system

Abstract

The system according to this embodiment aims to propose an optimal career path based on the user's skills, interests, and past experience, and to track learning progress and career change progress in real time. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a tracking unit. The reception unit receives input from the user regarding their skills, interests, and past experience. The analysis unit analyzes the information received by the reception unit and proposes the optimal career path. The provision unit provides online courses and workshops for the user to learn new skills. The tracking unit tracks the user's learning progress and career change progress in real time.

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that an optimal career path is not sufficiently proposed based on the skills, interests, and past experiences of a user, and the progress of learning and career changes is not sufficiently tracked in real time.

[0005] The system according to the embodiment aims to propose an optimal career path based on the skills, interests, and past experiences of a user, and to track the progress of learning and career changes in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a tracking unit. The reception unit receives input from the user regarding their skills, interests, and past experience. The analysis unit analyzes the information received by the reception unit and proposes the optimal career path. The provision unit provides online courses and workshops for the user to learn new skills. The tracking unit tracks the user's learning progress and career change progress in real time. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal career path based on the user's skills, interests, and past experience, and can track learning progress and career change progress in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 support system according to an embodiment of the present invention is a system that uses AI to analyze a user's skills, interests, and past experiences and proposes a new career path. This career support system allows the user to input their skills, interests, and past experiences, and the AI ​​analyzes this information to propose the optimal career path. Furthermore, it provides online courses and workshops for users to learn new skills, creating an environment where users can proactively challenge themselves in new careers. The AI ​​agent also tracks the user's learning progress and career change progress in real time and automatically reminds them of necessary actions. This allows users to efficiently pursue new careers and receive thorough support all the way to actual employment. For example, when a user inputs their skills, interests, and past experiences, they input their work history, educational background, and areas of interest. For example, if a user inputs "I am interested in programming," the AI ​​analyzes this information. Next, the AI ​​analyzes the input information and proposes the optimal career path for the user. The AI ​​comprehensively assesses the user's skills, interests, and past experiences and proposes suitable occupations and industries. For example, if a user is interested in programming, the AI ​​proposes occupations such as programmer or software engineer. Furthermore, it provides online courses and workshops for users to learn new skills. This allows users to improve their skills and prepare to take on new career challenges. For example, online courses to learn the basics of programming and workshops to actually create programs are offered. In addition, an AI agent tracks the user's learning progress and career change progress in real time and automatically reminds them of necessary actions. This allows users to always know their learning status and career change progress and take necessary actions without forgetting. For example, features such as tracking the progress of online courses and reminding users of the next lesson are provided. Through this system, users can efficiently take on new career challenges and receive thorough support all the way to actual employment.This allows the career support system to suggest the optimal career path based on the user's skills, interests, and past experience, and to support their learning progress and career changes.

[0029] The career support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a tracking unit. The reception unit receives input from the user regarding their skills, interests, and past experience. User skills include, but are not limited to, technical skills and soft skills. The reception unit also receives input from the user regarding their work history, educational background, and areas of interest. The analysis unit analyzes the information received by the reception unit and proposes the optimal career path. The analysis unit comprehensively assesses the user's skills, interests, and past experience and proposes suitable occupations and industries. For example, if the user is interested in programming, the analysis unit proposes occupations such as programmer or software engineer. The provision unit provides online courses and workshops for the user to learn new skills. The provision unit provides, for example, online courses to learn the basics of programming and workshops to actually create programs. The tracking unit tracks the user's learning progress and career change progress in real time. The tracking unit tracks the progress of online courses and provides reminders for the next lesson. As a result, the career support system according to the embodiment can propose an optimal career path based on the user's skills, interests, and past experience, and can support learning progress and career changes.

[0030] The reception desk accepts input from users regarding their skills, interests, and past experience. User skills include, but are not limited to, technical and soft skills. The reception desk accepts input from users regarding their work history, educational background, and areas of interest. Specifically, users can upload detailed resumes and CVs through a dedicated interface. Users can also use self-assessment sheets to describe their skill sets and areas of interest in detail. For example, technical skills can include specific skills such as programming languages, database management, and network construction. Soft skills include communication skills, leadership, and problem-solving abilities. Furthermore, users can describe their past projects and work experience in detail. This allows the reception desk to gain a comprehensive understanding of the user's skills and experience. The reception desk also accepts input from users regarding their interests and career goals. For example, users can input what types of jobs or industries they would like to work in in the future, and what skills they would like to acquire. This allows the reception desk to collect foundational information to more specifically support the user's career path.

[0031] The analysis department analyzes the information received by the reception department and proposes the optimal career path. For example, the analysis department comprehensively assesses the user's skills, interests, and past experience to suggest suitable job types and industries. Specifically, the analysis department uses AI to analyze the user's input information and derive the optimal career path. For example, it uses natural language processing technology to analyze the contents of the user's resume and self-assessment sheet to extract the user's skill set and interests. Furthermore, it uses machine learning algorithms to predict the most suitable job types and industries for the user based on past data and statistical information. For example, if the user is interested in programming, the analysis department will suggest job types such as programmer or software engineer. It can also suggest career paths in specific industries or companies based on the user's past experience and skill set. For example, if the user has experience in the financial industry, the analysis department will suggest a career path in the financial technology field. In this way, the analysis department can comprehensively assess the user's skills, interests, and past experience to propose the optimal career path. Furthermore, the analysis department can continuously improve its suggestions based on user feedback. For example, by providing feedback to the user on the proposed career path, the analysis unit can improve the accuracy of the proposed content.

[0032] The service provider offers online courses and workshops to help users learn new skills. For example, they offer online courses to learn the fundamentals of programming and workshops to create actual programs. Specifically, they provide diverse learning content tailored to users' skill levels and interests. Examples include introductory programming courses for beginners, database management courses for intermediate users, and machine learning algorithm courses for advanced users. They also offer workshops and hands-on sessions to develop practical skills. These include workshops where users can create programs and receive real-time feedback, and hands-on sessions where teams work on projects. Furthermore, they provide resources and tools to support users' learning progress. These include dashboards for managing online course progress and quizzes and exercises to review learned material. This allows the service provider to provide an environment where users can effectively learn new skills and support their career path.

[0033] The tracking unit tracks users' learning progress and career change progress in real time. For example, it tracks the progress of online courses and provides reminders for the next lesson. Specifically, the tracking unit collects user learning data and provides a dashboard to visualize progress. For example, it displays information in real time such as which courses the user is taking, which lessons have been completed, and which exercises they are working on. The tracking unit also provides reminders for the next lesson and adjusts the learning plan according to the user's learning progress. For example, when a user completes a particular lesson, it notifies them of a reminder for the next lesson and updates the learning plan. If a user falls behind in their learning, the tracking unit provides appropriate support and advice. Furthermore, the tracking unit also tracks the progress of users' career changes. For example, if a user changes jobs to a new profession, it tracks their progress and results and provides additional support as needed. In this way, the tracking unit can support users' learning progress and career changes in real time and help them achieve the optimal career path.

[0034] The tracking unit includes a reminder unit that tracks the user's learning progress and career change progress, and automatically reminds the user of necessary actions. For example, the tracking unit tracks the user's learning progress and reminds the user of the next learning step. For example, the tracking unit tracks the user's progress in an online course and reminds the user of the next lesson. The tracking unit can also track the user's career change progress and remind the user of necessary actions. For example, the tracking unit tracks the user's progress in job hunting and reminds the user of the next step. In this way, by tracking the user's learning progress and career change progress and automatically reminding the user of necessary actions, the system can support the user's career change. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's learning progress data into a generating AI and have the generating AI execute a reminder for the next learning step.

