system

Abstract

The system according to this embodiment aims to provide personalized career support by utilizing user data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a matching unit, a coaching unit, and a prediction unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The matching unit matches the learning plan generated by the generation unit with job information. The coaching unit provides interactive career coaching based on the job information matched by the matching unit. The prediction unit predicts trends in the labor market based on the information obtained by the coaching 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional carrier support services have problems such as limitations in personalization, information asymmetry, and lack of real-time performance.

[0005] The system according to the embodiment aims to provide personalized carrier support by utilizing user data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a matching unit, a coaching unit, and a prediction unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The matching unit matches the learning plan generated by the generation unit with job information. The coaching unit provides interactive career coaching based on the job information matched by the matching unit. The prediction unit predicts trends in the labor market based on the information obtained by the coaching unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized career support by utilizing user data. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The career support system according to an embodiment of the present invention proposes "CareerNavi AI," an innovative career support platform utilizing generative AI, in order to overcome the limitations of conventional career support services. This platform consists of five main components: an AI career analysis engine, a personalized learning recommendation system, an AI matching algorithm, an interactive career coaching bot, and a data analysis and prediction platform. The career support system combines user input data with external data sources to comprehensively analyze an individual's strengths, weaknesses, and potential aptitudes. For example, when a user inputs their past work experience and skills, the generative AI analyzes this data and evaluates the user's aptitudes. Next, the career support system generates an individually optimized learning plan based on the results of the AI ​​career analysis engine. For example, it provides a specific learning plan to acquire the skills necessary for the job the user aims for. Furthermore, the career support system matches job seekers with job information. In this process, it takes into account not only the matching of skill sets but also compatibility with corporate culture and long-term career prospects. For example, it recommends companies that match the user's values ​​and career goals. Through natural language dialogue with the user, the career support system provides answers to career-related questions, conducts mock interviews, and provides continuous motivation support. For example, if a user wants to practice for an interview, the generating AI will conduct a mock interview and provide immediate feedback. Finally, the career support system analyzes labor market trends in real time and predicts future skill demands. For instance, it predicts changes in skill demands in specific industries and provides users with appropriate career advice. This career support system is expected to enable high levels of individualization, elimination of information asymmetry, real-time response, high scalability, and continuous support. Through detailed analysis and individual optimization by the generating AI, it realizes career support tailored to each user's characteristics and goals, and reduces mismatches by providing more detailed and appropriate information to both job seekers and companies.By providing advice that reflects the latest labor market trends, we support adaptation to the rapidly changing employment environment and deliver high-quality career support to a large user base by leveraging generative AI. Our system supports long-term career development, promoting users' sustainable growth and improving the overall efficiency and productivity of the labor market through appropriate matching and continuous skills development support. Furthermore, by promoting self-analysis and continuous learning, we enhance users' career awareness and initiative. As a result, our career support system becomes an innovative platform that simultaneously supports users' career development and companies' talent acquisition and development, contributing to the optimization of the entire labor market.

[0029] The career support system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a matching unit, a coaching unit, and a prediction unit. The data collection unit collects user data. For example, the data collection unit can collect the user's career history, skills, and interests. The data collection unit can collect data using methods such as questionnaires, sensors, and online activity tracking. For example, the data collection unit can collect the user's work history and educational background in the form of a questionnaire. The data collection unit can also track the user's online activities to understand their interests and concerns. Furthermore, the data collection unit can collect the user's biometric data using sensors. For example, the data collection unit can measure the user's heart rate and activity level using a wearable device. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The analysis unit evaluates the user's strengths, weaknesses, and potential aptitudes. For example, the analysis unit evaluates strengths based on the user's past achievements and skill evaluations. The analysis unit can also evaluate weaknesses based on the user's past failures and lack of skills. Furthermore, the analysis unit can also evaluate potential aptitudes using aptitude tests and psychological tests. The generation unit generates learning plans based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate individually optimized learning plans using generation AI. The generation unit provides customized learning plans based on the user's goals and skill level. For example, the generation unit provides a specific learning plan to acquire the skills necessary for the job the user is aiming for. The generation unit can also adjust the learning plan according to the user's learning progress. The matching unit matches job postings with learning plans generated by the generation unit. The matching unit can, for example, use an AI matching algorithm to match the user's profile with job postings with high accuracy. The matching unit performs matching using criteria such as skill matching and interest matching. For example, the matching unit compares the user's skill set with the required skills of the job postings to perform matching. The matching unit can also recommend companies that match the user's values ​​and career goals.The Coaching Department provides interactive career coaching based on job postings matched by the Matching Department. The Coaching Department can, for example, conduct mock interviews using generative AI and provide immediate feedback. The Coaching Department provides answers to career-related questions and ongoing motivation support through natural language dialogue with users. For example, if a user wants to practice interviews, the Coaching Department's generative AI conducts a mock interview and provides immediate feedback. The Prediction Department forecasts labor market trends based on information obtained by the Coaching Department. The Prediction Department can, for example, use AI to analyze labor market trends in real time and predict future skill demands. The Prediction Department uses statistical models and machine learning algorithms to forecast labor market trends. For example, the Prediction Department can predict changes in skill demand in a specific industry and provide appropriate career advice to users. Thus, the career support system according to this embodiment can provide individually optimized career support by collecting and analyzing user data, generating learning plans, matching with job postings, conducting interactive career coaching, and forecasting labor market trends.

[0030] The data collection unit collects user data. For example, it can collect user history, skills, and interests. Specifically, the data collection unit collects data using methods such as surveys, sensors, and online activity tracking. For example, the data collection unit collects users' work history and educational background in the form of surveys. Surveys are provided through online forms and mobile apps and are designed to be easy for users to fill out. The data collection unit can also track users' online activities to understand their interests and preferences. For example, it tracks websites visited, online courses taken, and social media activity, and analyzes this data to identify users' interests and preferences. Furthermore, the data collection unit can collect users' biometric data using sensors. For example, it can measure users' heart rate and activity levels using wearable devices. This allows the data collection unit to understand the user's health status and stress levels, which can be used to support their careers. The data collection unit integrates these diverse data sources to create detailed user profiles. This allows the data collection unit to gain a comprehensive understanding of users' history, skills, and interests, and build a foundation for providing personalized career support.

[0031] The analytics department analyzes the data collected by the data collection department. For example, the analytics department can analyze the data using statistical analysis and machine learning algorithms. Specifically, the analytics department evaluates users' strengths, weaknesses, and potential aptitudes. For instance, it assesses strengths based on users' past achievements and skill assessments. It analyzes past projects, performance, and acquired qualifications to identify areas of expertise and skills. The analytics department can also assess weaknesses based on users' past failures and skill deficiencies. For example, it analyzes past failures and goals that were not achieved due to skill deficiencies to identify areas for improvement. Furthermore, the analytics department can assess potential aptitudes using aptitude tests and psychological assessments. This allows them to identify potential undiscovered talents and aptitudes and suggest new career directions. The analytics department comprehensively evaluates this data to provide insights for optimizing users' career paths.

[0032] The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate individually optimized learning plans using a generation AI. Specifically, the generation unit provides a customized learning plan based on the user's goals and skill level. For example, the generation unit provides a specific learning plan to acquire the skills necessary for the job the user is aiming for. The generation AI considers the user's current skill set and goals and suggests the most suitable learning resources and courses. Furthermore, the generation unit can also adjust the learning plan according to the user's learning progress. For example, if the user is struggling to acquire a particular skill, the generation unit provides additional resources and support and flexibly modifies the learning plan. This allows the generation unit to support users in efficiently and effectively acquiring skills and achieving their career goals.

[0033] The matching unit matches job postings with learning plans generated by the generation unit. The matching unit can accurately match user profiles with job postings using, for example, AI matching algorithms. Specifically, the matching unit performs matching using criteria such as skill matching and interest matching. For example, the matching unit compares the user's skill set with the required skills of the job posting to perform matching. This allows the system to identify job postings where the user's skills match those required by the company. The matching unit can also recommend companies that align with the user's values ​​and career goals. For example, it can identify and recommend companies that match the corporate culture and work style that the user values. Furthermore, the matching unit can provide job postings that are likely to interest the user based on their interests. In this way, the matching unit can achieve optimal matching for both the user and the company, supporting the user's career success.

