system

Abstract

The system according to this embodiment aims to support career advancement by providing personalized training plans based on the user's occupational data and skill level. [Solution] The system according to the embodiment comprises a hearing unit, an analysis unit, a generation unit, and a support unit. The hearing unit hears the user's occupational data and skill level. The analysis unit analyzes the data heard by the hearing unit to identify skill gaps. The generation unit generates an individualized training plan based on the skill gaps identified by the analysis unit. The support unit addresses the user's anxieties and concerns.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] ]>In the conventional technology, there is a problem that it is difficult to provide an individualized training plan based on a user's occupation data and skill level, and career advancement support is not sufficiently provided.

[0005] The system according to the embodiment aims to provide an individualized training plan based on a user's occupation data and skill level and support career advancement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a hearing unit, an analysis unit, a generation unit, and a support unit. The hearing unit hears the user's occupational data and skill level. The analysis unit analyzes the data heard by the hearing unit to identify skill gaps. The generation unit generates an individualized training plan based on the skill gaps identified by the analysis unit. The support unit addresses the user's anxieties and concerns. [Effects of the Invention]

[0007] The system according to this embodiment can provide a personalized training plan based on the user's occupational data and skill level, thereby supporting career advancement. [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, etc. The communication I / F manages communication between multiple 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) An AI agent system according to an embodiment of the present invention is a system that interviews a user about their current occupation and skill level and identifies skill gaps necessary for career advancement. This AI agent system takes the user's occupation data and skill level as input, and the AI ​​analyzes this data to identify skill gaps. Furthermore, the AI ​​generates an individualized training plan based on the identified skill gaps. This training plan is customized to the user's needs and includes specific action plans for efficient skill acquisition. The AI ​​also provides support to address the user's anxieties and concerns. For example, if a user feels anxious about the training plan, the AI ​​provides specific advice through dialogue to maintain the user's motivation. This mechanism allows users to improve their success rate in career advancement and shorten the time required for skill acquisition. Personalized support also improves user satisfaction. For example, a user inputs their occupation data and skill level through interactive AI counseling. The AI ​​then analyzes this data and identifies the skill gaps necessary for the user's career advancement. Furthermore, the AI ​​generates an individualized training plan based on the identified skill gaps. This training plan is customized to the user's needs and includes specific action plans for efficient skill acquisition. Furthermore, AI provides support to address user anxieties and concerns. For example, if a user feels anxious about their training plan, the AI ​​provides specific advice through dialogue to maintain the user's motivation. This mechanism allows users to improve their chances of career advancement and shorten the time it takes to acquire skills. Personalized support also improves user satisfaction. This enables the AI ​​agent system to gather user occupational data and skill levels, identify skill gaps, generate personalized training plans, and address user anxieties and concerns.

[0029] The AI ​​agent system according to this embodiment comprises a hearing unit, an analysis unit, a generation unit, and a support unit. The hearing unit hears the user's occupational data and skill level. The user's occupational data includes, but is not limited to, job title, industry, and position. The hearing unit collects the user's occupational data, for example, through an interview. The hearing unit can also collect information such as years of experience, qualifications, and test results to evaluate the user's skill level. For example, the hearing unit can conduct an online test to evaluate the user's skill level. The analysis unit analyzes the data heard by the hearing unit to identify skill gaps. Skill gaps are identified, for example, based on the difference between required skills and current skills, but is not limited to this example. The analysis unit identifies skill gaps using, for example, data analysis methods. The analysis unit can also identify skill gaps by adjusting the algorithm used. For example, the analysis unit identifies skill gaps using machine learning algorithms. The generation unit generates an individualized training plan based on the skill gaps identified by the analysis unit. Training plans are generated based on, for example, the type, duration, and content of the training, but are not limited to such examples. The generation unit customizes the training plan according to the user's needs, for example. The generation unit can also generate training plans by adjusting the algorithms used. For example, the generation unit generates training plans using machine learning algorithms. The support unit addresses the user's anxieties and concerns. The support unit provides, for example, specific advice regarding anxieties about the training plan or concerns about the career, but is not limited to such examples. The support unit addresses the user's anxieties and concerns through dialogue, for example. The support unit can also adjust how it responds to the user's anxieties and concerns. For example, the support unit customizes how it responds to the user's anxieties and concerns.As a result, the AI ​​agent system according to the embodiment can interview users about their occupational data and skill levels, identify skill gaps, generate personalized training plans, and address users' anxieties and concerns.

[0030] The interviewing department gathers user occupational data and skill levels through interviews. This data includes, but is not limited to, job title, industry, and position. The interviewing department collects this data through interviews, for example. Specifically, through dialogue with users, the department understands their detailed work history, current job responsibilities, industry characteristics, and responsibilities associated with their position. This allows for a comprehensive understanding of the user's occupational background. The interviewing department can also collect information such as years of experience, qualifications, and test results to assess the user's skill level. For example, the interviewing department can conduct online tests to evaluate the user's skill level. These online tests include specific questions designed to assess the user's expertise and technical skills, and quantitatively evaluate their skill level based on their responses. Furthermore, the interviewing department collects information such as the user's self-assessment, past project experience, and acquired qualifications and certifications to conduct a comprehensive skill assessment. This allows the interviewing unit to gain a detailed understanding of the user's occupational data and skill level, and provide the data necessary for processing in the subsequent analysis unit.

[0031] The analysis unit identifies skill gaps by analyzing data collected by the interview unit. Skill gaps are identified, for example, based on the difference between required skills and current skills, but are not limited to such examples. The analysis unit identifies skill gaps using data analysis methods, for example. Specifically, the analysis unit compares the user's current skill set with the skill set required for the target job, based on collected occupational data and skill levels. This makes it possible to clearly identify the skills the user possesses and the skills they lack. The analysis unit can also identify skill gaps by adjusting the algorithms it uses. For example, the analysis unit can identify skill gaps using machine learning algorithms. Machine learning algorithms learn from past data and skill requirements in similar jobs, enabling them to identify the user's skill gaps with high accuracy. Furthermore, the analysis unit can also predict skills that will be needed in the future, taking into account the user's career path and industry trends. This allows the analysis unit to accurately understand the user's current skill level and identify the skills necessary for future career growth.

[0032] The generation unit generates a personalized training plan based on the skill gaps identified by the analysis unit. The training plan is generated based on, for example, the type, duration, and content of the training, but is not limited to these examples. The generation unit customizes the training plan according to the user's needs. Specifically, it selects the most suitable training courses and materials and creates a training schedule to fill the user's skill gaps. For example, it suggests training methods tailored to the user's learning style and schedule, such as online courses, workshops, and hands-on projects. The generation unit can also generate training plans by adjusting the algorithms used. For example, it can generate training plans using machine learning algorithms. Machine learning algorithms can analyze past training data and the user's learning history to suggest optimal training content and pace. Furthermore, the generation unit continuously improves the training plan based on user feedback to support effective learning. This allows the generation unit to provide personalized training plans to support the user's skill improvement.

[0033] The support department addresses users' anxieties and concerns. For example, the support department provides specific advice regarding anxieties about training plans or career concerns, but is not limited to these examples. The support department addresses users' anxieties and concerns through dialogue, for example. Specifically, the support department provides consultation on training progress and career direction through regular meetings and online chats with users. The support department can also adjust how it addresses users' anxieties and concerns. For example, the support department customizes how it addresses users' anxieties and concerns. It provides specific advice and resources tailored to the user's individual circumstances and needs, supporting them so they can proceed with their training with confidence. Furthermore, the support department can also provide psychological support and suggest ways to maintain user motivation. In this way, the support department can address users' anxieties and concerns and help maximize the effectiveness of their training.