[0035] The analysis unit comprehensively assesses the user's skills, interests, and past experience to propose suitable job types and industries. For example, the analysis unit comprehensively assesses the user's skills, interests, and past experience using a scoring system. For example, based on the user's skills, interests, and past experience, the analysis unit proposes suitable job types and industries. For example, if the user is interested in programming, the analysis unit will propose job types such as programmer or software engineer. The analysis unit can also comprehensively assess the user's skills, interests, and past experience using a weighting algorithm. For example, based on the user's skills, interests, and past experience, the analysis unit proposes suitable job types and industries. In this way, by comprehensively assessing the user's skills, interests, and past experience, it can propose suitable job types and industries. 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 skills, interests, and past experience data into a generating AI and have the generating AI propose the optimal career path.

[0036] The service provider offers online courses and workshops for users to learn new skills. For example, the service provider may offer online courses to learn the basics of programming. For example, the service provider may offer workshops where users actually create programs. The service provider may also offer online courses to improve business skills for users to learn new skills. For example, the service provider may offer workshops to learn project management skills. In this way, the service provider can support users' skill improvement by offering online courses and workshops for users to learn new skills. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider may input user skill data into generative AI and have the generative AI suggest the most suitable online courses and workshops.

[0037] The reminder unit tracks the progress of the online course and reminds the user of the next lesson. For example, the reminder unit allows the user to track their progress in the online course and remind them of the next lesson. For example, the reminder unit allows the user to continue learning by receiving a reminder for the next lesson. The reminder unit can also track the user's learning progress and remind them of the next learning step. For example, the reminder unit allows the user to continue learning by receiving a reminder for the next learning step. This allows the system to support the user's learning by tracking their progress in the online course and reminding them of the next lesson. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reminder unit can input the user's learning progress data into a generative AI and have the generative AI execute a reminder for the next lesson.

[0038] The reception desk analyzes the user's past input history and suggests the optimal input method. For example, the reception desk automatically displays skills and interests that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest skills and interests that the user will use at specific times of day based on the user's past input history. For example, the reception desk predicts and suggests skills and interests that the user will use at specific times of day based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history data into a generative AI and have the generative AI suggest the optimal input method.

[0039] The reception desk customizes input fields based on the user's current job status and areas of interest. For example, when the user enters their current job status, the reception desk automatically displays relevant skills and interests as suggestions. For example, the reception desk prioritizes displaying relevant input fields based on the user's areas of interest. The reception desk can also customize input fields based on the user's job status and areas of interest to streamline the input process. For example, the reception desk streamlines the input process by customizing input fields based on the user's job status and areas of interest. This streamlines the input process by customizing input fields based on the user's current job status and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's job status and areas of interest data into a generative AI and have the generative AI perform the customization of input fields.

[0040] The reception desk prioritizes displaying input fields that are highly relevant, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception desk prioritizes displaying skills and interests related to that region. For example, if the user works in a specific region, the reception desk prioritizes displaying job descriptions and areas of interest related to that region. The reception desk can also customize highly relevant input fields based on the user's geographical location to streamline the input process. For example, the reception desk customizes highly relevant input fields based on the user's geographical location to streamline the input process. This allows the reception desk to prioritize displaying highly relevant input fields by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI display highly relevant input fields.

[0041] The reception desk analyzes the user's social media activity and suggests relevant input fields. For example, the reception desk automatically displays skills and interests that the user frequently mentions on social media as suggestions. For example, the reception desk suggests relevant job situations and areas of interest based on the user's social media activity. The reception desk can also analyze the user's social media activity and suggest the most suitable input fields. For example, the reception desk analyzes the user's social media activity and suggests the most suitable input fields. This allows the reception desk to suggest relevant input fields by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI suggest relevant input fields.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the user's skills and interests during the analysis. For example, if the user has a strong interest in a particular skill, the analysis unit will provide detailed analysis results related to that skill. For example, if the user has multiple interests, the analysis unit will provide analysis results corresponding to each interest. The analysis unit can also adjust the level of detail of the analysis based on the importance of the user's skills and interests to provide the optimal analysis results. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the user's skills and interests to provide the optimal analysis results. This allows the analysis unit to provide the optimal analysis results by adjusting the level of detail of the analysis based on the importance of the user's skills and interests. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user skill and interest data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit applies different analysis algorithms depending on the user's work history and educational background during analysis. For example, if the user has a specific work history, the analysis unit applies an analysis algorithm based on that work history. For example, if the user has a specific educational background, the analysis unit applies an analysis algorithm based on that educational background. The analysis unit can also select the optimal analysis algorithm according to the user's work history and educational background and provide the analysis results. For example, the analysis unit selects the optimal analysis algorithm according to the user's work history and educational background and provides the analysis results. This allows for the provision of optimal analysis results by applying different analysis algorithms according to the user's work history and educational background. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's work history and educational background data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0044] The analysis unit determines the priority of analysis based on the user's submission timing. For example, the analysis unit prioritizes the analysis of information submitted by the user within a specific deadline. For example, if the user is in a hurry, the analysis unit provides analysis results quickly based on the submission timing. The analysis unit can also adjust the priority of analysis according to the user's submission timing to provide the optimal analysis results. For example, the analysis unit adjusts the priority of analysis according to the user's submission timing to provide the optimal analysis results. This allows for the rapid provision of analysis results by determining the priority of analysis based on the user's submission timing. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input user submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0045] The analysis unit adjusts the order of analysis results based on user relevance during analysis. For example, the analysis unit prioritizes analyzing information related to the user's specific skills or interests. For example, the analysis unit prioritizes analyzing information related to the user's work history or educational background. The analysis unit can also adjust the order of analysis results based on user relevance to provide the optimal analysis results. For example, the analysis unit adjusts the order of analysis results based on user relevance to provide the optimal analysis results. This allows the analysis unit to provide the optimal analysis results by adjusting the order of analysis results based on user relevance. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user relevance data into a generative AI and have the generative AI perform the adjustment of the order of analysis results.

[0046] The service provider adjusts the difficulty level of courses and workshops based on the user's skill level at the time of delivery. For example, if the user is a beginner, the service provider will provide courses and workshops with basic content. For example, if the user is an intermediate learner, the service provider will provide courses and workshops with intermediate-level content. The service provider can also provide courses and workshops with advanced-level content if the user is an advanced learner. For example, if the service provider is an advanced learner, the service provider will provide courses and workshops with advanced-level content. By adjusting the difficulty level of courses and workshops based on the user's skill level, the service provider can provide learning content that is appropriate for the user. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's skill level data into a generative AI and have the generative AI perform the difficulty level adjustment of courses and workshops.

[0047] The service provider, at the time of delivery, proposes different courses and workshops according to the user's interests. For example, if the user has a specific interest, the service provider will propose courses and workshops related to that interest. For example, if the user has multiple interests, the service provider will propose courses and workshops corresponding to each interest. The service provider can also propose the most suitable courses and workshops based on the user's interests. For example, the service provider will propose the most suitable courses and workshops based on the user's interests. In this way, by proposing different courses and workshops according to the user's interests, the service provider can provide the user with the most suitable learning content. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input user interest data into a generative AI and have the generative AI propose different courses and workshops.

