by considering the user's past work history. For example, the analysis unit can prioritize the analysis of relevant job postings based on the user's past work history. The analysis unit can extract job postings related to specific skill sets from the user's work history. The analysis unit can also filter the most suitable job postings by considering the user's work history. This improves the accuracy of the analysis by considering past work history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's past work history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0088] The analysis unit can filter job postings based on the user's current skill set during analysis. For example, the analysis unit can prioritize displaying job postings relevant to the user's current skill set. The analysis unit can filter the most suitable job postings based on the user's skill set. The analysis unit can also extract relevant job postings based on the user's skill set. This allows for the provision of more appropriate job information by filtering job postings based on the user's current skill set. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skill set data into a generating AI and have the generating AI perform the filtering of job postings.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0090] The analysis unit can analyze job data while considering the geographical distribution of users. For example, the analysis unit can prioritize the analysis of job data relevant to a region based on the geographical distribution of users. The analysis unit can extract data relevant to the regional job market while considering the geographical distribution of users. Furthermore, the analysis unit can analyze job data tailored to regional trends based on the geographical distribution of users. This allows for the priority analysis of regionally relevant job data by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the analysis of job data.

[0091] The analysis unit can evaluate the relevance of job posting data by referring to the user's social media activity during analysis. For example, the analysis unit can evaluate relevant job posting data based on the user's social media activity. The analysis unit can evaluate the relevance of job posting data by considering the user's social media followers and areas of interest. The analysis unit can also analyze the content of the user's social media posts and evaluate the relevance of job posting data. In this way, highly relevant job posting data can be evaluated by referring to social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the relevance evaluation of job posting data.

[0092] The suggestion unit can estimate the user's emotions and adjust the career path suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest a simple career path and display only the essential information. If the user is relaxed, the suggestion unit can suggest a detailed career path and display all the information. Furthermore, if the user is in a hurry, the suggestion unit can prioritize displaying only the most important career paths. In this way, by adjusting the suggestion method according to the user's emotions, a more appropriate career path can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the suggestion method.

[0093] The suggestion unit can make optimal suggestions by considering the user's past career path. For example, the suggestion unit can suggest relevant career paths based on the user's past career path. The suggestion unit can extract career paths related to specific skill sets from the user's career path. The suggestion unit can also filter the optimal career path by considering the user's career path. This allows for the suggestion of more appropriate career paths by considering the past career path. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past career path data into a generating AI and have the generating AI execute the optimal suggestion.

[0094] The suggestion unit can filter career paths based on the user's current skill set when making suggestions. For example, the suggestion unit can prioritize displaying career paths related to the user's current skill set. The suggestion unit can filter the optimal career path based on the user's skill set. The suggestion unit can also extract relevant career paths based on the user's skill set. This allows for the suggestion of more appropriate career paths by filtering career paths based on the current skill set. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's skill set data into a generating AI and have the generating AI perform the career path filtering.

[0095] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can prioritize important suggestions and postpone others. If the user is relaxed, the suggestion unit can display all suggestions equally. If the user is in a hurry, the suggestion unit can display only the most important suggestions to allow for quick suggestions. This allows for the suggestion of more appropriate career paths by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0096] The suggestion unit can propose career paths while considering the user's geographical distribution. For example, the suggestion unit can prioritize suggesting region-related career paths based on the user's geographical distribution. The suggestion unit can also propose career paths related to the local job market, taking into account the user's geographical distribution. Furthermore, the suggestion unit can propose career paths aligned with local trends based on the user's geographical distribution. In this way, by considering geographical distribution, it is possible to propose region-related career paths. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's geographical distribution data into a generating AI and have the generating AI execute the career path proposal.

[0097] The proposal unit can evaluate the relevance of career paths by referring to the user's social media activity when making a proposal. For example, the proposal unit can evaluate relevant career paths based on the content of the user's social media activities. The proposal unit can evaluate the relevance of career paths by considering the user's social media followers and areas of interest. The proposal unit can also analyze the content of the user's social media posts and evaluate the relevance of career paths. In this way, by referring to social media activity, it is possible to evaluate highly relevant career paths. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI perform the career path relevance evaluation.

[0098] The service provider can estimate the user's emotions and adjust how the learning plan is delivered based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple learning plan and display only the most important information. If the user is relaxed, the service provider can provide a detailed learning plan and display all the information. Furthermore, if the user is in a hurry, the service provider can prioritize displaying only the most important learning plan. This allows for the provision of a more appropriate learning plan by adjusting the delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the delivery method.

