system

Abstract

The system according to this embodiment aims to provide each user with an optimal career plan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit and compares the user's performance with their goals. The generation unit generates an individually optimized career plan based on the analysis results obtained by the analysis unit. The provision unit provides the career plan generated by the generation unit to the user.

Description

Technical Field

[0004] ,

[0006] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, career counseling is patterned, and there is a problem that it is difficult to provide an optimal career plan according to individual needs.

[0005] The system according to the embodiment aims to provide an optimal career plan for each individual user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit and compares the user's performance with their goals. The data generation unit generates an individually optimized career plan based on the analysis results obtained by the analysis unit. The data provision unit provides the career plan generated by the data generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal career plan for each individual user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The career plan proposal system according to an embodiment of the present invention is a system that proposes an individually optimized career plan using an AI agent. To address the problem that conventional career counseling is patterned and fails to adequately meet individual needs, the career plan proposal system analyzes an individual's skills and goals and proposes an individually optimized career plan. The system collects data such as the user's past learning history, skill set, and work history. Based on this collected data, the AI ​​compares the user's performance and goals to generate an individually optimized career plan. The generated career plan includes short-term and long-term career steps. Furthermore, the career plan proposal system promotes growth by introducing online courses and providing networking opportunities to the user. In addition, the career plan proposal system analyzes resume data using natural language processing to construct individual career paths. By understanding industry trends through data analysis and identifying required skill sets, it provides optimal advice. The career plan proposal system evaluates the user's progress and adjusts the career plan through a continuous feedback loop. This mechanism improves young people's self-expression and provides concrete, actionable career steps. Better matching between companies and young people is achieved, creating benefits for both parties. For example, a career planning system collects data such as the user's past learning history, skill set, and work experience. For example, a career planning system compares the user's performance with their goals and generates an optimally tailored career plan. For example, a career planning system suggests short-term and long-term career steps. For example, a career planning system introduces online courses and provides networking opportunities. For example, a career planning system evaluates the user's progress and adjusts the career plan through a continuous feedback loop. In this way, a career planning system can propose an optimally tailored career plan for the user and provide enhanced support for life planning.

[0029] The career plan proposal system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. The collection unit collects data such as the user's past course history, skill set, and work history. The collection unit can collect, for example, course history such as online courses, seminars, and workshops. The collection unit can collect skill sets such as programming skills and communication skills. The collection unit can collect work history such as past job duties, positions, and periods of employment. The analysis unit analyzes the data collected by the collection unit and compares the user's performance with their goals. The analysis unit analyzes the data using, for example, statistical analysis and machine learning algorithms. The analysis unit analyzes resume data using, for example, natural language processing and constructs individual career paths. The analysis unit uses, for example, natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit generates an individually optimized career plan based on the analysis results obtained by the analysis unit. The generation unit proposes, for example, short-term and long-term career steps. The generation unit generates a plan based on, for example, the user's goals, skills, and work history. The generation unit generates a career plan that includes, for example, short-term goals, long-term goals, and means of achieving them. The delivery unit provides the career plan generated by the generation unit to the user. The delivery unit provides, for example, online courses and networking opportunities. The delivery unit provides, for example, online courses such as technical courses and business courses. The delivery unit provides, for example, networking opportunities such as industry events and online forums. The delivery unit evaluates the user's progress and adjusts the career plan through a continuous feedback loop. The delivery unit provides, for example, periodic evaluations and real-time feedback. As a result, the career plan proposal system according to the embodiment can propose an individually optimized career plan by collecting, analyzing, generating, and providing user data.

[0030] The data collection unit collects user data. For example, it collects data such as a user's past learning history, skill sets, and work experience. Specifically, it can collect learning history for online courses, seminars, and workshops. This includes the content of the courses the user took, the dates and times they took them, their grades, and whether or not they received a certificate. Furthermore, the data collection unit can collect skill sets such as programming skills, communication skills, and leadership skills. These skills may be self-reported by the user or objectively evaluated through online tests and evaluation systems. The data collection unit can also collect work experience, including past job duties, positions, and employment periods. This includes detailed information such as what tasks the user was responsible for, what results they achieved, what positions they held, and how long they worked in those roles. This data may be collected not only from user input but also from company HR systems and professional networking sites. The data collection unit integrates information from these diverse data sources to build a comprehensive database of the user's career. This allows the data collection unit to accurately understand users' past experiences and skills, providing the foundational data necessary for subsequent analysis and plan generation. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection to always reflect the user's latest situation. For example, it can periodically send notifications to users prompting them to update their data, and immediately reflect any new skills or work experience in the database. This allows the data collection unit to maintain up-to-date and accurate data on users' careers, improving the overall performance of the system.

[0031] The analytics department analyzes data collected by the data collection department and compares user performance with their goals. The analytics department analyzes data using methods such as statistical analysis and machine learning algorithms. Specifically, it evaluates users' skill levels and career progress based on their past learning history and work experience data. For example, it uses statistical analysis to analyze the grades and completion rates of courses taken by users to evaluate their learning performance. It also uses machine learning algorithms to analyze users' skill sets and work experience data to identify their strengths and weaknesses. Furthermore, the analytics department uses natural language processing techniques to analyze resume data and construct individual career paths. Specifically, it uses techniques such as morphological analysis, grammatical analysis, and semantic analysis to analyze information in detail from users' resumes and extract important information about their careers. For example, it uses morphological analysis to extract job descriptions and skills from resumes, and grammatical analysis to analyze the relationships between this information. Furthermore, it uses semantic analysis to understand users' career goals and aspirations, providing foundational data to propose the most suitable career path for them. This allows the analytics department to quickly and accurately analyze collected data and compare user performance with their goals. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term career path trend analysis. For example, based on historical data, it can analyze career progression patterns in specific skills and job types, providing users with future career prospects. The analytics department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings to users. This allows the analytics department to not only grasp the situation in real time but also to handle long-term career management and anomaly detection, improving the reliability and security of the entire system.

