system

Abstract

The system according to this embodiment aims to accurately understand individual career aspirations and provide optimal advice. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit receives career-related information from the user. The analysis unit analyzes the information received by the reception unit and understands the individual's career aspirations. The provision unit provides optimal advice based on the analysis results obtained by the analysis unit. The monitoring unit monitors the user's progress based on the advice provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0007] The system according to this embodiment can accurately grasp each individual's career aspirations and provide optimal advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The career support system according to an embodiment of the present invention is a system that accurately grasps individual career aspirations and provides optimal advice by utilizing data analysis and generative AI. In this career support system, the user inputs information about their career, and the generative AI analyzes the input information to accurately grasp individual career aspirations. The generative AI performs data analysis to understand the user's career aspirations. The generative AI provides optimal advice based on the user's career aspirations. In addition, the generative AI provides regular feedback to support the user's career development. Through this system, the user can maximize their own potential and build a highly satisfying career. Furthermore, by supporting the optimal career development of individuals, it becomes possible for society as a whole to build a more satisfying work environment. For example, the career support system allows the user to input information about their career. For example, the career support system can input information such as the user's work history, skills, and goals. Next, the career support system uses generative AI to analyze the input information and grasp the user's career aspirations. The generative AI analyzes the user's career aspirations using, for example, a natural language processing model or a machine learning model. Next, the career support system provides optimal advice to the user based on the results of the analysis by the generative AI. The AI-generated content provides advice such as career path suggestions and methods for skill development. Furthermore, the career support system monitors the user's progress and updates the advice as needed. This allows the career support system to continuously support the user's career development. By accurately understanding the user's career aspirations and providing optimal advice, the system can support their career development.

[0029] The career support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit receives career-related information from the user. This information includes, but is not limited to, work history, skills, and goals. The reception unit stores the career information entered by the user in a database, for example. The reception unit also allows the user to input career information using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice into text data. Furthermore, the reception unit can analyze the user's past career information input history and select the optimal input method. For example, if the reception unit has preferred using voice input in the past, it will prioritize suggesting voice input. The analysis unit uses a generative AI to analyze the information received by the reception unit and understand the individual's career aspirations. The analysis unit uses, for example, a natural language processing model or a machine learning model to analyze the user's career aspirations. The generative AI understands the user's career aspirations based on information such as the user's work history, skills, and goals. The provision unit uses a generation AI to provide optimal advice based on the analysis results obtained by the analysis unit. The provision unit provides advice such as career path suggestions and methods for skill development. The generation AI generates specific advice based on the user's career aspirations, for example. The monitoring unit monitors the user's progress based on the advice provided by the provision unit. The monitoring unit periodically checks, for example, the user's goal achievement and skill acquisition status. The monitoring unit records the user's progress in a database and updates the advice as needed. As a result, the career support system according to this embodiment can accurately grasp the user's career aspirations and provide optimal advice to support the user's career development.

[0030] The reception desk receives career information from users. This information includes, but is not limited to, work history, skills, and goals. The reception desk stores the career information entered by users in a database. Specifically, information entered by users through web forms or mobile applications is stored in a secure database. This allows users' career information to be centrally managed and used for subsequent analysis and provision. The reception desk also allows users to enter career information using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text data. The speech recognition technology uses an advanced speech recognition model with deep learning, enabling high-precision transcription of user speech. Furthermore, the reception desk can analyze the user's past career information input history and select the optimal input method. For example, if the reception desk has preferred using voice input in the past, it will prioritize suggesting voice input. This allows users to enter career information in the way that is most comfortable for them, improving the efficiency of system usage. The reception department processes user input data in real time and immediately reflects it in the database, enabling a rapid response. Furthermore, the reception department has a function to check the integrity and consistency of input data, verifying that there is no missing or incorrect information. This allows the reception department to accurately and efficiently receive carrier information from users, thereby enhancing the overall reliability of the system.

[0031] The analysis unit uses generative AI to analyze information received by the reception unit and understand individual career aspirations. The analysis unit analyzes users' career aspirations using, for example, natural language processing models and machine learning models. Specifically, the generative AI understands users' career aspirations based on information such as their work history, skills, and goals. For example, the generative AI tokenizes user input data and uses an encoder-decoder model to understand the context. This allows for the extraction and analysis of detailed information about the user's career. Furthermore, the generative AI refers to past data and similar user data, using them as supplementary information to more accurately understand the user's career aspirations. The analysis unit analyzes the user's career information multidimensionally, evaluating trends in work history, skill strengths, and goal achievement. For example, it analyzes in detail what types of jobs the user has worked in the past, what skills they possess, and what kind of career they aspire to in the future. This allows the analysis unit to comprehensively understand users' career aspirations and provide analysis results tailored to individual needs. Furthermore, the analysis unit also has the function to visualize the analysis results and present them to the user in an easy-to-understand manner. For example, by visually displaying the user's career path and skill distribution using graphs and charts, it enables the user to intuitively understand their own career situation. This allows the analysis unit to accurately grasp the user's career aspirations and build a foundation for providing concrete advice for the next step.

[0032] The service provider uses generative AI to provide optimal advice based on the analysis results obtained by the analysis unit. For example, the service provider offers advice such as career path suggestions and methods for skill development. The generative AI generates specific advice based on the user's career aspirations. Specifically, the generative AI proposes the optimal career path based on information such as the user's work history, skills, and goals. For example, if a user is interested in a particular job, it proposes specific steps to acquire the necessary skills and experience for that job. The service provider can also provide information on training programs and online courses to improve the user's skills. The generative AI assesses the user's current skill level and selects an appropriate training program. Furthermore, the service provider can provide job postings and networking opportunities tailored to the user's career goals. For example, if a user aims for a career in a specific industry, it provides job postings and industry event information for that industry. The service provider creates individual reports based on the generative AI's analysis results to provide personalized advice to the user. This allows the user to develop a concrete action plan for their career. Furthermore, the service provider can collect user feedback and continuously improve the advice provided. For example, the system analyzes how users reacted to the advice provided and incorporates that feedback into future advice. This allows the service provider to offer users the most appropriate advice and support their career development.