[0048] The service provider prioritizes providing highly relevant courses and workshops, taking into account the user's geographical location. For example, if a user lives in a specific region, the service provider will prioritize providing courses and workshops related to that region. For example, if a user works in a specific region, the service provider will provide courses and workshops based on their job responsibilities and areas of interest related to that region. The service provider can also customize and provide highly relevant courses and workshops based on the user's geographical location. For example, the service provider can customize and provide highly relevant courses and workshops based on the user's geographical location. This allows the service provider to prioritize providing highly relevant courses and workshops by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing highly relevant courses and workshops.

[0049] The service provider analyzes the user's social media activity at the time of delivery and suggests relevant courses and workshops. For example, the service provider suggests courses and workshops related to skills and interests that the user frequently mentions on social media. For example, the service provider suggests courses and workshops based on the user's social media activity and relevant job situation and areas of interest. The service provider can also analyze the user's social media activity and suggest the most suitable courses and workshops. For example, the service provider analyzes the user's social media activity and suggests the most suitable courses and workshops. This allows the service provider to suggest relevant courses and workshops by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input the user's social media activity data into generative AI and have the generative AI suggest relevant courses and workshops.

[0050] The tracking unit adjusts the level of detail in tracking based on the user's learning progress and the importance of career changes. For example, if a user has a strong interest in a particular skill, the tracking unit provides detailed tracking information about that skill. For example, if a user has multiple interests, the tracking unit provides tracking information tailored to each interest. The tracking unit can also adjust the level of detail in tracking based on the user's learning progress and the importance of career changes to provide optimal tracking information. For example, the tracking unit adjusts the level of detail in tracking based on the user's learning progress and the importance of career changes to provide optimal tracking information. This allows the tracking unit to provide optimal tracking information by adjusting the level of detail in tracking based on the user's learning progress and the importance of career changes. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the tracking unit can input the user's learning progress and career change data into a generative AI and have the generative AI perform the adjustment of the level of detail in tracking.

[0051] The tracking unit applies different tracking algorithms depending on the user's work history and educational background during tracking. For example, if the user has a specific work history, the tracking unit applies a tracking algorithm based on that work history. For example, if the user has a specific educational background, the tracking unit applies a tracking algorithm based on that educational background. The tracking unit can also select the most suitable tracking algorithm based on the user's work history and educational background and provide tracking information. For example, the tracking unit selects the most suitable tracking algorithm based on the user's work history and educational background and provides tracking information. This allows for the provision of optimal tracking information by applying different tracking algorithms depending on the user's work history and educational background. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's work history and educational background data into a generative AI and have the generative AI execute the application of different tracking algorithms.

[0052] The tracking unit determines tracking priorities based on the user's submission timing during tracking. For example, the tracking unit prioritizes tracking information submitted by the user within a specific deadline. For example, if the user is in a hurry, the tracking unit provides tracking results quickly based on the submission timing. The tracking unit can also adjust tracking priorities according to the user's submission timing to provide optimal tracking results. For example, the tracking unit adjusts tracking priorities according to the user's submission timing to provide optimal tracking results. This allows for the rapid provision of tracking results by determining tracking priorities based on the user's submission timing. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input user submission timing data into a generative AI and have the generative AI perform the tracking priority determination.

[0053] The tracking unit adjusts the order of tracking results based on user relevance during tracking. For example, the tracking unit prioritizes tracking information related to the user's specific skills or interests. For example, the tracking unit prioritizes tracking information related to the user's work history or educational background. The tracking unit can also adjust the order of tracking results based on user relevance to provide optimal tracking results. For example, the tracking unit adjusts the order of tracking results based on user relevance to provide optimal tracking results. This allows for the provision of optimal tracking results by adjusting the order of tracking results based on user relevance. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input user relevance data into a generative AI and have the generative AI perform the adjustment of the order of tracking results.

[0054] The reminder unit adjusts the level of detail in reminders based on the user's learning progress and the importance of their career change. For example, if a user has a strong interest in a particular skill, the reminder unit provides a detailed reminder about that skill. For example, if a user has multiple interests, the reminder unit provides reminders tailored to each interest. The reminder unit can also adjust the level of detail in reminders based on the user's learning progress and the importance of their career change to provide the most appropriate reminder. For example, the reminder unit adjusts the level of detail in reminders based on the user's learning progress and the importance of their career change to provide the most appropriate reminder. This allows the system to provide optimal reminder information by adjusting the level of detail in reminders based on the user's learning progress and the importance of their career change. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reminder unit can input the user's learning progress and career change data into a generative AI and have the generative AI perform the adjustment of the level of detail in reminders.

[0055] The reminder unit applies different reminder algorithms depending on the user's work history and educational background when sending reminders. For example, if the user has a specific work history, the reminder unit applies a reminder algorithm based on that work history. For example, if the user has a specific educational background, the reminder unit applies a reminder algorithm based on that educational background. The reminder unit can also select the most appropriate reminder algorithm based on the user's work history and educational background and provide reminder information. For example, the reminder unit selects the most appropriate reminder algorithm based on the user's work history and educational background and provides reminder information. This allows the system to provide optimal reminder information by applying different reminder algorithms depending on the user's work history and educational background. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the user's work history and educational background data into a generative AI and have the generative AI execute the application of different reminder algorithms.

[0056] The reminder unit determines the priority of reminders based on the user's submission timing. For example, the reminder unit prioritizes reminders for information submitted by the user within a specific deadline. For example, if the user is in a hurry, the reminder unit provides a quick reminder result based on the submission timing. The reminder unit can also adjust the priority of reminders according to the user's submission timing to provide the optimal reminder result. For example, the reminder unit adjusts the priority of reminders according to the user's submission timing to provide the optimal reminder result. This allows for the rapid provision of reminder results by determining the priority of reminders based on the user's submission timing. Some or all of the above processing in the reminder unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reminder unit can input user submission timing data into a generation AI and have the generation AI perform the reminder priority determination.

[0057] The reminder unit adjusts the order of reminder results based on user relevance when sending reminders. For example, the reminder unit prioritizes reminders of information related to the user's specific skills or interests. For example, the reminder unit prioritizes reminders of information related to the user's work history or educational background. The reminder unit can also adjust the order of reminder results based on user relevance to provide the most optimal reminder results. For example, the reminder unit adjusts the order of reminder results based on user relevance to provide the most optimal reminder results. This allows the system to provide the most optimal reminder results by adjusting the order of reminder results based on user relevance. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input user relevance data into a generative AI and have the generative AI perform the adjustment of the order of reminder results.

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

[0059] The reception desk can provide real-time feedback based on user input regarding skills, interests, and past experience. For example, if a user enters a specific skill, it can instantly display information on related job roles and industries. If a user enters an interest, it can also provide the latest trends and news related to that interest. Furthermore, if a user enters past experience, it can suggest recommended career paths and resources for skill development based on that experience. This allows users to receive immediate feedback on their input and consider more specific career path options.