[0034] The Coaching Department provides interactive career coaching based on job postings matched by the Matching Department. For example, the Coaching Department can conduct mock interviews using generative AI and provide immediate feedback. Specifically, the Coaching Department provides answers to career-related questions and ongoing motivation support through natural language dialogue with users. For instance, if a user wants to practice interview skills, the generative AI conducts a mock interview and provides immediate feedback. The generative AI analyzes the user's responses, attitude, and facial expressions, pointing out areas for improvement and strengths. Furthermore, when a user asks a career-related question, the generative AI provides an appropriate answer, resolving the user's doubts. The Coaching Department also provides support to maintain user motivation. For example, when a user reports progress toward achieving a goal, the generative AI sends an encouraging message. This allows the Coaching Department to support users in confidently advancing their careers and guide them to success.

[0035] The forecasting unit predicts labor market trends based on information obtained by the coaching unit. For example, the forecasting unit can use AI to analyze labor market trends in real time and predict future skill demand. Specifically, the forecasting unit uses statistical models and machine learning algorithms to predict labor market trends. For instance, the forecasting unit predicts changes in skill demand in specific industries and provides users with appropriate career advice. The forecasting unit analyzes historical data and current market trends to identify skills and occupations that will be in high demand in the future. This allows users to gain information to select promising career paths for the future. Furthermore, the forecasting unit can also predict skill demand by region, taking into account the characteristics of the labor market in each region. This allows users to find career opportunities in regions best suited to their skill sets. Based on these predictions, the forecasting unit can provide users with specific career advice and support their future career success.

[0036] The data collection unit can collect the user's background, skills, and interests. For example, it can collect the user's work history and educational background in the form of questionnaires. It can also track the user's online activity and understand their interests. Furthermore, the data collection unit can collect the user's biometric data using sensors. For example, it can measure the user's heart rate and activity level using wearable devices. This allows for more detailed data-driven career support by collecting the user's background, skills, and interests. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's online activity data into a generating AI and have the generating AI perform the task of understanding the user's interests.

[0037] The analysis department can analyze the collected data and evaluate the user's strengths, weaknesses, and potential aptitudes. For example, the analysis department can analyze the data using statistical analysis or machine learning algorithms. The analysis department evaluates strengths based on the user's past achievements and skill assessments. It can also evaluate weaknesses based on the user's past failures and skill deficiencies. Furthermore, the analysis department can evaluate potential aptitudes using aptitude tests and psychological tests. This allows for individually optimized career support by evaluating the user's strengths, weaknesses, and potential aptitudes. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the collected data into a generating AI and have the generating AI perform the evaluation of strengths and weaknesses.

[0038] The generation unit can generate individually optimized learning plans based on the analysis results. For example, the generation unit generates individually optimized learning plans using a generation AI. The generation unit provides customized learning plans based on the user's goals and skill level. For example, the generation unit provides a specific learning plan to acquire the skills necessary for the job the user is aiming for. The generation unit can also adjust the learning plan according to the user's learning progress. In this way, by generating individually optimized learning plans, it effectively supports the user's skill improvement. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI execute the generation of the learning plan.

[0039] The matching unit can accurately match user profiles with job postings. For example, the matching unit uses an AI matching algorithm to accurately match user profiles with job postings. The matching unit performs matching using criteria such as skill matching and interest matching. For example, the matching unit compares the user's skill set with the required skills of the job posting to perform matching. The matching unit can also recommend companies that match the user's values ​​and career goals. This reduces mismatches between users and companies by achieving high-precision matching. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input user profile data into a generating AI and have the generating AI perform the matching with job postings.

[0040] The coaching department can conduct mock interviews and provide immediate feedback. For example, the coaching department can use generative AI to conduct mock interviews and provide immediate feedback. The coaching department provides answers to career-related questions and ongoing motivation support through natural language dialogue with users. For example, if a user wants to practice interviews, the coaching department's generative AI will conduct a mock interview and provide immediate feedback. This improves the user's interview skills by conducting mock interviews and providing immediate feedback. Some or all of the above processes in the coaching department may be performed using generative AI or not. For example, the coaching department can input the user's interview data into the generative AI and have the generative AI generate the feedback.

[0041] The forecasting unit can analyze labor market trends in real time and predict future skill demand. For example, the forecasting unit can use AI to analyze labor market trends in real time and predict future skill demand. The forecasting unit can use statistical models and machine learning algorithms to predict labor market trends. For example, the forecasting unit can predict changes in skill demand in a specific industry and provide appropriate career advice to the user. This allows the forecasting unit to provide appropriate career advice to the user by analyzing labor market trends in real time and predicting future skill demand. Some or all of the above-described processes in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input labor market data into a generating AI and have the generating AI perform skill demand predictions.

[0042] The data collection unit can analyze the user's past career history and skill changes to select the optimal data collection method. For example, the data collection unit can focus on collecting relevant skills based on the user's past job experience. The data collection unit can also analyze the user's skill changes and ask questions appropriate to their current skill level. Furthermore, the data collection unit can prioritize collecting data related to specific industries or job types from the user's career history. This allows the optimal data collection method to be selected by analyzing the user's past career history and skill changes. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's career data into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can filter data based on the user's current career goals and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the job type the user is aiming for. The data collection unit can also ask relevant questions based on the user's areas of interest. Furthermore, the data collection unit can collect data on the skills and experience required according to the user's career goals. This allows for the collection of more relevant data by filtering the data based on the user's career goals and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's career goal data into a generating AI and have the generating AI perform the data filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if a user lives in a specific region, the data collection unit will prioritize the collection of job postings related to that region. The data collection unit can also collect data on region-specific skills and experience based on the user's geographical location information. Furthermore, if a user is on the move, the data collection unit can prioritize the collection of data related to their current location. This allows for the priority collection of region-specific data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to a user's interests based on their social media activity. The data collection unit can also analyze accounts that a user follows and groups they participate in and collect relevant data. Furthermore, the data collection unit can collect data related to a user's career goals based on their social media posts. In this way, data related to a user's interests can be collected by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0046] The analysis unit can evaluate a user's strengths and weaknesses by referring to their past performance data during analysis. For example, the analysis unit can evaluate strengths and weaknesses based on a user's past job evaluation data. It can also analyze a user's past project results to evaluate strengths and weaknesses. Furthermore, the analysis unit can evaluate strengths and weaknesses by referring to a user's past skill test results. This allows for an accurate evaluation of strengths and weaknesses by referring to a user's past performance data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user performance data into a generating AI and have the generating AI perform the strengths and weaknesses evaluation.

[0047] The analysis unit can apply different analysis algorithms during analysis depending on the user's career goals. For example, if the user aims for a technical position, the analysis unit can apply an analysis algorithm specialized in technical skills. Similarly, if the user aims for a management position, the analysis unit can apply an analysis algorithm specialized in leadership skills. Furthermore, if the user aims for a creative position, the analysis unit can apply an analysis algorithm specialized in creativity. This allows for more appropriate analysis results by applying analysis algorithms according to the user's career goals. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's career goal data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0048] The analysis unit can weight data based on the timing of user submissions during analysis. For example, the analysis unit can prioritize data recently submitted by users during analysis. It can also weight data based on data submitted by users within a specific period. Furthermore, the analysis unit can adjust the importance of data according to the timing of user submissions. This allows for more accurate analysis results by weighting data based on the timing of user submissions. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user submission data into a generating AI and have the generating AI perform the data weighting.

[0049] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to literature related to the user's research field. In addition, the analysis unit can improve the accuracy of its analysis based on literature cited by the user. Thus, by referring to the user's relevant literature, the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0050] The generation unit can generate an optimal learning plan by referring to the user's past learning history. For example, the generation unit can generate an optimal learning plan based on what the user has learned in the past. The generation unit can also analyze the user's past learning results and generate an optimal learning plan. Furthermore, the generation unit can suggest effective learning methods based on the user's past learning history. In this way, an optimal learning plan can be generated by referring to the user's past learning history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's learning history data into a generation AI and have the generation AI execute the generation of an optimal learning plan.