[0033] The monitoring unit monitors the user's progress based on the advice provided by the service provider. For example, the monitoring unit periodically checks the user's goal achievement and skill acquisition status. Specifically, it tracks the user's progress toward the goals they have set and evaluates their achievement. For instance, if a user is participating in a training program to acquire a specific skill, the monitoring unit periodically checks their progress and evaluates their achievement. The monitoring unit also records the user's progress in a database and updates the advice as needed. For example, if a user achieves a goal, it sets a new goal and provides corresponding advice. Furthermore, the monitoring unit has the functionality to visualize and present the user's progress in an easy-to-understand manner. For example, it uses graphs and charts to visually display the user's progress, allowing users to intuitively understand their own progress. This enables the monitoring unit to accurately grasp the user's progress and provide appropriate advice as needed. Additionally, the monitoring unit can collect user feedback and continuously improve the accuracy and effectiveness of monitoring. For example, it analyzes how users responded to the advice provided and incorporates this feedback into future monitoring. This allows the monitoring unit to accurately grasp the user's progress and provide optimal advice to support their career development.

[0034] The analysis unit can understand users' career aspirations using generative AI. For example, the analysis unit can analyze users' career aspirations using natural language processing models. For instance, the analysis unit can understand users' career aspirations based on information such as their work history, skills, and goals. The analysis unit can also analyze users' career aspirations using machine learning models. For example, the analysis unit can learn from users' past career information and predict their career aspirations. Furthermore, the analysis unit can build a system to understand users' career aspirations using generative AI. For example, the analysis unit can develop an algorithm in which generative AI analyzes users' career information and understands their career aspirations. This allows for an accurate understanding of users' career aspirations by using generative AI.

[0035] The service provider can provide optimal advice based on the user's career aspirations using generative AI. For example, the service provider can generate advice based on the user's career aspirations using a natural language processing model. For instance, the service provider can provide advice such as career path suggestions and methods for skill development based on information such as the user's work history, skills, and goals. The service provider can also generate advice based on the user's career aspirations using a machine learning model. For example, the service provider can learn the user's past career information and provide optimal advice. Furthermore, the service provider can build a system to provide advice based on the user's career aspirations using generative AI. For example, the service provider can develop an algorithm for generative AI to analyze the user's career information and provide optimal advice. This allows the service provider to provide optimal advice based on the user's career aspirations by using generative AI.

[0036] The monitoring unit can monitor the user's progress and update advice as needed. For example, the monitoring unit can periodically check the user's goal achievement and skill acquisition status. For example, the monitoring unit can record the user's progress in a database and update advice as needed. The monitoring unit can also monitor the user's progress in real time. For example, the monitoring unit can monitor the user's progress in real time and update advice as needed. Furthermore, the monitoring unit can build a system to analyze the user's progress and provide optimal advice. For example, the monitoring unit can develop an algorithm to analyze the user's progress and provide optimal advice. This allows for continuous support of the user's career development by monitoring the user's progress and updating advice as needed. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the user's progress into AI and have AI perform the analysis of the progress.

[0037] The service provider can offer specific advice tailored to the user's communication style and work methods. For example, the service provider can use generative AI to generate advice based on the user's communication style and work methods. Furthermore, the service provider can build a system using generative AI to provide advice based on the user's communication style and work methods. For example, the service provider can develop an algorithm that uses generative AI to analyze the user's communication style and work methods and provide optimal advice. This allows for more effective support of the user's career development by providing specific advice tailored to their communication style and work methods.

[0038] The monitoring unit can provide periodic feedback on the user's progress. For example, the monitoring unit can periodically check the user's goal achievement and skill acquisition status and provide feedback. For example, the monitoring unit can record the user's progress in a database and provide periodic feedback. The monitoring unit can also monitor the user's progress in real time and provide periodic feedback. For example, the monitoring unit can monitor the user's progress in real time and provide periodic feedback. Furthermore, the monitoring unit can build a system to analyze the user's progress and provide optimal feedback. For example, the monitoring unit can develop an algorithm to analyze the user's progress and provide optimal feedback. This allows for continuous support of the user's career development by providing periodic feedback on their progress. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's progress into AI and have AI perform the analysis of the progress.

[0039] The reception desk can analyze the user's past carrier information input history and select the optimal input method. For example, if the user has preferred using voice input in the past, the reception desk will prioritize suggesting voice input. For example, if the user has preferred using text input in the past, the reception desk will prioritize suggesting text input. The reception desk can also analyze patterns of input methods used by the user in the past and suggest the optimal input method. For example, the reception desk will select the optimal input method based on the user's past input data and input frequency. By analyzing the user's past carrier information input history, the reception desk can suggest the optimal input method to the user and promote efficient information input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​select the optimal input method.

[0040] The reception desk can filter the user's career information input based on their current job status and areas of interest. For example, the reception desk may prompt the user to input only relevant career information based on their current job status. For example, the reception desk may prompt the user to prioritize inputting relevant career information based on their areas of interest. The reception desk can also simplify input by filtering out unnecessary information based on the user's job status and areas of interest. For example, the reception desk may set filtering criteria based on the user's job status and areas of interest. This allows for the efficient input of highly relevant information by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's job status and areas of interest into an AI and leave the filtering to the AI.

[0041] The reception desk can prioritize inputting highly relevant information when users enter carrier information, taking into account their geographical location. For example, if a user is in a specific region, the reception desk may prompt them to prioritize inputting carrier information related to that region. For example, if a user is on the move, the reception desk may prompt them to input relevant carrier information based on their current location. The reception desk can also prompt users to prioritize inputting carrier information related to a specific region if they are interested in that region. For example, the reception desk may develop an algorithm to select highly relevant information based on the user's geographical location. This allows for efficient input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk may input the user's geographical location information into an AI and have the AI ​​select highly relevant information.

