system

Abstract

The system according to this embodiment aims to address users' anxieties and concerns by providing personalized training plans based on the user's occupation and skill level. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a support unit, a monitoring unit, and an adjustment unit. The collection unit collects information about the user's occupation and skill level. The analysis unit identifies skill gaps based on the information collected by the collection unit. The provision unit provides a training plan through interactive AI counseling. The support unit addresses the user's anxieties and concerns. The monitoring unit monitors the user's training progress in real time. The adjustment unit adjusts the training plan.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that an individualized training plan based on the user's occupation and skill level is not sufficiently provided to address the user's anxiety and concerns.

[0005] The system according to the embodiment aims to provide an individualized training plan based on the user's occupation and skill level and to address the user's anxiety and concerns.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a support unit, a monitoring unit, and an adjustment unit. The collection unit collects information about the user's occupation and skill level. The analysis unit identifies skill gaps based on the information collected by the collection unit. The provision unit provides a training plan through interactive AI counseling. The support unit addresses the user's anxieties and concerns. The monitoring unit monitors the user's training progress in real time. The adjustment unit adjusts the training plan. [Effects of the Invention]

[0007] The system according to this embodiment can provide an individualized training plan based on the user's occupation and skill level, and can address the user's anxieties and concerns. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that interviews users about their occupation and skill level and identifies skill gaps for career advancement. This AI agent system collects information about the user's occupation and skill level, and the AI ​​identifies the user's skill gaps. Subsequently, through conversational AI counseling, it provides the user with an optimal training plan and addresses the user's anxieties and concerns. For example, if a user inputs, "My current occupation is an engineer, and I want to improve my programming skills," the AI ​​evaluates the user's current skill level and identifies the necessary skills. Subsequently, through conversational AI counseling, it provides the user with an optimal training plan and gives specific advice on how to improve programming skills. The AI ​​also provides appropriate support for any anxieties or concerns the user may feel during training. This mechanism allows users to efficiently acquire the skills necessary for their career advancement. Furthermore, by addressing the user's anxieties and concerns through conversational AI counseling, the effectiveness of the training can be maximized. For example, by providing appropriate support for any anxieties or concerns the user may feel during training, the user can engage in training with peace of mind. In addition, the AI ​​monitors the user's training progress in real time and adjusts the training plan as needed. This allows users to train at their own pace and acquire skills efficiently. For example, if a user is falling behind in their training progress, the AI ​​can adjust the training plan to help them progress at an optimal pace. In this way, the AI ​​agent system identifies skill gaps based on the user's occupation and skill level, and supports the user's career advancement by providing personalized training plans through conversational AI counseling. It also addresses the user's anxieties and concerns to maximize the effectiveness of the training and support the user in efficiently acquiring skills.This allows the AI ​​agent system to identify skill gaps based on the user's occupation and skill level, and provide personalized training plans to support the user's career advancement.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a support unit, a monitoring unit, and an adjustment unit. The collection unit collects information about the user's occupation and skill level. For example, the collection unit stores information about the user's occupation and skill level in a database. The collection unit can also collect information about the user's past occupational history and skill set. For example, the collection unit analyzes what occupations the user has held in the past and what skills they possess. Furthermore, the collection unit can filter information based on the user's current career goals and areas of interest. For example, the collection unit prioritizes collecting information related to the occupation the user is aiming for and the areas of interest the user is interested in. The analysis unit identifies skill gaps based on the information collected by the collection unit. For example, the analysis unit analyzes the difference between the user's current skill level and their target skill level. Furthermore, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between the user's occupational history and skill set. For example, the analysis unit identifies skill gaps based on what occupations the user has held in the past and what skills they possess. The provision unit provides a training plan through conversational AI counseling. The service provider creates an optimal training plan based, for example, on the user's skill gaps. The service provider can also adjust the way the training plan is presented based on the user's emotions. For example, if the user is feeling anxious, the service provider will provide reassuring language. The support provider addresses the user's anxieties and concerns. For example, the support provider provides appropriate support for any anxieties or concerns the user may have during training. The support provider can also adjust the support method based on the user's emotions. For example, if the support provider is feeling anxious, they will provide reassuring support. The monitoring provider monitors the user's training progress in real time. For example, the monitoring provider monitors the user's progress in real time as they progress through the training and adjusts the training plan as needed. The monitoring provider can also adjust the monitoring method based on the user's emotions.For example, the monitoring unit provides a reassuring monitoring method if the user is feeling anxious. The adjustment unit adjusts the training plan. The adjustment unit adjusts the training plan, for example, according to the user's training progress. The adjustment unit can also adjust the training plan adjustment method based on the user's emotions. For example, the adjustment unit provides a reassuring adjustment method if the user is feeling anxious. As a result, the AI ​​agent system according to the embodiment can support the user's career advancement by identifying skill gaps based on the user's occupation and skill level and providing an individualized training plan.

[0030] The data collection unit collects information about the user's occupation and skill level. Specifically, it stores the occupation and skill level information entered by the user in a database. For example, when a user enters their current occupation, past work history, and skill set, the data collection unit accurately records this information and stores it in the database. The data collection unit can also collect information about the user's past work history and skill set. For example, it can import data from resumes and work histories to analyze what occupations the user has held in the past and what skills they possess. Furthermore, the data collection unit can filter information based on the user's current career goals and areas of interest. For example, it can prioritize collecting information related to the occupation the user is aiming for or the areas of interest they are interested in. This allows the data collection unit to efficiently collect and store the information necessary for the user's career advancement in the database. The data collection unit can collect information not only from user input but also from external data sources. For example, it can obtain data from online occupation databases and skill assessment platforms and integrate it with the user's information. This allows the data collection unit to collect more comprehensive and accurate information to help the user's career advancement.

[0031] The analysis unit identifies skill gaps based on the information collected by the data collection unit. Specifically, it analyzes the difference between the user's current skill level and their target skill level. For example, it compares the skills the user currently possesses with the skills required for their target occupation to identify which skills are lacking. The analysis unit can also improve the accuracy of its analysis by considering the interrelationships between the user's work history and skill set. For example, it identifies skill gaps based on the types of occupations the user has held in the past and the skills they possess. The analysis unit uses AI to analyze the data and identify the user's skill gaps. The AI ​​uses machine learning algorithms to analyze the user's work history and skill set to identify skill gaps. For example, the AI ​​identifies the skills required for the occupation the user is aiming for based on the user's past work history and skill set, and analyzes which skills are lacking. Furthermore, the analysis unit can improve the accuracy of its analysis based on the user's career goals and areas of interest. For example, it prioritizes the analysis of skills related to the occupation the user is aiming for and areas of interest to identify skill gaps. This allows the analysis unit to accurately identify the user's skill gaps and use this information to help the user advance their career.