[0032] The generation unit generates an individually optimized career plan based on the analysis results obtained by the analysis unit. For example, the generation unit proposes short-term and long-term career steps. Specifically, it generates a plan based on the user's goals, skills, and work history. For instance, for short-term goals, it clarifies the necessary skills and experience and proposes a concrete action plan based on them. This may include taking specific online courses, participating in specific projects, or acquiring specific skills. For long-term career goals, it details the steps the user should take. For example, if a user aspires to a management position in the future, it proposes a concrete plan for acquiring the necessary leadership skills and management experience. The generation unit generates a career plan that includes short-term goals, long-term goals, and means of achieving them. It is crucial to consider the user's current skill level and work history to set realistic and achievable goals. Furthermore, the generation unit can continuously update the career plan based on user feedback and adjust the plan according to the user's progress. For example, if a user acquires a specific skill, it proposes a new career step that utilizes that skill. Also, if a user fails to achieve their goals, it analyzes the cause and proposes solutions. This allows the generation unit to consistently provide users with the optimal career plan and support their career growth. Furthermore, the generation unit can automatically generate user career plans using AI. For example, it can use generation AI to automatically generate and propose career plans based on the user's goals and skills. This enables the generation unit to efficiently and effectively generate career plans and support users' career growth.

[0033] The service provider provides users with career plans generated by the service provider. For example, the service provider introduces online courses and networking opportunities. Specifically, it introduces online courses such as technical courses and business courses. This involves selecting the most suitable courses based on the user's skills and goals and encouraging them to take them. It also provides networking opportunities such as industry events and online forums. This allows users to interact with other professionals in the same industry or with similar interests, exchanging information and building networks. The service provider evaluates user progress and adjusts career plans through a continuous feedback loop. Specifically, it provides regular evaluations and real-time feedback. For example, it evaluates the progress of career plans based on the user's course performance and feedback, and adjusts the plan as needed. It also collects feedback from users' participation in networking events and incorporates it into future event selections. This allows the service provider to always provide users with the latest and most suitable information, supporting their career growth. Furthermore, the service provider improves its services based on user feedback. For example, it collects feedback from users to improve the quality of the courses and networking opportunities offered. The service provider also ensures reliable information transmission using multiple communication methods. For example, by using a combination of methods such as email, SMS, and in-app notifications, important information can be reliably delivered to users. This allows the service provider to deliver information quickly and reliably to users and support their career growth.

[0034] The data collection unit can collect data such as the user's past learning history, skill set, and work history. For example, the data collection unit can collect the user's past learning history. For example, the data collection unit can collect learning history such as online courses, seminars, and workshops. For example, the data collection unit can collect the user's skill set. For example, the data collection unit can collect skill sets such as programming skills and communication skills. For example, the data collection unit can collect the user's work history. For example, the data collection unit can collect work history such as past job duties, positions, and periods of employment. By collecting data such as the user's past learning history, skill set, and work history, it is possible to propose a more accurate career plan. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the user's past learning history, skill set, and work history into a generating AI and have the generating AI perform the data collection.

[0035] The analysis department can analyze resume data using natural language processing and construct individual career paths. For example, the analysis department can analyze resume data using natural language processing. The analysis department uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis department constructs individual career paths based on resume data. The analysis department can analyze data such as work history, education, and skills to construct career paths. This allows for more accurate analysis of resume data and the construction of individual career paths by using natural language processing. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input resume data into a generating AI and have the generating AI perform the data analysis.

[0036] The generation unit can propose short-term and long-term career steps. The generation unit can, for example, propose short-term and long-term career steps. The generation unit can, for example, generate a plan based on the user's goals, skills, and work history. The generation unit can, for example, generate a career plan that includes short-term goals, long-term goals, and means of achieving them. This allows the user's career plan to be concretely shown by proposing short-term and long-term career steps. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data such as the user's goals, skills, and work history into a generation AI and have the generation AI perform the generation of a career plan.

[0037] The service provider can introduce online courses and provide networking opportunities. For example, the service provider can introduce online courses such as technical courses and business courses. For example, the service provider can provide networking opportunities such as industry events and online forums. This promotes user growth by introducing online courses and providing networking opportunities. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input information about online courses and networking opportunities into a generating AI and have the generating AI provide the information.

[0038] The service provider can evaluate the user's progress and adjust their career plan through a continuous feedback loop. For example, the service provider can evaluate the user's progress through a continuous feedback loop. The service provider can, for example, perform periodic evaluations and provide real-time feedback. The service provider can, for example, adjust the career plan based on the user's progress. The service provider can, for example, update the career plan based on the user's progress data. This allows the service provider to provide a more appropriate career plan by evaluating the user's progress and adjusting the career plan through a continuous feedback loop. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user progress data into a generating AI and have the generating AI perform data evaluation and career plan adjustment.

[0039] The data collection unit can analyze the user's past learning history and skill set to select the optimal data collection method. For example, the data collection unit analyzes the user's past learning history. For example, the data collection unit prioritizes collecting relevant data based on the user's past course history. For example, the data collection unit analyzes the user's skill set. For example, the data collection unit can analyze the user's skill set and select an efficient method for collecting the necessary data. For example, the data collection unit can consider the user's work history and select an industry-specific data collection method. This allows the optimal data collection method to be selected by analyzing the user's past learning history and skill set. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's past learning history and skill set into a generating AI and have the generating AI perform data analysis and select a data collection method.

[0040] The data collection unit can filter data based on the user's current job status and areas of interest during data collection. For example, the data collection unit can consider the user's current job status and collect only relevant data. For example, the data collection unit can prioritize the collection of data of interest based on the user's areas of interest. For example, the data collection unit can combine the user's job status and areas of interest to filter and collect the most relevant data. This allows for the collection of highly relevant data by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current job status and areas of interest into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, if the user is on the move, the data collection unit can collect data related to the user's current location in real time. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform data collection.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media activity. For example, the data collection unit can analyze a user's social media activity and collect data related to topics of interest. For example, the data collection unit can collect relevant data based on information about accounts that a user follows. For example, the data collection unit can analyze the content of a user's posts and collect data related to areas of interest. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a user's social media activity into a generating AI and have the generating AI perform data analysis and collection.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's resume data during the analysis. For example, the analysis unit can prioritize the analysis of important items in the user's resume data. For example, the analysis unit can perform a detailed analysis based on the importance of the user's work history. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's skill set. This allows for prioritizing the analysis of important data by adjusting the level of detail of the analysis based on the importance of the user's resume data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's resume data into a generating AI and have the generating AI perform the process of adjusting the level of detail of the analysis based on the importance of the data.

