system

Abstract

The system according to this embodiment aims to help users clarify their future careers and develop concrete action plans and mindsets. [Solution] The system according to this embodiment comprises a plan provision unit, an experience provision unit, and a progress visualization unit. The plan provision unit provides action plans and mindsets to clarify the user's future career. The experience provision unit provides a virtual work experience utilizing VR technology. The progress visualization unit visualizes the progress.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 it is difficult for a user to clarify their future career and have a specific action plan or mindset.

[0005] The system according to the embodiment aims to enable the user to clarify their future career and have a specific action plan or mindset.

Means for Solving the Problems

[0006] The system according to the embodiment includes a plan providing unit, an experience providing unit, and a progress visualization unit. The plan providing unit provides an action plan and a mindset for clarifying the user's future career. The experience providing unit provides a virtual career experience utilizing VR technology. The progress visualization unit visualizes the progress. [Effects of the Invention]

[0007] The system according to this embodiment allows users to clarify their future careers and develop concrete action plans and mindsets. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 Future Design AI Partner according to an embodiment of the present invention is an AI agent service that helps users discover the person they want to become and supports them in designing their life to achieve that goal. When a user inputs what kind of person they want to become in the future, the AI ​​agent provides a concrete action plan and mindset modeled after the career paths and thinking of successful people. Furthermore, it is equipped with a virtual job experience utilizing VR technology and a graph function that visualizes progress toward achieving goals. This allows users to feel where they are and what they need to do to get closer to their goals, making it easier to maintain motivation. For example, if a user inputs "I want to go from XX to XX," the AI ​​agent analyzes the information and creates a video showing how to get from the current location to the destination. This video starts navigating according to the orientation of the user's smartphone, and the screen moves in accordance with the walking speed. This results in a simple structure that can be easily used by children and the elderly alike, making it enjoyable for everyone. Also, since the viewpoint of the smartphone is pointed at all axes, users will not get lost, and walking safety is ensured because the smartphone is held horizontally. Furthermore, the AI ​​agent provides personalized questions and diagnoses to deepen the user's self-understanding, supporting them in discovering their ideal self. Furthermore, it supports life planning based on users' ideals and current situations, helping them to envision specific career paths and life visions. It also provides learning content that complements the career education and economic / financial literacy lacking in compulsory education, supporting the formation of concrete images of future career choices and social contributions. This service also includes a mental support function that analyzes users' emotions and motivations and suggests encouragement and refreshing methods at the appropriate time. In addition, it provides an online community where users can interact, share information, and support each other's growth through cooperation. Through multilingual support and international information provision, it aims to create an environment where users around the world can grow together, contributing to the realization of economic and mental well-being and the development of society as a whole. In this way, Future Design AI Partner can help users discover the person they want to be and support them in designing their life to achieve that.

[0029] The Future Design AI Partner according to this embodiment comprises a plan provision unit, an experience provision unit, and a progress visualization unit. The plan provision unit provides action plans and mindsets to clarify the user's future career. For example, when the user inputs what kind of person they want to become in the future, the plan provision unit provides specific action plans and mindsets modeled after the career paths and thinking of successful people. The plan provision unit can, for example, provide step-by-step instructions and methods for setting goals. It can also provide methods for improving positive thinking and self-efficacy. The experience provision unit provides virtual work experiences utilizing VR technology. For example, the experience provision unit enables the user to form a concrete image of their future occupation through virtual work experiences. For example, the experience provision unit can provide types of occupations that can be experienced and scenarios for the experience. It can also provide realistic work experiences using VR technology. The progress visualization unit visualizes progress toward achieving goals. For example, the progress visualization unit can display the user's progress toward achieving goals in graphs and charts. The progress visualization unit can, for example, provide evaluation criteria and visualization formats for progress. It can also enable users to check their progress in real time. This allows the future design AI partner, according to the embodiment, to clarify the user's future career and support them through action plans, virtual work experiences, and progress visualization.

[0030] The planning department provides action plans and mindsets to clarify users' future careers. For example, when a user inputs what kind of person they want to become in the future, the planning department provides specific action plans and mindsets modeled after the career paths and thinking of successful individuals. Specifically, based on the goals and desired career entered by the user, the AI ​​refers to a database of past successful individuals and analyzes the behavior and thought patterns of people with similar career paths. This allows the department to provide users with specific advice on what skills they should acquire, what experiences they should gain, and what kind of networking they should engage in. The planning department can also provide step-by-step instructions and methods for setting goals. For example, it can set short-term and long-term goals and present specific action plans for each goal. Furthermore, it can provide methods for improving positive thinking and self-efficacy. This includes affirmations to boost daily self-esteem and mental exercises for stress management. As a result, users can have a clear vision for their careers and gain specific means to act effectively towards them. Based on user feedback, the planning department can continuously improve the content of the action plans and mindsets to provide more effective support.