[0032] The service provider offers training plans through interactive AI counseling. Specifically, it creates optimal training plans based on the user's skill gaps. For example, it suggests training courses and materials to address the user's skill deficiencies. The service provider can also adjust the presentation of the training plan based on the user's emotions. For example, if the user is feeling anxious, it will provide reassuring language. The service provider uses AI to interact with users and provide training plans tailored to their needs and emotions. The AI ​​analyzes user input using natural language processing technology and proposes appropriate training plans. For example, the AI ​​suggests optimal training courses and materials based on information about skill gaps entered by the user. The AI ​​also analyzes the user's emotions and provides reassuring language if the user is feeling anxious. This allows the service provider to provide optimal training plans to address user skill gaps and support their career advancement. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the training plans. For example, it can analyze how users reacted to the training plans and revise the content and presentation of the training plans. This allows the service provider to offer users more effective training plans and support their career advancement.

[0033] The support department addresses users' anxieties and concerns. Specifically, it provides appropriate support for anxieties and concerns that users may experience during training. For example, if a user encounters difficulties during training, the support department will send them encouraging messages. The support department can also adjust its support methods based on the user's emotions. For example, if a user is feeling anxious, the support department will provide reassuring support. The support department uses AI to analyze users' emotions and provide appropriate support. The AI ​​analyzes user input using natural language processing technology to identify the user's emotions. For example, the AI ​​can detect anxiety and concerns from the text entered by the user and generate appropriate support messages. The AI ​​also suggests the best support method for the user based on the user's past training history and feedback. This allows the support department to respond quickly and appropriately to users' anxieties and concerns and support their training. Furthermore, the support department can collect user feedback and continuously improve the accuracy and effectiveness of its support. For example, it can analyze how users reacted to support and review the content and methods of support. This allows the support department to provide more effective support to users and support their training.

[0034] The monitoring unit monitors users' training progress in real time. Specifically, it monitors the user's progress as they progress through training in real time and adjusts the training plan as needed. For example, it monitors the user's progress as they progress through training and revises the training plan if progress is behind schedule. The monitoring unit can also adjust its monitoring methods based on the user's emotions. For example, if the monitoring unit is feeling anxious, it provides monitoring methods that provide reassurance. The monitoring unit uses AI to analyze users' training progress and provide appropriate monitoring methods. The AI ​​uses machine learning algorithms to analyze users' training progress and grasp the progress status in real time. For example, the AI ​​analyzes data as the user progresses through training and revises the training plan if progress is behind schedule. The AI ​​also analyzes users' emotions and provides monitoring methods that provide reassurance if the user is feeling anxious. This allows the monitoring unit to monitor users' training progress in real time and take appropriate action. Furthermore, the monitoring unit can collect user feedback and continuously improve the accuracy and effectiveness of monitoring. For example, it can analyze how users reacted to monitoring and revise the content and methods of monitoring. This allows the monitoring unit to provide more effective monitoring to users and support user training.

[0035] The adjustment unit adjusts the training plan. Specifically, it adjusts the training plan according to the user's training progress. For example, if a user is falling behind in their training, the adjustment unit reviews the training plan and proposes a new plan to accelerate progress. The adjustment unit can also adjust the training plan based on the user's emotions. For example, if the adjustment unit is feeling anxious, it provides adjustments that reassure the user. The adjustment unit uses AI to analyze the user's training progress and provide appropriate adjustments. The AI ​​uses machine learning algorithms to analyze the user's training progress and adjusts the training plan according to the progress. For example, the AI ​​analyzes data from the user's training and, if progress is falling behind, reviews the training plan and proposes a new plan to accelerate progress. The AI ​​also analyzes the user's emotions and, if the user is feeling anxious, provides adjustments that reassure the user. This allows the adjustment unit to provide an appropriate training plan according to the user's training progress and support the user's career advancement. Furthermore, the adjustment unit can collect user feedback and continuously improve the accuracy and effectiveness of the training plan. For example, it can analyze how users reacted to the training plan and review the content and methods of the training plan. This allows the adjustment unit to provide users with more effective training plans and support their career advancement.

[0036] The data collection unit can analyze the user's past work history and select the optimal information collection method. For example, if the user previously worked as an engineer, the data collection unit will prioritize collecting information on technical skills. It can also collect information on leadership skills if the user previously worked in a management position. Furthermore, if the user has experience in multiple occupations, the data collection unit can collect skill information related to each occupation in a balanced manner. This allows for the collection of more relevant information by selecting the information collection method based on the user's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's work history data into a generating AI and have the generating AI select the optimal information collection method.

[0037] The data collection unit can filter information based on the user's current career goals and areas of interest. For example, if the user aspires to be a data scientist, the unit will prioritize collecting information on data analysis and machine learning. It can also collect information on project management if the user aspires to be a project manager. Furthermore, if the user is interested in a specific industry, the unit can collect the latest trends and skills related to that industry. This allows for more relevant information to be provided by filtering information based on the user's career goals and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's career goal data into a generating AI and have the generating AI perform the information filtering.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if a user works in a specific region, the data collection unit will prioritize collecting job postings for that region. Furthermore, if a user is pursuing a career abroad, the data collection unit can also collect overseas job postings and visa information. Additionally, if a user desires remote work, the data collection unit can prioritize collecting information related to remote work. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0039] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information about companies and industries that the user follows on social media. It can also collect information related to topics that the user shows interest in on social media. Furthermore, the data collection unit can collect information related to groups that the user participates in on social media. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0040] The analysis unit can improve the accuracy of its analysis by considering the interrelationship between the user's work history and skill set during the analysis process. For example, if the user previously worked as an engineer, the analysis unit can analyze the interrelationship between technical skills and work history. Furthermore, if the user previously worked in a management position, the analysis unit can also analyze the interrelationship between leadership skills and work history. Additionally, if the user has experience in multiple occupations, the analysis unit can analyze the interrelationship between the skill set associated with each occupation and their work history. This improves the accuracy of the analysis by considering the interrelationship between the user's work history and skill set. 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 the user's work history data and skill set data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0041] The analysis unit can apply different analysis algorithms to each user's occupation category during analysis. For example, the analysis unit can apply an analysis algorithm specialized in technical skills to users in technical professions. It can also apply an analysis algorithm specialized in leadership skills to users in management professions. Furthermore, it can apply an analysis algorithm specialized in creativity and design skills to users in creative professions. By applying different analysis algorithms to each user's occupation category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user occupation category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0042] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user works in a specific region, the analysis unit can consider job information in that region. Furthermore, if a user is aiming for a career abroad, the analysis unit can consider overseas job information and visa information. Additionally, if a user desires remote work, the analysis unit can consider information related to remote work. By considering the geographical distribution of users during analysis, more relevant information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0043] 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 perform analysis by referring to technical literature that the user has read in the past. It can also perform analysis by referring to materials from seminars and training sessions that the user has attended in the past. Furthermore, the analysis unit can perform analysis by referring to papers and articles that the user has written in the past. In this way, the accuracy of the analysis is improved by referring to the user's relevant literature. 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 the user's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0044] The service provider can adjust the level of detail of a training plan based on the user's skill level when providing the plan. For example, the service provider can provide a detailed plan starting with basic skills to beginner users. It can also provide a plan that includes advanced skills to intermediate users. Furthermore, it can provide a plan that delves deeper into specialized skills to advanced users. By adjusting the level of detail of the plan based on the user's skill level, a more appropriate training plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user skill level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the plan.