[0044] The analysis unit can apply different analysis algorithms depending on the user's work history and skill set during analysis. For example, the analysis unit can select an appropriate analysis algorithm based on the user's work history. For example, the analysis unit can apply the optimal analysis algorithm depending on the user's skill set. For example, the analysis unit can select the optimal analysis algorithm by combining the user's work history and skill set. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the user's work history and skill set. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's work history and skill set into a generating AI and have the generating AI perform the data analysis.

[0045] The analysis unit can determine the priority of analysis based on when the user submitted their resume. For example, the analysis unit may prioritize analyzing resumes that the user has recently submitted. For example, the analysis unit may prioritize analyzing resumes that the user submitted within a specific period. The analysis unit can determine the priority of analysis based on when the user submitted their resume. This allows for the prioritization of the analysis of the most recent data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input data on when the user submitted their resume into a generating AI and have the generating AI perform the data analysis and priority determination.

[0046] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, the analysis unit can adjust the order of analysis based on the relevance of the user's work history. For example, the analysis unit can adjust the order of analysis based on the relevance of the user's skill set. For example, the analysis unit can adjust the order of analysis by combining the relevance of the user's work history and skill set. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on user relevance. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user work history and skill set data into a generating AI and have the generating AI perform data analysis and order adjustment.

[0047] The generation unit can adjust the level of detail generated based on the user's short-term and long-term goals when generating a career plan. For example, the generation unit can generate a career plan that includes specific steps based on the user's short-term goals. For example, the generation unit can generate a career plan that includes an overall vision based on the user's long-term goals. For example, the generation unit can combine the user's short-term and long-term goals to generate a balanced career plan. This allows for the provision of a more specific career plan by adjusting the level of detail generated based on the user's short-term and long-term goals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's short-term and long-term goals into a generation AI and have the generation AI perform data analysis and career plan generation.

[0048] The generation unit can apply different generation algorithms depending on the user's work history and skill set when generating a career plan. For example, the generation unit can select an appropriate generation algorithm based on the user's work history. For example, the generation unit can apply the optimal generation algorithm depending on the user's skill set. For example, the generation unit can select the optimal generation algorithm by combining the user's work history and skill set. This allows for the provision of a more appropriate career plan by applying different generation algorithms depending on the user's work history and skill set. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's work history and skill set into a generation AI and have the generation AI perform data analysis and career plan generation.

[0049] The generation unit can determine the generation priority based on the user's goal achievement timeline when generating career plans. For example, the generation unit can prioritize generating career plans based on the user's short-term goal achievement timeline. For example, the generation unit can generate career plans that include an overall vision based on the user's long-term goal achievement timeline. For example, the generation unit can generate balanced career plans based on the user's goal achievement timeline. This allows for the provision of more appropriate career plans by determining the generation priority based on the user's goal achievement timeline. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's goal achievement timeline into a generation AI and have the generation AI perform data analysis and career plan generation.

[0050] The generation unit can adjust the generation order based on the user's relevance when generating career plans. For example, the generation unit can adjust the generation order based on the relevance of the user's work history. For example, the generation unit can adjust the generation order based on the relevance of the user's skill set. For example, the generation unit can adjust the generation order by combining the relevance of the user's work history and skill set. This allows for the provision of more appropriate career plans by adjusting the generation order based on the user's relevance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's work history and skill set into a generation AI and have the generation AI perform data analysis and career plan generation.

[0051] The service provider can select the optimal delivery method when providing a career plan by referring to the user's past feedback. For example, the service provider can select the optimal delivery method based on the delivery method the user has preferred in the past. For example, the service provider can analyze the user's past feedback and select a delivery method that reflects areas for improvement. For example, the service provider can select a customized delivery method based on the user's feedback history. This allows for the provision of a more appropriate career plan by referring to the user's past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's past feedback into a generating AI and have the generating AI perform data analysis and select a delivery method.

[0052] The service provider can customize the means of providing a career plan based on the user's current job situation. For example, the service provider can prioritize providing relevant information, taking into account the user's current job situation. For example, the service provider can select the optimal means of providing the career plan based on the user's job situation. For example, the service provider can select a customized means of providing the career plan by combining the user's job situation and areas of interest. This allows for the provision of a more appropriate career plan by customizing the means of providing the career plan based on the user's current job situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's current job situation into a generating AI and have the generating AI perform data analysis and select the means of providing the career plan.

[0053] The service provider can select the optimal delivery method when providing a career plan, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing a career plan relevant to that region. For example, the service provider can provide region-specific career plans based on the user's geographical location information. For example, if the user is on the move, the service provider can provide a career plan relevant to their current location in real time. This allows for the provision of a more appropriate career plan by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform data analysis and provide a career plan.

[0054] The service provider can analyze a user's social media activity and propose a means of providing a career plan when providing one. For example, the service provider can analyze a user's social media activity. For example, the service provider can analyze a user's social media activity and provide a career plan related to topics of interest. For example, the service provider can provide a relevant career plan based on information from accounts that a user follows. For example, the service provider can analyze a user's posts and provide a career plan related to areas of interest. By analyzing a user's social media activity, a more appropriate career plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on a user's social media activity into a generating AI and have the generating AI perform data analysis and provide a career plan.

[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 career plan suggestion system can analyze users' past feedback and improve its suggested career plans based on that feedback. For example, if a user has provided positive feedback on a previously suggested career plan, a similar plan can be suggested. If a user has provided negative feedback, a different approach can be tried. By continuously collecting user feedback and improving the suggestions, the system can provide more satisfying career plans.

[0057] The career plan suggestion system can propose career plans that take into account the user's geographical location. For example, if a user lives in a specific region, it can prioritize suggesting career opportunities in that region. If a user wishes to relocate, it can suggest career plans in their desired region. By utilizing the user's geographical location information, it can provide more realistic and actionable career plans.