[0034] The Feedback Collection Unit collects user feedback. The Feedback Collection Unit collects user feedback, for example, through surveys. For example, the Feedback Collection Unit can conduct online surveys to collect users' opinions and impressions. The Feedback Collection Unit can also collect user feedback through interviews. For example, the Feedback Collection Unit collects user feedback through face-to-face interviews. The Feedback Collection Unit can also collect user feedback through evaluation comments. For example, the Feedback Collection Unit encourages users to provide evaluation comments on their training plans. By collecting user feedback, the Feedback Collection Unit can improve the system and respond to user needs. Some or all of the above processing in the Feedback Collection Unit may be performed using AI, for example, or without AI. For example, the Feedback Collection Unit can input user feedback data into a generating AI and have the generating AI perform feedback analysis.

[0035] The Monitoring Unit monitors the progress of the training plan. For example, the Monitoring Unit periodically checks the user's training progress. For example, the Monitoring Unit may request periodic progress reports to confirm that the user is progressing according to the training plan. The Monitoring Unit can also monitor the user's training progress in real time. For example, the Monitoring Unit collects progress data through an online platform to confirm in real time whether the user is progressing according to the training plan. The Monitoring Unit can also monitor the degree of achievement and progress to evaluate the user's training progress. For example, the Monitoring Unit evaluates the extent to which the user has achieved the goals they have set. In this way, the Monitoring Unit can understand the user's progress by monitoring the progress of the training plan and provide appropriate support. Some or all of the above processes in the Monitoring Unit may be performed using AI, for example, or not using AI. For example, the Monitoring Unit may input user progress data into a generating AI and have the generating AI perform progress analysis.

[0036] The generation unit can generate a training plan customized to the user's needs. For example, the generation unit can customize the training plan based on the user's occupational data and skill level. For example, the generation unit can analyze the user's occupational data and identify the skills required for a specific occupation. The generation unit can also evaluate the user's skill level and identify skill gaps. For example, the generation unit can evaluate the user's skill level and identify the difference between the required skills and the user's current skills. Furthermore, the generation unit can adjust the training plan according to the user's needs. For example, the generation unit can adjust the plan based on the user's desired training period and training content. In this way, the generation unit can efficiently support the user's skill development by generating a training plan customized to the user's needs. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's occupational data and skill level into a generation AI and have the generation AI perform the generation of the training plan.

[0037] The support department can provide specific advice to address users' anxieties and concerns regarding their training plans. For example, if a user feels anxious about their training plan, the support department can provide specific advice. For instance, the support department can interview the user to understand the reasons for their anxiety and provide specific advice to alleviate those anxieties. The support department can also provide specific advice if a user has concerns about their career. For example, the support department can interview the user to understand the reasons for their concerns and provide specific advice to alleviate those concerns. Furthermore, the support department can provide specific advice to help users maintain their motivation. For example, the support department can provide specific advice to help users maintain their motivation regarding their training plan. In this way, the support department can help users maintain their motivation and support the execution of their training plans by providing specific advice to address their anxieties and concerns. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input data on users' anxieties and concerns into a generating AI and have the generating AI generate specific advice.

[0038] The interview unit can analyze the user's past work history and select the optimal interview method. For example, the interview unit can prioritize relevant questions based on the user's past work experience. For example, the interview unit selects relevant questions based on the user's past work history. The interview unit can also focus on questions related to specific skills based on the user's work history. For example, the interview unit selects questions related to specific skills based on the user's work history. The interview unit can also optimize the order of interviews based on the user's work history. For example, the interview unit adjusts the order of interviews based on the user's work history. In this way, the interview unit can select the optimal interview method and conduct effective interviews by analyzing the user's past work history. Some or all of the above processing in the interview unit may be performed using AI, for example, or without using AI. For example, the interviewing unit can input the user's occupational history data into a generating AI, which can then select the most suitable interviewing method.

[0039] The interviewing unit can customize the questions asked during the interview based on the user's current occupation and areas of interest. For example, the interviewing unit can ask questions about skills related to the user's current occupation. For example, the interviewing unit can select questions about relevant skills based on the user's occupational data. The interviewing unit can also ask questions that are likely to interest the user based on their areas of interest. For example, the interviewing unit can select questions that are likely to interest the user based on their areas of interest. The interviewing unit can also ask questions that combine the user's occupation and areas of interest. For example, the interviewing unit can select relevant questions based on the user's occupational data and areas of interest. In this way, the interviewing unit can collect useful information for the user by customizing the questions based on the user's current occupation and areas of interest. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's occupational data and areas of interest data into a generating AI and have the generating AI perform the customization of the questions.

[0040] The interviewing unit can prioritize acquiring highly relevant information during interviews, taking into account the user's geographical location. For example, the interviewing unit can acquire region-specific occupational information based on the user's location. The interviewing unit can also acquire information on nearby training facilities, taking into account the user's geographical location. The interviewing unit can also prioritize acquiring job postings related to the user's location. In this way, the interviewing unit can provide useful information to the user by prioritizing the acquisition of highly relevant information, taking into account the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant information.

[0041] The interviewing unit can analyze the user's social media activity during the interview and obtain relevant information. For example, the interviewing unit can analyze the content of the user's social media posts and obtain information about occupations of interest. For example, the interviewing unit can obtain information about occupations of interest based on the content of the user's social media posts. The interviewing unit can also analyze the user's followers and the accounts they follow and obtain relevant occupation information. For example, the interviewing unit can obtain relevant occupation information based on the user's followers and the accounts they follow. The interviewing unit can also obtain trend-based information by referring to the user's social media activity history. For example, the interviewing unit can obtain trend-based information based on the user's social media activity history. In this way, the interviewing unit can obtain information based on the user's interests by analyzing the user's social media activity. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0042] The analysis unit can optimize its analysis algorithm by referring to the user's past skill acquisition history during analysis. For example, the analysis unit can identify relevant skill gaps based on the skills the user has acquired in the past. For example, the analysis unit can identify relevant skill gaps based on the user's past skill acquisition history. The analysis unit can also select the optimal analysis algorithm by referring to the user's skill acquisition history. For example, the analysis unit can select the optimal analysis algorithm based on the user's skill acquisition history. The analysis unit can also analyze the user's past skill acquisition history and identify trends in skill gaps. For example, the analysis unit can identify trends in skill gaps based on the user's past skill acquisition history. As a result, the analysis unit can optimize its analysis algorithm by referring to the user's past skill acquisition history and provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skill acquisition history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0043] The analysis unit can apply different analysis methods depending on the user's occupation category during analysis. For example, the analysis unit can identify specific skill gaps based on the user's occupation category. The analysis unit can also select the optimal analysis method depending on the user's occupation category. For example, the analysis unit selects the optimal analysis method based on the user's occupation category. The analysis unit can also customize the analysis results based on the user's occupation category. For example, the analysis unit customizes the analysis results based on the user's occupation category. In this way, the analysis unit can provide the user with the optimal analysis results by applying different analysis methods depending on the user's occupation category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's occupation category data into a generating AI and have the generating AI execute the application of different analysis methods.

[0044] The analysis unit can determine the priority of analyses based on the user's submission timing during the analysis process. For example, if the user is in a hurry, the analysis unit will prioritize the analysis based on the submission timing. The analysis unit can also prioritize detailed analyses if the user has ample time. The analysis unit can also set an optimal analysis schedule based on the user's submission timing. This allows the analysis unit to perform rapid analyses that meet the user's needs by prioritizing analyses based on the user's submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0045] 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 the relevant literature provided by the user. The analysis unit can also supplement the analysis results by referring to literature related to the user's occupation category. For example, the analysis unit supplements the analysis results based on literature related to the user's occupation category. The analysis unit can also improve the accuracy of its analysis by analyzing literature related to the user's skill gap. For example, the analysis unit improves the accuracy of its analysis based on literature related to the user's skill gap. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature and provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0046] The generation unit can adjust the level of detail in the training plan based on the user's skill level during generation. For example, the generation unit can provide a detailed training plan according to the user's skill level. For example, the generation unit generates a detailed training plan based on the user's skill level. The generation unit can also provide a concise training plan based on the user's skill level. For example, the generation unit generates a concise training plan based on the user's skill level. The generation unit can also customize the content of the training plan, taking the user's skill level into consideration. For example, the generation unit customizes the content of the training plan based on the user's skill level. This allows the generation unit to provide the optimal training plan for the user by adjusting the level of detail in the training plan based on the user's skill level. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user skill level data into a generation AI and have the generation AI perform the adjustment of the level of detail in the training plan.