[0042] The reception desk can analyze a user's social media activity when they input career information and input relevant information. For example, the reception desk can prompt the user to input career information related to their areas of interest based on their social media activity. For example, the reception desk can prompt the user to input career information related to their current job situation based on their social media activity. The reception desk can also prompt the user to input information related to their future career aspirations based on their social media activity. For example, the reception desk can develop an algorithm to select relevant information based on the user's social media activity. This allows for the efficient input of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​select relevant information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the carrier information during the analysis. For example, the analysis unit performs a detailed analysis for highly important carrier information. For example, the analysis unit performs a concise analysis for less important carrier information. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the carrier information. For example, the analysis unit can develop an algorithm to evaluate the importance of the carrier information and adjust the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the carrier information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input carrier information importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of career information during analysis. For example, the analysis unit can apply a technical skills analysis algorithm to career information related to technical positions. For example, the analysis unit can apply a leadership skills analysis algorithm to career information related to management positions. Furthermore, the analysis unit can also apply a creativity analysis algorithm to career information related to creative positions. For example, the analysis unit can build a system for applying different analysis algorithms depending on the category of career information. This allows for more accurate analysis results by applying different analysis algorithms depending on the category of career information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input career information category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the career information during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted career information. For example, the analysis unit may postpone the analysis of older career information. The analysis unit can also adjust the priority of analysis in stages based on the submission date. For example, the analysis unit may develop an algorithm to evaluate the submission date of career information and determine the priority of analysis. This allows for efficient analysis by determining the priority of analysis based on the submission date of career information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input career information submission date data into a generative AI and have the generative AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the carrier information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant carrier information. For example, it may postpone the analysis of less relevant carrier information. The analysis unit can also adjust the order of analysis stepwise based on the relevance of the carrier information. For example, the analysis unit may develop an algorithm to evaluate the relevance of the carrier information and adjust the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the carrier information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input carrier information relevance data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0047] The service provider can adjust the level of detail of advice based on the importance of the career information when providing advice. For example, the service provider can provide detailed advice for highly important career information, and concise advice for less important career information. The service provider can also adjust the level of detail of the advice in stages according to the importance of the career information. For example, the service provider can develop an algorithm to evaluate the importance of career information and adjust the level of detail of the advice. This allows for the provision of efficient advice by adjusting the level of detail of the advice based on the importance of the career information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input career information importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the advice.

[0048] The service provider can apply different advice algorithms depending on the category of career information when providing advice. For example, for career information related to technical positions, the service provider can provide advice on improving technical skills. For example, for career information related to management positions, the service provider can provide advice on improving leadership skills. Furthermore, for career information related to creative positions, the service provider can also provide advice on improving creativity. For example, the service provider can build a system for applying different advice algorithms depending on the category of career information. This allows for the provision of more accurate advice by applying different advice algorithms depending on the category of career information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input career information category data into a generative AI and have the generative AI execute the application of the advice algorithm.

[0049] The service provider can determine the priority of advice based on when career information is submitted. For example, it may prioritize advice based on recently submitted career information. For example, it may postpone advice based on older career information. The service provider can also adjust the priority of advice in stages based on submission timing. For example, it may develop an algorithm to evaluate the submission timing of career information and determine the priority of advice. This allows for the efficient provision of advice by prioritizing advice based on the submission timing of career information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input career information submission timing data into a generative AI and have the generative AI determine the priority of advice.

[0050] The service provider can adjust the order of advice based on the relevance of career information when providing advice. For example, the service provider may prioritize advice based on highly relevant career information. For example, the service provider may postpone advice based on less relevant career information. The service provider can also adjust the order of advice stepwise based on the relevance of career information. For example, the service provider may develop an algorithm to evaluate the relevance of career information and adjust the order of advice. This allows for the provision of efficient advice by adjusting the order of advice based on the relevance of career information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider may input career information relevance data into a generative AI and have the generative AI perform the adjustment of the order of advice.

[0051] The monitoring unit can analyze the user's past progress and select the optimal monitoring method during monitoring. For example, the monitoring unit monitors progress based on goals the user has achieved in the past. For example, the monitoring unit determines the optimal monitoring frequency based on the user's past progress. The monitoring unit can also analyze the user's past progress and customize the monitoring method. For example, the monitoring unit develops an algorithm to select the optimal monitoring method based on the user's past progress data. This allows for efficient monitoring by selecting the optimal monitoring method through analysis of the user's past progress. Some or all of the above processes 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 progress data into AI and have the AI ​​select the optimal monitoring method.

[0052] The monitoring unit can customize the monitoring methods based on the user's current work situation during monitoring. For example, the monitoring unit can select appropriate monitoring methods based on the user's current work situation. For example, the monitoring unit can adjust the frequency and method of monitoring according to the user's work situation. The monitoring unit can also customize the content of monitoring based on the user's work situation. For example, the monitoring unit can develop an algorithm for customizing monitoring methods based on the user's work situation. This enables efficient monitoring by customizing the monitoring methods based on the user's current work 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 work situation data into AI and have the AI ​​perform the customization of monitoring methods.

[0053] The monitoring unit can select the optimal monitoring method while considering the user's geographical location information. For example, if the user is in a specific region, the monitoring unit will select a monitoring method relevant to that region. For example, if the user is on the move, the monitoring unit will adjust the monitoring method based on the user's current location. The monitoring unit can also select a monitoring method relevant to a specific region if the user is interested in that region. For example, the monitoring unit can develop an algorithm to select the optimal monitoring method based on the user's geographical location information. This allows for efficient monitoring by selecting the optimal monitoring method while considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location data into AI and have the AI ​​select the optimal monitoring method.

[0054] The monitoring unit can analyze a user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit can propose monitoring methods related to the user's areas of interest based on their social media activity. For example, the monitoring unit can propose monitoring methods related to the user's current job situation based on their social media activity. The monitoring unit can also propose monitoring methods related to the user's future career aspirations based on their social media activity. For example, the monitoring unit can develop an algorithm to propose the optimal monitoring method based on the user's social media activity. This allows for efficient monitoring by proposing the optimal monitoring method through analysis of 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 activity data into AI and have the AI ​​propose the optimal monitoring method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a user's past input history when they enter their career information and suggest the most suitable input method. For example, if a user has preferred using voice input in the past, it will be prioritized. Similarly, if a user has preferred using text input in the past, it will be prioritized. Furthermore, the reception desk can develop algorithms to select the optimal input method based on the user's input history. This allows the system to analyze a user's past input history, suggest the most suitable input method, and promote efficient information entry.