[0031] The Experience Provision Department provides virtual career experiences utilizing VR technology. For example, the department helps users develop a concrete image of their future career through virtual work experiences. Specifically, users can select their desired profession and experience a day in that profession through a VR headset. For instance, a user aspiring to be a doctor can experience simulations of examinations and surgeries in a virtual hospital. A user aspiring to be an engineer can experience project management and team meetings in a virtual office. The Experience Provision Department can provide various types of professions and experience scenarios. This includes specific job duties, workplace atmosphere, and communication with colleagues for each profession. Furthermore, the Experience Provision Department can also provide realistic career experiences using VR technology. This allows users to virtually experience the actual work environment and gain valuable information to determine if a profession is suitable for them. Based on user feedback, the Experience Provision Department can continuously improve the experience content to provide more realistic and effective career experiences. This enables users to develop a concrete image of their future career and effectively prepare for it.

[0032] The progress visualization unit visualizes progress toward achieving goals. For example, it can display a user's progress toward achieving their goals using graphs and charts. Specifically, it monitors the degree of achievement toward the goals set by the user in real time and visually displays the progress. For example, it can display progress toward short-term and long-term goals set by the user using bar graphs and line graphs, allowing users to grasp the degree of achievement and remaining tasks at a glance. The progress visualization unit can provide progress evaluation criteria and visualization formats. This includes evaluation criteria for goal achievement and color schemes to indicate progress. The progress visualization unit can also allow users to check their progress in real time. This allows users to always know their progress toward their goals and revise their action plans as needed. Furthermore, based on user feedback, the progress visualization unit can continuously improve the methods and evaluation criteria for progress visualization, providing more effective progress management. This allows users to obtain concrete means to effectively move toward their goals. The progress visualization unit can collaborate with other departments to comprehensively evaluate the user's progress and provide necessary support.

[0033] The Mental Support Department can provide mental support. For example, the Mental Support Department can analyze the user's emotions and motivation and provide mental support at the appropriate time. For example, the Mental Support Department can provide counseling and stress management methods. In addition, the Mental Support Department can provide refreshing methods and encouraging messages based on the user's emotions. This allows the Mental Support Department to analyze the user's emotions and motivation and provide mental support at the appropriate time. Some or all of the above processes in the Mental Support Department may be performed using AI, for example, or not using AI. For example, the Mental Support Department can input the user's emotional data into a generating AI and have the generating AI perform an emotional analysis.

[0034] The community provider can provide online communities. For example, the community provider can provide online communities where users can interact with each other and support each other's growth through information sharing and cooperation. The community provider can provide, for example, the types of communities and how to participate. The community provider can also provide an intuitive interface so that users can easily participate. In this way, the community provider can provide online communities where users can interact with each other and support each other's growth through information sharing and cooperation. Some or all of the above processing in the community provider may be performed using AI, for example, or not using AI. For example, the community provider can input user participation data into a generating AI and have the generating AI perform the community provision.

[0035] The learning provision unit can provide learning content that complements career education and economic / financial literacy. For example, the learning provision unit can provide learning content that complements career education and economic / financial literacy that are lacking in compulsory education. For example, the learning provision unit can provide educational themes and types of teaching materials. The learning provision unit can also provide online learning content so that users can learn at their own pace. In this way, the learning provision unit can provide learning content that complements career education and economic / financial literacy that are lacking in compulsory education. Some or all of the above processing in the learning provision unit may be performed using AI, for example, or not using AI. For example, the learning provision unit can input user learning data into a generating AI and have the generating AI execute the provision of learning content.

[0036] The planning unit can provide concrete action plans and mindsets based on the career paths and thinking of successful individuals. For example, the planning unit can provide action plans tailored to the user by referencing the career paths of successful individuals. For example, the planning unit can provide mindsets tailored to the user by modeling the thinking of successful individuals. Furthermore, the planning unit can provide concrete steps for the user to achieve their goals based on the career paths and thinking of successful individuals. In this way, the planning unit can provide concrete action plans and mindsets based on the career paths and thinking of successful individuals. Some or all of the above processing in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input successful individuals' career path data into a generating AI and have the generating AI execute the provision of action plans.

[0037] The experience provision unit can provide virtual job experiences using VR technology. For example, the experience provision unit can enable users to virtually experience jobs using VR technology. For example, the experience provision unit can provide the types of jobs that can be experienced and the scenarios for the experience. The experience provision unit can also provide realistic job experiences using VR technology. In this way, the experience provision unit can provide virtual job experiences using VR technology. Some or all of the above processing in the experience provision unit may be performed using AI, for example, or without AI. For example, the experience provision unit can input job experience data using VR technology into a generating AI and have the generating AI perform the provision of virtual job experiences.