[0060] The tracking unit can analyze user behavior patterns when tracking a user's learning progress and career change progress, and propose an optimal learning schedule. For example, if a user tends to study at a specific time of day, it can set learning reminders tailored to that time. Furthermore, if a user prefers a particular learning method (video, text, practice, etc.), it can prioritize providing learning content based on that method. It can also suggest the next steps based on the user's learning progress and provide advice to maintain motivation. This allows users to receive support optimized for their own learning style and schedule.

[0061] The analytics unit can take into account a user's future career goals when comprehensively assessing their skills, interests, and past experience. For example, if a user is aiming for a career in a specific job or industry, it can identify gaps in skills and experience to reach that goal and suggest resources for necessary skill development. If a user has a long-term career plan, it can also provide specific steps along that career path. Furthermore, it can suggest relevant networking events and connections with industry experts, depending on the user's career goals. This allows users to develop concrete action plans toward achieving their career objectives.

[0062] The service provider can customize online courses and workshops to suit users' learning styles when offering them to acquire new skills. For example, if a user prefers visual learning, a course with extensive video content and infographics can be provided. If a user prefers hands-on learning, hands-on workshops or project-based learning content can be offered. Furthermore, if a user prefers self-study, an online course that can be progressed at one's own pace can be provided, along with options for support as needed. This allows users to utilize learning resources optimized for their own learning style.

[0063] The reminder section can track online course progress and incorporate features to maintain user motivation when reminding users of the next lesson. For example, it can provide messages and badges that give users a sense of accomplishment when they achieve a specific goal. It can also send encouraging messages to help users move on to the next step as they progress through their learning. Furthermore, if a user is losing motivation, it can offer advice and success stories to boost their motivation. This allows users to enjoy their learning progress and maintain their motivation to continue learning.

[0064] The reception desk can analyze a user's past input history and suggest the most suitable input method. For example, it can automatically display skills and interests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest skills and interests that the user will use at specific times of the day based on their past input history. In this way, by analyzing a user's past input history, the system can suggest the most suitable input method.

[0065] The reception desk can customize input fields based on the user's current job status and areas of interest. For example, when a user enters their current job status, relevant skills and interests are automatically displayed as suggestions. It can also prioritize the display of relevant input fields based on the user's areas of interest. Furthermore, it is possible to streamline the input process by customizing input fields based on the user's job status and areas of interest. This streamlines the input process by customizing input fields based on the user's current job status and areas of interest.

[0066] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location. For example, if a user lives in a specific region, it can prioritize displaying skills and interests related to that region. Similarly, if a user works in a specific region, it can prioritize displaying job-related information and areas of interest related to that region. Furthermore, it's possible to customize highly relevant input fields based on the user's geographical location, thereby streamlining the input process. This allows for the prioritization of highly relevant input fields by considering the user's geographical location.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk accepts user input regarding skills, interests, and past experience. User skills include technical skills and soft skills. The reception desk accepts user input regarding their work history, educational background, and areas of interest. Step 2: The analysis department analyzes the information received by the reception department and proposes the optimal career path. The analysis department comprehensively assesses the user's skills, interests, and past experience to propose suitable job types and industries. For example, if the user is interested in programming, it will propose job types such as programmer or software engineer. Step 3: The service provider offers online courses and workshops for users to learn new skills. The service provider offers online courses to learn the basics of programming, as well as workshops to actually create programs. Step 4: The tracking unit tracks the user's learning progress and career change progress in real time. The tracking unit tracks the progress of online courses and provides reminders for the next lesson.

[0069] (Example of form 2) The career support system according to an embodiment of the present invention is a system that uses AI to analyze a user's skills, interests, and past experiences and proposes a new career path. This career support system allows the user to input their skills, interests, and past experiences, and the AI ​​analyzes this information to propose the optimal career path. Furthermore, it provides online courses and workshops for users to learn new skills, creating an environment where users can proactively challenge themselves in new careers. The AI ​​agent also tracks the user's learning progress and career change progress in real time and automatically reminds them of necessary actions. This allows users to efficiently pursue new careers and receive thorough support all the way to actual employment. For example, when a user inputs their skills, interests, and past experiences, they input their work history, educational background, and areas of interest. For example, if a user inputs "I am interested in programming," the AI ​​analyzes this information. Next, the AI ​​analyzes the input information and proposes the optimal career path for the user. The AI ​​comprehensively assesses the user's skills, interests, and past experiences and proposes suitable occupations and industries. For example, if a user is interested in programming, the AI ​​proposes occupations such as programmer or software engineer. Furthermore, it provides online courses and workshops for users to learn new skills. This allows users to improve their skills and prepare to take on new career challenges. For example, online courses to learn the basics of programming and workshops to actually create programs are offered. In addition, an AI agent tracks the user's learning progress and career change progress in real time and automatically reminds them of necessary actions. This allows users to always know their learning status and career change progress and take necessary actions without forgetting. For example, features such as tracking the progress of online courses and reminding users of the next lesson are provided. Through this system, users can efficiently take on new career challenges and receive thorough support all the way to actual employment.This allows the career support system to suggest the optimal career path based on the user's skills, interests, and past experience, and to support their learning progress and career changes.

[0070] The career support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a tracking unit. The reception unit receives input from the user regarding their skills, interests, and past experience. User skills include, but are not limited to, technical skills and soft skills. The reception unit also receives input from the user regarding their work history, educational background, and areas of interest. The analysis unit analyzes the information received by the reception unit and proposes the optimal career path. The analysis unit comprehensively assesses the user's skills, interests, and past experience and proposes suitable occupations and industries. For example, if the user is interested in programming, the analysis unit proposes occupations such as programmer or software engineer. The provision unit provides online courses and workshops for the user to learn new skills. The provision unit provides, for example, online courses to learn the basics of programming and workshops to actually create programs. The tracking unit tracks the user's learning progress and career change progress in real time. The tracking unit tracks the progress of online courses and provides reminders for the next lesson. As a result, the career support system according to the embodiment can propose an optimal career path based on the user's skills, interests, and past experience, and can support learning progress and career changes.

[0071] The reception desk accepts input from users regarding their skills, interests, and past experience. User skills include, but are not limited to, technical and soft skills. The reception desk accepts input from users regarding their work history, educational background, and areas of interest. Specifically, users can upload detailed resumes and CVs through a dedicated interface. Users can also use self-assessment sheets to describe their skill sets and areas of interest in detail. For example, technical skills can include specific skills such as programming languages, database management, and network construction. Soft skills include communication skills, leadership, and problem-solving abilities. Furthermore, users can describe their past projects and work experience in detail. This allows the reception desk to gain a comprehensive understanding of the user's skills and experience. The reception desk also accepts input from users regarding their interests and career goals. For example, users can input what types of jobs or industries they would like to work in in the future, and what skills they would like to acquire. This allows the reception desk to collect foundational information to more specifically support the user's career path.

[0072] The analysis department analyzes the information received by the reception department and proposes the optimal career path. For example, the analysis department comprehensively assesses the user's skills, interests, and past experience to suggest suitable job types and industries. Specifically, the analysis department uses AI to analyze the user's input information and derive the optimal career path. For example, it uses natural language processing technology to analyze the contents of the user's resume and self-assessment sheet to extract the user's skill set and interests. Furthermore, it uses machine learning algorithms to predict the most suitable job types and industries for the user based on past data and statistical information. For example, if the user is interested in programming, the analysis department will suggest job types such as programmer or software engineer. It can also suggest career paths in specific industries or companies based on the user's past experience and skill set. For example, if the user has experience in the financial industry, the analysis department will suggest a career path in the financial technology field. In this way, the analysis department can comprehensively assess the user's skills, interests, and past experience to propose the optimal career path. Furthermore, the analysis department can continuously improve its suggestions based on user feedback. For example, by providing feedback to the user on the proposed career path, the analysis unit can improve the accuracy of the proposed content.