[0057] The service provider can filter the advice provided based on the user's current job situation and areas of interest when offering advice based on the user's career information. For example, it can prompt the user to enter only relevant career information based on their current job situation. It can also prompt the user to prioritize entering relevant career information based on their areas of interest. Furthermore, it can filter out unnecessary information and simplify input based on the user's job situation and areas of interest. This allows users to efficiently input highly relevant information by filtering based on their current job situation and areas of interest.

[0058] When providing advice based on a user's carrier information, the service provider can prioritize inputting highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it can prompt them to prioritize inputting carrier information related to that region. Similarly, if a user is on the move, it can prompt them to input relevant carrier information based on their current location. Furthermore, if a user is interested in a particular region, it can prompt them to prioritize inputting carrier information related to that region. This allows for the efficient input of highly relevant information by considering the user's geographical location.

[0059] The analysis unit can adjust the level of detail in its analysis of a user's career information based on the importance of that information. For example, it can perform a detailed analysis on highly important career information, and a simplified analysis on less important information. Furthermore, it can adjust the level of detail in stages according to the importance of the career information. This allows for efficient analysis by adjusting the level of detail based on the importance of the career information.

[0060] The monitoring unit can analyze a user's past progress to select the optimal monitoring method. For example, it can monitor progress based on goals the user has achieved in the past. It can also determine the optimal monitoring frequency based on the user's past progress. Furthermore, it can customize the monitoring method by analyzing the user's past progress. This allows for efficient monitoring by selecting the optimal monitoring method through analysis of the user's past progress.

[0061] The analysis unit can apply different analysis algorithms depending on the category of the user's career information. For example, a technical skills analysis algorithm can be applied to career information related to technical positions. A leadership skills analysis algorithm can also be applied to career information related to management positions. Furthermore, a creativity analysis algorithm can be applied to career information related to creative positions. By applying different analysis algorithms according to the category of career information, more accurate analysis results can be provided.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives career information from users. This information includes work history, skills, and goals. The reception desk stores the user's entered career information in a database. It can also convert the user's voice into text data using speech recognition technology. Furthermore, the reception desk can analyze the user's past career information input history and select the optimal input method. Step 2: The analysis unit uses generative AI to analyze the information received by the reception unit and understand each individual's career aspirations. The analysis unit uses natural language processing models and machine learning models to analyze the user's career aspirations. Step 3: The service department uses the generation AI to provide optimal advice based on the analysis results obtained by the analysis department. The service department provides advice such as career path suggestions and methods for skill development. Step 4: The monitoring unit monitors the user's progress based on the advice provided by the service provider. The monitoring unit regularly checks the user's goal achievement and skill acquisition status, records the progress in the database, and updates the advice as needed.

[0064] (Example of form 2) The career support system according to an embodiment of the present invention is a system that accurately grasps individual career aspirations and provides optimal advice by utilizing data analysis and generative AI. In this career support system, the user inputs information about their career, and the generative AI analyzes the input information to accurately grasp individual career aspirations. The generative AI performs data analysis to understand the user's career aspirations. The generative AI provides optimal advice based on the user's career aspirations. In addition, the generative AI provides regular feedback to support the user's career development. Through this system, the user can maximize their own potential and build a highly satisfying career. Furthermore, by supporting the optimal career development of individuals, it becomes possible for society as a whole to build a more satisfying work environment. For example, the career support system allows the user to input information about their career. For example, the career support system can input information such as the user's work history, skills, and goals. Next, the career support system uses generative AI to analyze the input information and grasp the user's career aspirations. The generative AI analyzes the user's career aspirations using, for example, a natural language processing model or a machine learning model. Next, the career support system provides optimal advice to the user based on the results of the analysis by the generative AI. The AI-generated content provides advice such as career path suggestions and methods for skill development. Furthermore, the career support system monitors the user's progress and updates the advice as needed. This allows the career support system to continuously support the user's career development. By accurately understanding the user's career aspirations and providing optimal advice, the system can support their career development.

[0065] The career support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit receives career-related information from the user. This information includes, but is not limited to, work history, skills, and goals. The reception unit stores the career information entered by the user in a database, for example. The reception unit also allows the user to input career information using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice into text data. Furthermore, the reception unit can analyze the user's past career information input history and select the optimal input method. For example, if the reception unit has preferred using voice input in the past, it will prioritize suggesting voice input. The analysis unit uses a generative AI to analyze the information received by the reception unit and understand the individual's career aspirations. The analysis unit uses, for example, a natural language processing model or a machine learning model to analyze the user's career aspirations. The generative AI understands the user's career aspirations based on information such as the user's work history, skills, and goals. The provision unit uses a generation AI to provide optimal advice based on the analysis results obtained by the analysis unit. The provision unit provides advice such as career path suggestions and methods for skill development. The generation AI generates specific advice based on the user's career aspirations, for example. The monitoring unit monitors the user's progress based on the advice provided by the provision unit. The monitoring unit periodically checks, for example, the user's goal achievement and skill acquisition status. The monitoring unit records the user's progress in a database and updates the advice as needed. As a result, the career support system according to this embodiment can accurately grasp the user's career aspirations and provide optimal advice to support the user's career development.