[0038] The progress visualization unit can visualize progress toward achieving a goal. For example, the progress visualization unit can display the user's progress toward achieving the goal in graphs or charts. For example, the progress visualization unit can provide progress evaluation criteria and visualization formats. The progress visualization unit can also allow the user to check their progress in real time. In this way, the progress visualization unit can visualize progress toward achieving the goal. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without using AI. For example, the progress visualization unit can input the user's progress data into a generating AI and have the generating AI perform the visualization of the progress.

[0039] The planning unit can analyze a user's past career history and provide an optimal action plan. For example, the planning unit can provide a similar action plan based on the user's past successful projects. For example, the planning unit can provide an action plan necessary for skill development based on the user's past career history. The planning unit can also analyze a user's past career history and provide an action plan suitable for a career change. In this way, the planning unit can analyze a user's past career history and provide an optimal action plan. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's career history data into a generating AI and have the generating AI perform the task of providing an action plan.

[0040] The planning unit can adjust the level of detail of an action plan based on the user's current skill level when providing the plan. For example, if the user has a high skill level, the planning unit can provide a detailed action plan. If the user has a low skill level, the planning unit can provide a basic action plan. The planning unit can also provide a step-by-step action plan according to the user's skill level. This allows the planning unit to adjust the level of detail of the plan based on the user's current skill level. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's skill level data into a generating AI and have the generating AI perform the adjustment of the action plan.

[0041] The planning unit can prioritize providing highly relevant plans when providing action plans, taking into account the user's geographical location information. For example, if the user lives in an urban area, the planning unit can provide action plans that can be implemented in urban areas. For example, if the user lives in a rural area, the planning unit can provide action plans that can be implemented in rural areas. The planning unit can also provide action plans that take regional characteristics into account based on the user's geographical location information. This allows the planning unit to provide highly relevant action plans that take into account the user's geographical location information. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's geographical location information data into a generating AI and have the generating AI execute the provision of action plans.

[0042] The planning unit can analyze the user's social media activity and provide relevant plans when providing action plans. For example, the planning unit can provide action plans based on topics the user has shown interest in on social media. For example, the planning unit can provide action plans related to areas of interest based on the user's social media activity. The planning unit can also analyze the user's social media activity and provide action plans based on trends. This allows the planning unit to analyze the user's social media activity and provide relevant action plans. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing action plans.

[0043] The experience provision unit can provide the most suitable virtual work experience by referring to the user's past work experience when providing virtual work experience. For example, the experience provision unit can provide experiences related to the user's past work experience. For example, the experience provision unit can provide experiences necessary for skill improvement based on the user's past work experience. Furthermore, the experience provision unit can analyze the user's past work experience and provide experiences suitable for a career change. In this way, the experience provision unit can provide the most suitable virtual work experience by referring to the user's past work experience. Some or all of the above processing in the experience provision unit may be performed using AI, for example, or without using AI. For example, the experience provision unit can input the user's work experience data into a generating AI and have the generating AI perform the provision of virtual work experience.

[0044] The experience delivery unit can adjust the level of detail of a virtual job experience based on the user's current occupation. For example, if the user's current occupation is advanced, the experience delivery unit can provide a detailed job experience. For example, if the user's current occupation is at a beginner level, the experience delivery unit can provide a basic job experience. The experience delivery unit can also provide a step-by-step job experience depending on the user's current occupation. In this way, the experience delivery unit can adjust the level of detail of the experience based on the user's current occupation. Some or all of the above processing in the experience delivery unit may be performed using AI, for example, or without AI. For example, the experience delivery unit can input the user's occupation data into a generating AI and have the generating AI perform the adjustment of the level of detail of the experience.

[0045] The experience provision unit can prioritize providing highly relevant experiences by considering the user's geographical location when providing virtual work experiences. For example, if the user lives in an urban area, the experience provision unit can provide work experiences that can be performed in an urban area. For example, if the user lives in a rural area, the experience provision unit can provide work experiences that can be performed in a rural area. Furthermore, the experience provision unit can also provide work experiences that take regional characteristics into account based on the user's geographical location. In this way, the experience provision unit can provide highly relevant virtual work experiences by considering the user's geographical location. Some or all of the above processing in the experience provision unit may be performed using AI, for example, or without using AI. For example, the experience provision unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of virtual work experiences.