[0051] The generation unit can apply different learning algorithms depending on the user's career goals when generating a learning plan. For example, if the user aims for a technical position, the generation unit can apply a learning algorithm specialized in technical skills. Similarly, if the user aims for a management position, the generation unit can apply a learning algorithm specialized in leadership skills. Furthermore, if the user aims for a creative position, the generation unit can apply a learning algorithm specialized in creativity. This allows for the provision of a more appropriate learning plan by applying a learning algorithm according to the user's career goals. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's career goal data into a generation AI and have the generation AI execute the application of the learning algorithm.

[0052] The generation unit can generate an optimal learning plan by considering the user's geographical location information. For example, if the user lives in a specific region, the generation unit can provide a learning plan related to that region. The generation unit can also provide a learning plan related to region-specific skills and experiences based on the user's geographical location information. Furthermore, if the user is on the move, the generation unit can provide a learning plan related to their current location. In this way, by considering the user's geographical location information, region-specific learning plans can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's location information data into a generation AI and have the generation AI perform the generation of an optimal learning plan.

[0053] The generation unit can analyze a user's social media activity and generate relevant learning plans when creating learning plans. For example, the generation unit can provide learning plans related to the user's interests based on their social media activity. The generation unit can also analyze accounts the user follows and groups they participate in and provide relevant learning plans. Furthermore, the generation unit can provide learning plans related to career goals based on the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to provide learning plans related to their interests. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the generation of relevant learning plans.

[0054] The matching unit can select the most suitable job postings by referring to the user's past work experience during the matching process. For example, the matching unit can prioritize selecting relevant job postings based on the user's past work experience. It can also select job postings that match the user's skill set based on their past work experience. Furthermore, the matching unit can analyze the user's past work experience and select job postings that align with their career goals. This allows the matching unit to select the most suitable job postings by referring to the user's past work experience. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input the user's work experience data into a generating AI and have the generating AI select the most suitable job postings.

[0055] The matching unit can apply different matching algorithms to users according to their career goals during the matching process. For example, if a user is aiming for a technical position, the matching unit can apply a matching algorithm specialized in technical skills. Similarly, if a user is aiming for a management position, the matching unit can apply a matching algorithm specialized in leadership skills. Furthermore, if a user is aiming for a creative position, the matching unit can apply a matching algorithm specialized in creativity. This allows for the provision of more appropriate job information by applying matching algorithms according to the user's career goals. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's career goal data into a generating AI and have the generating AI execute the application of matching algorithms.

[0056] The matching unit can select the most suitable job postings by considering the user's geographical location during the matching process. For example, if the user lives in a specific region, the matching unit will prioritize selecting job postings related to that region. The matching unit can also select region-specific job postings based on the user's geographical location. Furthermore, if the user is on the move, the matching unit can prioritize selecting job postings related to their current location. This allows the system to provide region-specific job postings by considering the user's geographical location. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's location data into a generating AI and have the generating AI select the most suitable job postings.

[0057] The matching unit can analyze a user's social media activity during the matching process and provide relevant job information. For example, the matching unit can provide job information related to a user's interests based on their social media activity. It can also analyze accounts a user follows and groups they participate in and provide relevant job information. Furthermore, it can provide job information related to a user's career goals based on their social media posts. This allows the matching unit to provide job information related to a user's interests by analyzing their social media activity. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's social media data into a generating AI and have the generating AI provide relevant job information.

[0058] The coaching department can provide optimal feedback during coaching sessions by referring to the user's past interview history. For example, the coaching department can provide specific feedback based on the user's past interview history. The coaching department can also analyze the user's past interview results and suggest areas for improvement. Furthermore, the coaching department can provide feedback that highlights the user's successes based on their past interview history. This allows for the provision of optimal feedback by referring to the user's past interview history. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input the user's interview history data into a generating AI and have the generating AI generate the feedback.

[0059] The coaching department can apply different coaching algorithms to users according to their career goals during coaching sessions. For example, if a user is aiming for a technical position, the coaching department can apply a coaching algorithm specializing in technical skills. Similarly, if a user is aiming for a management position, the coaching department can apply a coaching algorithm specializing in leadership skills. Furthermore, if a user is aiming for a creative position, the coaching department can apply a coaching algorithm specializing in creativity. This allows for more appropriate coaching by applying coaching algorithms according to the user's career goals. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input the user's career goal data into a generating AI and have the generating AI apply the coaching algorithms.

[0060] The coaching unit can provide optimal coaching content by considering the user's geographical location during coaching sessions. For example, if the user lives in a specific region, the coaching unit can provide coaching content relevant to that region. Furthermore, based on the user's geographical location, the coaching unit can also provide coaching content related to region-specific skills and experiences. Additionally, if the user is on the move, the coaching unit can provide coaching content relevant to their current location. This allows for the provision of region-specific coaching content by considering the user's geographical location. Some or all of the above processing in the coaching unit may be performed using AI, or not. For example, the coaching unit can input the user's location data into a generating AI and have the generating AI provide optimal coaching content.

[0061] The coaching department can analyze a user's social media activity during coaching sessions and provide relevant coaching content. For example, the coaching department can provide coaching content related to a user's interests based on their social media activity. It can also analyze accounts a user follows and groups they participate in and provide relevant coaching content. Furthermore, the coaching department can provide coaching content related to career goals based on the user's social media posts. This allows the coaching department to provide coaching content related to a user's interests by analyzing their social media activity. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input a user's social media data into a generating AI and have the generating AI provide relevant coaching content.

[0062] The forecasting unit can predict future skill demand by referring to historical labor market data during the forecasting process. For example, the forecasting unit can predict future skill demand based on historical labor market data. The forecasting unit can also analyze historical labor market trends and predict future skill demand. Furthermore, the forecasting unit can predict skill demand in a specific industry by referring to historical labor market data. This allows for accurate prediction of future skill demand by referring to historical labor market data. Some or all of the above-described processes in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input historical labor market data into a generating AI and have the generating AI perform the skill demand forecast.

[0063] The prediction unit can apply different prediction algorithms depending on the specific industry during the prediction process. For example, to predict skill demand in the technology industry, the prediction unit can apply a prediction algorithm specialized in technical skills. It can also apply a prediction algorithm specialized in leadership skills to predict skill demand in management positions. Furthermore, it can apply a prediction algorithm specialized in creativity to predict skill demand in the creative industry. This allows for more accurate predictions by applying prediction algorithms tailored to specific industries. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input specific industry data into a generating AI and have the generating AI perform the application of the prediction algorithm.

[0064] The prediction unit can provide optimal predictions by considering the user's geographical location information during the prediction process. For example, if the user lives in a specific region, the prediction unit can provide labor market trend predictions related to that region. The prediction unit can also provide region-specific skill demand predictions based on the user's geographical location information. Furthermore, if the user is on the move, the prediction unit can provide labor market trend predictions related to their current location. In this way, by considering the user's geographical location information, region-specific labor market trend predictions can be provided. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the user's location information data into a generating AI and have the generating AI perform the task of providing optimal predictions.

[0065] The prediction unit can analyze the user's social media activity during prediction and provide relevant prediction results. For example, the prediction unit can provide labor market trend predictions related to the user's interests based on the user's social media activity. The prediction unit can also analyze the accounts the user follows and the groups the user participates in and provide relevant labor market trend predictions. Furthermore, the prediction unit can provide labor market trend predictions related to career goals based on the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to provide labor market trend predictions related to interests. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant prediction results.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] Career support systems can analyze a user's past career path and simulate future career paths. For example, they can simulate possible future career paths based on the user's past job experience and skill development. They can also present different scenarios depending on the user's career goals. Furthermore, they can predict how a user's career path will change if they acquire specific skills. This makes it easier for users to visualize their future career path concretely and develop specific action plans to achieve their goals.

[0068] Career support systems can provide region-specific career advice by taking into account the user's geographical location. For example, if a user lives in a specific region, the system can provide advice based on the labor market trends and job openings in that region. It can also provide information on region-specific skill demands and industry trends. Furthermore, if a user is considering relocating, the system can provide career advice related to their destination region. This enables appropriate career advice that takes the user's geographical location into account.

[0069] Career support systems can analyze users' social media activity and provide relevant career advice. For example, they can offer career advice related to users' interests based on the accounts they follow and the groups they participate in on social media. They can also analyze users' posts and provide advice related to their career goals. Furthermore, they can suggest networking opportunities based on users' social media activity. This enables effective career advice that leverages users' social media activity.