[0073] The service provider offers online courses and workshops to help users learn new skills. For example, they offer online courses to learn the fundamentals of programming and workshops to create actual programs. Specifically, they provide diverse learning content tailored to users' skill levels and interests. Examples include introductory programming courses for beginners, database management courses for intermediate users, and machine learning algorithm courses for advanced users. They also offer workshops and hands-on sessions to develop practical skills. These include workshops where users can create programs and receive real-time feedback, and hands-on sessions where teams work on projects. Furthermore, they provide resources and tools to support users' learning progress. These include dashboards for managing online course progress and quizzes and exercises to review learned material. This allows the service provider to provide an environment where users can effectively learn new skills and support their career path.

[0074] The tracking unit tracks users' learning progress and career change progress in real time. For example, it tracks the progress of online courses and provides reminders for the next lesson. Specifically, the tracking unit collects user learning data and provides a dashboard to visualize progress. For example, it displays information in real time such as which courses the user is taking, which lessons have been completed, and which exercises they are working on. The tracking unit also provides reminders for the next lesson and adjusts the learning plan according to the user's learning progress. For example, when a user completes a particular lesson, it notifies them of a reminder for the next lesson and updates the learning plan. If a user falls behind in their learning, the tracking unit provides appropriate support and advice. Furthermore, the tracking unit also tracks the progress of users' career changes. For example, if a user changes jobs to a new profession, it tracks their progress and results and provides additional support as needed. In this way, the tracking unit can support users' learning progress and career changes in real time and help them achieve the optimal career path.

[0075] The tracking unit includes a reminder unit that tracks the user's learning progress and career change progress, and automatically reminds the user of necessary actions. For example, the tracking unit tracks the user's learning progress and reminds the user of the next learning step. For example, the tracking unit tracks the user's progress in an online course and reminds the user of the next lesson. The tracking unit can also track the user's career change progress and remind the user of necessary actions. For example, the tracking unit tracks the user's progress in job hunting and reminds the user of the next step. In this way, by tracking the user's learning progress and career change progress and automatically reminding the user of necessary actions, the system can support the user's career change. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's learning progress data into a generating AI and have the generating AI execute a reminder for the next learning step.

[0076] The analysis unit comprehensively assesses the user's skills, interests, and past experience to propose suitable job types and industries. For example, the analysis unit comprehensively assesses the user's skills, interests, and past experience using a scoring system. For example, based on the user's skills, interests, and past experience, the analysis unit proposes suitable job types and industries. For example, if the user is interested in programming, the analysis unit will propose job types such as programmer or software engineer. The analysis unit can also comprehensively assess the user's skills, interests, and past experience using a weighting algorithm. For example, based on the user's skills, interests, and past experience, the analysis unit proposes suitable job types and industries. In this way, by comprehensively assessing the user's skills, interests, and past experience, it can propose suitable job types and industries. 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 skills, interests, and past experience data into a generating AI and have the generating AI propose the optimal career path.

[0077] The service provider offers online courses and workshops for users to learn new skills. For example, the service provider may offer online courses to learn the basics of programming. For example, the service provider may offer workshops where users actually create programs. The service provider may also offer online courses to improve business skills for users to learn new skills. For example, the service provider may offer workshops to learn project management skills. In this way, the service provider can support users' skill improvement by offering online courses and workshops for users to learn new skills. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider may input user skill data into generative AI and have the generative AI suggest the most suitable online courses and workshops.

[0078] The reminder unit tracks the progress of the online course and reminds the user of the next lesson. For example, the reminder unit allows the user to track their progress in the online course and remind them of the next lesson. For example, the reminder unit allows the user to continue learning by receiving a reminder for the next lesson. The reminder unit can also track the user's learning progress and remind them of the next learning step. For example, the reminder unit allows the user to continue learning by receiving a reminder for the next learning step. This allows the system to support the user's learning by tracking their progress in the online course and reminding them of the next lesson. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reminder unit can input the user's learning progress data into a generative AI and have the generative AI execute a reminder for the next lesson.

[0079] The reception desk estimates the user's emotions and adjusts the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception desk provides an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception desk provides an interface with bright colors to make the input process enjoyable. The reception desk can also provide a simple and highly visible interface to facilitate the input process if the user is tired. For example, if the reception desk is tired, it provides a simple and highly visible interface to facilitate the input process. In this way, the user's input process can be made more comfortable by adjusting the design of the input interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the design adjustment of the input interface.

[0080] The reception desk analyzes the user's past input history and suggests the optimal input method. For example, the reception desk automatically displays skills and interests that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest skills and interests that the user will use at specific times of day based on the user's past input history. For example, the reception desk predicts and suggests skills and interests that the user will use at specific times of day based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history data into a generative AI and have the generative AI suggest the optimal input method.

[0081] The reception desk customizes input fields based on the user's current job status and areas of interest. For example, when the user enters their current job status, the reception desk automatically displays relevant skills and interests as suggestions. For example, the reception desk prioritizes displaying relevant input fields based on the user's areas of interest. The reception desk can also customize input fields based on the user's job status and areas of interest to streamline the input process. For example, the reception desk streamlines the input process by customizing input fields based on the user's job status and areas of interest. This streamlines the input process by customizing input fields based on the user's current job status and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's job status and areas of interest data into a generative AI and have the generative AI perform the customization of input fields.

[0082] The reception desk estimates the user's emotions and prioritizes input based on the estimated emotions. For example, if the user is stressed, the reception desk prioritizes displaying important input items and minimizes the input steps. For example, if the user is relaxed, the reception desk provides detailed input options and suggests customizable input methods. The reception desk can also prioritize voice input if the user is in a hurry, allowing them to quickly input skills and interests. For example, if the reception desk prioritizes voice input if the user is in a hurry, allowing them to quickly input skills and interests. This streamlines the user's input process by prioritizing input based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the input prioritization.

[0083] The reception desk prioritizes displaying input fields that are highly relevant, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception desk prioritizes displaying skills and interests related to that region. For example, if the user works in a specific region, the reception desk prioritizes displaying job descriptions and areas of interest related to that region. The reception desk can also customize highly relevant input fields based on the user's geographical location to streamline the input process. For example, the reception desk customizes highly relevant input fields based on the user's geographical location to streamline the input process. This allows the reception desk to prioritize displaying highly relevant input fields by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI display highly relevant input fields.

[0084] The reception desk analyzes the user's social media activity and suggests relevant input fields. For example, the reception desk automatically displays skills and interests that the user frequently mentions on social media as suggestions. For example, the reception desk suggests relevant job situations and areas of interest based on the user's social media activity. The reception desk can also analyze the user's social media activity and suggest the most suitable input fields. For example, the reception desk analyzes the user's social media activity and suggests the most suitable input fields. This allows the reception desk to suggest relevant input fields by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI suggest relevant input fields.

[0085] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides analysis results that include detailed information. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, if the analysis unit is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis results based on the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis results.