[0046] The experience provision unit can analyze the user's social media activity and provide relevant experiences when providing virtual career experiences. For example, the experience provision unit can provide experiences based on the occupations the user has shown interest in on social media. For example, the experience provision unit can provide career experiences related to the user's areas of interest based on the user's social media activity. The experience provision unit can also analyze the user's social media activity and provide career experiences based on trends. In this way, the experience provision unit can analyze the user's social media activity and provide relevant virtual career experiences. Some or all of the above processing in the experience provision unit may be performed using AI, for example, or without AI. For example, the experience provision unit can input the user's social media data into a generating AI and have the generating AI perform the provision of virtual career experiences.

[0047] The progress visualization unit can provide the optimal display method by referring to the user's past progress data when visualizing progress. For example, the progress visualization unit can provide the optimal display method based on the user's past progress data. For example, the progress visualization unit can provide a display method that increases motivation based on the user's past progress data. The progress visualization unit can also analyze the user's past progress data and provide an efficient display method. As a result, the progress visualization unit can provide the optimal progress display by referring to the user's past progress data. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without using AI. For example, the progress visualization unit can input the user's past progress data into a generating AI and have the generating AI perform the task of providing a progress display method.

[0048] The progress visualization unit can adjust the level of detail displayed based on the user's current goals when visualizing progress. For example, if the user's current goals are advanced, the progress visualization unit can provide a detailed progress display. For example, if the user's current goals are at a beginner level, the progress visualization unit can provide a basic progress display. The progress visualization unit can also provide a step-by-step progress display depending on the user's current goals. This allows the progress visualization unit to adjust the level of detail displayed based on the user's current goals. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without AI. For example, the progress visualization unit can input user goal data into a generating AI and have the generating AI adjust the method of displaying progress.

[0049] The progress visualization unit can prioritize displaying highly relevant progress while considering the user's geographical location information when visualizing progress. For example, if the user lives in an urban area, the progress visualization unit can display progress that can be completed in an urban area. For example, if the user lives in a rural area, the progress visualization unit can display progress that can be completed in a rural area. Furthermore, the progress visualization unit can also display progress that takes regional characteristics into account based on the user's geographical location information. In this way, the progress visualization unit can provide highly relevant progress while considering the user's geographical location information. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without using AI. For example, the progress visualization unit can input the user's geographical location information data into a generating AI and have the generating AI execute the display of progress.

[0050] The progress visualization unit can analyze the user's social media activity and display relevant progress when visualizing progress. For example, the progress visualization unit can display progress based on topics the user has shown interest in on social media. For example, the progress visualization unit can display progress related to areas of interest from the user's social media activity. The progress visualization unit can also analyze the user's social media activity and display progress based on trends. In this way, the progress visualization unit can analyze the user's social media activity and provide relevant progress. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without AI. For example, the progress visualization unit can input the user's social media data into a generating AI and have the generating AI perform the display of progress.

[0051] The mental support unit can provide optimal support by referring to the user's past mental state when providing mental support. For example, the mental support unit can provide optimal mental support based on the user's past mental state. For example, the mental support unit can provide support that is effective in reducing stress based on the user's past mental state. The mental support unit can also analyze the user's past mental state and provide support that helps improve mental health. In this way, the mental support unit can provide optimal mental support by referring to the user's past mental state. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without using AI. For example, the mental support unit can input the user's past mental state data into a generating AI and have the generating AI perform the provision of mental support.

[0052] The mental support unit can prioritize providing highly relevant support by considering the user's geographical location when providing mental support. For example, if the user lives in an urban area, the mental support unit can provide mental support that can be performed in an urban area. For example, if the user lives in a rural area, the mental support unit can provide mental support that can be performed in a rural area. In addition, the mental support unit can provide mental support that takes regional characteristics into account based on the user's geographical location. This allows the mental support unit to provide highly relevant mental support by considering the user's geographical location. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of mental support.

[0053] The community provider unit can provide the optimal way for a user to participate in a community by referring to the user's past community participation history. For example, the community provider unit can provide the optimal way to participate based on the user's past community participation history. For example, the community provider unit can provide activities that the user is interested in based on the user's past community participation history. The community provider unit can also analyze the user's past community participation history and provide methods that make participation easier. In this way, the community provider unit can provide the optimal way to participate by referring to the user's past community participation history. Some or all of the above processing in the community provider unit may be performed using AI, for example, or without AI. For example, the community provider unit can input the user's past community participation history data into a generating AI and have the generating AI perform the community provision.

[0054] The community provider unit can prioritize providing highly relevant communities by considering the user's geographical location information when providing communities. For example, if the user lives in an urban area, the community provider unit can provide community activities that can be performed in urban areas. For example, if the user lives in a rural area, the community provider unit can provide community activities that can be performed in rural areas. Furthermore, the community provider unit can also provide community activities that take regional characteristics into account based on the user's geographical location information. This allows the community provider unit to provide highly relevant community activities by considering the user's geographical location information. Some or all of the above processing in the community provider unit may be performed using AI, for example, or without AI. For example, the community provider unit can input the user's geographical location information data into a generating AI and have the generating AI perform the community provision.