[0045] The service provider can apply different planning algorithms depending on the user's occupation category when providing training plans. For example, the service provider can apply a planning algorithm specialized in technical skills to users in technical professions. It can also apply a planning algorithm specialized in leadership skills to users in management professions. Furthermore, it can apply a planning algorithm specialized in creativity and design skills to users in creative professions. This allows for the provision of more effective training plans by applying different planning algorithms according to the user's occupation category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user occupation category data into a generating AI and have the generating AI apply different planning algorithms.

[0046] The service provider can prioritize training plans based on the user's submission timing when providing them. For example, if a user submits early, the service provider will prioritize providing the plan. Alternatively, if a user submits at the last minute, the service provider can provide a plan quickly. Furthermore, if a user submits at a specific date and time, the service provider can provide a plan tailored to that date and time. This allows for the provision of more effective training plans by prioritizing them based on the user's submission timing. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user submission timing data into a generating AI and have the generating AI determine the plan prioritization.

[0047] The service provider can adjust the order of training plans based on user relevance when providing them. For example, if a user wants to prioritize learning a specific skill, the service provider will first provide a plan related to that skill. The service provider can also provide a balanced plan if a user wants to learn multiple skills simultaneously. Furthermore, if a user wants to learn skills in a specific order, the service provider can provide a plan tailored to that order. This allows for the provision of more effective training plans by adjusting the order of plans based on user relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user relevance data into a generating AI and have the generating AI adjust the order of the plans.

[0048] The support department can analyze the user's past anxieties and concerns during support to select the most appropriate support method. For example, the support department can analyze anxieties the user has felt in the past and provide reassuring support in similar situations. The support department can also provide detailed explanations for topics the user has expressed concerns about in the past. Furthermore, the support department can provide specific support to resolve problems the user has experienced in the past. This allows for more effective support by analyzing the user's past 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 the user's past anxiety data into a generating AI and have the generating AI select the most appropriate support method.

[0049] The support unit can customize the means of support based on the user's current living situation. For example, if the user is busy, the support unit can provide effective support in a short amount of time. Furthermore, if the user has more time, the support unit can provide more detailed support. In addition, if the user is in a specific living situation, the support unit can provide support tailored to that situation. This allows for more effective support by customizing the means of support based on the user's current living situation. 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 living situation data into a generating AI and have the generating AI perform the customization of the support means.

[0050] The support department can select the most appropriate support method when providing support, taking into account the user's geographical location. For example, if the user works in a specific region, the support department can provide support while considering job information in that region. Furthermore, if the user is aiming for a career abroad, the support department can provide support while considering overseas job information and visa information. Additionally, if the user desires remote work, the support department can provide support while considering information related to remote work. This allows for more effective support by considering the user's geographical location. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input the user's geographical location data into a generating AI and have the generating AI select the most appropriate support method.

[0051] The support department can analyze a user's social media activity and propose support measures during support sessions. For example, the support department can provide information about companies and industries that the user follows on social media. It can also provide information related to topics that the user shows interest in on social media. Furthermore, the support department can provide information related to groups that the user participates in on social media. This allows for more effective support 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. For example, the support department can input the user's social media data into a generating AI and have the generating AI propose support measures.

[0052] The monitoring unit can analyze the user's past training progress during monitoring and select the optimal monitoring method. For example, the monitoring unit can provide the optimal monitoring method based on the training progress the user has achieved in the past. It can also provide a monitoring method that includes improvement measures based on the user's past training progress that was behind. Furthermore, the monitoring unit can provide a monitoring method that reinforces the user's past successful training progress. This makes more effective monitoring possible by analyzing the user's past training progress. 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.

[0053] The monitoring unit can customize the monitoring methods based on the user's current living situation during monitoring. For example, if the user is busy, the monitoring unit can provide a quick and effective monitoring method. Furthermore, if the user has ample time, the monitoring unit can provide a more detailed monitoring method. In addition, if the user is in a specific living situation, the monitoring unit can provide a monitoring method tailored to that situation. This allows for more effective monitoring by customizing the monitoring methods based on the user's current living situation. 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 living situation data into a generating AI and have the generating AI perform the customization of the monitoring methods.

[0054] The monitoring unit can select the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user works in a specific region, the monitoring unit will monitor while considering job information in that region. Furthermore, if the user is aiming for a career overseas, the monitoring unit can monitor while considering overseas job information and visa information. In addition, if the user desires remote work, the monitoring unit can monitor while considering information related to remote work. This allows for more effective monitoring by 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 data into a generating AI and have the generating AI select the optimal monitoring method.

[0055] The monitoring unit can analyze a user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit can provide information about companies and industries that the user follows on social media. It can also provide information related to topics that the user shows interest in on social media. Furthermore, it can provide information related to groups that the user participates in on social media. This allows for more effective monitoring by analyzing the user's social media activity. 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 social media data into a generating AI and have the generating AI execute suggestions for monitoring methods.

[0056] The adjustment unit can analyze the user's past training progress and select the optimal adjustment method during the adjustment process. For example, the adjustment unit can provide the optimal adjustment method based on the training progress the user has achieved in the past. It can also provide an adjustment method that includes improvement measures based on the user's past training progress that has fallen behind. Furthermore, the adjustment unit can provide an adjustment method that reinforces the user's past successful training progress. This allows for more effective adjustments by analyzing the user's past training progress. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past training progress data into a generating AI and have the generating AI select the optimal adjustment method.

[0057] The adjustment unit can customize the means of adjustment based on the user's current living situation during the adjustment process. For example, if the user is busy, the adjustment unit can provide a quick and effective adjustment method. Furthermore, if the user has ample time, the adjustment unit can provide a more detailed adjustment method. In addition, if the user is in a specific living situation, the adjustment unit can provide an adjustment method tailored to that situation. This allows for more effective adjustments by customizing the means of adjustment based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user living situation data into a generating AI and have the generating AI perform the customization of the adjustment means.

[0058] The adjustment unit can select the optimal adjustment method by considering the user's geographical location information during the adjustment process. For example, if the user works in a specific region, the adjustment unit will consider job information in that region when making adjustments. Furthermore, if the user is aiming for a career abroad, the adjustment unit can also consider overseas job information and visa information when making adjustments. Additionally, if the user desires remote work, the adjustment unit can consider information related to remote work when making adjustments. This allows for more effective adjustments by considering the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal adjustment method.

[0059] The adjustment unit can analyze the user's social media activity during the adjustment process and propose adjustment methods. For example, the adjustment unit can provide information about companies and industries that the user follows on social media. It can also provide information related to topics that the user shows interest in on social media. Furthermore, it can provide information related to groups that the user participates in on social media. This allows for more effective adjustments by analyzing the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media data into a generating AI and have the generating AI execute suggestions for adjustment methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze the user's past work history and select the most appropriate information collection method. For example, if the user previously worked as an engineer, it can prioritize collecting information on technical skills. If the user previously worked in a management position, it can also collect information on leadership skills. Furthermore, if the user has experience in multiple occupations, it can collect skill information related to each occupation in a balanced manner. This allows for the collection of more relevant information by selecting an information collection method based on the user's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's work history data into a generating AI and have the generating AI select the most appropriate information collection method.