[0086] The analysis unit adjusts the level of detail of the analysis based on the importance of the user's skills and interests during the analysis. For example, if the user has a strong interest in a particular skill, the analysis unit will provide detailed analysis results related to that skill. For example, if the user has multiple interests, the analysis unit will provide analysis results corresponding to each interest. The analysis unit can also adjust the level of detail of the analysis based on the importance of the user's skills and interests to provide the optimal analysis results. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the user's skills and interests to provide the optimal analysis results. This allows the analysis unit to provide the optimal analysis results by adjusting the level of detail of the analysis based on the importance of the user's skills and interests. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user skill and interest data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0087] The analysis unit applies different analysis algorithms depending on the user's work history and educational background during analysis. For example, if the user has a specific work history, the analysis unit applies an analysis algorithm based on that work history. For example, if the user has a specific educational background, the analysis unit applies an analysis algorithm based on that educational background. The analysis unit can also select the optimal analysis algorithm according to the user's work history and educational background and provide the analysis results. For example, the analysis unit selects the optimal analysis algorithm according to the user's work history and educational background and provides the analysis results. This allows for the provision of optimal analysis results by applying different analysis algorithms according to the user's work history and educational background. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's work history and educational background data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0088] The analysis unit estimates the user's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit will provide detailed analysis results and suggest a customizable analysis method. The analysis unit can also provide analysis results quickly and prioritize displaying concise information if the user is in a hurry. For example, if the analysis unit is in a hurry, it will provide analysis results quickly and prioritize displaying concise information. This ensures that analysis results important to the user are prioritized by determining the priority of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the priority determination of the analysis results.

[0089] The analysis unit determines the priority of analysis based on the user's submission timing. For example, the analysis unit prioritizes the analysis of information submitted by the user within a specific deadline. For example, if the user is in a hurry, the analysis unit provides analysis results quickly based on the submission timing. The analysis unit can also adjust the priority of analysis according to the user's submission timing to provide the optimal analysis results. For example, the analysis unit adjusts the priority of analysis according to the user's submission timing to provide the optimal analysis results. This allows for the rapid provision of analysis results by determining the priority of analysis based on the user's submission timing. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input user submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0090] The analysis unit adjusts the order of analysis results based on user relevance during analysis. For example, the analysis unit prioritizes analyzing information related to the user's specific skills or interests. For example, the analysis unit prioritizes analyzing information related to the user's work history or educational background. The analysis unit can also adjust the order of analysis results based on user relevance to provide the optimal analysis results. For example, the analysis unit adjusts the order of analysis results based on user relevance to provide the optimal analysis results. This allows the analysis unit to provide the optimal analysis results by adjusting the order of analysis results based on user relevance. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user relevance data into a generative AI and have the generative AI perform the adjustment of the order of analysis results.

[0091] The service provider estimates the user's emotions and adjusts the content of the courses and workshops offered based on those emotions. For example, if the user is nervous, the service provider offers courses or workshops with basic content. For example, if the user is relaxed, the service provider offers courses or workshops with more detailed content. The service provider can also offer courses or workshops that can be completed in a short amount of time if the user is in a hurry. For example, if the service provider is in a hurry, the service provider offers courses or workshops that can be completed in a short amount of time. In this way, by adjusting the content of the courses and workshops offered based on the user's emotions, the service provider can provide the user with the optimal learning environment. 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 not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the course or workshop content.

[0092] The service provider adjusts the difficulty level of courses and workshops based on the user's skill level at the time of delivery. For example, if the user is a beginner, the service provider will provide courses and workshops with basic content. For example, if the user is an intermediate learner, the service provider will provide courses and workshops with intermediate-level content. The service provider can also provide courses and workshops with advanced-level content if the user is an advanced learner. For example, if the service provider is an advanced learner, the service provider will provide courses and workshops with advanced-level content. By adjusting the difficulty level of courses and workshops based on the user's skill level, the service provider can provide learning content that is appropriate for the user. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's skill level data into a generative AI and have the generative AI perform the difficulty level adjustment of courses and workshops.

[0093] The service provider, at the time of delivery, proposes different courses and workshops according to the user's interests. For example, if the user has a specific interest, the service provider will propose courses and workshops related to that interest. For example, if the user has multiple interests, the service provider will propose courses and workshops corresponding to each interest. The service provider can also propose the most suitable courses and workshops based on the user's interests. For example, the service provider will propose the most suitable courses and workshops based on the user's interests. In this way, by proposing different courses and workshops according to the user's interests, the service provider can provide the user with the most suitable learning content. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input user interest data into a generative AI and have the generative AI propose different courses and workshops.

[0094] The service provider estimates the user's emotions and adjusts the order of courses and workshops offered based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize important courses and workshops. For example, if the user is relaxed, the service provider will offer detailed courses and workshops and suggest customizable learning methods. The service provider can also prioritize courses and workshops that allow for quick learning if the user is in a hurry. For example, if the service provider is in a hurry, the service provider will prioritize courses and workshops that allow for quick learning. This allows the service provider to provide the optimal learning order for the user by adjusting the order of courses and workshops offered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the order of courses and workshops.

[0095] The service provider prioritizes providing highly relevant courses and workshops, taking into account the user's geographical location. For example, if a user lives in a specific region, the service provider will prioritize providing courses and workshops related to that region. For example, if a user works in a specific region, the service provider will provide courses and workshops based on their job responsibilities and areas of interest related to that region. The service provider can also customize and provide highly relevant courses and workshops based on the user's geographical location. For example, the service provider can customize and provide highly relevant courses and workshops based on the user's geographical location. This allows the service provider to prioritize providing highly relevant courses and workshops by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing highly relevant courses and workshops.

[0096] The service provider analyzes the user's social media activity at the time of delivery and suggests relevant courses and workshops. For example, the service provider suggests courses and workshops related to skills and interests that the user frequently mentions on social media. For example, the service provider suggests courses and workshops based on the user's social media activity and relevant job situation and areas of interest. The service provider can also analyze the user's social media activity and suggest the most suitable courses and workshops. For example, the service provider analyzes the user's social media activity and suggests the most suitable courses and workshops. This allows the service provider to suggest relevant courses and workshops by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input the user's social media activity data into generative AI and have the generative AI suggest relevant courses and workshops.

[0097] The tracking unit estimates the user's emotions and adjusts the tracking method based on the estimated emotions. For example, if the user is tense, the tracking unit provides a simple and highly visible tracking method. For example, if the user is relaxed, the tracking unit provides a tracking method that includes detailed information. The tracking unit can also provide a concise tracking method if the user is in a hurry. For example, if the tracking unit provides a concise tracking method if the user is in a hurry, the tracking unit provides a concise tracking method. By adjusting the tracking method based on the user's emotions, the optimal tracking method for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the tracking method.

[0098] The tracking unit adjusts the level of detail in tracking based on the user's learning progress and the importance of career changes. For example, if a user has a strong interest in a particular skill, the tracking unit provides detailed tracking information about that skill. For example, if a user has multiple interests, the tracking unit provides tracking information tailored to each interest. The tracking unit can also adjust the level of detail in tracking based on the user's learning progress and the importance of career changes to provide optimal tracking information. For example, the tracking unit adjusts the level of detail in tracking based on the user's learning progress and the importance of career changes to provide optimal tracking information. This allows the tracking unit to provide optimal tracking information by adjusting the level of detail in tracking based on the user's learning progress and the importance of career changes. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the tracking unit can input the user's learning progress and career change data into a generative AI and have the generative AI perform the adjustment of the level of detail in tracking.