[0066] The reception desk receives career information from users. This information includes, but is not limited to, work history, skills, and goals. The reception desk stores the career information entered by users in a database. Specifically, information entered by users through web forms or mobile applications is stored in a secure database. This allows users' career information to be centrally managed and used for subsequent analysis and provision. The reception desk also allows users to enter career information using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text data. The speech recognition technology uses an advanced speech recognition model with deep learning, enabling high-precision transcription of user speech. Furthermore, the reception desk can analyze the user's past career information input history and select the optimal input method. For example, if the reception desk has preferred using voice input in the past, it will prioritize suggesting voice input. This allows users to enter career information in the way that is most comfortable for them, improving the efficiency of system usage. The reception department processes user input data in real time and immediately reflects it in the database, enabling a rapid response. Furthermore, the reception department has a function to check the integrity and consistency of input data, verifying that there is no missing or incorrect information. This allows the reception department to accurately and efficiently receive carrier information from users, thereby enhancing the overall reliability of the system.

[0067] The analysis unit uses generative AI to analyze information received by the reception unit and understand individual career aspirations. The analysis unit analyzes users' career aspirations using, for example, natural language processing models and machine learning models. Specifically, the generative AI understands users' career aspirations based on information such as their work history, skills, and goals. For example, the generative AI tokenizes user input data and uses an encoder-decoder model to understand the context. This allows for the extraction and analysis of detailed information about the user's career. Furthermore, the generative AI refers to past data and similar user data, using them as supplementary information to more accurately understand the user's career aspirations. The analysis unit analyzes the user's career information multidimensionally, evaluating trends in work history, skill strengths, and goal achievement. For example, it analyzes in detail what types of jobs the user has worked in the past, what skills they possess, and what kind of career they aspire to in the future. This allows the analysis unit to comprehensively understand users' career aspirations and provide analysis results tailored to individual needs. Furthermore, the analysis unit also has the function to visualize the analysis results and present them to the user in an easy-to-understand manner. For example, by visually displaying the user's career path and skill distribution using graphs and charts, it enables the user to intuitively understand their own career situation. This allows the analysis unit to accurately grasp the user's career aspirations and build a foundation for providing concrete advice for the next step.

[0068] The service provider uses generative AI to provide optimal advice based on the analysis results obtained by the analysis unit. For example, the service provider offers advice such as career path suggestions and methods for skill development. The generative AI generates specific advice based on the user's career aspirations. Specifically, the generative AI proposes the optimal career path based on information such as the user's work history, skills, and goals. For example, if a user is interested in a particular job, it proposes specific steps to acquire the necessary skills and experience for that job. The service provider can also provide information on training programs and online courses to improve the user's skills. The generative AI assesses the user's current skill level and selects an appropriate training program. Furthermore, the service provider can provide job postings and networking opportunities tailored to the user's career goals. For example, if a user aims for a career in a specific industry, it provides job postings and industry event information for that industry. The service provider creates individual reports based on the generative AI's analysis results to provide personalized advice to the user. This allows the user to develop a concrete action plan for their career. Furthermore, the service provider can collect user feedback and continuously improve the advice provided. For example, the system analyzes how users reacted to the advice provided and incorporates that feedback into future advice. This allows the service provider to offer users the most appropriate advice and support their career development.

[0069] The monitoring unit monitors the user's progress based on the advice provided by the service provider. For example, the monitoring unit periodically checks the user's goal achievement and skill acquisition status. Specifically, it tracks the user's progress toward the goals they have set and evaluates their achievement. For instance, if a user is participating in a training program to acquire a specific skill, the monitoring unit periodically checks their progress and evaluates their achievement. The monitoring unit also records the user's progress in a database and updates the advice as needed. For example, if a user achieves a goal, it sets a new goal and provides corresponding advice. Furthermore, the monitoring unit has the functionality to visualize and present the user's progress in an easy-to-understand manner. For example, it uses graphs and charts to visually display the user's progress, allowing users to intuitively understand their own progress. This enables the monitoring unit to accurately grasp the user's progress and provide appropriate advice as needed. Additionally, the monitoring unit can collect user feedback and continuously improve the accuracy and effectiveness of monitoring. For example, it analyzes how users responded to the advice provided and incorporates this feedback into future monitoring. This allows the monitoring unit to accurately grasp the user's progress and provide optimal advice to support their career development.

[0070] The analysis unit can understand users' career aspirations using generative AI. For example, the analysis unit can analyze users' career aspirations using natural language processing models. For instance, the analysis unit can understand users' career aspirations based on information such as their work history, skills, and goals. The analysis unit can also analyze users' career aspirations using machine learning models. For example, the analysis unit can learn from users' past career information and predict their career aspirations. Furthermore, the analysis unit can build a system to understand users' career aspirations using generative AI. For example, the analysis unit can develop an algorithm in which generative AI analyzes users' career information and understands their career aspirations. This allows for an accurate understanding of users' career aspirations by using generative AI.

[0071] The service provider can provide optimal advice based on the user's career aspirations using generative AI. For example, the service provider can generate advice based on the user's career aspirations using a natural language processing model. For instance, the service provider can provide advice such as career path suggestions and methods for skill development based on information such as the user's work history, skills, and goals. The service provider can also generate advice based on the user's career aspirations using a machine learning model. For example, the service provider can learn the user's past career information and provide optimal advice. Furthermore, the service provider can build a system to provide advice based on the user's career aspirations using generative AI. For example, the service provider can develop an algorithm for generative AI to analyze the user's career information and provide optimal advice. This allows the service provider to provide optimal advice based on the user's career aspirations by using generative AI.

[0072] The monitoring unit can monitor the user's progress and update advice as needed. For example, the monitoring unit can periodically check the user's goal achievement and skill acquisition status. For example, the monitoring unit can record the user's progress in a database and update advice as needed. The monitoring unit can also monitor the user's progress in real time. For example, the monitoring unit can monitor the user's progress in real time and update advice as needed. Furthermore, the monitoring unit can build a system to analyze the user's progress and provide optimal advice. For example, the monitoring unit can develop an algorithm to analyze the user's progress and provide optimal advice. This allows for continuous support of the user's career development by monitoring the user's progress and updating advice as needed. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the user's progress into AI and have AI perform the analysis of the progress.

[0073] The service provider can offer specific advice tailored to the user's communication style and work methods. For example, the service provider can use generative AI to generate advice based on the user's communication style and work methods. Furthermore, the service provider can build a system using generative AI to provide advice based on the user's communication style and work methods. For example, the service provider can develop an algorithm that uses generative AI to analyze the user's communication style and work methods and provide optimal advice. This allows for more effective support of the user's career development by providing specific advice tailored to their communication style and work methods.