[0062] The data collection unit can filter information based on the user's current career goals and areas of interest. For example, if the user aspires to be a data scientist, it can prioritize collecting information on data analysis and machine learning. If the user aspires to be a project manager, it can also collect information on project management. Furthermore, if the user is interested in a particular industry, it can collect the latest trends and skills related to that industry. This allows for the provision of more relevant information by filtering information based on the user's career goals and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's career goal data into a generating AI and have the generating AI perform the information filtering.

[0063] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if a user works in a specific region, it can prioritize the collection of job postings in that region. If a user is pursuing a career abroad, it can also collect overseas job postings and visa information. Furthermore, if a user desires remote work, it can prioritize the collection of information related to remote work. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0064] The analysis unit can improve the accuracy of the analysis by considering the interrelationship between the user's work history and skill set during the analysis. For example, if the user has worked as an engineer in the past, the analysis can examine the interrelationship between technical skills and work history. Similarly, if the user has worked as a manager in the past, the analysis can examine the interrelationship between leadership skills and work history. Furthermore, if the user has experience in multiple occupations, the analysis can examine the interrelationship between the skill set related to each occupation and their work history. This improves the accuracy of the analysis by considering the interrelationship between the user's work history and skill set. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's work history data and skill set data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0065] The analysis unit can apply different analysis algorithms to each user's occupation category during analysis. For example, a user in a technical position can be given an analysis algorithm specialized in technical skills. Similarly, a user in a management position can be given an analysis algorithm specialized in leadership skills. Furthermore, a user in a creative position can be given an analysis algorithm specialized in creativity and design skills. By applying different analysis algorithms to each user's occupation category, the accuracy of the analysis is improved. 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 occupation category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects information about the user's occupation and skill level. The data collection unit stores the occupation and skill level information entered by the user in a database, and also collects information about the user's past work history and skill set. Furthermore, the data collection unit filters the information based on the user's current career goals and areas of interest. Step 2: The analysis unit identifies skill gaps based on the information collected by the data collection unit. The analysis unit analyzes the difference between the user's current skill level and their target skill level, and improves the accuracy of the analysis by considering the user's work history and the interrelationships of their skill set. Step 3: The service provider provides a training plan through interactive AI counseling. The service provider creates an optimal training plan based on the user's skill gaps and adjusts how the training plan is presented based on the user's emotions. Step 4: The support team addresses user anxieties and concerns. The support team provides appropriate support for any anxieties or concerns users may have during training, and adjusts the support method based on the user's feelings. Step 5: The monitoring unit monitors the user's training progress in real time. The monitoring unit monitors the user's progress as they proceed with training in real time and adjusts the training plan as needed. Step 6: The adjustment unit adjusts the training plan. The adjustment unit adjusts the training plan according to the user's training progress and adjusts the method of adjusting the training plan based on the user's emotions.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that interviews users about their occupation and skill level and identifies skill gaps for career advancement. This AI agent system collects information about the user's occupation and skill level, and the AI ​​identifies the user's skill gaps. Subsequently, through conversational AI counseling, it provides the user with an optimal training plan and addresses the user's anxieties and concerns. For example, if a user inputs, "My current occupation is an engineer, and I want to improve my programming skills," the AI ​​evaluates the user's current skill level and identifies the necessary skills. Subsequently, through conversational AI counseling, it provides the user with an optimal training plan and gives specific advice on how to improve programming skills. The AI ​​also provides appropriate support for any anxieties or concerns the user may feel during training. This mechanism allows users to efficiently acquire the skills necessary for their career advancement. Furthermore, by addressing the user's anxieties and concerns through conversational AI counseling, the effectiveness of the training can be maximized. For example, by providing appropriate support for any anxieties or concerns the user may feel during training, the user can engage in training with peace of mind. In addition, the AI ​​monitors the user's training progress in real time and adjusts the training plan as needed. This allows users to train at their own pace and acquire skills efficiently. For example, if a user is falling behind in their training progress, the AI ​​can adjust the training plan to help them progress at an optimal pace. In this way, the AI ​​agent system identifies skill gaps based on the user's occupation and skill level, and supports the user's career advancement by providing personalized training plans through conversational AI counseling. It also addresses the user's anxieties and concerns to maximize the effectiveness of the training and support the user in efficiently acquiring skills.This allows the AI ​​agent system to identify skill gaps based on the user's occupation and skill level, and provide personalized training plans to support the user's career advancement.

[0069] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a support unit, a monitoring unit, and an adjustment unit. The collection unit collects information about the user's occupation and skill level. For example, the collection unit stores information about the user's occupation and skill level in a database. The collection unit can also collect information about the user's past occupational history and skill set. For example, the collection unit analyzes what occupations the user has held in the past and what skills they possess. Furthermore, the collection unit can filter information based on the user's current career goals and areas of interest. For example, the collection unit prioritizes collecting information related to the occupation the user is aiming for and the areas of interest the user is interested in. The analysis unit identifies skill gaps based on the information collected by the collection unit. For example, the analysis unit analyzes the difference between the user's current skill level and their target skill level. Furthermore, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between the user's occupational history and skill set. For example, the analysis unit identifies skill gaps based on what occupations the user has held in the past and what skills they possess. The provision unit provides a training plan through conversational AI counseling. The service provider creates an optimal training plan based, for example, on the user's skill gaps. The service provider can also adjust the way the training plan is presented based on the user's emotions. For example, if the user is feeling anxious, the service provider will provide reassuring language. The support provider addresses the user's anxieties and concerns. For example, the support provider provides appropriate support for any anxieties or concerns the user may have during training. The support provider can also adjust the support method based on the user's emotions. For example, if the support provider is feeling anxious, they will provide reassuring support. The monitoring provider monitors the user's training progress in real time. For example, the monitoring provider monitors the user's progress in real time as they progress through the training and adjusts the training plan as needed. The monitoring provider can also adjust the monitoring method based on the user's emotions.For example, the monitoring unit provides a reassuring monitoring method if the user is feeling anxious. The adjustment unit adjusts the training plan. The adjustment unit adjusts the training plan, for example, according to the user's training progress. The adjustment unit can also adjust the training plan adjustment method based on the user's emotions. For example, the adjustment unit provides a reassuring adjustment method if the user is feeling anxious. As a result, the AI ​​agent system according to the embodiment can support the user's career advancement by identifying skill gaps based on the user's occupation and skill level and providing an individualized training plan.