[0047] The generation unit can apply different training algorithms depending on the user's occupation category during generation. For example, the generation unit can select the optimal training algorithm based on the user's occupation category. The generation unit can also customize the content of the training plan depending on the user's occupation category. The generation unit can also optimize the training algorithm based on the user's occupation category. In this way, the generation unit can provide the user with the optimal training plan by applying different training algorithms depending on the user's occupation category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user occupation category data into a generation AI and have the generation AI execute the application of the training algorithm.

[0048] The generation unit can prioritize training plans based on the user's submission timing during generation. For example, if the user is in a hurry, the generation unit will prioritize training plans considering the submission timing. The generation unit can also prioritize detailed training plans if the user has ample time. The generation unit can also set an optimal training schedule based on the user's submission timing. This allows the generation unit to provide a rapid training plan that meets the user's needs by prioritizing training plans based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user submission timing data into a generation AI and have the generation AI perform the training plan prioritization.

[0049] The generation unit can improve the accuracy of the training plan by referring to the user's relevant literature during generation. For example, the generation unit improves the accuracy of the training plan based on the relevant literature provided by the user. The generation unit can also supplement the training plan by referring to literature related to the user's occupation category. For example, the generation unit supplements the training plan based on literature related to the user's occupation category. The generation unit can also improve the accuracy of the training plan by analyzing literature related to the user's skill gaps. For example, the generation unit improves the accuracy of the training plan based on literature related to the user's skill gaps. In this way, the generation unit can improve the accuracy of the training plan by referring to the user's relevant literature and provide a more accurate training plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's relevant literature data into a generation AI and have the generation AI perform the training plan accuracy improvement.

[0050] The support unit can select the optimal support method by referring to the user's past feedback during support. For example, the support unit can select the optimal support method based on the user's past feedback. The support unit can also customize the support content by referring to the user's feedback history. For example, the support unit can customize the support content based on the user's feedback history. The support unit can also analyze the user's past feedback and select a method to maximize the effectiveness of the support. For example, the support unit can select a method to maximize the effectiveness of the support based on the user's past feedback. In this way, the support unit can select the optimal support method by referring to the user's past feedback and provide effective support. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal support method.

[0051] The support unit can apply different support methods depending on the user's occupation category during support. For example, the support unit can select a specific support method based on the user's occupation category. The support unit can also customize the support content depending on the user's occupation category. For example, the support unit can customize the support content based on the user's occupation category. The support unit can also apply the most suitable support method based on the user's occupation category. For example, the support unit can apply the most suitable support method based on the user's occupation category. In this way, the support unit can provide the best possible support to the user by applying different support methods depending on the user's occupation category. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user occupation category data into a generating AI and have the generating AI execute the application of support methods.

[0052] The support unit can select the optimal support method when providing support, taking into account the user's geographical location. For example, the support unit can select a region-specific support method based on the user's location. The support unit can also provide information on nearby support facilities, taking into account the user's geographical location. The support unit can also prioritize providing support resources related to the user's location. For example, the support unit provides relevant support resources based on the user's location. In this way, the support unit can provide beneficial support to the user by selecting the optimal support method, taking into account the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI select the support method.

[0053] The support department can analyze the user's social media activity and customize the support provided during support. For example, the support department can analyze the user's social media posts and provide support that is of interest to the user. For example, the support department can provide support that is of interest to the user based on the user's social media posts. The support department can also analyze the user's followers and followed accounts and provide relevant support information. For example, the support department can provide relevant support information based on the user's followers and followed accounts. The support department can also provide trend-based support based on the user's social media activity history. For example, the support department can provide trend-based support based on the user's social media activity history. In this way, the support department can provide support that is of interest to the user by analyzing the user's social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's social media data into a generating AI and have the generating AI perform the customization of the support content.

[0054] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit can select the optimal collection method based on the user's past feedback history. The feedback collection unit can also customize the content to be collected by referring to the user's feedback history. For example, the feedback collection unit can customize the content to be collected based on the user's feedback history. The feedback collection unit can also analyze the user's past feedback history and select a method that maximizes the effectiveness of the collection. For example, the feedback collection unit can select a method that maximizes the effectiveness of the collection based on the user's past feedback history. In this way, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history and conduct effective feedback collection. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without using AI. For example, the feedback collection unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal collection method.

[0055] The feedback collection unit can prioritize collecting highly relevant feedback by considering the user's geographical location information during feedback collection. For example, the feedback collection unit can prioritize collecting region-specific feedback based on the user's location. The feedback collection unit can also prioritize collecting nearby feedback by referring to the user's geographical location information. The feedback collection unit can also prioritize collecting feedback related to the user's location. The feedback collection unit can collect relevant feedback based on the user's location. In this way, the feedback collection unit can collect useful feedback for the user by prioritizing the collection of highly relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant feedback.

[0056] The monitoring unit can select the optimal monitoring method by referring to the user's past training progress during monitoring. For example, the monitoring unit selects the optimal monitoring method based on the user's past training progress. The monitoring unit can also customize the monitoring content by referring to the user's training progress history. For example, the monitoring unit customizes the monitoring content based on the user's training progress history. The monitoring unit can also analyze the user's past training progress and select a method to maximize the effectiveness of monitoring. For example, the monitoring unit selects a method to maximize the effectiveness of monitoring based on the user's past training progress. In this way, the monitoring unit can select the optimal monitoring method by referring to the user's past training progress and perform effective monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past training progress data into a generating AI and have the generating AI select the optimal monitoring method.

[0057] The monitoring unit can select the optimal monitoring method while considering the user's geographical location information. For example, the monitoring unit can select a region-specific monitoring method based on the user's location. The monitoring unit can also provide information on nearby monitoring facilities based on the user's geographical location information. The monitoring unit can also prioritize providing monitoring resources related to the user's location. For example, the monitoring unit provides relevant monitoring resources based on the user's location. In this way, the monitoring unit can perform monitoring that is beneficial to the user by selecting the optimal monitoring method while considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI select the monitoring method.

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

[0059] The analysis unit can refer to a user's past work history and skill acquisition history when analyzing a user's occupational data and skill level. For example, the analysis unit can analyze what occupations a user has held in the past and identify the gap between those occupations and the skills required for their current occupation. The analysis unit can also consider qualifications and skills acquired in the past and evaluate how these skills are useful in their current occupation. Furthermore, based on the user's past skill acquisition history, the analysis unit can identify trends in skill gaps and predict skills that will be needed in the future. As a result, the analysis unit can identify and analyze skill gaps with greater accuracy by referring to the user's past work history and skill acquisition history.

[0060] The generator can identify the user's skill gaps and then customize the training plan according to the user's learning style. For example, if the user is a visual learner, the generator can provide a training plan that includes many visual materials. If the user is an auditory learner, the generator can also provide a training plan that focuses on audio materials. Furthermore, if the user prefers hands-on learning, the generator can provide a plan that includes hands-on training sessions. In this way, the generator can support users in efficiently acquiring skills by customizing the training plan according to their learning style.

[0061] The support team can address users' anxieties and concerns about their training plans by referencing their past successes and providing advice. For example, they can offer advice that builds confidence in the current training plan based on goals and successful projects the user has achieved in the past. They can also suggest specific solutions to current anxieties and concerns by referencing difficulties and challenges the user has overcome in the past. Furthermore, they can offer advice to boost motivation by reflecting on the user's past successes. In this way, the support team can provide more effective support by referencing the user's past successes.