[0070] Career support systems can analyze a user's past learning history and recommend the most suitable learning resources. For example, they can recommend relevant learning resources based on what the user has learned in the past. They can also analyze the user's learning outcomes and suggest effective learning methods. Furthermore, they can prioritize recommending resources necessary for skill development based on the user's learning history. This enables effective recommendation of learning resources by leveraging the user's past learning history.

[0071] Career support systems can provide information on region-specific networking events and career fairs, taking into account the user's geographical location. For example, if a user lives in a specific region, the system can provide information on networking events and career fairs held in that region. It can also provide information on region-specific industry associations and professional groups. Furthermore, if a user is considering relocating, the system can provide information on networking events and career fairs in their destination region. This enables effective networking support that takes the user's geographical location into account.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection unit collects user data. The data collection unit can collect, for example, the user's background, skills, and interests. The data collection unit can collect data using methods such as questionnaires, sensors, and online activity tracking. For example, the data collection unit can collect the user's work history and educational background in the form of questionnaires. The data collection unit can also track the user's online activity to understand their interests and preferences. Furthermore, the data collection unit can collect the user's biometric data using sensors. For example, the data collection unit can measure the user's heart rate and activity level using wearable devices. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis department evaluates the user's strengths, weaknesses, and potential aptitudes. For example, the analysis department evaluates strengths based on the user's past achievements and skill assessments. The analysis department can also evaluate weaknesses based on the user's past failures and lack of skills. Furthermore, the analysis department can evaluate potential aptitudes using aptitude tests and psychological tests. Step 3: The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate an individually optimized learning plan using a generation AI. The generation unit provides a customized learning plan based on the user's goals and skill level. For example, the generation unit provides a specific learning plan to acquire the skills necessary for the job the user is aiming for. The generation unit can also adjust the learning plan according to the user's learning progress. Step 4: The matching unit matches job postings with the learning plan generated by the generation unit. The matching unit can, for example, use an AI matching algorithm to match the user's profile with job postings with high accuracy. The matching unit performs matching using criteria such as skill matching and interest matching. For example, the matching unit compares the user's skill set with the required skills of the job postings to perform matching. The matching unit can also recommend companies that match the user's values ​​and career goals. Step 5: The coaching department conducts interactive career coaching based on job postings matched by the matching department. The coaching department can, for example, use generative AI to conduct mock interviews and provide immediate feedback. The coaching department provides answers to career-related questions and ongoing motivation support through natural language dialogue with the user. For example, if a user wants to practice interviews, the coaching department's generative AI will conduct a mock interview and provide immediate feedback. Step 6: The forecasting unit predicts labor market trends based on the information obtained by the coaching unit. The forecasting unit can, for example, use AI to analyze labor market trends in real time and predict future skill demand. The forecasting unit predicts labor market trends using statistical models and machine learning algorithms. For example, the forecasting unit predicts changes in skill demand in a specific industry and provides appropriate career advice to the user.

[0074] (Example of form 2) The career support system according to an embodiment of the present invention proposes "CareerNavi AI," an innovative career support platform utilizing generative AI, in order to overcome the limitations of conventional career support services. This platform consists of five main components: an AI career analysis engine, a personalized learning recommendation system, an AI matching algorithm, an interactive career coaching bot, and a data analysis and prediction platform. The career support system combines user input data with external data sources to comprehensively analyze an individual's strengths, weaknesses, and potential aptitudes. For example, when a user inputs their past work experience and skills, the generative AI analyzes this data and evaluates the user's aptitudes. Next, the career support system generates an individually optimized learning plan based on the results of the AI ​​career analysis engine. For example, it provides a specific learning plan to acquire the skills necessary for the job the user aims for. Furthermore, the career support system matches job seekers with job information. In this process, it takes into account not only the matching of skill sets but also compatibility with corporate culture and long-term career prospects. For example, it recommends companies that match the user's values ​​and career goals. Through natural language dialogue with the user, the career support system provides answers to career-related questions, conducts mock interviews, and provides continuous motivation support. For example, if a user wants to practice for an interview, the generating AI will conduct a mock interview and provide immediate feedback. Finally, the career support system analyzes labor market trends in real time and predicts future skill demands. For instance, it predicts changes in skill demands in specific industries and provides users with appropriate career advice. This career support system is expected to enable high levels of individualization, elimination of information asymmetry, real-time response, high scalability, and continuous support. Through detailed analysis and individual optimization by the generating AI, it realizes career support tailored to each user's characteristics and goals, and reduces mismatches by providing more detailed and appropriate information to both job seekers and companies.By providing advice that reflects the latest labor market trends, we support adaptation to the rapidly changing employment environment and deliver high-quality career support to a large user base by leveraging generative AI. Our system supports long-term career development, promoting users' sustainable growth and improving the overall efficiency and productivity of the labor market through appropriate matching and continuous skills development support. Furthermore, by promoting self-analysis and continuous learning, we enhance users' career awareness and initiative. As a result, our career support system becomes an innovative platform that simultaneously supports users' career development and companies' talent acquisition and development, contributing to the optimization of the entire labor market.

[0075] The career support system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a matching unit, a coaching unit, and a prediction unit. The data collection unit collects user data. For example, the data collection unit can collect the user's career history, skills, and interests. The data collection unit can collect data using methods such as questionnaires, sensors, and online activity tracking. For example, the data collection unit can collect the user's work history and educational background in the form of a questionnaire. The data collection unit can also track the user's online activities to understand their interests and concerns. Furthermore, the data collection unit can collect the user's biometric data using sensors. For example, the data collection unit can measure the user's heart rate and activity level using a wearable device. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The analysis unit evaluates the user's strengths, weaknesses, and potential aptitudes. For example, the analysis unit evaluates strengths based on the user's past achievements and skill evaluations. The analysis unit can also evaluate weaknesses based on the user's past failures and lack of skills. Furthermore, the analysis unit can also evaluate potential aptitudes using aptitude tests and psychological tests. The generation unit generates learning plans based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate individually optimized learning plans using generation AI. The generation unit provides customized learning plans based on the user's goals and skill level. For example, the generation unit provides a specific learning plan to acquire the skills necessary for the job the user is aiming for. The generation unit can also adjust the learning plan according to the user's learning progress. The matching unit matches job postings with learning plans generated by the generation unit. The matching unit can, for example, use an AI matching algorithm to match the user's profile with job postings with high accuracy. The matching unit performs matching using criteria such as skill matching and interest matching. For example, the matching unit compares the user's skill set with the required skills of the job postings to perform matching. The matching unit can also recommend companies that match the user's values ​​and career goals.The Coaching Department provides interactive career coaching based on job postings matched by the Matching Department. The Coaching Department can, for example, conduct mock interviews using generative AI and provide immediate feedback. The Coaching Department provides answers to career-related questions and ongoing motivation support through natural language dialogue with users. For example, if a user wants to practice interviews, the Coaching Department's generative AI conducts a mock interview and provides immediate feedback. The Prediction Department forecasts labor market trends based on information obtained by the Coaching Department. The Prediction Department can, for example, use AI to analyze labor market trends in real time and predict future skill demands. The Prediction Department uses statistical models and machine learning algorithms to forecast labor market trends. For example, the Prediction Department can predict changes in skill demand in a specific industry and provide appropriate career advice to users. Thus, the career support system according to this embodiment can provide individually optimized career support by collecting and analyzing user data, generating learning plans, matching with job postings, conducting interactive career coaching, and forecasting labor market trends.

[0076] The data collection unit collects user data. For example, it can collect user history, skills, and interests. Specifically, the data collection unit collects data using methods such as surveys, sensors, and online activity tracking. For example, the data collection unit collects users' work history and educational background in the form of surveys. Surveys are provided through online forms and mobile apps and are designed to be easy for users to fill out. The data collection unit can also track users' online activities to understand their interests and preferences. For example, it tracks websites visited, online courses taken, and social media activity, and analyzes this data to identify users' interests and preferences. Furthermore, the data collection unit can collect users' biometric data using sensors. For example, it can measure users' heart rate and activity levels using wearable devices. This allows the data collection unit to understand the user's health status and stress levels, which can be used to support their careers. The data collection unit integrates these diverse data sources to create detailed user profiles. This allows the data collection unit to gain a comprehensive understanding of users' history, skills, and interests, and build a foundation for providing personalized career support.