[0070] The data collection unit collects information about the user's occupation and skill level. Specifically, it stores the occupation and skill level information entered by the user in a database. For example, when a user enters their current occupation, past work history, and skill set, the data collection unit accurately records this information and stores it in the database. The data collection unit can also collect information about the user's past work history and skill set. For example, it can import data from resumes and work histories to analyze what occupations the user has held in the past and what skills they possess. Furthermore, the data collection unit can filter information based on the user's current career goals and areas of interest. For example, it can prioritize collecting information related to the occupation the user is aiming for or the areas of interest they are interested in. This allows the data collection unit to efficiently collect and store the information necessary for the user's career advancement in the database. The data collection unit can collect information not only from user input but also from external data sources. For example, it can obtain data from online occupation databases and skill assessment platforms and integrate it with the user's information. This allows the data collection unit to collect more comprehensive and accurate information to help the user's career advancement.

[0071] The analysis unit identifies skill gaps based on the information collected by the data collection unit. Specifically, it analyzes the difference between the user's current skill level and their target skill level. For example, it compares the skills the user currently possesses with the skills required for their target occupation to identify which skills are lacking. The analysis unit can also improve the accuracy of its analysis by considering the interrelationships between the user's work history and skill set. For example, it identifies skill gaps based on the types of occupations the user has held in the past and the skills they possess. The analysis unit uses AI to analyze the data and identify the user's skill gaps. The AI ​​uses machine learning algorithms to analyze the user's work history and skill set to identify skill gaps. For example, the AI ​​identifies the skills required for the occupation the user is aiming for based on the user's past work history and skill set, and analyzes which skills are lacking. Furthermore, the analysis unit can improve the accuracy of its analysis based on the user's career goals and areas of interest. For example, it prioritizes the analysis of skills related to the occupation the user is aiming for and areas of interest to identify skill gaps. This allows the analysis unit to accurately identify the user's skill gaps and use this information to help the user advance their career.

[0072] The service provider offers training plans through interactive AI counseling. Specifically, it creates optimal training plans based on the user's skill gaps. For example, it suggests training courses and materials to address the user's skill deficiencies. The service provider can also adjust the presentation of the training plan based on the user's emotions. For example, if the user is feeling anxious, it will provide reassuring language. The service provider uses AI to interact with users and provide training plans tailored to their needs and emotions. The AI ​​analyzes user input using natural language processing technology and proposes appropriate training plans. For example, the AI ​​suggests optimal training courses and materials based on information about skill gaps entered by the user. The AI ​​also analyzes the user's emotions and provides reassuring language if the user is feeling anxious. This allows the service provider to provide optimal training plans to address user skill gaps and support their career advancement. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the training plans. For example, it can analyze how users reacted to the training plans and revise the content and presentation of the training plans. This allows the service provider to offer users more effective training plans and support their career advancement.

[0073] The support department addresses users' anxieties and concerns. Specifically, it provides appropriate support for anxieties and concerns that users may experience during training. For example, if a user encounters difficulties during training, the support department will send them encouraging messages. The support department can also adjust its support methods based on the user's emotions. For example, if a user is feeling anxious, the support department will provide reassuring support. The support department uses AI to analyze users' emotions and provide appropriate support. The AI ​​analyzes user input using natural language processing technology to identify the user's emotions. For example, the AI ​​can detect anxiety and concerns from the text entered by the user and generate appropriate support messages. The AI ​​also suggests the best support method for the user based on the user's past training history and feedback. This allows the support department to respond quickly and appropriately to users' anxieties and concerns and support their training. Furthermore, the support department can collect user feedback and continuously improve the accuracy and effectiveness of its support. For example, it can analyze how users reacted to support and review the content and methods of support. This allows the support department to provide more effective support to users and support their training.

[0074] The monitoring unit monitors users' training progress in real time. Specifically, it monitors the user's progress as they progress through training in real time and adjusts the training plan as needed. For example, it monitors the user's progress as they progress through training and revises the training plan if progress is behind schedule. The monitoring unit can also adjust its monitoring methods based on the user's emotions. For example, if the monitoring unit is feeling anxious, it provides monitoring methods that provide reassurance. The monitoring unit uses AI to analyze users' training progress and provide appropriate monitoring methods. The AI ​​uses machine learning algorithms to analyze users' training progress and grasp the progress status in real time. For example, the AI ​​analyzes data as the user progresses through training and revises the training plan if progress is behind schedule. The AI ​​also analyzes users' emotions and provides monitoring methods that provide reassurance if the user is feeling anxious. This allows the monitoring unit to monitor users' training progress in real time and take appropriate action. Furthermore, the monitoring unit can collect user feedback and continuously improve the accuracy and effectiveness of monitoring. For example, it can analyze how users reacted to monitoring and revise the content and methods of monitoring. This allows the monitoring unit to provide more effective monitoring to users and support user training.

[0075] The adjustment unit adjusts the training plan. Specifically, it adjusts the training plan according to the user's training progress. For example, if a user is falling behind in their training, the adjustment unit reviews the training plan and proposes a new plan to accelerate progress. The adjustment unit can also adjust the training plan based on the user's emotions. For example, if the adjustment unit is feeling anxious, it provides adjustments that reassure the user. The adjustment unit uses AI to analyze the user's training progress and provide appropriate adjustments. The AI ​​uses machine learning algorithms to analyze the user's training progress and adjusts the training plan according to the progress. For example, the AI ​​analyzes data from the user's training and, if progress is falling behind, reviews the training plan and proposes a new plan to accelerate progress. The AI ​​also analyzes the user's emotions and, if the user is feeling anxious, provides adjustments that reassure the user. This allows the adjustment unit to provide an appropriate training plan according to the user's training progress and support the user's career advancement. Furthermore, the adjustment unit can collect user feedback and continuously improve the accuracy and effectiveness of the training plan. For example, it can analyze how users reacted to the training plan and review the content and methods of the training plan. This allows the adjustment unit to provide users with more effective training plans and support their career advancement.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may delay information collection until the user is relaxed. Alternatively, if the user is focused, the data collection unit can start collecting information immediately. Furthermore, if the user is tired, the data collection unit can adjust the schedule to collect information after a break. This allows for more effective information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0077] The data collection unit can analyze the user's past work history and select the optimal information collection method. For example, if the user previously worked as an engineer, the data collection unit will prioritize collecting information on technical skills. It can also collect information on leadership skills if the user previously worked in a management position. Furthermore, if the user has experience in multiple occupations, the data collection unit can collect skill information related to each occupation in a balanced manner. This allows for the collection of more relevant information by selecting the information collection method based on the user's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's work history data into a generating AI and have the generating AI select the optimal information collection method.