[0062] The monitoring unit can adjust the frequency of monitoring according to the user's learning pace when monitoring the progress of the user's training plan. For example, if the user is progressing quickly, the monitoring unit can set a lower monitoring frequency. Conversely, if the user is progressing slowly, the monitoring unit can set a higher monitoring frequency. Furthermore, if the user is experiencing difficulties in a particular training session, the monitoring unit can intensify monitoring for that session. In this way, the monitoring unit can provide optimal support to the user by adjusting the monitoring frequency according to the user's learning pace.

[0063] The feedback collection unit can adjust its collection method based on the content of user feedback. For example, if a user provides detailed feedback, the feedback collection unit can collect more detailed information through face-to-face interviews. Alternatively, if a user provides concise feedback, the feedback collection unit can quickly collect that feedback through online questionnaires. Furthermore, if a user provides evaluation comments for a specific training session, the feedback collection unit can focus on collecting feedback for that session. This allows the feedback collection unit to collect feedback more effectively by adjusting its collection method based on the content of user feedback.

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

[0065] Step 1: The interview team gathers user occupational data and skill levels. User occupational data includes job title, industry, position, etc., and is collected through interviews. The interview team can also collect information such as years of experience, qualifications, and test results, and may conduct online tests. Step 2: The analysis unit analyzes the data collected by the interview unit to identify skill gaps. Skill gaps are identified based on the difference between required skills and current skills, and are determined using data analysis methods and machine learning algorithms. Step 3: The generation unit generates a personalized training plan based on the skill gaps identified by the analysis unit. The training plan is generated based on the type, duration, and content of the training, and is customized according to the user's needs. The generation unit can also generate the training plan using machine learning algorithms. Step 4: The support team addresses user anxieties and concerns. The support team provides specific advice regarding anxieties about training plans and career concerns, and addresses users' anxieties and concerns through dialogue. The support team can also customize how they address users' anxieties and concerns.

[0066] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that interviews a user about their current occupation and skill level and identifies skill gaps necessary for career advancement. This AI agent system takes the user's occupation data and skill level as input, and the AI ​​analyzes this data to identify skill gaps. Furthermore, the AI ​​generates an individualized training plan based on the identified skill gaps. This training plan is customized to the user's needs and includes specific action plans for efficient skill acquisition. The AI ​​also provides support to address the user's anxieties and concerns. For example, if a user feels anxious about the training plan, the AI ​​provides specific advice through dialogue to maintain the user's motivation. This mechanism allows users to improve their success rate in career advancement and shorten the time required for skill acquisition. Personalized support also improves user satisfaction. For example, a user inputs their occupation data and skill level through interactive AI counseling. The AI ​​then analyzes this data and identifies the skill gaps necessary for the user's career advancement. Furthermore, the AI ​​generates an individualized training plan based on the identified skill gaps. This training plan is customized to the user's needs and includes specific action plans for efficient skill acquisition. Furthermore, AI provides support to address user anxieties and concerns. For example, if a user feels anxious about their training plan, the AI ​​provides specific advice through dialogue to maintain the user's motivation. This mechanism allows users to improve their chances of career advancement and shorten the time it takes to acquire skills. Personalized support also improves user satisfaction. This enables the AI ​​agent system to gather user occupational data and skill levels, identify skill gaps, generate personalized training plans, and address user anxieties and concerns.

[0067] The AI ​​agent system according to this embodiment comprises a hearing unit, an analysis unit, a generation unit, and a support unit. The hearing unit hears the user's occupational data and skill level. The user's occupational data includes, but is not limited to, job title, industry, and position. The hearing unit collects the user's occupational data, for example, through an interview. The hearing unit can also collect information such as years of experience, qualifications, and test results to evaluate the user's skill level. For example, the hearing unit can conduct an online test to evaluate the user's skill level. The analysis unit analyzes the data heard by the hearing unit to identify skill gaps. Skill gaps are identified, for example, based on the difference between required skills and current skills, but is not limited to this example. The analysis unit identifies skill gaps using, for example, data analysis methods. The analysis unit can also identify skill gaps by adjusting the algorithm used. For example, the analysis unit identifies skill gaps using machine learning algorithms. The generation unit generates an individualized training plan based on the skill gaps identified by the analysis unit. Training plans are generated based on, for example, the type, duration, and content of the training, but are not limited to such examples. The generation unit customizes the training plan according to the user's needs, for example. The generation unit can also generate training plans by adjusting the algorithms used. For example, the generation unit generates training plans using machine learning algorithms. The support unit addresses the user's anxieties and concerns. The support unit provides, for example, specific advice regarding anxieties about the training plan or concerns about the career, but is not limited to such examples. The support unit addresses the user's anxieties and concerns through dialogue, for example. The support unit can also adjust how it responds to the user's anxieties and concerns. For example, the support unit customizes how it responds to the user's anxieties and concerns.As a result, the AI ​​agent system according to the embodiment can interview users about their occupational data and skill levels, identify skill gaps, generate personalized training plans, and address users' anxieties and concerns.

[0068] The interviewing department gathers user occupational data and skill levels through interviews. This data includes, but is not limited to, job title, industry, and position. The interviewing department collects this data through interviews, for example. Specifically, through dialogue with users, the department understands their detailed work history, current job responsibilities, industry characteristics, and responsibilities associated with their position. This allows for a comprehensive understanding of the user's occupational background. The interviewing department can also collect information such as years of experience, qualifications, and test results to assess the user's skill level. For example, the interviewing department can conduct online tests to evaluate the user's skill level. These online tests include specific questions designed to assess the user's expertise and technical skills, and quantitatively evaluate their skill level based on their responses. Furthermore, the interviewing department collects information such as the user's self-assessment, past project experience, and acquired qualifications and certifications to conduct a comprehensive skill assessment. This allows the interviewing unit to gain a detailed understanding of the user's occupational data and skill level, and provide the data necessary for processing in the subsequent analysis unit.

[0069] The analysis unit identifies skill gaps by analyzing data collected by the interview unit. Skill gaps are identified, for example, based on the difference between required skills and current skills, but are not limited to such examples. The analysis unit identifies skill gaps using data analysis methods, for example. Specifically, the analysis unit compares the user's current skill set with the skill set required for the target job, based on collected occupational data and skill levels. This makes it possible to clearly identify the skills the user possesses and the skills they lack. The analysis unit can also identify skill gaps by adjusting the algorithms it uses. For example, the analysis unit can identify skill gaps using machine learning algorithms. Machine learning algorithms learn from past data and skill requirements in similar jobs, enabling them to identify the user's skill gaps with high accuracy. Furthermore, the analysis unit can also predict skills that will be needed in the future, taking into account the user's career path and industry trends. This allows the analysis unit to accurately understand the user's current skill level and identify the skills necessary for future career growth.

[0070] The generation unit generates a personalized training plan based on the skill gaps identified by the analysis unit. The training plan is generated based on, for example, the type, duration, and content of the training, but is not limited to these examples. The generation unit customizes the training plan according to the user's needs. Specifically, it selects the most suitable training courses and materials and creates a training schedule to fill the user's skill gaps. For example, it suggests training methods tailored to the user's learning style and schedule, such as online courses, workshops, and hands-on projects. The generation unit can also generate training plans by adjusting the algorithms used. For example, it can generate training plans using machine learning algorithms. Machine learning algorithms can analyze past training data and the user's learning history to suggest optimal training content and pace. Furthermore, the generation unit continuously improves the training plan based on user feedback to support effective learning. This allows the generation unit to provide personalized training plans to support the user's skill improvement.

[0071] The support department addresses users' anxieties and concerns. For example, the support department provides specific advice regarding anxieties about training plans or career concerns, but is not limited to these examples. The support department addresses users' anxieties and concerns through dialogue, for example. Specifically, the support department provides consultation on training progress and career direction through regular meetings and online chats with users. The support department can also adjust how it addresses users' anxieties and concerns. For example, the support department customizes how it addresses users' anxieties and concerns. It provides specific advice and resources tailored to the user's individual circumstances and needs, supporting them so they can proceed with their training with confidence. Furthermore, the support department can also provide psychological support and suggest ways to maintain user motivation. In this way, the support department can address users' anxieties and concerns and help maximize the effectiveness of their training.