[0077] The analytics department analyzes the data collected by the data collection department. For example, the analytics department can analyze the data using statistical analysis and machine learning algorithms. Specifically, the analytics department evaluates users' strengths, weaknesses, and potential aptitudes. For instance, it assesses strengths based on users' past achievements and skill assessments. It analyzes past projects, performance, and acquired qualifications to identify areas of expertise and skills. The analytics department can also assess weaknesses based on users' past failures and skill deficiencies. For example, it analyzes past failures and goals that were not achieved due to skill deficiencies to identify areas for improvement. Furthermore, the analytics department can assess potential aptitudes using aptitude tests and psychological assessments. This allows them to identify potential undiscovered talents and aptitudes and suggest new career directions. The analytics department comprehensively evaluates this data to provide insights for optimizing users' career paths.

[0078] The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate individually optimized learning plans using a generation AI. Specifically, the generation unit provides a customized learning plan based on the user's goals and skill level. For example, the generation unit provides a specific learning plan to acquire the skills necessary for the job the user is aiming for. The generation AI considers the user's current skill set and goals and suggests the most suitable learning resources and courses. Furthermore, the generation unit can also adjust the learning plan according to the user's learning progress. For example, if the user is struggling to acquire a particular skill, the generation unit provides additional resources and support and flexibly modifies the learning plan. This allows the generation unit to support users in efficiently and effectively acquiring skills and achieving their career goals.

[0079] The matching unit matches job postings with learning plans generated by the generation unit. The matching unit can accurately match user profiles with job postings using, for example, AI matching algorithms. Specifically, the matching unit performs matching using criteria such as skill matching and interest matching. For example, the matching unit compares the user's skill set with the required skills of the job posting to perform matching. This allows the system to identify job postings where the user's skills match those required by the company. The matching unit can also recommend companies that align with the user's values ​​and career goals. For example, it can identify and recommend companies that match the corporate culture and work style that the user values. Furthermore, the matching unit can provide job postings that are likely to interest the user based on their interests. In this way, the matching unit can achieve optimal matching for both the user and the company, supporting the user's career success.

[0080] The Coaching Department provides interactive career coaching based on job postings matched by the Matching Department. For example, the Coaching Department can conduct mock interviews using generative AI and provide immediate feedback. Specifically, the Coaching Department provides answers to career-related questions and ongoing motivation support through natural language dialogue with users. For instance, if a user wants to practice interview skills, the generative AI conducts a mock interview and provides immediate feedback. The generative AI analyzes the user's responses, attitude, and facial expressions, pointing out areas for improvement and strengths. Furthermore, when a user asks a career-related question, the generative AI provides an appropriate answer, resolving the user's doubts. The Coaching Department also provides support to maintain user motivation. For example, when a user reports progress toward achieving a goal, the generative AI sends an encouraging message. This allows the Coaching Department to support users in confidently advancing their careers and guide them to success.

[0081] The forecasting unit predicts labor market trends based on information obtained by the coaching unit. For example, the forecasting unit can use AI to analyze labor market trends in real time and predict future skill demand. Specifically, the forecasting unit uses statistical models and machine learning algorithms to predict labor market trends. For instance, the forecasting unit predicts changes in skill demand in specific industries and provides users with appropriate career advice. The forecasting unit analyzes historical data and current market trends to identify skills and occupations that will be in high demand in the future. This allows users to gain information to select promising career paths for the future. Furthermore, the forecasting unit can also predict skill demand by region, taking into account the characteristics of the labor market in each region. This allows users to find career opportunities in regions best suited to their skill sets. Based on these predictions, the forecasting unit can provide users with specific career advice and support their future career success.

[0082] The data collection unit can collect the user's background, skills, and interests. For example, it can collect the user's work history and educational background in the form of questionnaires. It can also track the user's online activity and understand their interests. Furthermore, the data collection unit can collect the user's biometric data using sensors. For example, it can measure the user's heart rate and activity level using wearable devices. This allows for more detailed data-driven career support by collecting the user's background, skills, and interests. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's online activity data into a generating AI and have the generating AI perform the task of understanding the user's interests.

[0083] The analysis department can analyze the collected data and evaluate the user's strengths, weaknesses, and potential aptitudes. For example, the analysis department can analyze the data using statistical analysis or machine learning algorithms. The analysis department evaluates strengths based on the user's past achievements and skill assessments. It can also evaluate weaknesses based on the user's past failures and skill deficiencies. Furthermore, the analysis department can evaluate potential aptitudes using aptitude tests and psychological tests. This allows for individually optimized career support by evaluating the user's strengths, weaknesses, and potential aptitudes. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the collected data into a generating AI and have the generating AI perform the evaluation of strengths and weaknesses.

[0084] The generation unit can generate individually optimized learning plans based on the analysis results. For example, the generation unit generates individually optimized learning plans using a generation AI. The generation unit provides customized learning plans based on the user's goals and skill level. For example, the generation unit provides a specific learning plan to acquire the skills necessary for the job the user is aiming for. The generation unit can also adjust the learning plan according to the user's learning progress. In this way, by generating individually optimized learning plans, it effectively supports the user's skill improvement. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI execute the generation of the learning plan.

[0085] The matching unit can accurately match user profiles with job postings. For example, the matching unit uses an AI matching algorithm to accurately match user profiles with job postings. The matching unit performs matching using criteria such as skill matching and interest matching. For example, the matching unit compares the user's skill set with the required skills of the job posting to perform matching. The matching unit can also recommend companies that match the user's values ​​and career goals. This reduces mismatches between users and companies by achieving high-precision matching. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input user profile data into a generating AI and have the generating AI perform the matching with job postings.

[0086] The coaching department can conduct mock interviews and provide immediate feedback. For example, the coaching department can use generative AI to conduct mock interviews and provide immediate feedback. The coaching department provides answers to career-related questions and ongoing motivation support through natural language dialogue with users. For example, if a user wants to practice interviews, the coaching department's generative AI will conduct a mock interview and provide immediate feedback. This improves the user's interview skills by conducting mock interviews and providing immediate feedback. Some or all of the above processes in the coaching department may be performed using generative AI or not. For example, the coaching department can input the user's interview data into the generative AI and have the generative AI generate the feedback.

[0087] The forecasting unit can analyze labor market trends in real time and predict future skill demand. For example, the forecasting unit can use AI to analyze labor market trends in real time and predict future skill demand. The forecasting unit can use statistical models and machine learning algorithms to predict labor market trends. For example, the forecasting unit can predict changes in skill demand in a specific industry and provide appropriate career advice to the user. This allows the forecasting unit to provide appropriate career advice to the user by analyzing labor market trends in real time and predicting future skill demand. Some or all of the above-described processes in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input labor market data into a generating AI and have the generating AI perform skill demand predictions.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily suspend data collection and resume it when the user is relaxed. The data collection unit can also collect detailed data when the user is focused. Furthermore, if the user is tired, the data collection unit can ask only simple questions and collect detailed data later. This allows for more appropriate data collection by adjusting the timing of data collection based on 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 data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data collection unit can analyze the user's past career history and skill changes to select the optimal data collection method. For example, the data collection unit can focus on collecting relevant skills based on the user's past job experience. The data collection unit can also analyze the user's skill changes and ask questions appropriate to their current skill level. Furthermore, the data collection unit can prioritize collecting data related to specific industries or job types from the user's career history. This allows the optimal data collection method to be selected by analyzing the user's past career history and skill changes. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's career data into a generating AI and have the generating AI select the optimal data collection method.

[0090] The data collection unit can filter data based on the user's current career goals and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the job type the user is aiming for. The data collection unit can also ask relevant questions based on the user's areas of interest. Furthermore, the data collection unit can collect data on the skills and experience required according to the user's career goals. This allows for the collection of more relevant data by filtering the data based on the user's career goals and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's career goal data into a generating AI and have the generating AI perform the data filtering.