[0078] The data collection unit can filter information based on the user's current career goals and areas of interest. For example, if the user aspires to be a data scientist, the unit will prioritize collecting information on data analysis and machine learning. It can also collect information on project management if the user aspires to be a project manager. Furthermore, if the user is interested in a specific industry, the unit can collect the latest trends and skills related to that industry. This allows for more relevant information to be provided by filtering information based on the user's career goals and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's career goal data into a generating AI and have the generating AI perform the information filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information that provides a sense of security. Similarly, if the user is excited, the data collection unit may prioritize collecting challenging information. Furthermore, if the user is relaxed, the data collection unit can collect a wide range of information in a balanced manner. This allows for more effective information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 data collection unit may be performed using AI, or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0080] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if a user works in a specific region, the data collection unit will prioritize collecting job postings for that region. Furthermore, if a user is pursuing a career abroad, the data collection unit can also collect overseas job postings and visa information. Additionally, if a user desires remote work, the data collection unit can prioritize collecting information related to remote work. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0081] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information about companies and industries that the user follows on social media. It can also collect information related to topics that the user shows interest in on social media. Furthermore, the data collection unit can collect information related to groups that the user participates in on social media. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0082] The analysis unit can estimate the user's emotions and adjust the method of identifying skill gaps based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can identify skill gaps step by step and provide reassurance. If the user is excited, the analysis unit can quickly identify skill gaps and set challenging goals. Furthermore, if the user is relaxed, the analysis unit can perform a detailed skill gap analysis and propose a comprehensive training plan. This allows for more effective skill gap identification by adjusting the method of identifying skill gaps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI adjust the method of identifying skill gaps.

[0083] The analysis unit can improve the accuracy of its analysis by considering the interrelationship between the user's work history and skill set during the analysis process. For example, if the user previously worked as an engineer, the analysis unit can analyze the interrelationship between technical skills and work history. Furthermore, if the user previously worked in a management position, the analysis unit can also analyze the interrelationship between leadership skills and work history. Additionally, if the user has experience in multiple occupations, the analysis unit can analyze the interrelationship between the skill set associated with each occupation and their work history. This improves the accuracy of the analysis by considering the interrelationship between the user's work history and skill set. 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 the user's work history data and skill set data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0084] The analysis unit can apply different analysis algorithms to each user's occupation category during analysis. For example, the analysis unit can apply an analysis algorithm specialized in technical skills to users in technical professions. It can also apply an analysis algorithm specialized in leadership skills to users in management professions. Furthermore, it can apply an analysis algorithm specialized in creativity and design skills to users in creative professions. By applying different analysis algorithms to each user's occupation category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user occupation category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, more effective information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0086] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user works in a specific region, the analysis unit can consider job information in that region. Furthermore, if a user is aiming for a career abroad, the analysis unit can consider overseas job information and visa information. Additionally, if a user desires remote work, the analysis unit can consider information related to remote work. By considering the geographical distribution of users during analysis, more relevant information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the analysis.

[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 perform analysis by referring to technical literature that the user has read in the past. It can also perform analysis by referring to materials from seminars and training sessions that the user has attended in the past. Furthermore, the analysis unit can perform analysis by referring to papers and articles that the user has written in the past. In this way, the accuracy of the analysis is improved by referring to the user's relevant literature. 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 the user's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0088] The service provider can estimate the user's emotions and adjust the presentation of the training plan based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide a reassuring presentation. It can also provide a challenging presentation if the user is excited. Furthermore, if the user is relaxed, it can provide a presentation that includes detailed explanations. This allows for a more effective training plan by adjusting the presentation of the training plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI adjust the presentation of the training plan.

[0089] The service provider can adjust the level of detail of a training plan based on the user's skill level when providing the plan. For example, the service provider can provide a detailed plan starting with basic skills to beginner users. It can also provide a plan that includes advanced skills to intermediate users. Furthermore, it can provide a plan that delves deeper into specialized skills to advanced users. By adjusting the level of detail of the plan based on the user's skill level, a more appropriate training plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user skill level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the plan.

[0090] The service provider can apply different planning algorithms depending on the user's occupation category when providing training plans. For example, the service provider can apply a planning algorithm specialized in technical skills to users in technical professions. It can also apply a planning algorithm specialized in leadership skills to users in management professions. Furthermore, it can apply a planning algorithm specialized in creativity and design skills to users in creative professions. This allows for the provision of more effective training plans by applying different planning algorithms according to the user's occupation category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user occupation category data into a generating AI and have the generating AI apply different planning algorithms.

[0091] The service provider can estimate the user's emotions and adjust the length of the training plan based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide a short-term, achievable plan. If the user is excited, the service provider can also provide a long-term, challenging plan. Furthermore, if the user is relaxed, the service provider can provide a longer plan that includes detailed explanations. This allows for the provision of a more effective training plan by adjusting the length of the training plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into the generative AI and have the generative AI adjust the length of the training plan.

[0092] The service provider can prioritize training plans based on the user's submission timing when providing them. For example, if a user submits early, the service provider will prioritize providing the plan. Alternatively, if a user submits at the last minute, the service provider can provide a plan quickly. Furthermore, if a user submits at a specific date and time, the service provider can provide a plan tailored to that date and time. This allows for the provision of more effective training plans by prioritizing them based on the user's submission timing. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user submission timing data into a generating AI and have the generating AI determine the plan prioritization.

[0093] The service provider can adjust the order of training plans based on user relevance when providing them. For example, if a user wants to prioritize learning a specific skill, the service provider will first provide a plan related to that skill. The service provider can also provide a balanced plan if a user wants to learn multiple skills simultaneously. Furthermore, if a user wants to learn skills in a specific order, the service provider can provide a plan tailored to that order. This allows for the provision of more effective training plans by adjusting the order of plans based on user relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user relevance data into a generating AI and have the generating AI adjust the order of the plans.

[0094] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is feeling anxious, the support unit can provide reassuring support. It can also provide challenging support if the user is excited. Furthermore, if the user is relaxed, it can provide support that includes detailed explanations. This allows for more effective support by adjusting the support methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 support unit may be performed using AI, or not. For example, the support unit can input user facial expression data into the generative AI and have the generative AI adjust the support methods.

[0095] The support department can analyze the user's past anxieties and concerns during support to select the most appropriate support method. For example, the support department can analyze anxieties the user has felt in the past and provide reassuring support in similar situations. The support department can also provide detailed explanations for topics the user has expressed concerns about in the past. Furthermore, the support department can provide specific support to resolve problems the user has experienced in the past. This allows for more effective support by analyzing the user's past 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 the user's past anxiety data into a generating AI and have the generating AI select the most appropriate support method.

[0096] The support unit can customize the means of support based on the user's current living situation. For example, if the user is busy, the support unit can provide effective support in a short amount of time. Furthermore, if the user has more time, the support unit can provide more detailed support. In addition, if the user is in a specific living situation, the support unit can provide support tailored to that situation. This allows for more effective support by customizing the means of support based on the user's current living situation. 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 living situation data into a generating AI and have the generating AI perform the customization of the support means.

[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 providing support. It can also prioritize challenging support if the user is excited. Furthermore, if the user is relaxed, it can provide balanced support. This allows for more effective support by prioritizing support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI, or not. For example, the support unit can input user facial expression data into a generative AI and have the generative AI determine the priority of support.

[0098] The support department can select the most appropriate support method when providing support, taking into account the user's geographical location. For example, if the user works in a specific region, the support department can provide support while considering job information in that region. Furthermore, if the user is aiming for a career abroad, the support department can provide support while considering overseas job information and visa information. Additionally, if the user desires remote work, the support department can provide support while considering information related to remote work. This allows for more effective support by considering the user's geographical location. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input the user's geographical location data into a generating AI and have the generating AI select the most appropriate support method.