[0072] The Feedback Collection Unit collects user feedback. The Feedback Collection Unit collects user feedback, for example, through surveys. For example, the Feedback Collection Unit can conduct online surveys to collect users' opinions and impressions. The Feedback Collection Unit can also collect user feedback through interviews. For example, the Feedback Collection Unit collects user feedback through face-to-face interviews. The Feedback Collection Unit can also collect user feedback through evaluation comments. For example, the Feedback Collection Unit encourages users to provide evaluation comments on their training plans. By collecting user feedback, the Feedback Collection Unit can improve the system and respond to user needs. Some or all of the above processing in the Feedback Collection Unit may be performed using AI, for example, or without AI. For example, the Feedback Collection Unit can input user feedback data into a generating AI and have the generating AI perform feedback analysis.

[0073] The Monitoring Unit monitors the progress of the training plan. For example, the Monitoring Unit periodically checks the user's training progress. For example, the Monitoring Unit may request periodic progress reports to confirm that the user is progressing according to the training plan. The Monitoring Unit can also monitor the user's training progress in real time. For example, the Monitoring Unit collects progress data through an online platform to confirm in real time whether the user is progressing according to the training plan. The Monitoring Unit can also monitor the degree of achievement and progress to evaluate the user's training progress. For example, the Monitoring Unit evaluates the extent to which the user has achieved the goals they have set. In this way, the Monitoring Unit can understand the user's progress by monitoring the progress of the training plan and provide appropriate support. Some or all of the above processes in the Monitoring Unit may be performed using AI, for example, or not using AI. For example, the Monitoring Unit may input user progress data into a generating AI and have the generating AI perform progress analysis.

[0074] The generation unit can generate a training plan customized to the user's needs. For example, the generation unit can customize the training plan based on the user's occupational data and skill level. For example, the generation unit can analyze the user's occupational data and identify the skills required for a specific occupation. The generation unit can also evaluate the user's skill level and identify skill gaps. For example, the generation unit can evaluate the user's skill level and identify the difference between the required skills and the user's current skills. Furthermore, the generation unit can adjust the training plan according to the user's needs. For example, the generation unit can adjust the plan based on the user's desired training period and training content. In this way, the generation unit can efficiently support the user's skill development by generating a training plan customized to the user's needs. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's occupational data and skill level into a generation AI and have the generation AI perform the generation of the training plan.

[0075] The support department can provide specific advice to address users' anxieties and concerns regarding their training plans. For example, if a user feels anxious about their training plan, the support department can provide specific advice. For instance, the support department can interview the user to understand the reasons for their anxiety and provide specific advice to alleviate those anxieties. The support department can also provide specific advice if a user has concerns about their career. For example, the support department can interview the user to understand the reasons for their concerns and provide specific advice to alleviate those concerns. Furthermore, the support department can provide specific advice to help users maintain their motivation. For example, the support department can provide specific advice to help users maintain their motivation regarding their training plan. In this way, the support department can help users maintain their motivation and support the execution of their training plans by providing specific advice to address their anxieties and concerns. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input data on users' anxieties and concerns into a generating AI and have the generating AI generate specific advice.

[0076] The interview unit can estimate the user's emotions and adjust the timing of the interview based on the estimated emotions. For example, if the user is feeling stressed, the interview unit will start the interview at a time when the user is likely to relax. For example, the interview unit will select a time when the user is relaxed to conduct the interview. The interview unit can also conduct the interview without missing the opportunity if the user is concentrating. For example, the interview unit will select a time when the user is concentrating to conduct the interview. Furthermore, if the user is tired, the interview unit can adjust the interview to take a break. For example, the interview unit will conduct the interview after the user has taken a break. In this way, the interview unit can reduce the user's stress and conduct an effective interview by adjusting the timing of the interview 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the hearing unit may be performed using AI, for example, or without AI. For example, the hearing unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0077] The interview unit can analyze the user's past work history and select the optimal interview method. For example, the interview unit can prioritize relevant questions based on the user's past work experience. For example, the interview unit selects relevant questions based on the user's past work history. The interview unit can also focus on questions related to specific skills based on the user's work history. For example, the interview unit selects questions related to specific skills based on the user's work history. The interview unit can also optimize the order of interviews based on the user's work history. For example, the interview unit adjusts the order of interviews based on the user's work history. In this way, the interview unit can select the optimal interview method and conduct effective interviews by analyzing the user's past work history. Some or all of the above processing in the interview unit may be performed using AI, for example, or without using AI. For example, the interviewing unit can input the user's occupational history data into a generating AI, which can then select the most suitable interviewing method.

[0078] The interviewing unit can customize the questions asked during the interview based on the user's current occupation and areas of interest. For example, the interviewing unit can ask questions about skills related to the user's current occupation. For example, the interviewing unit can select questions about relevant skills based on the user's occupational data. The interviewing unit can also ask questions that are likely to interest the user based on their areas of interest. For example, the interviewing unit can select questions that are likely to interest the user based on their areas of interest. The interviewing unit can also ask questions that combine the user's occupation and areas of interest. For example, the interviewing unit can select relevant questions based on the user's occupational data and areas of interest. In this way, the interviewing unit can collect useful information for the user by customizing the questions based on the user's current occupation and areas of interest. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's occupational data and areas of interest data into a generating AI and have the generating AI perform the customization of the questions.

[0079] The interviewing unit can estimate the user's emotions and determine the priority of the interview based on the estimated emotions. For example, if the user is feeling anxious, the interviewing unit will prioritize questions that provide reassurance. For example, if the user is feeling anxious, the interviewing unit will select questions that provide reassurance. The interviewing unit can also prioritize questions that allow the user to answer calmly if the user is excited. For example, if the user is excited, the interviewing unit will select questions that allow the user to answer calmly. The interviewing unit can also prioritize detailed questions if the user is relaxed. For example, if the user is relaxed, the interviewing unit will select detailed questions. In this way, by determining the priority of the interview based on the user's emotions, the interviewing unit can conduct effective interviews that take the user's emotions into consideration. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the hearing unit may be performed using AI, for example, or without AI. For example, the hearing unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0080] The interviewing unit can prioritize acquiring highly relevant information during interviews, taking into account the user's geographical location. For example, the interviewing unit can acquire region-specific occupational information based on the user's location. The interviewing unit can also acquire information on nearby training facilities, taking into account the user's geographical location. The interviewing unit can also prioritize acquiring job postings related to the user's location. In this way, the interviewing unit can provide useful information to the user by prioritizing the acquisition of highly relevant information, taking into account the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant information.

[0081] The interviewing unit can analyze the user's social media activity during the interview and obtain relevant information. For example, the interviewing unit can analyze the content of the user's social media posts and obtain information about occupations of interest. For example, the interviewing unit can obtain information about occupations of interest based on the content of the user's social media posts. The interviewing unit can also analyze the user's followers and the accounts they follow and obtain relevant occupation information. For example, the interviewing unit can obtain relevant occupation information based on the user's followers and the accounts they follow. The interviewing unit can also obtain trend-based information by referring to the user's social media activity history. For example, the interviewing unit can obtain trend-based information based on the user's social media activity history. In this way, the interviewing unit can obtain information based on the user's interests by analyzing the user's social media activity. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0082] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis. The analysis unit can also perform a rapid analysis if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can perform a rapid data analysis. The analysis unit can also provide reassuring analysis results if the user is feeling anxious. For example, if the analysis unit is feeling anxious, the analysis unit can provide reassuring analysis results. In this way, the analysis unit can provide the optimal analysis results for the user by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI perform adjustments to the accuracy of the analysis.