[0074] The monitoring unit can provide periodic feedback on the user's progress. For example, the monitoring unit can periodically check the user's goal achievement and skill acquisition status and provide feedback. For example, the monitoring unit can record the user's progress in a database and provide periodic feedback. The monitoring unit can also monitor the user's progress in real time and provide periodic feedback. For example, the monitoring unit can monitor the user's progress in real time and provide periodic feedback. Furthermore, the monitoring unit can build a system to analyze the user's progress and provide optimal feedback. For example, the monitoring unit can develop an algorithm to analyze the user's progress and provide optimal feedback. This allows for continuous support of the user's career development by providing periodic feedback on their progress. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's progress into AI and have AI perform the analysis of the progress.

[0075] The reception desk can estimate the user's emotions and adjust the timing of career information input based on the estimated emotions. For example, if the user is stressed, the reception desk may prompt the user to input career information during a time when they can relax. For example, if the user is focused, the reception desk may prompt the user to input career information at that time. Also, if the user is tired, the reception desk may prompt the user to input career information after they have rested. For example, the reception desk may develop an algorithm to estimate the user's emotions and determine the optimal input timing. By adjusting the timing of career information input based on the user's emotions, it is possible to reduce user stress and promote efficient information input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can analyze the user's past carrier information input history and select the optimal input method. For example, if the user has preferred using voice input in the past, the reception desk will prioritize suggesting voice input. For example, if the user has preferred using text input in the past, the reception desk will prioritize suggesting text input. The reception desk can also analyze patterns of input methods used by the user in the past and suggest the optimal input method. For example, the reception desk will select the optimal input method based on the user's past input data and input frequency. By analyzing the user's past carrier information input history, the reception desk can suggest the optimal input method to the user and promote efficient information input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​select the optimal input method.

[0077] The reception desk can filter the user's career information input based on their current job status and areas of interest. For example, the reception desk may prompt the user to input only relevant career information based on their current job status. For example, the reception desk may prompt the user to prioritize inputting relevant career information based on their areas of interest. The reception desk can also simplify input by filtering out unnecessary information based on the user's job status and areas of interest. For example, the reception desk may set filtering criteria based on the user's job status and areas of interest. This allows for the efficient input of highly relevant information by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's job status and areas of interest into an AI and leave the filtering to the AI.

[0078] The reception unit can estimate the user's emotions and determine the priority of the career information to be entered based on the estimated emotions. For example, if the user is stressed, the reception unit may prompt the user to enter less important information first. For example, if the user is relaxed, the reception unit may prompt the user to enter more important information first. Also, if the user is in a hurry, the reception unit may prompt the user to enter the most important information first. For example, the reception unit may develop an algorithm to estimate the user's emotions and determine the priority of the career information to be entered. This reduces user stress and promotes efficient information input by prioritizing the career information to be entered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can prioritize inputting highly relevant information when users enter carrier information, taking into account their geographical location. For example, if a user is in a specific region, the reception desk may prompt them to prioritize inputting carrier information related to that region. For example, if a user is on the move, the reception desk may prompt them to input relevant carrier information based on their current location. The reception desk can also prompt users to prioritize inputting carrier information related to a specific region if they are interested in that region. For example, the reception desk may develop an algorithm to select highly relevant information based on the user's geographical location. This allows for efficient input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk may input the user's geographical location information into an AI and have the AI ​​select highly relevant information.

[0080] The reception desk can analyze a user's social media activity when they input career information and input relevant information. For example, the reception desk can prompt the user to input career information related to their areas of interest based on their social media activity. For example, the reception desk can prompt the user to input career information related to their current job situation based on their social media activity. The reception desk can also prompt the user to input information related to their future career aspirations based on their social media activity. For example, the reception desk can develop an algorithm to select relevant information based on the user's social media activity. This allows for the efficient input of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​select relevant information.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise analysis results. The analysis unit can also provide visually appealing analysis results if the user is excited. For example, the analysis unit develops an algorithm to estimate the user's emotions and adjust the presentation of the analysis. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the carrier information during the analysis. For example, the analysis unit performs a detailed analysis for highly important carrier information. For example, the analysis unit performs a concise analysis for less important carrier information. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the carrier information. For example, the analysis unit can develop an algorithm to evaluate the importance of the carrier information and adjust the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the carrier information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input carrier information importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of career information during analysis. For example, the analysis unit can apply a technical skills analysis algorithm to career information related to technical positions. For example, the analysis unit can apply a leadership skills analysis algorithm to career information related to management positions. Furthermore, the analysis unit can also apply a creativity analysis algorithm to career information related to creative positions. For example, the analysis unit can build a system for applying different analysis algorithms depending on the category of career information. This allows for more accurate analysis results by applying different analysis algorithms depending on the category of career information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input career information category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. The analysis unit can also provide a visually appealing analysis if the user is excited. For example, the analysis unit develops an algorithm to estimate the user's emotions and adjust the length of the analysis. This allows for analysis results that are easy for the user to understand by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can determine the priority of analysis based on the submission date of the career information during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted career information. For example, the analysis unit may postpone the analysis of older career information. The analysis unit can also adjust the priority of analysis in stages based on the submission date. For example, the analysis unit may develop an algorithm to evaluate the submission date of career information and determine the priority of analysis. This allows for efficient analysis by determining the priority of analysis based on the submission date of career information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input career information submission date data into a generative AI and have the generative AI perform the determination of the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the carrier information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant carrier information. For example, it may postpone the analysis of less relevant carrier information. The analysis unit can also adjust the order of analysis stepwise based on the relevance of the carrier information. For example, the analysis unit may develop an algorithm to evaluate the relevance of the carrier information and adjust the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the carrier information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input carrier information relevance data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0087] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed advice. For example, if the user is stressed, the service provider can provide concise advice. The service provider can also provide visually appealing advice if the user is excited. For example, the service provider can develop an algorithm to estimate the user's emotions and adjust the way advice is expressed. This allows the service provider to provide advice that is easy for the user to understand by adjusting the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The service provider can adjust the level of detail of advice based on the importance of the career information when providing advice. For example, the service provider can provide detailed advice for highly important career information, and concise advice for less important career information. The service provider can also adjust the level of detail of the advice in stages according to the importance of the career information. For example, the service provider can develop an algorithm to evaluate the importance of career information and adjust the level of detail of the advice. This allows for the provision of efficient advice by adjusting the level of detail of the advice based on the importance of the career information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input career information importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the advice.