[0091] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting only important data. Furthermore, if the user is excited, the data collection unit may prioritize collecting data that is of interest to them. This allows for more effective data collection by prioritizing data based on 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 data collection unit may be performed using AI or not. For example, the data collection unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0092] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if a user lives in a specific region, the data collection unit will prioritize the collection of job postings related to that region. The data collection unit can also collect data on region-specific skills and experience based on the user's geographical location information. Furthermore, if a user is on the move, the data collection unit can prioritize the collection of data related to their current location. This allows for the priority collection of region-specific data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0093] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to a user's interests based on their social media activity. The data collection unit can also analyze accounts that a user follows and groups they participate in and collect relevant data. Furthermore, the data collection unit can collect data related to a user's career goals based on their social media posts. In this way, data related to a user's interests can be collected by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0094] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis. If the user is in a hurry, the analysis unit can perform a simplified data analysis. Furthermore, if the user is excited, the analysis unit can perform a visually easy-to-understand data analysis. By adjusting the data analysis method based on the user's emotions, more appropriate data analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The analysis unit can evaluate a user's strengths and weaknesses by referring to their past performance data during analysis. For example, the analysis unit can evaluate strengths and weaknesses based on a user's past job evaluation data. It can also analyze a user's past project results to evaluate strengths and weaknesses. Furthermore, the analysis unit can evaluate strengths and weaknesses by referring to a user's past skill test results. This allows for an accurate evaluation of strengths and weaknesses by referring to a user's past performance data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user performance data into a generating AI and have the generating AI perform the strengths and weaknesses evaluation.

[0096] The analysis unit can apply different analysis algorithms during analysis depending on the user's career goals. For example, if the user aims for a technical position, the analysis unit can apply an analysis algorithm specialized in technical skills. Similarly, if the user aims for a management position, the analysis unit can apply an analysis algorithm specialized in leadership skills. Furthermore, if the user aims for a creative position, the analysis unit can apply an analysis algorithm specialized in creativity. This allows for more appropriate analysis results by applying analysis algorithms according to the user's career goals. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's career goal data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0097] 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 nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also 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 of the analysis results based on the user's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0098] The analysis unit can weight data based on the timing of user submissions during analysis. For example, the analysis unit can prioritize data recently submitted by users during analysis. It can also weight data based on data submitted by users within a specific period. Furthermore, the analysis unit can adjust the importance of data according to the timing of user submissions. This allows for more accurate analysis results by weighting data based on the timing of user submissions. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user submission data into a generating AI and have the generating AI perform the data weighting.

[0099] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to literature related to the user's research field. In addition, the analysis unit can improve the accuracy of its analysis based on literature cited by the user. Thus, by referring to the user's relevant literature, the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0100] The generation unit can estimate the user's emotions and adjust the content of the learning plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed learning plan. If the user is in a hurry, the generation unit can also provide a simplified learning plan. Furthermore, if the user is excited, the generation unit can provide a visually easy-to-understand learning plan. This allows for more effective learning support by adjusting the content of the learning plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the learning plan content.

[0101] The generation unit can generate an optimal learning plan by referring to the user's past learning history. For example, the generation unit can generate an optimal learning plan based on what the user has learned in the past. The generation unit can also analyze the user's past learning results and generate an optimal learning plan. Furthermore, the generation unit can suggest effective learning methods based on the user's past learning history. In this way, an optimal learning plan can be generated by referring to the user's past learning history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's learning history data into a generation AI and have the generation AI execute the generation of an optimal learning plan.

[0102] The generation unit can apply different learning algorithms depending on the user's career goals when generating a learning plan. For example, if the user aims for a technical position, the generation unit can apply a learning algorithm specialized in technical skills. Similarly, if the user aims for a management position, the generation unit can apply a learning algorithm specialized in leadership skills. Furthermore, if the user aims for a creative position, the generation unit can apply a learning algorithm specialized in creativity. This allows for the provision of a more appropriate learning plan by applying a learning algorithm according to the user's career goals. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's career goal data into a generation AI and have the generation AI execute the application of the learning algorithm.

[0103] The generation unit can estimate the user's emotions and prioritize learning plans based on those emotions. For example, if the user is relaxed, the generation unit may prioritize providing detailed learning plans. It can also prioritize important learning plans if the user is in a hurry. Furthermore, if the user is excited, it may prioritize providing engaging learning plans. This allows for more effective learning support by prioritizing learning plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of learning plans.

[0104] The generation unit can generate an optimal learning plan by considering the user's geographical location information. For example, if the user lives in a specific region, the generation unit can provide a learning plan related to that region. The generation unit can also provide a learning plan related to region-specific skills and experiences based on the user's geographical location information. Furthermore, if the user is on the move, the generation unit can provide a learning plan related to their current location. In this way, by considering the user's geographical location information, region-specific learning plans can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's location information data into a generation AI and have the generation AI perform the generation of an optimal learning plan.

[0105] The generation unit can analyze a user's social media activity and generate relevant learning plans when creating learning plans. For example, the generation unit can provide learning plans related to the user's interests based on their social media activity. The generation unit can also analyze accounts the user follows and groups they participate in and provide relevant learning plans. Furthermore, the generation unit can provide learning plans related to career goals based on the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to provide learning plans related to their interests. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the generation of relevant learning plans.

[0106] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit can apply detailed matching criteria. If the user is in a hurry, the matching unit can also apply simplified matching criteria. Furthermore, if the user is excited, the matching unit can apply visually easy-to-understand matching criteria. This allows for more appropriate matching by adjusting the matching criteria based on 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 matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the matching criteria.

[0107] The matching unit can select the most suitable job postings by referring to the user's past work experience during the matching process. For example, the matching unit can prioritize selecting relevant job postings based on the user's past work experience. It can also select job postings that match the user's skill set based on their past work experience. Furthermore, the matching unit can analyze the user's past work experience and select job postings that align with their career goals. This allows the matching unit to select the most suitable job postings by referring to the user's past work experience. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input the user's work experience data into a generating AI and have the generating AI select the most suitable job postings.

[0108] The matching unit can apply different matching algorithms to users according to their career goals during the matching process. For example, if a user is aiming for a technical position, the matching unit can apply a matching algorithm specialized in technical skills. Similarly, if a user is aiming for a management position, the matching unit can apply a matching algorithm specialized in leadership skills. Furthermore, if a user is aiming for a creative position, the matching unit can apply a matching algorithm specialized in creativity. This allows for the provision of more appropriate job information by applying matching algorithms according to the user's career goals. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's career goal data into a generating AI and have the generating AI execute the application of matching algorithms.

[0109] The matching unit can estimate the user's emotions and adjust the display method of the matching results based on the estimated emotions. For example, if the user is nervous, the matching unit can provide a simple and highly visible display method. If the user is relaxed, the matching unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the matching unit can provide a display method that gets straight to the point. By adjusting the display method of the matching results based on the user's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0110] The matching unit can select the most suitable job postings by considering the user's geographical location during the matching process. For example, if the user lives in a specific region, the matching unit will prioritize selecting job postings related to that region. The matching unit can also select region-specific job postings based on the user's geographical location. Furthermore, if the user is on the move, the matching unit can prioritize selecting job postings related to their current location. This allows the system to provide region-specific job postings by considering the user's geographical location. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's location data into a generating AI and have the generating AI select the most suitable job postings.

[0111] The matching unit can analyze a user's social media activity during the matching process and provide relevant job information. For example, the matching unit can provide job information related to a user's interests based on their social media activity. It can also analyze accounts a user follows and groups they participate in and provide relevant job information. Furthermore, it can provide job information related to a user's career goals based on their social media posts. This allows the matching unit to provide job information related to a user's interests by analyzing their social media activity. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's social media data into a generating AI and have the generating AI provide relevant job information.

[0112] The coaching unit can estimate the user's emotions and adjust the coaching content based on those emotions. For example, if the user is relaxed, the coaching unit can provide detailed coaching content. If the user is in a hurry, the coaching unit can provide simplified coaching content. Furthermore, if the user is excited, the coaching unit can provide visually easy-to-understand coaching content. By adjusting the coaching content based on the user's emotions, more effective coaching becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coaching unit may be performed using AI or not. For example, the coaching unit can input user emotion data into a generative AI and have the generative AI adjust the coaching content.