[0055] The learning delivery unit can provide optimal content by referring to the user's past learning history when delivering learning content. For example, the learning delivery unit can provide optimal learning content based on the user's past learning history. For example, the learning delivery unit can provide learning content necessary for skill improvement based on the user's past learning history. The learning delivery unit can also analyze the user's past learning history and provide efficient learning content. In this way, the learning delivery unit can provide optimal learning content by referring to the user's past learning history. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or without using AI. For example, the learning delivery unit can input the user's past learning history data into a generating AI and have the generating AI execute the provision of learning content.

[0056] The learning delivery unit can prioritize providing highly relevant content by considering the user's geographical location when delivering learning content. For example, if the user lives in an urban area, the learning delivery unit can provide learning content that can be executed in an urban area. For example, if the user lives in a rural area, the learning delivery unit can provide learning content that can be executed in a rural area. The learning delivery unit can also provide learning content that takes regional characteristics into account based on the user's geographical location. This allows the learning delivery unit to provide highly relevant learning content by considering the user's geographical location. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or without AI. For example, the learning delivery unit can input the user's geographical location data into a generating AI and have the generating AI execute the provision of learning content.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The planning unit can adjust action plans considering the user's health condition. For example, if a user inputs their health checkup results, the planning unit can provide an action plan based on those results to maintain or improve their health. Furthermore, if a user sets a specific health goal, the unit can provide concrete steps to achieve that goal. In addition, the planning unit can provide an action plan that includes exercise and dietary advice, tailored to the user's health condition. This allows the planning unit to provide action plans that take the user's health into consideration.

[0059] The experience provision department can customize the content of virtual career experiences based on the user's hobbies and interests. For example, if a user is interested in music, they can be offered a music-related career experience. Similarly, if a user is interested in sports, they can be offered a sports-related career experience. Furthermore, if a user is interested in art, they can be offered an art-related career experience. This allows the experience provision department to customize the content of virtual career experiences based on the user's hobbies and interests.

[0060] The progress visualization unit can adjust the display method of progress according to the user's learning style. For example, users with a visual learning style can be provided with progress displays using graphs and charts. Users with an auditory learning style can be provided with a function to report progress via voice. Furthermore, users with a tactile learning style can be provided with interactive progress displays. In this way, the progress visualization unit can provide progress displays that are tailored to the user's learning style.

[0061] The Mental Support Department can analyze users' sleep data and provide appropriate mental support. For example, if a user is sleep-deprived, it can provide relaxing mental support. Conversely, if a user is getting good quality sleep, it can provide mental support to boost motivation. Furthermore, it can suggest stress management and refreshing methods based on the user's sleep patterns. In this way, the Mental Support Department can provide mental support that takes the user's sleep data into consideration.

[0062] The community provider can offer communities based on users' occupations and areas of expertise. For example, it can provide a community for healthcare professionals to share medical information and provide support. It can also provide a community for engineers to facilitate technical information exchange and collaboration. Furthermore, it can provide a community for educators to share knowledge and experience related to education. In this way, the community provider can offer communities tailored to users' occupations and areas of expertise.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The planning department provides action plans and mindsets to clarify the user's future career. When the user inputs what kind of person they want to become in the future, the department provides specific action plans and mindsets modeled after the career paths and thinking of successful individuals. For example, it can provide step-by-step instructions, goal-setting methods, and methods for improving positive thinking and self-efficacy. Step 2: The Experience Provision Department will provide virtual job experiences utilizing VR technology. The goal is to enable users to form a concrete image of their future careers through these virtual job experiences. For example, they can provide a variety of jobs to experience and scenarios for those experiences, and then use VR technology to provide a realistic job experience. Step 3: The progress visualization section visualizes progress toward achieving the goal. It displays the user's progress toward achieving the goal using graphs and charts, and provides evaluation criteria and visualization formats for progress. It can also allow users to check their progress in real time.