[0058] A career plan suggestion system can analyze a user's social media activity and suggest career plans related to topics of interest. For example, if a user frequently posts about a particular industry, the system can suggest career plans within that industry. It can also suggest relevant career plans based on information about accounts the user follows. By leveraging a user's social media activity, a more personalized career plan can be provided.

[0059] The career plan proposal system can apply different generation algorithms depending on the user's work history and skill set. For example, it can select an appropriate generation algorithm based on the user's work history. It can apply the optimal generation algorithm according to the user's skill set. It can select the optimal generation algorithm by combining the user's work history and skill set. As a result, by applying different generation algorithms according to the user's work history and skill set, it can provide a more appropriate career plan.

[0060] The career plan proposal system can analyze a user's past learning history and skill set to select the optimal data collection method. For example, it can analyze a user's past learning history and prioritize the collection of relevant data. It can analyze a user's skill set and select an efficient method for collecting necessary data. It can consider a user's work history and select an industry-specific data collection method. In this way, by analyzing a user's past learning history and skill set, the system can select the optimal data collection method.

[0061] The career plan suggestion system can filter data based on the user's current job situation and areas of interest. For example, it can collect only relevant data considering the user's current job situation. It can prioritize the collection of data of interest based on the user's areas of interest. By combining the user's job situation and areas of interest, it can filter and collect the most relevant data. This allows for the collection of highly relevant data by filtering based on the user's current job situation and areas of interest.

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

[0063] Step 1: The data collection unit collects user data. For example, it collects data such as the user's past learning history, skill set, and work history. Specifically, it collects learning history such as online courses, seminars, and workshops, skill sets such as programming skills and communication skills, and work history such as past job duties, positions, and periods of employment. Step 2: The analysis unit analyzes the data collected by the data collection unit and compares user performance with their goals. For example, it analyzes the data using statistical analysis and machine learning algorithms, and analyzes resume data using natural language processing to construct individual career paths. Specifically, it uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The generation unit generates an individually optimized career plan based on the analysis results obtained by the analysis unit. For example, it proposes short-term and long-term career steps and generates a plan based on the user's goals, skills, and work history. Specifically, it generates a career plan that includes short-term goals, long-term goals, and means of achieving them. Step 4: The delivery unit provides the user with the career plan generated by the generation unit. For example, it may introduce online courses and provide networking opportunities. Specifically, it may introduce online courses such as technical courses and business courses, and provide networking opportunities such as industry events and online forums. It also evaluates the user's progress and adjusts the career plan through a continuous feedback loop. Specifically, it provides regular evaluations and real-time feedback.

[0064] (Example of form 2) The career plan proposal system according to an embodiment of the present invention is a system that proposes an individually optimized career plan using an AI agent. To address the problem that conventional career counseling is patterned and fails to adequately meet individual needs, the career plan proposal system analyzes an individual's skills and goals and proposes an individually optimized career plan. The system collects data such as the user's past learning history, skill set, and work history. Based on this collected data, the AI ​​compares the user's performance and goals to generate an individually optimized career plan. The generated career plan includes short-term and long-term career steps. Furthermore, the career plan proposal system promotes growth by introducing online courses and providing networking opportunities to the user. In addition, the career plan proposal system analyzes resume data using natural language processing to construct individual career paths. By understanding industry trends through data analysis and identifying required skill sets, it provides optimal advice. The career plan proposal system evaluates the user's progress and adjusts the career plan through a continuous feedback loop. This mechanism improves young people's self-expression and provides concrete, actionable career steps. Better matching between companies and young people is achieved, creating benefits for both parties. For example, a career planning system collects data such as the user's past learning history, skill set, and work experience. For example, a career planning system compares the user's performance with their goals and generates an optimally tailored career plan. For example, a career planning system suggests short-term and long-term career steps. For example, a career planning system introduces online courses and provides networking opportunities. For example, a career planning system evaluates the user's progress and adjusts the career plan through a continuous feedback loop. In this way, a career planning system can propose an optimally tailored career plan for the user and provide enhanced support for life planning.

[0065] The career plan proposal system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. The collection unit collects data such as the user's past course history, skill set, and work history. The collection unit can collect, for example, course history such as online courses, seminars, and workshops. The collection unit can collect skill sets such as programming skills and communication skills. The collection unit can collect work history such as past job duties, positions, and periods of employment. The analysis unit analyzes the data collected by the collection unit and compares the user's performance with their goals. The analysis unit analyzes the data using, for example, statistical analysis and machine learning algorithms. The analysis unit analyzes resume data using, for example, natural language processing and constructs individual career paths. The analysis unit uses, for example, natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit generates an individually optimized career plan based on the analysis results obtained by the analysis unit. The generation unit proposes, for example, short-term and long-term career steps. The generation unit generates a plan based on, for example, the user's goals, skills, and work history. The generation unit generates a career plan that includes, for example, short-term goals, long-term goals, and means of achieving them. The delivery unit provides the career plan generated by the generation unit to the user. The delivery unit provides, for example, online courses and networking opportunities. The delivery unit provides, for example, online courses such as technical courses and business courses. The delivery unit provides, for example, networking opportunities such as industry events and online forums. The delivery unit evaluates the user's progress and adjusts the career plan through a continuous feedback loop. The delivery unit provides, for example, periodic evaluations and real-time feedback. As a result, the career plan proposal system according to the embodiment can propose an individually optimized career plan by collecting, analyzing, generating, and providing user data.

[0066] The data collection unit collects user data. For example, it collects data such as a user's past learning history, skill sets, and work experience. Specifically, it can collect learning history for online courses, seminars, and workshops. This includes the content of the courses the user took, the dates and times they took them, their grades, and whether or not they received a certificate. Furthermore, the data collection unit can collect skill sets such as programming skills, communication skills, and leadership skills. These skills may be self-reported by the user or objectively evaluated through online tests and evaluation systems. The data collection unit can also collect work experience, including past job duties, positions, and employment periods. This includes detailed information such as what tasks the user was responsible for, what results they achieved, what positions they held, and how long they worked in those roles. This data may be collected not only from user input but also from company HR systems and professional networking sites. The data collection unit integrates information from these diverse data sources to build a comprehensive database of the user's career. This allows the data collection unit to accurately understand users' past experiences and skills, providing the foundational data necessary for subsequent analysis and plan generation. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection to always reflect the user's latest situation. For example, it can periodically send notifications to users prompting them to update their data, and immediately reflect any new skills or work experience in the database. This allows the data collection unit to maintain up-to-date and accurate data on users' careers, improving the overall performance of the system.