[0083] The analysis unit can optimize its analysis algorithm by referring to the user's past skill acquisition history during analysis. For example, the analysis unit can identify relevant skill gaps based on the skills the user has acquired in the past. For example, the analysis unit can identify relevant skill gaps based on the user's past skill acquisition history. The analysis unit can also select the optimal analysis algorithm by referring to the user's skill acquisition history. For example, the analysis unit can select the optimal analysis algorithm based on the user's skill acquisition history. The analysis unit can also analyze the user's past skill acquisition history and identify trends in skill gaps. For example, the analysis unit can identify trends in skill gaps based on the user's past skill acquisition history. As a result, the analysis unit can optimize its analysis algorithm by referring to the user's past skill acquisition history and provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skill acquisition history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0084] The analysis unit can apply different analysis methods depending on the user's occupation category during analysis. For example, the analysis unit can identify specific skill gaps based on the user's occupation category. The analysis unit can also select the optimal analysis method depending on the user's occupation category. For example, the analysis unit selects the optimal analysis method based on the user's occupation category. The analysis unit can also customize the analysis results based on the user's occupation category. For example, the analysis unit customizes the analysis results based on the user's occupation category. In this way, the analysis unit can provide the user with the optimal analysis results by applying different analysis methods depending on the user's occupation category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's occupation category data into a generating AI and have the generating AI execute the application of different analysis methods.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is nervous, the analysis unit can select a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the analysis unit can select a display method that includes detailed information. The analysis unit can also provide a display method that gets straight to the point if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can select a display method that gets straight to the point. In this way, the analysis unit can provide a display method that is easy for the user to understand by adjusting the display method of the analysis results 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the display method.

[0086] The analysis unit can determine the priority of analyses based on the user's submission timing during the analysis process. For example, if the user is in a hurry, the analysis unit will prioritize the analysis based on the submission timing. The analysis unit can also prioritize detailed analyses if the user has ample time. The analysis unit can also set an optimal analysis schedule based on the user's submission timing. This allows the analysis unit to perform rapid analyses that meet the user's needs by prioritizing analyses based on the user's submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0087] 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 the relevant literature provided by the user. The analysis unit can also supplement the analysis results by referring to literature related to the user's occupation category. For example, the analysis unit supplements the analysis results based on literature related to the user's occupation category. The analysis unit can also improve the accuracy of its analysis by analyzing literature related to the user's skill gap. For example, the analysis unit improves the accuracy of its analysis based on literature related to the user's skill gap. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature and provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0088] The generation unit can estimate the user's emotions and adjust the content of the training plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed training plan. For example, if the user is relaxed, the generation unit can generate a detailed training plan. The generation unit can also provide a training plan that can be executed quickly if the user is in a hurry. For example, if the user is in a hurry, the generation unit can generate a training plan that can be executed quickly. The generation unit can also provide a training plan that provides reassurance if the user is feeling anxious. For example, if the generation unit is feeling anxious, the generation unit can generate a training plan that provides reassurance. In this way, the generation unit can provide the optimal training plan for the user by adjusting the content of the training plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the content of the training plan.

[0089] The generation unit can adjust the level of detail in the training plan based on the user's skill level during generation. For example, the generation unit can provide a detailed training plan according to the user's skill level. For example, the generation unit generates a detailed training plan based on the user's skill level. The generation unit can also provide a concise training plan based on the user's skill level. For example, the generation unit generates a concise training plan based on the user's skill level. The generation unit can also customize the content of the training plan, taking the user's skill level into consideration. For example, the generation unit customizes the content of the training plan based on the user's skill level. This allows the generation unit to provide the optimal training plan for the user by adjusting the level of detail in the training plan based on the user's skill level. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user skill level data into a generation AI and have the generation AI perform the adjustment of the level of detail in the training plan.

[0090] The generation unit can apply different training algorithms depending on the user's occupation category during generation. For example, the generation unit can select the optimal training algorithm based on the user's occupation category. The generation unit can also customize the content of the training plan depending on the user's occupation category. The generation unit can also optimize the training algorithm based on the user's occupation category. In this way, the generation unit can provide the user with the optimal training plan by applying different training algorithms depending on the user's occupation category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user occupation category data into a generation AI and have the generation AI execute the application of the training algorithm.

[0091] The generation unit can estimate the user's emotions and determine the priority of training plans based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will prioritize training plans that provide a sense of security. For example, if the user is feeling anxious, the generation unit will select training plans that provide a sense of security. The generation unit can also prioritize training plans that allow the user to approach calmly if they are excited. For example, if the user is excited, the generation unit will select training plans that allow the user to approach calmly. The generation unit can also prioritize detailed training plans if the user is relaxed. For example, if the user is relaxed, the generation unit will select detailed training plans. In this way, the generation unit can provide the optimal training plan for the user by determining the priority of training plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the priorities for the training plan.

[0092] The generation unit can prioritize training plans based on the user's submission timing during generation. For example, if the user is in a hurry, the generation unit will prioritize training plans considering the submission timing. The generation unit can also prioritize detailed training plans if the user has ample time. The generation unit can also set an optimal training schedule based on the user's submission timing. This allows the generation unit to provide a rapid training plan that meets the user's needs by prioritizing training plans based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user submission timing data into a generation AI and have the generation AI perform the training plan prioritization.

[0093] The generation unit can improve the accuracy of the training plan by referring to the user's relevant literature during generation. For example, the generation unit improves the accuracy of the training plan based on the relevant literature provided by the user. The generation unit can also supplement the training plan by referring to literature related to the user's occupation category. For example, the generation unit supplements the training plan based on literature related to the user's occupation category. The generation unit can also improve the accuracy of the training plan by analyzing literature related to the user's skill gaps. For example, the generation unit improves the accuracy of the training plan based on literature related to the user's skill gaps. In this way, the generation unit can improve the accuracy of the training plan by referring to the user's relevant literature and provide a more accurate training plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's relevant literature data into a generation AI and have the generation AI perform the training plan accuracy improvement.

[0094] The support unit can estimate the user's emotions and adjust the support content based on the estimated emotions. For example, if the user is feeling anxious, the support unit can provide reassuring support. For example, if the user is feeling anxious, the support unit can select reassuring support. The support unit can also provide detailed support if the user is relaxed. For example, if the user is relaxed, the support unit can select detailed support. The support unit can also provide prompt support if the user is in a hurry. For example, if the support unit is in a hurry, the support unit can select prompt support. In this way, the support unit can provide the optimal support for the user by adjusting the support content 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 support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​adjust the support content accordingly.

[0095] The support unit can select the optimal support method by referring to the user's past feedback during support. For example, the support unit can select the optimal support method based on the user's past feedback. The support unit can also customize the support content by referring to the user's feedback history. For example, the support unit can customize the support content based on the user's feedback history. The support unit can also analyze the user's past feedback and select a method to maximize the effectiveness of the support. For example, the support unit can select a method to maximize the effectiveness of the support based on the user's past feedback. In this way, the support unit can select the optimal support method by referring to the user's past feedback and provide effective support. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal support method.

[0096] The support unit can apply different support methods depending on the user's occupation category during support. For example, the support unit can select a specific support method based on the user's occupation category. The support unit can also customize the support content depending on the user's occupation category. For example, the support unit can customize the support content based on the user's occupation category. The support unit can also apply the most suitable support method based on the user's occupation category. For example, the support unit can apply the most suitable support method based on the user's occupation category. In this way, the support unit can provide the best possible support to the user by applying different support methods depending on the user's occupation category. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user occupation category data into a generating AI and have the generating AI execute the application of support methods.

[0097] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is feeling anxious, the support unit will prioritize support that provides a sense of security. For example, if the user is feeling anxious, the support unit will select support that provides a sense of security. The support unit can also prioritize support that allows the user to approach the situation calmly if the user is agitated. For example, if the user is agitated, the support unit will select support that allows the user to approach the situation calmly. The support unit can also prioritize detailed support if the user is relaxed. For example, if the user is relaxed, the support unit will select detailed support. In this way, the support unit can provide the optimal support for the user by determining the priority of support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​determine the priority of support requests.