[0065] (Example of form 2) The Future Design AI Partner according to an embodiment of the present invention is an AI agent service that helps users discover the person they want to become and supports them in designing their life to achieve that goal. When a user inputs what kind of person they want to become in the future, the AI ​​agent provides a concrete action plan and mindset modeled after the career paths and thinking of successful people. Furthermore, it is equipped with a virtual job experience utilizing VR technology and a graph function that visualizes progress toward achieving goals. This allows users to feel where they are and what they need to do to get closer to their goals, making it easier to maintain motivation. For example, if a user inputs "I want to go from XX to XX," the AI ​​agent analyzes the information and creates a video showing how to get from the current location to the destination. This video starts navigating according to the orientation of the user's smartphone, and the screen moves in accordance with the walking speed. This results in a simple structure that can be easily used by children and the elderly alike, making it enjoyable for everyone. Also, since the viewpoint of the smartphone is pointed at all axes, users will not get lost, and walking safety is ensured because the smartphone is held horizontally. Furthermore, the AI ​​agent provides personalized questions and diagnoses to deepen the user's self-understanding, supporting them in discovering their ideal self. Furthermore, it supports life planning based on users' ideals and current situations, helping them to envision specific career paths and life visions. It also provides learning content that complements the career education and economic / financial literacy lacking in compulsory education, supporting the formation of concrete images of future career choices and social contributions. This service also includes a mental support function that analyzes users' emotions and motivations and suggests encouragement and refreshing methods at the appropriate time. In addition, it provides an online community where users can interact, share information, and support each other's growth through cooperation. Through multilingual support and international information provision, it aims to create an environment where users around the world can grow together, contributing to the realization of economic and mental well-being and the development of society as a whole. In this way, Future Design AI Partner can help users discover the person they want to be and support them in designing their life to achieve that.

[0066] The Future Design AI Partner according to this embodiment comprises a plan provision unit, an experience provision unit, and a progress visualization unit. The plan provision unit provides action plans and mindsets to clarify the user's future career. For example, when the user inputs what kind of person they want to become in the future, the plan provision unit provides specific action plans and mindsets modeled after the career paths and thinking of successful people. The plan provision unit can, for example, provide step-by-step instructions and methods for setting goals. It can also provide methods for improving positive thinking and self-efficacy. The experience provision unit provides virtual work experiences utilizing VR technology. For example, the experience provision unit enables the user to form a concrete image of their future occupation through virtual work experiences. For example, the experience provision unit can provide types of occupations that can be experienced and scenarios for the experience. It can also provide realistic work experiences using VR technology. The progress visualization unit visualizes progress toward achieving goals. For example, the progress visualization unit can display the user's progress toward achieving goals in graphs and charts. The progress visualization unit can, for example, provide evaluation criteria and visualization formats for progress. It can also enable users to check their progress in real time. This allows the future design AI partner, according to the embodiment, to clarify the user's future career and support them through action plans, virtual work experiences, and progress visualization.

[0067] The planning department provides action plans and mindsets to clarify users' future careers. For example, when a user inputs what kind of person they want to become in the future, the planning department provides specific action plans and mindsets modeled after the career paths and thinking of successful individuals. Specifically, based on the goals and desired career entered by the user, the AI ​​refers to a database of past successful individuals and analyzes the behavior and thought patterns of people with similar career paths. This allows the department to provide users with specific advice on what skills they should acquire, what experiences they should gain, and what kind of networking they should engage in. The planning department can also provide step-by-step instructions and methods for setting goals. For example, it can set short-term and long-term goals and present specific action plans for each goal. Furthermore, it can provide methods for improving positive thinking and self-efficacy. This includes affirmations to boost daily self-esteem and mental exercises for stress management. As a result, users can have a clear vision for their careers and gain specific means to act effectively towards them. Based on user feedback, the planning department can continuously improve the content of the action plans and mindsets to provide more effective support.

[0068] The Experience Provision Department provides virtual career experiences utilizing VR technology. For example, the department helps users develop a concrete image of their future career through virtual work experiences. Specifically, users can select their desired profession and experience a day in that profession through a VR headset. For instance, a user aspiring to be a doctor can experience simulations of examinations and surgeries in a virtual hospital. A user aspiring to be an engineer can experience project management and team meetings in a virtual office. The Experience Provision Department can provide various types of professions and experience scenarios. This includes specific job duties, workplace atmosphere, and communication with colleagues for each profession. Furthermore, the Experience Provision Department can also provide realistic career experiences using VR technology. This allows users to virtually experience the actual work environment and gain valuable information to determine if a profession is suitable for them. Based on user feedback, the Experience Provision Department can continuously improve the experience content to provide more realistic and effective career experiences. This enables users to develop a concrete image of their future career and effectively prepare for it.

[0069] The progress visualization unit visualizes progress toward achieving goals. For example, it can display a user's progress toward achieving their goals using graphs and charts. Specifically, it monitors the degree of achievement toward the goals set by the user in real time and visually displays the progress. For example, it can display progress toward short-term and long-term goals set by the user using bar graphs and line graphs, allowing users to grasp the degree of achievement and remaining tasks at a glance. The progress visualization unit can provide progress evaluation criteria and visualization formats. This includes evaluation criteria for goal achievement and color schemes to indicate progress. The progress visualization unit can also allow users to check their progress in real time. This allows users to always know their progress toward their goals and revise their action plans as needed. Furthermore, based on user feedback, the progress visualization unit can continuously improve the methods and evaluation criteria for progress visualization, providing more effective progress management. This allows users to obtain concrete means to effectively move toward their goals. The progress visualization unit can collaborate with other departments to comprehensively evaluate the user's progress and provide necessary support.