[0067] The analytics department analyzes data collected by the data collection department and compares user performance with their goals. The analytics department analyzes data using methods such as statistical analysis and machine learning algorithms. Specifically, it evaluates users' skill levels and career progress based on their past learning history and work experience data. For example, it uses statistical analysis to analyze the grades and completion rates of courses taken by users to evaluate their learning performance. It also uses machine learning algorithms to analyze users' skill sets and work experience data to identify their strengths and weaknesses. Furthermore, the analytics department uses natural language processing techniques to analyze resume data and construct individual career paths. Specifically, it uses techniques such as morphological analysis, grammatical analysis, and semantic analysis to analyze information in detail from users' resumes and extract important information about their careers. For example, it uses morphological analysis to extract job descriptions and skills from resumes, and grammatical analysis to analyze the relationships between this information. Furthermore, it uses semantic analysis to understand users' career goals and aspirations, providing foundational data to propose the most suitable career path for them. This allows the analytics department to quickly and accurately analyze collected data and compare user performance with their goals. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term career path trend analysis. For example, based on historical data, it can analyze career progression patterns in specific skills and job types, providing users with future career prospects. The analytics department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings to users. This allows the analytics department to not only grasp the situation in real time but also to handle long-term career management and anomaly detection, improving the reliability and security of the entire system.

[0068] The generation unit generates an individually optimized career plan based on the analysis results obtained by the analysis unit. For example, the generation unit proposes short-term and long-term career steps. Specifically, it generates a plan based on the user's goals, skills, and work history. For instance, for short-term goals, it clarifies the necessary skills and experience and proposes a concrete action plan based on them. This may include taking specific online courses, participating in specific projects, or acquiring specific skills. For long-term career goals, it details the steps the user should take. For example, if a user aspires to a management position in the future, it proposes a concrete plan for acquiring the necessary leadership skills and management experience. The generation unit generates a career plan that includes short-term goals, long-term goals, and means of achieving them. It is crucial to consider the user's current skill level and work history to set realistic and achievable goals. Furthermore, the generation unit can continuously update the career plan based on user feedback and adjust the plan according to the user's progress. For example, if a user acquires a specific skill, it proposes a new career step that utilizes that skill. Also, if a user fails to achieve their goals, it analyzes the cause and proposes solutions. This allows the generation unit to consistently provide users with the optimal career plan and support their career growth. Furthermore, the generation unit can automatically generate user career plans using AI. For example, it can use generation AI to automatically generate and propose career plans based on the user's goals and skills. This enables the generation unit to efficiently and effectively generate career plans and support users' career growth.

[0069] The service provider provides users with career plans generated by the service provider. For example, the service provider introduces online courses and networking opportunities. Specifically, it introduces online courses such as technical courses and business courses. This involves selecting the most suitable courses based on the user's skills and goals and encouraging them to take them. It also provides networking opportunities such as industry events and online forums. This allows users to interact with other professionals in the same industry or with similar interests, exchanging information and building networks. The service provider evaluates user progress and adjusts career plans through a continuous feedback loop. Specifically, it provides regular evaluations and real-time feedback. For example, it evaluates the progress of career plans based on the user's course performance and feedback, and adjusts the plan as needed. It also collects feedback from users' participation in networking events and incorporates it into future event selections. This allows the service provider to always provide users with the latest and most suitable information, supporting their career growth. Furthermore, the service provider improves its services based on user feedback. For example, it collects feedback from users to improve the quality of the courses and networking opportunities offered. The service provider also ensures reliable information transmission using multiple communication methods. For example, by using a combination of methods such as email, SMS, and in-app notifications, important information can be reliably delivered to users. This allows the service provider to deliver information quickly and reliably to users and support their career growth.

[0070] The data collection unit can collect data such as the user's past learning history, skill set, and work history. For example, the data collection unit can collect the user's past learning history. For example, the data collection unit can collect learning history such as online courses, seminars, and workshops. For example, the data collection unit can collect the user's skill set. For example, the data collection unit can collect skill sets such as programming skills and communication skills. For example, the data collection unit can collect the user's work history. For example, the data collection unit can collect work history such as past job duties, positions, and periods of employment. By collecting data such as the user's past learning history, skill set, and work history, it is possible to propose a more accurate career plan. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the user's past learning history, skill set, and work history into a generating AI and have the generating AI perform the data collection.

[0071] The analysis department can analyze resume data using natural language processing and construct individual career paths. For example, the analysis department can analyze resume data using natural language processing. The analysis department uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis department constructs individual career paths based on resume data. The analysis department can analyze data such as work history, education, and skills to construct career paths. This allows for more accurate analysis of resume data and the construction of individual career paths by using natural language processing. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input resume data into a generating AI and have the generating AI perform the data analysis.

[0072] The generation unit can propose short-term and long-term career steps. The generation unit can, for example, propose short-term and long-term career steps. The generation unit can, for example, generate a plan based on the user's goals, skills, and work history. The generation unit can, for example, generate a career plan that includes short-term goals, long-term goals, and means of achieving them. This allows the user's career plan to be concretely shown by proposing short-term and long-term career steps. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data such as the user's goals, skills, and work history into a generation AI and have the generation AI perform the generation of a career plan.

[0073] The service provider can introduce online courses and provide networking opportunities. For example, the service provider can introduce online courses such as technical courses and business courses. For example, the service provider can provide networking opportunities such as industry events and online forums. This promotes user growth by introducing online courses and providing networking opportunities. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input information about online courses and networking opportunities into a generating AI and have the generating AI provide the information.