[0099] The support department can analyze a user's social media activity and propose support measures during support sessions. For example, the support department can provide information about companies and industries that the user follows on social media. It can also provide information related to topics that the user shows interest in on social media. Furthermore, the support department can provide information related to groups that the user participates in on social media. This allows for more effective support 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. For example, the support department can input the user's social media data into a generating AI and have the generating AI propose support measures.

[0100] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can provide a reassuring monitoring method. It can also provide a challenging monitoring method if the user is excited. Furthermore, if the user is relaxed, it can provide a monitoring method that includes detailed explanations. This allows for more effective monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user facial expression data into the generative AI and have the generative AI adjust the monitoring method.

[0101] The monitoring unit can analyze the user's past training progress during monitoring and select the optimal monitoring method. For example, the monitoring unit can provide the optimal monitoring method based on the training progress the user has achieved in the past. It can also provide a monitoring method that includes improvement measures based on the user's past training progress that was behind. Furthermore, the monitoring unit can provide a monitoring method that reinforces the user's past successful training progress. This makes more effective monitoring possible by analyzing the user's past training progress. 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.

[0102] The monitoring unit can customize the monitoring methods based on the user's current living situation during monitoring. For example, if the user is busy, the monitoring unit can provide a quick and effective monitoring method. Furthermore, if the user has ample time, the monitoring unit can provide a more detailed monitoring method. In addition, if the user is in a specific living situation, the monitoring unit can provide a monitoring method tailored to that situation. This allows for more effective monitoring by customizing the monitoring methods based on the user's current living situation. 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 living situation data into a generating AI and have the generating AI perform the customization of the monitoring methods.

[0103] 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. It can also prioritize challenging monitoring if the user is excited. Furthermore, if the user is relaxed, the monitoring unit can provide balanced monitoring. This allows for more effective monitoring by prioritizing monitoring according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user facial expression data into the generative AI and have the generative AI determine monitoring priorities.

[0104] The monitoring unit can select the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user works in a specific region, the monitoring unit will monitor while considering job information in that region. Furthermore, if the user is aiming for a career overseas, the monitoring unit can monitor while considering overseas job information and visa information. In addition, if the user desires remote work, the monitoring unit can monitor while considering information related to remote work. This allows for more effective monitoring by 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 data into a generating AI and have the generating AI select the optimal monitoring method.

[0105] The monitoring unit can analyze a user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit can provide information about companies and industries that the user follows on social media. It can also provide information related to topics that the user shows interest in on social media. Furthermore, it can provide information related to groups that the user participates in on social media. This allows for more effective monitoring by analyzing the user's social media activity. 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 social media data into a generating AI and have the generating AI execute suggestions for monitoring methods.

[0106] The adjustment unit can estimate the user's emotions and adjust the training plan adjustment method based on the estimated user emotions. For example, if the user is feeling anxious, the adjustment unit can provide a reassuring adjustment method. It can also provide a challenging adjustment method if the user is excited. Furthermore, if the user is relaxed, it can provide an adjustment method that includes detailed explanations. This allows for more effective training plan adjustment by adjusting the training plan adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user facial expression data into the generative AI and have the generative AI perform the adjustment of the training plan adjustment method.

[0107] The adjustment unit can analyze the user's past training progress and select the optimal adjustment method during the adjustment process. For example, the adjustment unit can provide the optimal adjustment method based on the training progress the user has achieved in the past. It can also provide an adjustment method that includes improvement measures based on the user's past training progress that has fallen behind. Furthermore, the adjustment unit can provide an adjustment method that reinforces the user's past successful training progress. This allows for more effective adjustments by analyzing the user's past training progress. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past training progress data into a generating AI and have the generating AI select the optimal adjustment method.

[0108] The adjustment unit can customize the means of adjustment based on the user's current living situation during the adjustment process. For example, if the user is busy, the adjustment unit can provide a quick and effective adjustment method. Furthermore, if the user has ample time, the adjustment unit can provide a more detailed adjustment method. In addition, if the user is in a specific living situation, the adjustment unit can provide an adjustment method tailored to that situation. This allows for more effective adjustments by customizing the means of adjustment based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user living situation data into a generating AI and have the generating AI perform the customization of the adjustment means.

[0109] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is feeling anxious, the adjustment unit will prioritize adjustments. It can also prioritize challenging adjustments if the user is excited. Furthermore, if the user is relaxed, the adjustment unit can perform balanced adjustments. This allows for more effective adjustments by determining the priority of adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user facial expression data into the generative AI and have the generative AI determine the priority of adjustments.

[0110] The adjustment unit can select the optimal adjustment method by considering the user's geographical location information during the adjustment process. For example, if the user works in a specific region, the adjustment unit will consider job information in that region when making adjustments. Furthermore, if the user is aiming for a career abroad, the adjustment unit can also consider overseas job information and visa information when making adjustments. Additionally, if the user desires remote work, the adjustment unit can consider information related to remote work when making adjustments. This allows for more effective adjustments by considering the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal adjustment method.

[0111] The adjustment unit can analyze the user's social media activity during the adjustment process and propose adjustment methods. For example, the adjustment unit can provide information about companies and industries that the user follows on social media. It can also provide information related to topics that the user shows interest in on social media. Furthermore, it can provide information related to groups that the user participates in on social media. This allows for more effective adjustments by analyzing the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media data into a generating AI and have the generating AI execute suggestions for adjustment methods.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and adjust the method of identifying skill gaps based on the estimated emotions. For example, if the user is feeling anxious, the skill gap identification can be performed step by step to provide reassurance. If the user is excited, the skill gap can be identified quickly, and challenging goals can be set. Furthermore, if the user is relaxed, a detailed skill gap analysis can be performed, and a comprehensive training plan can be proposed. This allows for more effective skill gap identification by adjusting the method of identifying skill gaps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI adjust the method of identifying skill gaps.

[0114] The service provider can estimate the user's emotions and adjust the presentation of the training plan based on the estimated emotions. For example, if the user is feeling anxious, it can provide a reassuring presentation. If the user is excited, it can provide a challenging presentation. Furthermore, if the user is relaxed, it can provide a presentation that includes detailed explanations. By adjusting the presentation of the training plan according to the user's emotions, a more effective training plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the user's facial expression data into the generative AI and have the generative AI adjust the presentation of the training plan.

[0115] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is feeling anxious, it can provide reassuring support. If the user is excited, it can provide challenging support. Furthermore, if the user is relaxed, it can provide support that includes detailed explanations. By adjusting the support methods according to the user's emotions, more effective support can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not using AI. For example, the support unit can input user facial expression data into the generative AI and have the generative AI adjust the support methods.

[0116] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is feeling anxious, it can provide a reassuring monitoring method. If the user is excited, it can provide a challenging monitoring method. Furthermore, if the user is relaxed, it can provide a monitoring method that includes detailed explanations. By adjusting the monitoring method according to the user's emotions, more effective monitoring becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user facial expression data into the generative AI and have the generative AI perform the adjustment of the monitoring method.