[0070] The Mental Support Department can provide mental support. For example, the Mental Support Department can analyze the user's emotions and motivation and provide mental support at the appropriate time. For example, the Mental Support Department can provide counseling and stress management methods. In addition, the Mental Support Department can provide refreshing methods and encouraging messages based on the user's emotions. This allows the Mental Support Department to analyze the user's emotions and motivation and provide mental support at the appropriate time. Some or all of the above processes in the Mental Support Department may be performed using AI, for example, or not using AI. For example, the Mental Support Department can input the user's emotional data into a generating AI and have the generating AI perform an emotional analysis.

[0071] The community provider can provide online communities. For example, the community provider can provide online communities where users can interact with each other and support each other's growth through information sharing and cooperation. The community provider can provide, for example, the types of communities and how to participate. The community provider can also provide an intuitive interface so that users can easily participate. In this way, the community provider can provide online communities where users can interact with each other and support each other's growth through information sharing and cooperation. Some or all of the above processing in the community provider may be performed using AI, for example, or not using AI. For example, the community provider can input user participation data into a generating AI and have the generating AI perform the community provision.

[0072] The learning provision unit can provide learning content that complements career education and economic / financial literacy. For example, the learning provision unit can provide learning content that complements career education and economic / financial literacy that are lacking in compulsory education. For example, the learning provision unit can provide educational themes and types of teaching materials. The learning provision unit can also provide online learning content so that users can learn at their own pace. In this way, the learning provision unit can provide learning content that complements career education and economic / financial literacy that are lacking in compulsory education. Some or all of the above processing in the learning provision unit may be performed using AI, for example, or not using AI. For example, the learning provision unit can input user learning data into a generating AI and have the generating AI execute the provision of learning content.

[0073] The planning unit can provide concrete action plans and mindsets based on the career paths and thinking of successful individuals. For example, the planning unit can provide action plans tailored to the user by referencing the career paths of successful individuals. For example, the planning unit can provide mindsets tailored to the user by modeling the thinking of successful individuals. Furthermore, the planning unit can provide concrete steps for the user to achieve their goals based on the career paths and thinking of successful individuals. In this way, the planning unit can provide concrete action plans and mindsets based on the career paths and thinking of successful individuals. Some or all of the above processing in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input successful individuals' career path data into a generating AI and have the generating AI execute the provision of action plans.

[0074] The experience provision unit can provide virtual job experiences using VR technology. For example, the experience provision unit can enable users to virtually experience jobs using VR technology. For example, the experience provision unit can provide the types of jobs that can be experienced and the scenarios for the experience. The experience provision unit can also provide realistic job experiences using VR technology. In this way, the experience provision unit can provide virtual job experiences using VR technology. Some or all of the above processing in the experience provision unit may be performed using AI, for example, or without AI. For example, the experience provision unit can input job experience data using VR technology into a generating AI and have the generating AI perform the provision of virtual job experiences.

[0075] The progress visualization unit can visualize progress toward achieving a goal. For example, the progress visualization unit can display the user's progress toward achieving the goal in graphs or charts. For example, the progress visualization unit can provide progress evaluation criteria and visualization formats. The progress visualization unit can also allow the user to check their progress in real time. In this way, the progress visualization unit can visualize progress toward achieving the goal. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without using AI. For example, the progress visualization unit can input the user's progress data into a generating AI and have the generating AI perform the visualization of the progress.

[0076] The planning unit can estimate the user's emotions and adjust the content of the action plan based on the estimated emotions. For example, if the user is feeling stressed, the planning unit can provide an action plan that helps them relax. For example, if the user wants to increase their motivation, the planning unit can provide an action plan that includes challenging goals. Furthermore, if the user is feeling anxious, the planning unit can provide an action plan that provides a sense of security. In this way, the planning unit can adjust the content of the action plan 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 planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the action plan.

[0077] The planning unit can analyze a user's past career history and provide an optimal action plan. For example, the planning unit can provide a similar action plan based on the user's past successful projects. For example, the planning unit can provide an action plan necessary for skill development based on the user's past career history. The planning unit can also analyze a user's past career history and provide an action plan suitable for a career change. In this way, the planning unit can analyze a user's past career history and provide an optimal action plan. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's career history data into a generating AI and have the generating AI perform the task of providing an action plan.