[0099] The service provider can provide the optimal learning plan by considering the user's past learning history when providing a learning plan. For example, the service provider can prioritize providing relevant learning plans based on the user's past learning history. The service provider can extract learning plans related to a specific skill set from the user's learning history. Furthermore, the service provider can filter the optimal learning plan by considering the user's learning history. This allows for the provision of a more appropriate learning plan by considering past learning history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing the optimal plan.

[0100] The service provider can customize learning content based on the user's current skill set when providing a learning plan. For example, the service provider can prioritize providing learning content related to the user's current skill set. The service provider can customize the optimal learning content based on the user's skill set. The service provider can also extract relevant learning content based on the user's skill set. This allows for the provision of a more appropriate learning plan by customizing the learning content based on the current skill set. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's skill set data into a generating AI and have the generating AI perform the customization of the learning content.

[0101] The service provider can estimate the user's emotions and prioritize learning plans based on those emotions. For example, if the user is stressed, the service provider can prioritize displaying important learning plans and postpone others. If the user is relaxed, the service provider can display all learning plans equally. If the user is in a hurry, the service provider can display only the most important learning plans and provide them quickly. This allows for the provision of more appropriate learning plans by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of learning plans.

[0102] The service provider can provide optimal learning plans by considering the geographical distribution of users. For example, the service provider can prioritize providing learning plans relevant to a region based on the geographical distribution of users. The service provider can also provide learning plans relevant to the local job market by considering the geographical distribution of users. Furthermore, the service provider can provide learning plans tailored to local trends based on the geographical distribution of users. In this way, by considering geographical distribution, it is possible to provide learning plans relevant to a region. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the geographical distribution data of users into a generating AI and have the generating AI perform the task of providing optimal learning plans.

[0103] The service provider can suggest learning content by referring to the user's social media activity when providing a learning plan. For example, the service provider can suggest relevant learning content based on the user's social media activity. The service provider can suggest learning content by considering the user's social media followers and areas of interest. The service provider can also analyze the user's social media posts and suggest relevant learning content. In this way, relevant learning content can be suggested by referring to social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the suggestion of learning content.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The suggestion function can estimate the user's emotions and adjust the career path suggestion method based on those emotions. For example, if the user is stressed, it can suggest a simple career path and display only the essential information. If the user is relaxed, it can suggest a detailed career path and display all the information. If the user is in a hurry, it can prioritize displaying only the most important career paths. In this way, by adjusting the suggestion method according to the user's emotions, it can suggest a more appropriate career path.

[0106] The system can estimate the user's emotions and adjust how the learning plan is delivered based on those emotions. For example, if the user is stressed, a simple learning plan can be provided, displaying only the most important information. If the user is relaxed, a detailed learning plan can be provided, displaying all the information. If the user is in a hurry, only the most important learning plan can be prioritized. This allows the system to provide a more appropriate learning plan by adjusting the delivery method according to the user's emotions.

[0107] The analytics department can estimate the user's emotions and adjust the analysis method of job data based on those estimated emotions. For example, if the user is stressed, it can provide a simple analysis and display only the most important information. If the user is relaxed, it can provide a detailed analysis and display all the information. If the user is in a hurry, it can prioritize displaying only the most important job data. In this way, by adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided.

[0108] The reception desk can estimate the user's emotions and adjust the input method for skills and career paths based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. If the user is in a hurry, it can prioritize voice input to allow for quick input of skills and career paths. In this way, a more appropriate input interface can be provided by adjusting the input method according to the user's emotions.

[0109] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, important suggestions will be displayed first, while others will be delayed. If the user is relaxed, all suggestions can be displayed equally. If the user is in a hurry, only the most important suggestions can be displayed, allowing for quick suggestions. By prioritizing suggestions according to the user's emotions, a more appropriate career path can be suggested.

[0110] The analytics department can improve the accuracy of its analysis by considering the user's past work history. For example, it can prioritize the analysis of relevant job postings based on the user's past work history. It can extract job postings related to specific skill sets from the user's work history. It can also filter the most suitable job postings by considering the user's work history. In this way, the accuracy of the analysis can be improved by considering past work history.

[0111] The proposal function can provide optimal suggestions by considering the user's past career path. For example, it can suggest relevant career paths based on the user's past career path. It can extract career paths related to specific skill sets from the user's career path. It can also filter the optimal career path by considering the user's career path. In this way, by considering past career paths, it can propose more appropriate career paths.

[0112] The system can provide optimal learning plans by considering the user's past learning history. For example, it can prioritize providing relevant learning plans based on the user's past learning history. It can extract learning plans related to specific skill sets from the user's learning history. It can also filter optimal learning plans by considering the user's learning history. This allows for the provision of more appropriate learning plans by taking past learning history into account.