[0098] The support unit can select the optimal support method when providing support, taking into account the user's geographical location. For example, the support unit can select a region-specific support method based on the user's location. The support unit can also provide information on nearby support facilities, taking into account the user's geographical location. The support unit can also prioritize providing support resources related to the user's location. For example, the support unit provides relevant support resources based on the user's location. In this way, the support unit can provide beneficial support to the user by selecting the optimal support method, taking into account the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI select the support method.

[0099] The support department can analyze the user's social media activity and customize the support provided during support. For example, the support department can analyze the user's social media posts and provide support that is of interest to the user. For example, the support department can provide support that is of interest to the user based on the user's social media posts. The support department can also analyze the user's followers and followed accounts and provide relevant support information. For example, the support department can provide relevant support information based on the user's followers and followed accounts. The support department can also provide trend-based support based on the user's social media activity history. For example, the support department can provide trend-based support based on the user's social media activity history. In this way, the support department can provide support that is of interest to the user by analyzing the user's social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's social media data into a generating AI and have the generating AI perform the customization of the support content.

[0100] The feedback collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated emotions. For example, the feedback collection unit will determine the right time to collect feedback if the user is relaxed. The feedback collection unit can also collect feedback quickly if the user is in a hurry. The feedback collection unit can also collect feedback quickly if the user is feeling anxious, at a time that will provide reassurance. In this way, the feedback collection unit can collect feedback at the optimal time for the user by adjusting the timing of feedback collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 processing described above in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of feedback collection.

[0101] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit can select the optimal collection method based on the user's past feedback history. The feedback collection unit can also customize the content to be collected by referring to the user's feedback history. For example, the feedback collection unit can customize the content to be collected based on the user's feedback history. The feedback collection unit can also analyze the user's past feedback history and select a method that maximizes the effectiveness of the collection. For example, the feedback collection unit can select a method that maximizes the effectiveness of the collection based on the user's past feedback history. In this way, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history and conduct effective feedback collection. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without using AI. For example, the feedback collection unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal collection method.

[0102] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated emotions. For example, if the user is feeling anxious, the feedback collection unit will prioritize collecting reassuring feedback. For example, if the user is feeling anxious, the feedback collection unit will select feedback that provides reassurance. The feedback collection unit can also prioritize collecting feedback that allows the user to approach the task calmly if the user is excited. For example, if the user is excited, the feedback collection unit will select feedback that allows the user to approach the task calmly. The feedback collection unit can also prioritize collecting detailed feedback if the user is relaxed. For example, if the user is relaxed, the feedback collection unit will select detailed feedback. In this way, the feedback collection unit can collect the most optimal feedback for the user by determining the priority of feedback collection 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 processing described above in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input user emotion data into a generating AI and have the generating AI perform the task of determining the priority of feedback collection.

[0103] The feedback collection unit can prioritize collecting highly relevant feedback by considering the user's geographical location information during feedback collection. For example, the feedback collection unit can prioritize collecting region-specific feedback based on the user's location. The feedback collection unit can also prioritize collecting nearby feedback by referring to the user's geographical location information. The feedback collection unit can also prioritize collecting feedback related to the user's location. The feedback collection unit can collect relevant feedback based on the user's location. In this way, the feedback collection unit can collect useful feedback for the user by prioritizing the collection of highly relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant feedback.

[0104] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, the monitoring unit may lower the monitoring frequency if the user is relaxed. The monitoring unit may also increase the monitoring frequency if the user is in a hurry. The monitoring unit may also adjust the monitoring frequency to provide reassurance if the user is feeling anxious. In this way, the monitoring unit can perform optimal monitoring for the user by adjusting the monitoring frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI adjust the monitoring frequency.

[0105] The monitoring unit can select the optimal monitoring method by referring to the user's past training progress during monitoring. For example, the monitoring unit selects the optimal monitoring method based on the user's past training progress. The monitoring unit can also customize the monitoring content by referring to the user's training progress history. For example, the monitoring unit customizes the monitoring content based on the user's training progress history. The monitoring unit can also analyze the user's past training progress and select a method to maximize the effectiveness of monitoring. For example, the monitoring unit selects a method to maximize the effectiveness of monitoring based on the user's past training progress. In this way, the monitoring unit can select the optimal monitoring method by referring to the user's past training progress and perform effective monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past training progress data into a generating AI and have the generating AI select the optimal monitoring method.

[0106] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit will prioritize monitoring that provides a sense of security. For example, if the user is feeling anxious, the monitoring unit will select monitoring that provides a sense of security. The monitoring unit can also prioritize monitoring that allows the user to approach the task calmly if the user is excited. For example, if the user is excited, the monitoring unit will select monitoring that allows the user to approach the task calmly. The monitoring unit can also prioritize detailed monitoring if the user is relaxed. For example, if the user is relaxed, the monitoring unit will select detailed monitoring. In this way, the monitoring unit can perform optimal monitoring for the user by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI determine the prioritization of monitoring tasks.

[0107] The monitoring unit can select the optimal monitoring method while considering the user's geographical location information. For example, the monitoring unit can select a region-specific monitoring method based on the user's location. The monitoring unit can also provide information on nearby monitoring facilities based on the user's geographical location information. The monitoring unit can also prioritize providing monitoring resources related to the user's location. For example, the monitoring unit provides relevant monitoring resources based on the user's location. In this way, the monitoring unit can perform monitoring that is beneficial to the user by selecting the optimal monitoring method while considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI select the monitoring method.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analysis unit can refer to a user's past work history and skill acquisition history when analyzing a user's occupational data and skill level. For example, the analysis unit can analyze what occupations a user has held in the past and identify the gap between those occupations and the skills required for their current occupation. The analysis unit can also consider qualifications and skills acquired in the past and evaluate how these skills are useful in their current occupation. Furthermore, based on the user's past skill acquisition history, the analysis unit can identify trends in skill gaps and predict skills that will be needed in the future. As a result, the analysis unit can identify and analyze skill gaps with greater accuracy by referring to the user's past work history and skill acquisition history.

[0110] The generator can identify the user's skill gaps and then customize the training plan according to the user's learning style. For example, if the user is a visual learner, the generator can provide a training plan that includes many visual materials. If the user is an auditory learner, the generator can also provide a training plan that focuses on audio materials. Furthermore, if the user prefers hands-on learning, the generator can provide a plan that includes hands-on training sessions. In this way, the generator can support users in efficiently acquiring skills by customizing the training plan according to their learning style.

[0111] The support team can address users' anxieties and concerns about their training plans by referencing their past successes and providing advice. For example, they can offer advice that builds confidence in the current training plan based on goals and successful projects the user has achieved in the past. They can also suggest specific solutions to current anxieties and concerns by referencing difficulties and challenges the user has overcome in the past. Furthermore, they can offer advice to boost motivation by reflecting on the user's past successes. In this way, the support team can provide more effective support by referencing the user's past successes.

[0112] The monitoring unit can adjust the frequency of monitoring according to the user's learning pace when monitoring the progress of the user's training plan. For example, if the user is progressing quickly, the monitoring unit can set a lower monitoring frequency. Conversely, if the user is progressing slowly, the monitoring unit can set a higher monitoring frequency. Furthermore, if the user is experiencing difficulties in a particular training session, the monitoring unit can intensify monitoring for that session. In this way, the monitoring unit can provide optimal support to the user by adjusting the monitoring frequency according to the user's learning pace.

[0113] The feedback collection unit can adjust its collection method based on the content of user feedback. For example, if a user provides detailed feedback, the feedback collection unit can collect more detailed information through face-to-face interviews. Alternatively, if a user provides concise feedback, the feedback collection unit can quickly collect that feedback through online questionnaires. Furthermore, if a user provides evaluation comments for a specific training session, the feedback collection unit can focus on collecting feedback for that session. This allows the feedback collection unit to collect feedback more effectively by adjusting its collection method based on the content of user feedback.