[0117] The adjustment unit can estimate the user's emotions and adjust the training plan adjustment method based on the estimated user emotions. For example, if the user is feeling anxious, it can provide an adjustment method that provides reassurance. If the user is excited, it can provide a challenging adjustment method. Furthermore, if the user is relaxed, it can provide an adjustment method that includes detailed explanations. This allows for more effective training plan adjustment by adjusting the training plan adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's facial expression data into the generative AI and have the generative AI perform the adjustment of the training plan adjustment method.

[0118] The data collection unit can analyze the user's past work history and select the most appropriate information collection method. For example, if the user previously worked as an engineer, it can prioritize collecting information on technical skills. If the user previously worked in a management position, it can also collect information on leadership skills. Furthermore, if the user has experience in multiple occupations, it can collect skill information related to each occupation in a balanced manner. This allows for the collection of more relevant information by selecting an information collection method based on the user's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's work history data into a generating AI and have the generating AI select the most appropriate information collection method.

[0119] The data collection unit can filter information based on the user's current career goals and areas of interest. For example, if the user aspires to be a data scientist, it can prioritize collecting information on data analysis and machine learning. If the user aspires to be a project manager, it can also collect information on project management. Furthermore, if the user is interested in a particular industry, it can collect the latest trends and skills related to that industry. This allows for the provision of more relevant information by filtering information based on the user's career goals and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's career goal data into a generating AI and have the generating AI perform the information filtering.

[0120] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if a user works in a specific region, it can prioritize the collection of job postings in that region. If a user is pursuing a career abroad, it can also collect overseas job postings and visa information. Furthermore, if a user desires remote work, it can prioritize the collection of information related to remote work. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0121] The analysis unit can improve the accuracy of the analysis by considering the interrelationship between the user's work history and skill set during the analysis. For example, if the user has worked as an engineer in the past, the analysis can examine the interrelationship between technical skills and work history. Similarly, if the user has worked as a manager in the past, the analysis can examine the interrelationship between leadership skills and work history. Furthermore, if the user has experience in multiple occupations, the analysis can examine the interrelationship between the skill set related to each occupation and their work history. This improves the accuracy of the analysis by considering the interrelationship between the user's work history and skill set. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's work history data and skill set data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0122] The analysis unit can apply different analysis algorithms to each user's occupation category during analysis. For example, a user in a technical position can be given an analysis algorithm specialized in technical skills. Similarly, a user in a management position can be given an analysis algorithm specialized in leadership skills. Furthermore, a user in a creative position can be given an analysis algorithm specialized in creativity and design skills. By applying different analysis algorithms to each user's occupation category, the accuracy of the analysis is improved. 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 occupation category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects information about the user's occupation and skill level. The data collection unit stores the occupation and skill level information entered by the user in a database, and also collects information about the user's past work history and skill set. Furthermore, the data collection unit filters the information based on the user's current career goals and areas of interest. Step 2: The analysis unit identifies skill gaps based on the information collected by the data collection unit. The analysis unit analyzes the difference between the user's current skill level and their target skill level, and improves the accuracy of the analysis by considering the user's work history and the interrelationships of their skill set. Step 3: The service provider provides a training plan through interactive AI counseling. The service provider creates an optimal training plan based on the user's skill gaps and adjusts how the training plan is presented based on the user's emotions. Step 4: The support team addresses user anxieties and concerns. The support team provides appropriate support for any anxieties or concerns users may have during training, and adjusts the support method based on the user's feelings. Step 5: The monitoring unit monitors the user's training progress in real time. The monitoring unit monitors the user's progress as they proceed with training in real time and adjusts the training plan as needed. Step 6: The adjustment unit adjusts the training plan. The adjustment unit adjusts the training plan according to the user's training progress and adjusts the method of adjusting the training plan based on the user's emotions.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, support unit, monitoring unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information on the user's occupation and skill level. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies skill gaps based on the collected information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides a training plan through interactive AI counseling. The support unit is implemented by the control unit 46A of the smart device 14 and addresses the user's anxieties and concerns. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors the user's training progress in real time. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and adjusts the training plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 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.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the 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.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, support unit, monitoring unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information on the user's occupation and skill level. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies skill gaps based on the collected information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides a training plan through interactive AI counseling. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and addresses the user's anxieties and concerns. The monitoring unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and monitors the user's training progress in real time. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and adjusts the training plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, support unit, monitoring unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information on the user's occupation and skill level. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies skill gaps based on the collected information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides a training plan through interactive AI counseling. The support unit is implemented by the control unit 46A of the headset terminal 314 and addresses the user's anxieties and concerns. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors the user's training progress in real time. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and adjusts the training plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, support unit, monitoring unit, and adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information on the user's occupation and skill level. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies skill gaps based on the collected information. The provision unit is implemented by the control unit 46A of the robot 414 and provides a training plan through interactive AI counseling. The support unit is implemented by the control unit 46A of the robot 414 and addresses the user's anxieties and concerns. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors the user's training progress in real time. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and adjusts the training plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection unit that collects information about the user's occupation and skill level, An analysis unit identifies skill gaps based on the information collected by the aforementioned collection unit, The service department provides training plans through interactive AI counseling, A support department to address user anxieties and concerns, A monitoring unit that monitors the user's training progress in real time, It includes an adjustment unit for adjusting the training plan. A system characterized by the following features. (Note 2) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past work history and select the appropriate information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current career goals and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate user sentiment and adjust the method for identifying skill gaps based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the interrelationship between the user's work history and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied for each user's occupation category. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, During analysis, the analysis is performed based on the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the training plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing a training plan, adjust the level of detail in the plan based on the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing training plans, different planning algorithms are applied depending on the user's occupational category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the training plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing training plans, we prioritize the plans based on when the users submit them. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing training plans, the order of the plans is adjusted based on the relevance of the users. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is During support, we analyze the user's past anxieties and concerns to select the appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The aforementioned support unit is During support, the appropriate support method is selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, During monitoring, the system analyzes the user's past training progress to select the appropriate monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, During monitoring, the monitoring methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, During monitoring, the appropriate monitoring method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned monitoring unit, During monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, It estimates the user's emotions and adjusts the training plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, During adjustments, the system analyzes the user's past training progress to select the appropriate adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, During the adjustment process, the adjustment methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, During adjustment, the appropriate adjustment method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The adjustment unit is, During the adjustment process, we analyze users' social media activity and propose adjustment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information about the user's occupation and skill level, An analysis unit identifies skill gaps based on the information collected by the aforementioned collection unit, The service department provides training plans through interactive AI counseling, A support department to address user anxieties and concerns, A monitoring unit that monitors the user's training progress in real time, It includes an adjustment unit for adjusting the training plan. A system characterized by the following features.

2. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past work history and select the appropriate information gathering method. The system according to feature 1.

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

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

8. The aforementioned analysis unit, We estimate user sentiment and adjust the method for identifying skill gaps based on the estimated user sentiment. The system according to feature 1.

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