[0099] The tracking unit applies different tracking algorithms depending on the user's work history and educational background during tracking. For example, if the user has a specific work history, the tracking unit applies a tracking algorithm based on that work history. For example, if the user has a specific educational background, the tracking unit applies a tracking algorithm based on that educational background. The tracking unit can also select the most suitable tracking algorithm based on the user's work history and educational background and provide tracking information. For example, the tracking unit selects the most suitable tracking algorithm based on the user's work history and educational background and provides tracking information. This allows for the provision of optimal tracking information by applying different tracking algorithms depending on the user's work history and educational background. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's work history and educational background data into a generative AI and have the generative AI execute the application of different tracking algorithms.

[0100] The tracking unit estimates the user's emotions and prioritizes tracking results based on the estimated emotions. For example, if the user is stressed, the tracking unit prioritizes displaying important tracking results. For example, if the user is relaxed, the tracking unit provides detailed tracking results and suggests a customizable tracking method. The tracking unit can also provide tracking results quickly and prioritize displaying concise information if the user is in a hurry. For example, if the tracking unit is in a hurry, it provides tracking results quickly and prioritizes displaying concise information. This allows the system to prioritize tracking results that are important to the user by prioritizing them based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI perform the priority determination of tracking results.

[0101] The tracking unit determines tracking priorities based on the user's submission timing during tracking. For example, the tracking unit prioritizes tracking information submitted by the user within a specific deadline. For example, if the user is in a hurry, the tracking unit provides tracking results quickly based on the submission timing. The tracking unit can also adjust tracking priorities according to the user's submission timing to provide optimal tracking results. For example, the tracking unit adjusts tracking priorities according to the user's submission timing to provide optimal tracking results. This allows for the rapid provision of tracking results by determining tracking priorities based on the user's submission timing. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input user submission timing data into a generative AI and have the generative AI perform the tracking priority determination.

[0102] The tracking unit adjusts the order of tracking results based on user relevance during tracking. For example, the tracking unit prioritizes tracking information related to the user's specific skills or interests. For example, the tracking unit prioritizes tracking information related to the user's work history or educational background. The tracking unit can also adjust the order of tracking results based on user relevance to provide optimal tracking results. For example, the tracking unit adjusts the order of tracking results based on user relevance to provide optimal tracking results. This allows for the provision of optimal tracking results by adjusting the order of tracking results based on user relevance. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input user relevance data into a generative AI and have the generative AI perform the adjustment of the order of tracking results.

[0103] The reminder unit estimates the user's emotions and adjusts the reminder method based on the estimated emotions. For example, if the user is tense, the reminder unit will give a calm reminder. For example, if the user is relaxed, the reminder unit will give a cheerful reminder. The reminder unit can also give a quick and concise reminder if the user is in a hurry. For example, if the reminder unit gives a quick and concise reminder if the user is in a hurry, the reminder unit will give a quick and concise reminder. In this way, by adjusting the reminder method based on the user's emotions, the system can provide the user with the most optimal reminder method. 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 reminder unit may be performed using AI or not using AI. For example, the reminder unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the reminder method.

[0104] The reminder unit adjusts the level of detail in reminders based on the user's learning progress and the importance of their career change. For example, if a user has a strong interest in a particular skill, the reminder unit provides a detailed reminder about that skill. For example, if a user has multiple interests, the reminder unit provides reminders tailored to each interest. The reminder unit can also adjust the level of detail in reminders based on the user's learning progress and the importance of their career change to provide the most appropriate reminder. For example, the reminder unit adjusts the level of detail in reminders based on the user's learning progress and the importance of their career change to provide the most appropriate reminder. This allows the system to provide optimal reminder information by adjusting the level of detail in reminders based on the user's learning progress and the importance of their career change. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reminder unit can input the user's learning progress and career change data into a generative AI and have the generative AI perform the adjustment of the level of detail in reminders.

[0105] The reminder unit applies different reminder algorithms depending on the user's work history and educational background when sending reminders. For example, if the user has a specific work history, the reminder unit applies a reminder algorithm based on that work history. For example, if the user has a specific educational background, the reminder unit applies a reminder algorithm based on that educational background. The reminder unit can also select the most appropriate reminder algorithm based on the user's work history and educational background and provide reminder information. For example, the reminder unit selects the most appropriate reminder algorithm based on the user's work history and educational background and provides reminder information. This allows the system to provide optimal reminder information by applying different reminder algorithms depending on the user's work history and educational background. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the user's work history and educational background data into a generative AI and have the generative AI execute the application of different reminder algorithms.

[0106] The reminder section estimates the user's emotions and determines the priority of reminders based on the estimated emotions. For example, if the user is stressed, the reminder section will prioritize important reminders. For example, if the user is relaxed, the reminder section will provide detailed reminders and suggest customizable reminder methods. The reminder section can also provide quick reminders and prioritize the display of concise information if the user is in a hurry. For example, if the reminder section is in a hurry, it will provide quick reminders and prioritize the display of concise information. This ensures that important reminders are prioritized for the user by determining the priority of reminders based on their 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 reminder section may be performed using AI or not using AI. For example, the reminder unit can input user emotion data into a generating AI and have the AI ​​determine the priority of reminders.

[0107] The reminder unit determines the priority of reminders based on the user's submission timing. For example, the reminder unit prioritizes reminders for information submitted by the user within a specific deadline. For example, if the user is in a hurry, the reminder unit provides a quick reminder result based on the submission timing. The reminder unit can also adjust the priority of reminders according to the user's submission timing to provide the optimal reminder result. For example, the reminder unit adjusts the priority of reminders according to the user's submission timing to provide the optimal reminder result. This allows for the rapid provision of reminder results by determining the priority of reminders based on the user's submission timing. Some or all of the above processing in the reminder unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reminder unit can input user submission timing data into a generation AI and have the generation AI perform the reminder priority determination.

[0108] The reminder unit adjusts the order of reminder results based on user relevance when sending reminders. For example, the reminder unit prioritizes reminders of information related to the user's specific skills or interests. For example, the reminder unit prioritizes reminders of information related to the user's work history or educational background. The reminder unit can also adjust the order of reminder results based on user relevance to provide the most optimal reminder results. For example, the reminder unit adjusts the order of reminder results based on user relevance to provide the most optimal reminder results. This allows the system to provide the most optimal reminder results by adjusting the order of reminder results based on user relevance. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input user relevance data into a generative AI and have the generative AI perform the adjustment of the order of reminder results.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The reception desk can provide real-time feedback based on user input regarding skills, interests, and past experience. For example, if a user enters a specific skill, it can instantly display information on related job roles and industries. If a user enters an interest, it can also provide the latest trends and news related to that interest. Furthermore, if a user enters past experience, it can suggest recommended career paths and resources for skill development based on that experience. This allows users to receive immediate feedback on their input and consider more specific career path options.

[0111] The tracking unit can analyze user behavior patterns when tracking a user's learning progress and career change progress, and propose an optimal learning schedule. For example, if a user tends to study at a specific time of day, it can set learning reminders tailored to that time. Furthermore, if a user prefers a particular learning method (video, text, practice, etc.), it can prioritize providing learning content based on that method. It can also suggest the next steps based on the user's learning progress and provide advice to maintain motivation. This allows users to receive support optimized for their own learning style and schedule.