[0114] The interview function can estimate the user's emotions and adjust the interview content based on those estimations. For example, if the user is feeling anxious, the interview function prioritizes questions that provide reassurance. Conversely, if the user is relaxed, the interview function can ask more detailed questions. Furthermore, if the user is agitated, the interview function can select questions that allow them to answer calmly. In this way, the interview function can conduct the most optimal interview for the user by adjusting the interview content based on the user's emotions.

[0115] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation method of the analysis results based on the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand.

[0116] The generation unit can estimate the user's emotions and adjust the pace of the training plan based on those emotions. For example, if the user is relaxed, the generation unit will provide a training plan at a normal pace. If the user is in a hurry, the generation unit can provide a training plan that progresses quickly. Furthermore, if the user is feeling anxious, the generation unit can slow down the pace to provide reassurance. In this way, the generation unit can provide the user with the optimal training plan by adjusting the pace of the training plan based on their emotions.

[0117] The support department can estimate the user's emotions and adjust the support content based on those estimates. For example, if the user is feeling anxious, the support department can provide reassuring support. If the user is relaxed, the support department can provide detailed support. Furthermore, if the user is in a hurry, the support department can provide prompt support. In this way, the support department can provide the best possible support to the user by adjusting the support content based on their emotions.

[0118] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on those emotions. For example, if the user is relaxed, the feedback collection unit can collect detailed feedback. If the user is in a hurry, it can collect concise feedback. Furthermore, if the user is feeling anxious, the feedback collection unit can select a feedback collection method that provides reassurance. In this way, the feedback collection unit can provide the most optimal feedback for the user by adjusting the feedback collection method based on the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The interview team gathers user occupational data and skill levels. User occupational data includes job title, industry, position, etc., and is collected through interviews. The interview team can also collect information such as years of experience, qualifications, and test results, and may conduct online tests. Step 2: The analysis unit analyzes the data collected by the interview unit to identify skill gaps. Skill gaps are identified based on the difference between required skills and current skills, and are determined using data analysis methods and machine learning algorithms. Step 3: The generation unit generates a personalized training plan based on the skill gaps identified by the analysis unit. The training plan is generated based on the type, duration, and content of the training, and is customized according to the user's needs. The generation unit can also generate the training plan using machine learning algorithms. Step 4: The support team addresses user anxieties and concerns. The support team provides specific advice regarding anxieties about training plans and career concerns, and addresses users' anxieties and concerns through dialogue. The support team can also customize how they address users' anxieties and concerns.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the hearing unit, analysis unit, generation unit, support unit, feedback collection unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the hearing unit collects the user's occupational data and skill level by the control unit 46A of the smart device 14. The analysis unit identifies skill gaps by the identification processing unit 290 of the data processing unit 12. The generation unit generates an individualized training plan by the identification processing unit 290 of the data processing unit 12. The support unit addresses the user's anxieties and concerns by the control unit 46A of the smart device 14. The feedback collection unit collects user feedback by the control unit 46A of the smart device 14. The monitoring unit monitors the progress of the training plan by the identification processing unit 290 of the data processing unit 12. 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.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the hearing unit, analysis unit, generation unit, support unit, feedback collection unit, and monitoring unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the hearing unit collects the user's occupational data and skill level by the control unit 46A of the smart glasses 214. The analysis unit identifies skill gaps by the identification processing unit 290 of the data processing unit 12. The generation unit generates an individualized training plan by the identification processing unit 290 of the data processing unit 12. The support unit addresses the user's anxieties and concerns by the control unit 46A of the smart glasses 214. The feedback collection unit collects user feedback by the control unit 46A of the smart glasses 214. The monitoring unit monitors the progress of the training plan by the identification processing unit 290 of the data processing unit 12. 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] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[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 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.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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 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.

[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 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.

[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 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.

[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 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.

[0156] Each of the multiple elements described above, including the hearing unit, analysis unit, generation unit, support unit, feedback collection unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the hearing unit collects the user's occupational data and skill level by the control unit 46A of the headset terminal 314. The analysis unit identifies skill gaps by the identification processing unit 290 of the data processing unit 12. The generation unit generates an individualized training plan by the identification processing unit 290 of the data processing unit 12. The support unit addresses the user's anxieties and concerns by the control unit 46A of the headset terminal 314. The feedback collection unit collects user feedback by the control unit 46A of the headset terminal 314. The monitoring unit monitors the progress of the training plan by the identification processing unit 290 of the data processing unit 12. 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[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 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.

[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 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).

[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] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the hearing unit, analysis unit, generation unit, support unit, feedback collection unit, and monitoring unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the hearing unit collects the user's occupational data and skill level by the control unit 46A of the robot 414. The analysis unit identifies skill gaps by the identification processing unit 290 of the data processing unit 12. The generation unit generates an individualized training plan by the identification processing unit 290 of the data processing unit 12. The support unit addresses the user's anxieties and concerns by the control unit 46A of the robot 414. The feedback collection unit collects user feedback by the control unit 46A of the robot 414. The monitoring unit monitors the progress of the training plan by the identification processing unit 290 of the data processing unit 12. 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The interview department gathers user occupational data and skill levels, An analysis unit analyzes the data collected by the aforementioned interview unit to identify skill gaps, A generation unit generates an individualized training plan based on the skill gap identified by the analysis unit, It includes a support department to address users' anxieties and concerns. A system characterized by the following features. (Note 2) It includes a feedback collection unit for collecting user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a monitoring unit to monitor the progress of the training plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a customized training plan tailored to the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is Provide specific advice to address users' anxieties and concerns regarding their training plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned hearing section is, The system estimates the user's emotions and adjusts the timing of interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned hearing section is, Analyze the user's past work history and select the most suitable interview method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned hearing section is, During the interview, the questions are customized based on the user's current occupation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned hearing section is, The system estimates the user's emotions and determines the priority of interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned hearing section is, During the interview process, we prioritize obtaining highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned hearing section is, During the interview, we analyze the user's social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past skill acquisition history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the user's occupation category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, 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 16) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the user submitted their data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, 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 18) The generating unit is It estimates the user's emotions and adjusts the training plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the level of detail in the training plan is adjusted based on the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, different training algorithms are applied depending on the user's occupation category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and prioritizes the training plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the training plan is prioritized based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the accuracy of the training plan is improved by referencing relevant user literature. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is The system estimates the user's emotions and adjusts the support provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is During support, we refer to the user's past feedback to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When providing support, different support methods are applied depending on the user's occupational category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is During support, we analyze the user's social media activity to customize the support content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback collection unit is It estimates the user's emotions and adjusts the timing of feedback collection based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned feedback collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned feedback collection unit is It estimates the user's emotions and prioritizes feedback collection based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned feedback collection unit is When collecting feedback, the system prioritizes collecting highly relevant feedback by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The monitoring unit, During monitoring, the optimal monitoring method is selected by referring to the user's past training progress. The system described in Appendix 3, characterized by the features described herein. (Note 36) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 37) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The interview department gathers user occupational data and skill levels, An analysis unit analyzes the data collected by the aforementioned interview unit to identify skill gaps, A generation unit generates an individualized training plan based on the skill gap identified by the analysis unit, It includes a support department to address users' anxieties and concerns. A system characterized by the following features.

2. It includes a feedback collection unit for collecting user feedback. The system according to feature 1.

3. It includes a monitoring unit to monitor the progress of the training plan. The system according to feature 1.

4. The generating unit is Generate a customized training plan tailored to the user's needs. The system according to feature 1.

5. The aforementioned support unit is Provide specific advice to address users' anxieties and concerns regarding their training plans. The system according to feature 1.

6. The aforementioned hearing section is, The system estimates the user's emotions and adjusts the timing of interviews based on those estimated emotions. The system according to feature 1.

7. The aforementioned hearing section is, Analyze the user's past work history and select the most suitable interview method. The system according to feature 1.

8. The aforementioned hearing section is, During the interview, the questions are customized based on the user's current occupation and areas of interest. The system according to feature 1.

9. The aforementioned hearing section is, The system estimates the user's emotions and determines the priority of interviews based on those estimated emotions. The system according to feature 1.

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