[0113] The coaching department can provide optimal feedback during coaching sessions by referring to the user's past interview history. For example, the coaching department can provide specific feedback based on the user's past interview history. The coaching department can also analyze the user's past interview results and suggest areas for improvement. Furthermore, the coaching department can provide feedback that highlights the user's successes based on their past interview history. This allows for the provision of optimal feedback by referring to the user's past interview history. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input the user's interview history data into a generating AI and have the generating AI generate the feedback.

[0114] The coaching department can apply different coaching algorithms to users according to their career goals during coaching sessions. For example, if a user is aiming for a technical position, the coaching department can apply a coaching algorithm specializing in technical skills. Similarly, if a user is aiming for a management position, the coaching department can apply a coaching algorithm specializing in leadership skills. Furthermore, if a user is aiming for a creative position, the coaching department can apply a coaching algorithm specializing in creativity. This allows for more appropriate coaching by applying coaching algorithms according to the user's career goals. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input the user's career goal data into a generating AI and have the generating AI apply the coaching algorithms.

[0115] The coaching unit can estimate the user's emotions and determine coaching priorities based on those emotions. For example, if the user is relaxed, the coaching unit may prioritize providing detailed coaching content. If the user is in a hurry, the coaching unit may also prioritize providing important coaching content. Furthermore, if the user is excited, the coaching unit may prioritize providing engaging coaching content. This allows for more effective coaching by prioritizing coaching based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coaching unit may be performed using AI or not. For example, the coaching unit can input user emotion data into a generative AI and have the generative AI determine coaching priorities.

[0116] The coaching unit can provide optimal coaching content by considering the user's geographical location during coaching sessions. For example, if the user lives in a specific region, the coaching unit can provide coaching content relevant to that region. Furthermore, based on the user's geographical location, the coaching unit can also provide coaching content related to region-specific skills and experiences. Additionally, if the user is on the move, the coaching unit can provide coaching content relevant to their current location. This allows for the provision of region-specific coaching content by considering the user's geographical location. Some or all of the above processing in the coaching unit may be performed using AI, or not. For example, the coaching unit can input the user's location data into a generating AI and have the generating AI provide optimal coaching content.

[0117] The coaching department can analyze a user's social media activity during coaching sessions and provide relevant coaching content. For example, the coaching department can provide coaching content related to a user's interests based on their social media activity. It can also analyze accounts a user follows and groups they participate in and provide relevant coaching content. Furthermore, the coaching department can provide coaching content related to career goals based on the user's social media posts. This allows the coaching department to provide coaching content related to a user's interests by analyzing their social media activity. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input a user's social media data into a generating AI and have the generating AI provide relevant coaching content.

[0118] The prediction unit can estimate the user's emotions and adjust the method of predicting labor market trends based on the estimated user emotions. For example, if the user is relaxed, the prediction unit can provide a detailed labor market trend prediction. If the user is in a hurry, the prediction unit can also provide a simplified labor market trend prediction. Furthermore, if the user is excited, the prediction unit can provide a visually easy-to-understand labor market trend prediction. This allows for more accurate predictions by adjusting the method of predicting labor market trends based on 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 prediction unit may be performed using AI or not. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the method of predicting labor market trends.

[0119] The forecasting unit can predict future skill demand by referring to historical labor market data during the forecasting process. For example, the forecasting unit can predict future skill demand based on historical labor market data. The forecasting unit can also analyze historical labor market trends and predict future skill demand. Furthermore, the forecasting unit can predict skill demand in a specific industry by referring to historical labor market data. This allows for accurate prediction of future skill demand by referring to historical labor market data. Some or all of the above-described processes in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input historical labor market data into a generating AI and have the generating AI perform the skill demand forecast.

[0120] The prediction unit can apply different prediction algorithms depending on the specific industry during the prediction process. For example, to predict skill demand in the technology industry, the prediction unit can apply a prediction algorithm specialized in technical skills. It can also apply a prediction algorithm specialized in leadership skills to predict skill demand in management positions. Furthermore, it can apply a prediction algorithm specialized in creativity to predict skill demand in the creative industry. This allows for more accurate predictions by applying prediction algorithms tailored to specific industries. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input specific industry data into a generating AI and have the generating AI perform the application of the prediction algorithm.

[0121] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. By adjusting the display method of the prediction results based on the user's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0122] The prediction unit can provide optimal predictions by considering the user's geographical location information during the prediction process. For example, if the user lives in a specific region, the prediction unit can provide labor market trend predictions related to that region. The prediction unit can also provide region-specific skill demand predictions based on the user's geographical location information. Furthermore, if the user is on the move, the prediction unit can provide labor market trend predictions related to their current location. In this way, by considering the user's geographical location information, region-specific labor market trend predictions can be provided. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the user's location information data into a generating AI and have the generating AI perform the task of providing optimal predictions.

[0123] The prediction unit can analyze the user's social media activity during prediction and provide relevant prediction results. For example, the prediction unit can provide labor market trend predictions related to the user's interests based on the user's social media activity. The prediction unit can also analyze the accounts the user follows and the groups the user participates in and provide relevant labor market trend predictions. Furthermore, the prediction unit can provide labor market trend predictions related to career goals based on the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to provide labor market trend predictions related to interests. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant prediction results.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] Career support systems can estimate a user's emotions and adjust the content of career advice based on those emotions. For example, if a user is feeling anxious, the system can provide encouraging and reassuring advice. If a user is confident, it can also provide advice on setting challenging goals. Furthermore, if a user is excited, it can effectively utilize that energy by presenting a concrete action plan. This allows for more effective support by providing appropriate career advice tailored to the user's emotions.

[0126] Career support systems can analyze a user's past career path and simulate future career paths. For example, they can simulate possible future career paths based on the user's past job experience and skill development. They can also present different scenarios depending on the user's career goals. Furthermore, they can predict how a user's career path will change if they acquire specific skills. This makes it easier for users to visualize their future career path concretely and develop specific action plans to achieve their goals.

[0127] The career support system can estimate the user's emotions and manage the progress of their learning plan based on those emotions. For example, if the user is feeling stressed, the system can gradually adjust the progress of their learning plan. Conversely, if the user is highly motivated, the system can accelerate the learning plan. Furthermore, if the user is tired, it can provide advice encouraging them to rest. This enables flexible management of the learning plan progress in response to the user's emotions, resulting in effective learning support.

[0128] Career support systems can provide region-specific career advice by taking into account the user's geographical location. For example, if a user lives in a specific region, the system can provide advice based on the labor market trends and job openings in that region. It can also provide information on region-specific skill demands and industry trends. Furthermore, if a user is considering relocating, the system can provide career advice related to their destination region. This enables appropriate career advice that takes the user's geographical location into account.

[0129] The career support system can estimate the user's emotions and adjust the content of the mock interview based on those estimates. For example, if the user is nervous, it will prioritize questions that help them relax. Conversely, if the user is confident, it can ask more challenging questions. Furthermore, if the user is excited, it can effectively utilize that energy by asking questions that simulate specific situations. This enables appropriate mock interviews tailored to the user's emotions, helping to improve their interview skills.

[0130] Career support systems can analyze users' social media activity and provide relevant career advice. For example, they can offer career advice related to users' interests based on the accounts they follow and the groups they participate in on social media. They can also analyze users' posts and provide advice related to their career goals. Furthermore, they can suggest networking opportunities based on users' social media activity. This enables effective career advice that leverages users' social media activity.

[0131] The career support system can estimate the user's emotions and adjust how job postings are displayed based on those emotions. For example, if the user is stressed, it can provide simple, easy-to-read job postings. If the user is relaxed, it can provide job postings with more detailed information. Furthermore, if the user is in a hurry, it can provide job postings that are to the point. By adjusting how job postings are displayed based on the user's emotions, it becomes possible to provide job postings that are easier to understand.

[0132] Career support systems can analyze a user's past learning history and recommend the most suitable learning resources. For example, they can recommend relevant learning resources based on what the user has learned in the past. They can also analyze the user's learning outcomes and suggest effective learning methods. Furthermore, they can prioritize recommending resources necessary for skill development based on the user's learning history. This enables effective recommendation of learning resources by leveraging the user's past learning history.