[0074] The service provider can evaluate the user's progress and adjust their career plan through a continuous feedback loop. For example, the service provider can evaluate the user's progress through a continuous feedback loop. The service provider can, for example, perform periodic evaluations and provide real-time feedback. The service provider can, for example, adjust the career plan based on the user's progress. The service provider can, for example, update the career plan based on the user's progress data. This allows the service provider to provide a more appropriate career plan by evaluating the user's progress and adjusting the career plan through a continuous feedback loop. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user progress data into a generating AI and have the generating AI perform data evaluation and career plan adjustment.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit estimates the user's emotions. For example, the data collection unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is stressed, the data collection unit can delay the timing of data collection and collect it when the user is relaxed. For example, if the user is concentrating, the data collection unit can advance the timing of data collection to collect it more efficiently. For example, if the user is tired, the data collection unit can adjust the timing of data collection and collect it after rest. In this way, by adjusting the timing of data collection based on the user's emotions, data can be collected at a more appropriate time. 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 data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the user's facial expressions and voice data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0076] The data collection unit can analyze the user's past learning history and skill set to select the optimal data collection method. For example, the data collection unit analyzes the user's past learning history. For example, the data collection unit prioritizes collecting relevant data based on the user's past course history. For example, the data collection unit analyzes the user's skill set. For example, the data collection unit can analyze the user's skill set and select an efficient method for collecting the necessary data. For example, the data collection unit can consider the user's work history and select an industry-specific data collection method. This allows the optimal data collection method to be selected by analyzing the user's past learning history and skill set. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's past learning history and skill set into a generating AI and have the generating AI perform data analysis and select a data collection method.

[0077] The data collection unit can filter data based on the user's current job status and areas of interest during data collection. For example, the data collection unit can consider the user's current job status and collect only relevant data. For example, the data collection unit can prioritize the collection of data of interest based on the user's areas of interest. For example, the data collection unit can combine the user's job status and areas of interest to filter and collect the most relevant data. This allows for the collection of highly relevant data by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current job status and areas of interest into a generating AI and have the generating AI perform data filtering.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, the data collection unit estimates the user's emotions. For example, the data collection unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important data and prioritize collecting important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial expressions and voice data into a generating AI, which can then perform emotion estimation and determine data prioritization.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, if the user is on the move, the data collection unit can collect data related to the user's current location in real time. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform data collection.

[0080] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media activity. For example, the data collection unit can analyze a user's social media activity and collect data related to topics of interest. For example, the data collection unit can collect relevant data based on information about accounts that a user follows. For example, the data collection unit can analyze the content of a user's posts and collect data related to areas of interest. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a user's social media activity into a generating AI and have the generating AI perform data analysis and collection.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions. For example, the analysis unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visual presentation. For example, if the user is relaxed, the analysis unit can provide a presentation that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a presentation that gets straight to the point. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expressions and voice data into the generating AI, and have the generating AI perform emotion estimation and adjust the expression method of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's resume data during the analysis. For example, the analysis unit can prioritize the analysis of important items in the user's resume data. For example, the analysis unit can perform a detailed analysis based on the importance of the user's work history. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's skill set. This allows for prioritizing the analysis of important data by adjusting the level of detail of the analysis based on the importance of the user's resume data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's resume data into a generating AI and have the generating AI perform the process of adjusting the level of detail of the analysis based on the importance of the data.

[0083] The analysis unit can apply different analysis algorithms depending on the user's work history and skill set during analysis. For example, the analysis unit can select an appropriate analysis algorithm based on the user's work history. For example, the analysis unit can apply the optimal analysis algorithm depending on the user's skill set. For example, the analysis unit can select the optimal analysis algorithm by combining the user's work history and skill set. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the user's work history and skill set. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's work history and skill set into a generating AI and have the generating AI perform the data analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions. For example, the analysis unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation and adjustment of the analysis length.

[0085] The analysis unit can determine the priority of analysis based on when the user submitted their resume. For example, the analysis unit may prioritize analyzing resumes that the user has recently submitted. For example, the analysis unit may prioritize analyzing resumes that the user submitted within a specific period. The analysis unit can determine the priority of analysis based on when the user submitted their resume. This allows for the prioritization of the analysis of the most recent data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input data on when the user submitted their resume into a generating AI and have the generating AI perform the data analysis and priority determination.

[0086] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, the analysis unit can adjust the order of analysis based on the relevance of the user's work history. For example, the analysis unit can adjust the order of analysis based on the relevance of the user's skill set. For example, the analysis unit can adjust the order of analysis by combining the relevance of the user's work history and skill set. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on user relevance. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user work history and skill set data into a generating AI and have the generating AI perform data analysis and order adjustment.

[0087] The generation unit can estimate the user's emotions and adjust the method of generating the career plan based on the estimated user emotions. For example, the generation unit estimates the user's emotions. For example, the generation unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is relaxed, the generation unit can generate a detailed career plan. For example, if the user is in a hurry, the generation unit can generate a concise career plan. For example, if the user is excited, the generation unit can generate a career plan with visually stimulating effects. This allows for the provision of a more appropriate career plan by adjusting the method of generating the career plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expressions and voice data into the generation AI, which can then perform emotion estimation and adjust the method for generating a career plan.

[0088] The generation unit can adjust the level of detail generated based on the user's short-term and long-term goals when generating a career plan. For example, the generation unit can generate a career plan that includes specific steps based on the user's short-term goals. For example, the generation unit can generate a career plan that includes an overall vision based on the user's long-term goals. For example, the generation unit can combine the user's short-term and long-term goals to generate a balanced career plan. This allows for the provision of a more specific career plan by adjusting the level of detail generated based on the user's short-term and long-term goals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's short-term and long-term goals into a generation AI and have the generation AI perform data analysis and career plan generation.

[0089] The generation unit can apply different generation algorithms depending on the user's work history and skill set when generating a career plan. For example, the generation unit can select an appropriate generation algorithm based on the user's work history. For example, the generation unit can apply the optimal generation algorithm depending on the user's skill set. For example, the generation unit can select the optimal generation algorithm by combining the user's work history and skill set. This allows for the provision of a more appropriate career plan by applying different generation algorithms depending on the user's work history and skill set. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's work history and skill set into a generation AI and have the generation AI perform data analysis and career plan generation.