[0113] The analytics department can filter job postings based on the user's current skill set. For example, it can prioritize displaying job postings relevant to the user's current skill set. It can filter the most relevant job postings based on the user's skill set. It can also extract relevant job postings based on the user's skill set. This allows for more appropriate job information to be provided by filtering job postings based on the user's current skill set.

[0114] The learning platform can customize learning content based on the user's current skill set. For example, it can prioritize providing learning content relevant to the user's current skill set. It can customize the optimal learning content based on the user's skill set. It can also extract relevant learning content based on the user's skill set. This allows for the provision of a more appropriate learning plan by customizing learning content based on the user's current skill set.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives input from users regarding their skills and desired career path. For example, a user can access a job search website and enter their current skills and desired career path. The reception desk stores the user's input information in a database and uses it for subsequent processing. Step 2: The analysis department analyzes the vast amount of job data based on the information received by the reception department. For example, it analyzes millions of job postings in real time and extracts the most suitable job information based on the user's skills and desired career path. The analysis department uses data mining and statistical analysis techniques to identify job postings that are suitable for the user from the job data. Step 3: The proposal department proposes the optimal career path based on the analysis results obtained by the analysis department. For example, based on the user's skills and desired career path, it proposes a career path that will help them find a job with high market value in the future and systematically acquire the necessary skills. The proposal department can propose the optimal career path by taking into account the user's learning ability and background. Step 4: The provisioning department provides a learning plan based on the career path proposed by the proposaling department. For example, they provide a learning plan and learning materials to help the user systematically acquire the necessary skills. The provisioning department can also coordinate compensation and schedules with engineers who will act as freelance tutors.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input of the user's skills and desired career path. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes a large amount of job posting data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal career path. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 and provides a learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input of the user's skills and desired career path. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes a vast amount of job posting data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal career path. The provision unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides a learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 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.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the 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.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 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.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives input of the user's skills and desired career path. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes a large amount of job posting data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal career path. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides a learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives input from the user regarding their skills and desired career path. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes a vast amount of job posting data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal career path. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides a learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A reception desk that accepts user input regarding skills and desired career path, An analysis department analyzes a vast amount of job posting data based on the information received by the aforementioned reception department, A proposal unit that proposes the optimal career path based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides a learning plan based on the career path proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, We provide learning plans and learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, I coordinate the fees and schedules with engineers who will work as freelance tutors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is We propose the optimal career path considering the user's learning ability and background. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose user career paths by working backward from the future. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Analyzing millions of job postings in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is Understanding the correlations between thousands of different skills The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Predicting the probability of success based on a vast number of successful job changes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Track market value fluctuations in real time. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts how skills and career paths are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyze the user's past input history and provide the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their skills and career path, the input fields are customized based on their current job responsibilities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When users enter their skills and career paths, the system prioritizes displaying the most relevant input fields by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When users input their skills and career paths, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of job posting data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by considering the user's past work history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, job postings are filtered based on the user's current skill set. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When analyzing job postings, consider the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, we refer to users' social media activity to assess the relevance of job posting data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, We estimate the user's emotions and adjust the career path suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we consider the user's past career path to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, career paths are filtered based on the user's current skill set. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we suggest career paths that take into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, we will refer to the user's social media activity to assess the relevance of their career path. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the learning plan is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing a learning plan, we take into account the user's past learning history to provide the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing a learning plan, customize the learning content based on the user's current skill set. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing a learning plan, we will provide the optimal learning plan considering the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing a learning plan, we suggest learning content based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user input regarding skills and desired career path, An analysis department analyzes a vast amount of job posting data based on the information received by the aforementioned reception department, A proposal unit that proposes the optimal career path based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides a learning plan based on the career path proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned supply unit is, We provide learning plans and learning materials. The system according to feature 1.

3. The aforementioned supply unit is, I coordinate the fees and schedules with engineers who will work as freelance tutors. The system according to feature 1.

4. The aforementioned analysis unit is We propose the optimal career path considering the user's learning ability and background. The system according to feature 1.

5. The aforementioned proposal section is, We propose user career paths by working backward from the future. The system according to feature 1.

6. The aforementioned analysis unit is Analyzing millions of job postings in real time. The system according to feature 1.

7. The aforementioned analysis unit is Understanding the correlations between thousands of different skills The system according to feature 1.

8. The aforementioned analysis unit is Predicting the probability of success based on a vast number of successful job changes. The system according to feature 1.

Images