[0133] A career support system can estimate a user's emotions and assist in setting career goals based on those emotions. For example, if a user is feeling anxious, it can set realistic and achievable goals. If a user is confident, it can set challenging goals. Furthermore, if a user is excited, it can set goals that include specific action plans. This enables the setting of appropriate career goals tailored to the user's emotions, resulting in effective support for achieving those goals.

[0134] Career support systems can provide information on region-specific networking events and career fairs, taking into account the user's geographical location. For example, if a user lives in a specific region, the system can provide information on networking events and career fairs held in that region. It can also provide information on region-specific industry associations and professional groups. Furthermore, if a user is considering relocating, the system can provide information on networking events and career fairs in their destination region. This enables effective networking support that takes the user's geographical location into account.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The data collection unit collects user data. The data collection unit can collect, for example, the user's background, skills, and interests. The data collection unit can collect data using methods such as questionnaires, sensors, and online activity tracking. For example, the data collection unit can collect the user's work history and educational background in the form of questionnaires. The data collection unit can also track the user's online activity to understand their interests and preferences. Furthermore, the data collection unit can collect the user's biometric data using sensors. For example, the data collection unit can measure the user's heart rate and activity level using wearable devices. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis department evaluates the user's strengths, weaknesses, and potential aptitudes. For example, the analysis department evaluates strengths based on the user's past achievements and skill assessments. The analysis department can also evaluate weaknesses based on the user's past failures and lack of skills. Furthermore, the analysis department can evaluate potential aptitudes using aptitude tests and psychological tests. Step 3: The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate an individually optimized learning plan using a generation AI. The generation unit provides a customized learning plan based on the user's goals and skill level. For example, the generation unit provides a specific learning plan to acquire the skills necessary for the job the user is aiming for. The generation unit can also adjust the learning plan according to the user's learning progress. Step 4: The matching unit matches job postings with the learning plan generated by the generation unit. The matching unit can, for example, use an AI matching algorithm to match the user's profile with job postings with high accuracy. The matching unit performs matching using criteria such as skill matching and interest matching. For example, the matching unit compares the user's skill set with the required skills of the job postings to perform matching. The matching unit can also recommend companies that match the user's values ​​and career goals. Step 5: The coaching department conducts interactive career coaching based on job postings matched by the matching department. The coaching department can, for example, use generative AI to conduct mock interviews and provide immediate feedback. The coaching department provides answers to career-related questions and ongoing motivation support through natural language dialogue with the user. For example, if a user wants to practice interviews, the coaching department's generative AI will conduct a mock interview and provide immediate feedback. Step 6: The forecasting unit predicts labor market trends based on the information obtained by the coaching unit. The forecasting unit can, for example, use AI to analyze labor market trends in real time and predict future skill demand. The forecasting unit predicts labor market trends using statistical models and machine learning algorithms. For example, the forecasting unit predicts changes in skill demand in a specific industry and provides appropriate career advice to the user.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, matching unit, coaching unit, and prediction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The generation unit generates a learning plan using the specific processing unit 290 of the data processing unit 12 and provides it to the user using the control unit 46A of the smart device 14. The matching unit matches job information with the user's profile using the specific processing unit 290 of the data processing unit 12, and the coaching unit conducts a mock interview using the control unit 46A of the smart device 14. The prediction unit predicts labor market trends using the specific processing unit 290 of the data processing unit 12 and provides the user with appropriate career advice. 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.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, matching unit, coaching unit, and prediction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The generation unit generates a learning plan using the specific processing unit 290 of the data processing unit 12 and provides it to the user using the control unit 46A of the smart glasses 214. The matching unit matches job information with the user's profile using the specific processing unit 290 of the data processing unit 12, and the coaching unit conducts a mock interview using the control unit 46A of the smart glasses 214. The prediction unit predicts labor market trends using the specific processing unit 290 of the data processing unit 12 and provides the user with appropriate career advice. 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.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, matching unit, coaching unit, and prediction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The generation unit generates a learning plan using the specific processing unit 290 of the data processing unit 12 and provides it to the user using the control unit 46A of the headset terminal 314. The matching unit matches job information with the user's profile using the specific processing unit 290 of the data processing unit 12, and the coaching unit conducts a mock interview using the control unit 46A of the headset terminal 314. The prediction unit predicts labor market trends using the specific processing unit 290 of the data processing unit 12 and provides the user with appropriate career advice. 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.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, matching unit, coaching unit, and prediction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The generation unit generates a learning plan using the specific processing unit 290 of the data processing unit 12 and provides it to the user using the control unit 46A of the robot 414. The matching unit matches job information with the user's profile using the specific processing unit 290 of the data processing unit 12, and the coaching unit conducts a mock interview using the control unit 46A of the robot 414. The prediction unit predicts labor market trends using the specific processing unit 290 of the data processing unit 12 and provides the user with appropriate career advice. 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a learning plan based on the analysis results obtained by the aforementioned analysis unit, A matching unit that matches job information based on the learning plan generated by the generation unit, The Coaching Department conducts interactive career coaching based on job information matched by the Matching Department, The system includes a forecasting unit that predicts trends in the labor market based on information obtained by the coaching unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather user background, skills, and interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to evaluate the user's strengths, weaknesses, and potential aptitudes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate an individually optimized learning plan based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The matching unit is Highly accurate matching of user profiles with job postings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned coaching department, We conduct mock interviews and provide immediate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The prediction unit, We analyze labor market trends in real time and predict future skill demands. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past experience and skill development to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current career goals and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, strengths and weaknesses are evaluated by referring to the user's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the user's career goals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, data is weighted based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a learning plan, the system references the user's past learning history to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a learning plan, different learning algorithms are applied depending on the user's career goals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating 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 24) The generating unit is When generating a learning plan, the system takes the user's geographical location into consideration to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating a learning plan, the system analyzes the user's social media activity and generates a relevant learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is During the matching process, the system selects the most suitable job postings by referencing the user's past work experience. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is During the matching process, different matching algorithms are applied depending on the user's career goals. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is The system estimates the user's emotions and adjusts how matching results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is During the matching process, the system selects the most suitable job postings by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The matching unit is During the matching process, the system analyzes the user's social media activity and provides relevant job information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned coaching department, The system estimates the user's emotions and adjusts the coaching content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned coaching department, During coaching sessions, we refer to the user's past interview history to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned coaching department, During coaching sessions, different coaching algorithms are applied depending on the user's career goals. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned coaching department, It estimates the user's emotions and determines the priority of coaching based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned coaching department, During coaching sessions, we provide optimal coaching content while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned coaching department, During coaching sessions, we analyze the user's social media activity and provide relevant coaching content. The system described in Appendix 1, characterized by the features described herein. (Note 38) The prediction unit, We estimate user sentiment and adjust the method of predicting labor market trends based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The prediction unit, When making predictions, historical labor market data is used to forecast future skill demands. The system described in Appendix 1, characterized by the features described herein. (Note 40) The prediction unit, When making predictions, different prediction algorithms are applied depending on the specific industry. The system described in Appendix 1, characterized by the features described herein. (Note 41) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The prediction unit, When making predictions, the system takes the user's geographical location into consideration to provide the best possible forecast. The system described in Appendix 1, characterized by the features described herein. (Note 43) The prediction unit, During the prediction process, the system analyzes the user's social media activity and provides relevant prediction results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 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 data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a learning plan based on the analysis results obtained by the aforementioned analysis unit, A matching unit that matches job information based on the learning plan generated by the generation unit, The Coaching Department conducts interactive career coaching based on job information matched by the Matching Department, The system includes a forecasting unit that predicts trends in the labor market based on information obtained by the coaching unit. A system characterized by the following features.

2. The aforementioned collection unit is Gather user background, skills, and interests. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to evaluate the user's strengths, weaknesses, and potential aptitudes. The system according to feature 1.

4. The generating unit is Generate an individually optimized learning plan based on the analysis results. The system according to feature 1.

5. The matching unit is Highly accurate matching of user profiles with job postings. The system according to feature 1.

6. The aforementioned coaching department, We conduct mock interviews and provide immediate feedback. The system according to feature 1.

7. The prediction unit, We analyze labor market trends in real time and predict future skill demands. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past experience and skill development to select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current career goals and areas of interest. The system according to feature 1.

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