[0112] The analytics unit can take into account a user's future career goals when comprehensively assessing their skills, interests, and past experience. For example, if a user is aiming for a career in a specific job or industry, it can identify gaps in skills and experience to reach that goal and suggest resources for necessary skill development. If a user has a long-term career plan, it can also provide specific steps along that career path. Furthermore, it can suggest relevant networking events and connections with industry experts, depending on the user's career goals. This allows users to develop concrete action plans toward achieving their career objectives.

[0113] The service provider can customize online courses and workshops to suit users' learning styles when offering them to acquire new skills. For example, if a user prefers visual learning, a course with extensive video content and infographics can be provided. If a user prefers hands-on learning, hands-on workshops or project-based learning content can be offered. Furthermore, if a user prefers self-study, an online course that can be progressed at one's own pace can be provided, along with options for support as needed. This allows users to utilize learning resources optimized for their own learning style.

[0114] The reminder section can track online course progress and incorporate features to maintain user motivation when reminding users of the next lesson. For example, it can provide messages and badges that give users a sense of accomplishment when they achieve a specific goal. It can also send encouraging messages to help users move on to the next step as they progress through their learning. Furthermore, if a user is losing motivation, it can offer advice and success stories to boost their motivation. This allows users to enjoy their learning progress and maintain their motivation to continue learning.

[0115] The reception desk can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is stressed, it can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, it can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, it can provide a simple and highly visible interface to make the input process easier. In this way, by adjusting the input interface design based on the user's emotions, the user's input process can be made more comfortable.

[0116] The reception desk can analyze a user's past input history and suggest the most suitable input method. For example, it can automatically display skills and interests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest skills and interests that the user will use at specific times of the day based on their past input history. In this way, by analyzing a user's past input history, the system can suggest the most suitable input method.

[0117] The reception desk can customize input fields based on the user's current job status and areas of interest. For example, when a user enters their current job status, relevant skills and interests are automatically displayed as suggestions. It can also prioritize the display of relevant input fields based on the user's areas of interest. Furthermore, it is possible to streamline the input process by customizing input fields based on the user's job status and areas of interest. This streamlines the input process by customizing input fields based on the user's current job status and areas of interest.

[0118] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, it can prioritize important input fields and minimize the input process. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of skills and interests. In this way, prioritizing input based on the user's emotions can streamline the user's input process.

[0119] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location. For example, if a user lives in a specific region, it can prioritize displaying skills and interests related to that region. Similarly, if a user works in a specific region, it can prioritize displaying job-related information and areas of interest related to that region. Furthermore, it's possible to customize highly relevant input fields based on the user's geographical location, thereby streamlining the input process. This allows for the prioritization of highly relevant input fields by considering the user's geographical location.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk accepts user input regarding skills, interests, and past experience. User skills include technical skills and soft skills. The reception desk accepts user input regarding their work history, educational background, and areas of interest. Step 2: The analysis department analyzes the information received by the reception department and proposes the optimal career path. The analysis department comprehensively assesses the user's skills, interests, and past experience to propose suitable job types and industries. For example, if the user is interested in programming, it will propose job types such as programmer or software engineer. Step 3: The service provider offers online courses and workshops for users to learn new skills. The service provider offers online courses to learn the basics of programming, as well as workshops to actually create programs. Step 4: The tracking unit tracks the user's learning progress and career change progress in real time. The tracking unit tracks the progress of online courses and provides reminders for the next lesson.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and tracking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input of the user's skills, interests, and past experience. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to propose an optimal career path. The provision unit is implemented by the output device 40 of the smart device 14 and provides online courses and workshops for the user to learn new skills. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the user's learning progress and career change in real time. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and tracking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input of the user's skills, interests, and past experiences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to propose an optimal career path. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides online courses and workshops for the user to learn new skills. The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12 and tracks the user's learning progress and career change in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and tracking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives input of the user's skills, interests, and past experience. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to propose an optimal career path. The provision unit is implemented by the display 343 of the headset terminal 314 and provides online courses and workshops for the user to learn new skills. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the user's learning progress and career change in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and tracking unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives input from the user regarding their skills, interests, and past experience. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to propose an optimal career path. The provision unit is implemented by the speaker 240 of the robot 414 and provides online courses and workshops for the user to learn new skills. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the user's learning progress and career change in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A reception desk that accepts user input regarding skills, interests, and past experience, An analysis unit analyzes the information received by the reception unit and proposes the optimal career path, The service department provides online courses and workshops for users to learn new skills, It includes a tracking unit that tracks the user's learning progress and career change progress in real time. A system characterized by the following features. (Note 2) The aforementioned tracking unit is It includes a reminder section that tracks the user's learning progress and career change status, and automatically reminds them of necessary actions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We comprehensively assess the user's skills, interests, and past experience to suggest suitable job roles and industries. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We offer online courses and workshops for users to learn new skills. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reminder unit, Track your online course progress and receive reminders for your next lesson. The system described in Appendix 2, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Customize input fields 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 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system prioritizes displaying input fields that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the user's skills and interests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's work history and educational background. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the user submitted their data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis results is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the user's emotions and adjust the content of the courses and workshops we offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When offering a course or workshop, the difficulty level will be adjusted based on the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When offering the service, we suggest different courses and workshops based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order of courses and workshops offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing content, the system prioritizes offering highly relevant courses and workshops, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest relevant courses and workshops. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tracking unit is We estimate the user's emotions and adjust the tracking method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tracking unit is During tracking, adjust the level of detail based on the user's learning progress and the importance of their career change. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tracking unit is During tracking, different tracking algorithms are applied depending on the user's work history and educational background. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tracking unit is It estimates the user's emotions and prioritizes tracking results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tracking unit is During tracking, tracking priorities are determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tracking unit is During tracking, the order of tracking results is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reminder unit, It estimates the user's emotions and adjusts the reminder method based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned reminder unit, When sending reminders, adjust the level of detail based on the user's learning progress and the importance of their career change. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned reminder unit, When sending reminders, different reminder algorithms are applied depending on the user's work history and educational background. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned reminder unit, It estimates the user's emotions and determines the priority of reminders based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned reminder unit, When sending reminders, prioritize them based on when the user submitted their work. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned reminder unit, When sending reminders, adjust the order of reminder results based on user relevance. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user input regarding skills, interests, and past experience, An analysis unit analyzes the information received by the reception unit and proposes the optimal career path, The service department provides online courses and workshops for users to learn new skills, It includes a tracking unit that tracks the user's learning progress and career change progress in real time. A system characterized by the following features.

2. The aforementioned tracking unit is It includes a reminder section that tracks the user's learning progress and career change status, and automatically reminds them of necessary actions. The system according to feature 1.

3. The aforementioned analysis unit, We comprehensively assess the user's skills, interests, and past experience to suggest suitable job roles and industries. The system according to feature 1.

4. The aforementioned supply unit is, We offer online courses and workshops for users to learn new skills. The system according to feature 1.

5. The aforementioned reminder unit, Track your online course progress and receive reminders for your next lesson. The system according to feature 2.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is Customize input fields based on the user's current job status and areas of interest. The system according to feature 1.

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

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