[0090] The generation unit can estimate the user's emotions and adjust the length of the career plan based on the estimated emotions. For example, the generation unit estimates the user's emotions. For example, the generation unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise career plan. For example, if the user is relaxed, the generation unit can generate a longer career plan that includes detailed explanations. For example, if the user is excited, the generation unit can generate a career plan with visually stimulating effects. This allows for the provision of a more appropriate career plan by adjusting the length of the career plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expressions and voice data into the generation AI, which can then perform emotion estimation and adjust the length of the career plan.

[0091] The generation unit can determine the generation priority based on the user's goal achievement timeline when generating career plans. For example, the generation unit can prioritize generating career plans based on the user's short-term goal achievement timeline. For example, the generation unit can generate career plans that include an overall vision based on the user's long-term goal achievement timeline. For example, the generation unit can generate balanced career plans based on the user's goal achievement timeline. This allows for the provision of more appropriate career plans by determining the generation priority based on the user's goal achievement timeline. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's goal achievement timeline into a generation AI and have the generation AI perform data analysis and career plan generation.

[0092] The generation unit can adjust the generation order based on the user's relevance when generating career plans. For example, the generation unit can adjust the generation order based on the relevance of the user's work history. For example, the generation unit can adjust the generation order based on the relevance of the user's skill set. For example, the generation unit can adjust the generation order by combining the relevance of the user's work history and skill set. This allows for the provision of more appropriate career plans by adjusting the generation order based on the user's relevance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's work history and skill set into a generation AI and have the generation AI perform data analysis and career plan generation.

[0093] The service provider can estimate the user's emotions and adjust the way the career plan is delivered based on the estimated emotions. For example, the service provider estimates the user's emotions. For example, the service provider analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible delivery method. For example, if the user is relaxed, the service provider can provide a delivery method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a delivery method that gets straight to the point. By adjusting the way the career plan is delivered based on the user's emotions, a more appropriate career plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expressions and voice data into a generating AI, which can then perform emotion estimation and adjust the method of providing career plans.

[0094] The service provider can select the optimal delivery method when providing a career plan by referring to the user's past feedback. For example, the service provider can select the optimal delivery method based on the delivery method the user has preferred in the past. For example, the service provider can analyze the user's past feedback and select a delivery method that reflects areas for improvement. For example, the service provider can select a customized delivery method based on the user's feedback history. This allows for the provision of a more appropriate career plan by referring to the user's past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's past feedback into a generating AI and have the generating AI perform data analysis and select a delivery method.

[0095] The service provider can customize the means of providing a career plan based on the user's current job situation. For example, the service provider can prioritize providing relevant information, taking into account the user's current job situation. For example, the service provider can select the optimal means of providing the career plan based on the user's job situation. For example, the service provider can select a customized means of providing the career plan by combining the user's job situation and areas of interest. This allows for the provision of a more appropriate career plan by customizing the means of providing the career plan based on the user's current job situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's current job situation into a generating AI and have the generating AI perform data analysis and select the means of providing the career plan.

[0096] The service provider can estimate the user's emotions and prioritize career plans based on those estimated emotions. For example, the service provider estimates the user's emotions. For example, the service provider analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is stressed, the service provider can postpone less important career plans and prioritize important ones. For example, if the user is relaxed, the service provider can prioritize providing detailed career plans. For example, if the user is in a hurry, the service provider can prioritize providing career plans that can be delivered quickly. This allows for the provision of more appropriate career plans by prioritizing career plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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, for example, or without AI. For example, the service provider can input user facial expressions and voice data into a generating AI, which can then perform emotion estimation and determine the priorities of a career plan.

[0097] The service provider can select the optimal delivery method when providing a career plan, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing a career plan relevant to that region. For example, the service provider can provide region-specific career plans based on the user's geographical location information. For example, if the user is on the move, the service provider can provide a career plan relevant to their current location in real time. This allows for the provision of a more appropriate career plan by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform data analysis and provide a career plan.

[0098] The service provider can analyze a user's social media activity and propose a means of providing a career plan when providing one. For example, the service provider can analyze a user's social media activity. For example, the service provider can analyze a user's social media activity and provide a career plan related to topics of interest. For example, the service provider can provide a relevant career plan based on information from accounts that a user follows. For example, the service provider can analyze a user's posts and provide a career plan related to areas of interest. By analyzing a user's social media activity, a more appropriate career plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on a user's social media activity into a generating AI and have the generating AI perform data analysis and provide a career plan.

[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 career plan suggestion system can estimate the user's emotions and adjust the suggested career plan based on those emotions. For example, if the user is feeling stressed, the system can suggest a relaxing career plan. If the user is excited, it can suggest a challenging career plan. If the user is feeling anxious, it can suggest a stable career plan. This allows the system to provide the optimal career plan tailored to the user's emotions.

[0101] The career plan suggestion system can analyze users' past feedback and improve its suggested career plans based on that feedback. For example, if a user has provided positive feedback on a previously suggested career plan, a similar plan can be suggested. If a user has provided negative feedback, a different approach can be tried. By continuously collecting user feedback and improving the suggestions, the system can provide more satisfying career plans.

[0102] The career plan suggestion system can propose career plans that take into account the user's geographical location. For example, if a user lives in a specific region, it can prioritize suggesting career opportunities in that region. If a user wishes to relocate, it can suggest career plans in their desired region. By utilizing the user's geographical location information, it can provide more realistic and actionable career plans.

[0103] A career plan suggestion system can analyze a user's social media activity and suggest career plans related to topics of interest. For example, if a user frequently posts about a particular industry, the system can suggest career plans within that industry. It can also suggest relevant career plans based on information about accounts the user follows. By leveraging a user's social media activity, a more personalized career plan can be provided.

[0104] The career plan suggestion system can estimate the user's emotions and prioritize career plans based on those emotions. For example, if the user is stressed, it can postpone less important career plans and prioritize important ones. If the user is relaxed, it can prioritize detailed career plans. If the user is in a hurry, it can prioritize career plans that can be proposed quickly. In this way, it can provide the optimal career plan based on the user's emotions.