[0089] The service provider can apply different advice algorithms depending on the category of career information when providing advice. For example, for career information related to technical positions, the service provider can provide advice on improving technical skills. For example, for career information related to management positions, the service provider can provide advice on improving leadership skills. Furthermore, for career information related to creative positions, the service provider can also provide advice on improving creativity. For example, the service provider can build a system for applying different advice algorithms depending on the category of career information. This allows for the provision of more accurate advice by applying different advice algorithms depending on the category of career information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input career information category data into a generative AI and have the generative AI execute the application of the advice algorithm.

[0090] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider will provide short, concise advice. For example, if the user is relaxed, the service provider will provide detailed advice. The service provider can also provide visually appealing advice if the user is excited. For example, the service provider can develop an algorithm to estimate the user's emotions and adjust the length of the advice. This allows the service provider to provide advice that is easy for the user to understand by adjusting the length of the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The service provider can determine the priority of advice based on when career information is submitted. For example, it may prioritize advice based on recently submitted career information. For example, it may postpone advice based on older career information. The service provider can also adjust the priority of advice in stages based on submission timing. For example, it may develop an algorithm to evaluate the submission timing of career information and determine the priority of advice. This allows for the efficient provision of advice by prioritizing advice based on the submission timing of career information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input career information submission timing data into a generative AI and have the generative AI determine the priority of advice.

[0092] The service provider can adjust the order of advice based on the relevance of career information when providing advice. For example, the service provider may prioritize advice based on highly relevant career information. For example, the service provider may postpone advice based on less relevant career information. The service provider can also adjust the order of advice stepwise based on the relevance of career information. For example, the service provider may develop an algorithm to evaluate the relevance of career information and adjust the order of advice. This allows for the provision of efficient advice by adjusting the order of advice based on the relevance of career information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider may input career information relevance data into a generative AI and have the generative AI perform the adjustment of the order of advice.

[0093] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, the monitoring unit performs detailed monitoring when the user is relaxed. For example, the monitoring unit performs concise monitoring when the user is stressed. The monitoring unit can also perform visually engaging monitoring when the user is excited. For example, the monitoring unit develops an algorithm to estimate the user's emotions and adjust the monitoring method. This allows the monitoring results to be easily understood by the user by adjusting the monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The monitoring unit can analyze the user's past progress and select the optimal monitoring method during monitoring. For example, the monitoring unit monitors progress based on goals the user has achieved in the past. For example, the monitoring unit determines the optimal monitoring frequency based on the user's past progress. The monitoring unit can also analyze the user's past progress and customize the monitoring method. For example, the monitoring unit develops an algorithm to select the optimal monitoring method based on the user's past progress data. This allows for efficient monitoring by selecting the optimal monitoring method through analysis of the user's past progress. Some or all of the above processes 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 progress data into AI and have the AI ​​select the optimal monitoring method.

[0095] The monitoring unit can customize the monitoring methods based on the user's current work situation during monitoring. For example, the monitoring unit can select appropriate monitoring methods based on the user's current work situation. For example, the monitoring unit can adjust the frequency and method of monitoring according to the user's work situation. The monitoring unit can also customize the content of monitoring based on the user's work situation. For example, the monitoring unit can develop an algorithm for customizing monitoring methods based on the user's work situation. This enables efficient monitoring by customizing the monitoring methods based on the user's current work 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 work situation data into AI and have the AI ​​perform the customization of monitoring methods.

[0096] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is stressed, the monitoring unit will start with less important monitoring. For example, if the user is relaxed, the monitoring unit will start with more important monitoring. Also, if the user is in a hurry, the monitoring unit can start with the most important monitoring. For example, the monitoring unit can develop an algorithm to estimate the user's emotions and determine monitoring priorities. This allows for efficient monitoring by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The monitoring unit can select the optimal monitoring method while considering the user's geographical location information. For example, if the user is in a specific region, the monitoring unit will select a monitoring method relevant to that region. For example, if the user is on the move, the monitoring unit will adjust the monitoring method based on the user's current location. The monitoring unit can also select a monitoring method relevant to a specific region if the user is interested in that region. For example, the monitoring unit can develop an algorithm to select the optimal monitoring method based on the user's geographical location information. This allows for efficient monitoring by selecting the optimal monitoring method while considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location data into AI and have the AI ​​select the optimal monitoring method.

[0098] The monitoring unit can analyze a user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit can propose monitoring methods related to the user's areas of interest based on their social media activity. For example, the monitoring unit can propose monitoring methods related to the user's current job situation based on their social media activity. The monitoring unit can also propose monitoring methods related to the user's future career aspirations based on their social media activity. For example, the monitoring unit can develop an algorithm to propose the optimal monitoring method based on the user's social media activity. This allows for efficient monitoring by proposing the optimal monitoring method through analysis of 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 activity data into AI and have the AI ​​propose the optimal monitoring method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can analyze a user's past input history when they enter their career information and suggest the most suitable input method. For example, if a user has preferred using voice input in the past, it will be prioritized. Similarly, if a user has preferred using text input in the past, it will be prioritized. Furthermore, the reception desk can develop algorithms to select the optimal input method based on the user's input history. This allows the system to analyze a user's past input history, suggest the most suitable input method, and promote efficient information entry.