[0105] The career plan proposal system can apply different generation algorithms depending on the user's work history and skill set. For example, it can select an appropriate generation algorithm based on the user's work history. It can apply the optimal generation algorithm according to the user's skill set. It can select the optimal generation algorithm by combining the user's work history and skill set. As a result, by applying different generation algorithms according to the user's work history and skill set, it can provide a more appropriate career plan.

[0106] The career plan suggestion system can estimate the user's emotions and adjust the length of the career plan based on those emotions. For example, if the user is in a hurry, it can suggest a short, to-the-point career plan. If the user is relaxed, it can suggest a longer career plan with detailed explanations. If the user is excited, it can suggest a career plan with visually stimulating effects. By adjusting the length of the career plan based on the user's emotions, it can provide a more appropriate career plan.

[0107] The career plan proposal system can analyze a user's past learning history and skill set to select the optimal data collection method. For example, it can analyze a user's past learning history and prioritize the collection of relevant data. It can analyze a user's skill set and select an efficient method for collecting necessary data. It can consider a user's work history and select an industry-specific data collection method. In this way, by analyzing a user's past learning history and skill set, the system can select the optimal data collection method.

[0108] The career plan suggestion system can estimate the user's emotions and adjust the career plan generation method based on those emotions. For example, if the user is relaxed, it can generate a detailed career plan. If the user is in a hurry, it can generate a concise career plan. If the user is excited, it can generate a career plan with visually stimulating effects. By adjusting the career plan generation method based on the user's emotions, it can provide a more appropriate career plan.

[0109] The career plan suggestion system can filter data based on the user's current job situation and areas of interest. For example, it can collect only relevant data considering the user's current job situation. It can prioritize the collection of data of interest based on the user's areas of interest. By combining the user's job situation and areas of interest, it can filter and collect the most relevant data. This allows for the collection of highly relevant data by filtering based on the user's current job situation and areas of interest.

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

[0111] Step 1: The data collection unit collects user data. For example, it collects data such as the user's past learning history, skill set, and work history. Specifically, it collects learning history such as online courses, seminars, and workshops, skill sets such as programming skills and communication skills, and work history such as past job duties, positions, and periods of employment. Step 2: The analysis unit analyzes the data collected by the data collection unit and compares user performance with their goals. For example, it analyzes the data using statistical analysis and machine learning algorithms, and analyzes resume data using natural language processing to construct individual career paths. Specifically, it uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The generation unit generates an individually optimized career plan based on the analysis results obtained by the analysis unit. For example, it proposes short-term and long-term career steps and generates a plan based on the user's goals, skills, and work history. Specifically, it generates a career plan that includes short-term goals, long-term goals, and means of achieving them. Step 4: The delivery unit provides the user with the career plan generated by the generation unit. For example, it may introduce online courses and provide networking opportunities. Specifically, it may introduce online courses such as technical courses and business courses, and provide networking opportunities such as industry events and online forums. It also evaluates the user's progress and adjusts the career plan through a continuous feedback loop. Specifically, it provides regular evaluations and real-time feedback.

[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 collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the control unit 46A of the smart device 14 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data using statistical analysis or machine learning algorithms using the specific processing unit 290 of the data processing unit 12. The generation unit generates an individually optimized career plan using the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the career plan generated by the control unit 46A of the smart device 14, and provides information on online courses and networking opportunities. 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 collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user data using the control unit 46A of the smart glasses 214 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data using statistical analysis or machine learning algorithms using the specific processing unit 290 of the data processing unit 12. The generation unit generates an individually optimized career plan using the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the career plan generated by the control unit 46A of the smart glasses 214, and provides information on online courses and networking opportunities. 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 collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user data using the control unit 46A of the headset terminal 314 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data using statistical analysis or machine learning algorithms using, for example, the specific processing unit 290 of the data processing unit 12. The generation unit generates an individually optimized career plan using, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the career plan generated by the control unit 46A of the headset terminal 314, and provides information on online courses and networking opportunities. 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 collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user data using the control unit 46A of the robot 414 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data using statistical analysis or machine learning algorithms using the specific processing unit 290 of the data processing unit 12. The generation unit generates an individually optimized career plan using the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the career plan generated by the control unit 46A of the robot 414, and provides information on online courses and networking opportunities. 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 data collection unit that collects user data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that compares the user's performance with their goals. A generation unit generates an individually optimized career plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides the user with the carrier plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as the user's past course history, skill set, and work experience. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyzing resume data using natural language processing and constructing individual career paths. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We propose short-term and long-term career steps. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We introduce online courses and provide networking opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Through a continuous feedback loop, we evaluate user progress and adjust career plans. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past learning history and skill set to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, 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 collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the user's resume data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the user's work history and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is 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 is During analysis, the analysis prioritization is determined based on when the user submitted their resume. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, the order of analysis is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the career plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a career plan, adjust the level of detail based on the user's short-term and long-term goals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a career plan, different generation algorithms are applied depending on the user's work history and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the career plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a career plan, the priority of generation is determined based on the user's target achievement date. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating career plans, the generation order is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we deliver career plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing career plans, we select the optimal delivery method by referring to users' past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a career plan, the method of delivery is customized based on the user's current job situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of career plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing career plans, the optimal delivery method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing career plans, we analyze the user's social media activity and propose methods for providing those plans. 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 data collection unit that collects user data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that compares the user's performance with their goals. A generation unit generates an individually optimized career plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides the user with the carrier plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as the user's past course history, skill set, and work experience. The system according to feature 1.

3. The aforementioned analysis unit is Analyzing resume data using natural language processing and constructing individual career paths. The system according to feature 1.

4. The generating unit is We propose short-term and long-term career steps. The system according to feature 1.

5. The aforementioned supply unit is, We introduce online courses and provide networking opportunities. The system according to feature 1.

6. The aforementioned supply unit is, Through a continuous feedback loop, we evaluate user progress and adjust career plans. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past learning history and skill set to select the optimal data collection method. The system according to feature 1.

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