[0101] The analysis unit can estimate the user's emotions when analyzing the user's career information and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is stressed, it can provide concise analysis results. Furthermore, if the user is excited, it can provide visually appealing analysis results. In this way, by adjusting the level of detail of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0102] The service provider can filter the advice provided based on the user's current job situation and areas of interest when offering advice based on the user's career information. For example, it can prompt the user to enter only relevant career information based on their current job situation. It can also prompt the user to prioritize entering relevant career information based on their areas of interest. Furthermore, it can filter out unnecessary information and simplify input based on the user's job situation and areas of interest. This allows users to efficiently input highly relevant information by filtering based on their current job situation and areas of interest.

[0103] The monitoring unit can estimate the user's emotions when monitoring their progress and adjust the monitoring method based on those emotions. For example, if the user is relaxed, detailed monitoring can be performed. If the user is stressed, concise monitoring can be performed. Furthermore, if the user is excited, visually engaging monitoring can be performed. By adjusting the monitoring method based on the user's emotions, the system can provide monitoring results that are easy for the user to understand.

[0104] When providing advice based on a user's carrier information, the service provider can prioritize inputting highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it can prompt them to prioritize inputting carrier information related to that region. Similarly, if a user is on the move, it can prompt them to input relevant carrier information based on their current location. Furthermore, if a user is interested in a particular region, it can prompt them to prioritize inputting carrier information related to that region. This allows for the efficient input of highly relevant information by considering the user's geographical location.

[0105] The analysis unit can adjust the level of detail in its analysis of a user's career information based on the importance of that information. For example, it can perform a detailed analysis on highly important career information, and a simplified analysis on less important information. Furthermore, it can adjust the level of detail in stages according to the importance of the career information. This allows for efficient analysis by adjusting the level of detail based on the importance of the career information.

[0106] The service provider can estimate the user's emotions when providing advice based on their career information, and adjust the way the advice is presented based on those emotions. For example, if the user is relaxed, detailed advice can be provided. If the user is stressed, concise advice can be provided. Furthermore, if the user is excited, visually appealing advice can be provided. In this way, by adjusting the way advice is presented based on the user's emotions, it is possible to provide advice that is easy for the user to understand.

[0107] The monitoring unit can analyze a user's past progress to select the optimal monitoring method. For example, it can monitor progress based on goals the user has achieved in the past. It can also determine the optimal monitoring frequency based on the user's past progress. Furthermore, it can customize the monitoring method by analyzing the user's past progress. This allows for efficient monitoring by selecting the optimal monitoring method through analysis of the user's past progress.

[0108] The analysis unit can apply different analysis algorithms depending on the category of the user's career information. For example, a technical skills analysis algorithm can be applied to career information related to technical positions. A leadership skills analysis algorithm can also be applied to career information related to management positions. Furthermore, a creativity analysis algorithm can be applied to career information related to creative positions. By applying different analysis algorithms according to the category of career information, more accurate analysis results can be provided.

[0109] The service provider can estimate the user's emotions when providing advice based on the user's career information, and adjust the length of the advice based on those emotions. For example, if the user is in a hurry, it can provide short, concise advice. If the user is relaxed, it can provide more detailed advice. Furthermore, if the user is excited, it can provide visually appealing advice. By adjusting the length of the advice based on the user's emotions, it can provide advice that is easy for the user to understand.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives career information from users. This information includes work history, skills, and goals. The reception desk stores the user's entered career information in a database. It can also convert the user's voice into text data using speech recognition technology. Furthermore, the reception desk can analyze the user's past career information input history and select the optimal input method. Step 2: The analysis unit uses generative AI to analyze the information received by the reception unit and understand each individual's career aspirations. The analysis unit uses natural language processing models and machine learning models to analyze the user's career aspirations. Step 3: The service department uses the generation AI to provide optimal advice based on the analysis results obtained by the analysis department. The service department provides advice such as career path suggestions and methods for skill development. Step 4: The monitoring unit monitors the user's progress based on the advice provided by the service provider. The monitoring unit regularly checks the user's goal achievement and skill acquisition status, records the progress in the database, and updates the advice as needed.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives career information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's career aspirations using generated AI. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides optimal advice based on the analysis results. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the user's progress. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives career information from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's career aspirations using generated AI. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides optimal advice based on the analysis results. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the user's progress. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives information about the user's career. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's career aspirations using generated AI. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides optimal advice based on the analysis results. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the user's progress. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives information about the user's career. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's career aspirations using generated AI. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides optimal advice based on the analysis results. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the user's progress. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that receives career-related information from users, The analysis unit analyzes the information received by the reception unit to understand the individual career aspirations, A provisioning unit that provides optimal advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a monitoring unit that monitors the user's progress based on the advice provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Generative AI is used to understand users' career aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Generative AI provides optimal advice based on the user's career aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, Monitor user progress and update advice as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide specific advice tailored to the user's communication style and work methods. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, Provide regular feedback on the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of entering carrier information based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past carrier information input history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering career information, filtering is performed based on the user's current job status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input carrier information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering carrier information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering career information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the carrier information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of carrier information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the career information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the carrier information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, we adjust the level of detail based on the importance of the career information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of career information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when career information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of career information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, the system analyzes the user's past progress to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, During monitoring, the monitoring methods are customized based on the user's current job status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The 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 29) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The 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. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives career-related information from users, The analysis unit analyzes the information received by the reception unit to understand the individual career aspirations, A provisioning unit that provides optimal advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a monitoring unit that monitors the user's progress based on the advice provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned analysis unit, Generative AI is used to understand users' career aspirations. The system according to feature 1.

3. The aforementioned supply unit is, Generative AI provides optimal advice based on the user's career aspirations. The system according to feature 1.

4. The monitoring unit, Monitor user progress and update advice as needed. The system according to feature 1.

5. The aforementioned supply unit is, We provide specific advice tailored to the user's communication style and work methods. The system according to feature 1.

6. The monitoring unit, Provide regular feedback on the user's progress. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of entering carrier information based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past carrier information input history and selects the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering career information, filtering is performed based on the user's current job status and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input carrier information based on the estimated user emotions. The system according to feature 1.

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