[0078] The planning unit can adjust the level of detail of an action plan based on the user's current skill level when providing the plan. For example, if the user has a high skill level, the planning unit can provide a detailed action plan. If the user has a low skill level, the planning unit can provide a basic action plan. The planning unit can also provide a step-by-step action plan according to the user's skill level. This allows the planning unit to adjust the level of detail of the plan based on the user's current skill level. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's skill level data into a generating AI and have the generating AI perform the adjustment of the action plan.

[0079] The planning unit can estimate the user's emotions and determine the priority of action plans based on the estimated emotions. For example, if the user is feeling stressed, the planning unit may prioritize relaxing actions. For example, if the user wants to increase their motivation, the planning unit may prioritize challenging actions. Also, if the user is feeling anxious, the planning unit may prioritize actions that provide a sense of security. In this way, the planning unit can determine the priority of action plans 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 planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI determine the priority of action plans.

[0080] The planning unit can prioritize providing highly relevant plans when providing action plans, taking into account the user's geographical location information. For example, if the user lives in an urban area, the planning unit can provide action plans that can be implemented in urban areas. For example, if the user lives in a rural area, the planning unit can provide action plans that can be implemented in rural areas. The planning unit can also provide action plans that take regional characteristics into account based on the user's geographical location information. This allows the planning unit to provide highly relevant action plans that take into account the user's geographical location information. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's geographical location information data into a generating AI and have the generating AI execute the provision of action plans.

[0081] The planning unit can analyze the user's social media activity and provide relevant plans when providing action plans. For example, the planning unit can provide action plans based on topics the user has shown interest in on social media. For example, the planning unit can provide action plans related to areas of interest based on the user's social media activity. The planning unit can also analyze the user's social media activity and provide action plans based on trends. This allows the planning unit to analyze the user's social media activity and provide relevant action plans. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing action plans.

[0082] The experience delivery unit can estimate the user's emotions and adjust the content of the virtual work experience based on the estimated emotions. For example, if the user is feeling stressed, the experience delivery unit can provide a relaxing work experience. For example, if the user wants to increase their motivation, the experience delivery unit can provide a challenging work experience. Furthermore, if the user is feeling anxious, the experience delivery unit can provide a reassuring work experience. In this way, the experience delivery unit can adjust the content of the virtual work experience 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 experience delivery unit may be performed using AI, for example, or without AI. For example, the experience delivery unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the virtual work experience.

[0083] The experience provision unit can provide the most suitable virtual work experience by referring to the user's past work experience when providing virtual work experience. For example, the experience provision unit can provide experiences related to the user's past work experience. For example, the experience provision unit can provide experiences necessary for skill improvement based on the user's past work experience. Furthermore, the experience provision unit can analyze the user's past work experience and provide experiences suitable for a career change. In this way, the experience provision unit can provide the most suitable virtual work experience by referring to the user's past work experience. Some or all of the above processing in the experience provision unit may be performed using AI, for example, or without using AI. For example, the experience provision unit can input the user's work experience data into a generating AI and have the generating AI perform the provision of virtual work experience.

[0084] The experience delivery unit can adjust the level of detail of a virtual job experience based on the user's current occupation. For example, if the user's current occupation is advanced, the experience delivery unit can provide a detailed job experience. For example, if the user's current occupation is at a beginner level, the experience delivery unit can provide a basic job experience. The experience delivery unit can also provide a step-by-step job experience depending on the user's current occupation. In this way, the experience delivery unit can adjust the level of detail of the experience based on the user's current occupation. Some or all of the above processing in the experience delivery unit may be performed using AI, for example, or without AI. For example, the experience delivery unit can input the user's occupation data into a generating AI and have the generating AI perform the adjustment of the level of detail of the experience.

[0085] The experience delivery unit can estimate the user's emotions and determine the priority of virtual job experiences based on the estimated emotions. For example, if the user is feeling stressed, the experience delivery unit may prioritize relaxing job experiences. For example, if the user wants to increase their motivation, the experience delivery unit may prioritize challenging job experiences. Also, if the user is feeling anxious, the experience delivery unit may prioritize job experiences that provide a sense of security. In this way, the experience delivery unit can determine the priority of virtual job experiences 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 experience delivery unit may be performed using AI, for example, or not using AI. For example, the experience delivery unit can input user emotion data into a generative AI and have the generative AI determine the priority of virtual job experiences.

[0086] The experience provision unit can prioritize providing highly relevant experiences by considering the user's geographical location when providing virtual work experiences. For example, if the user lives in an urban area, the experience provision unit can provide work experiences that can be performed in an urban area. For example, if the user lives in a rural area, the experience provision unit can provide work experiences that can be performed in a rural area. Furthermore, the experience provision unit can also provide work experiences that take regional characteristics into account based on the user's geographical location. In this way, the experience provision unit can provide highly relevant virtual work experiences by considering the user's geographical location. Some or all of the above processing in the experience provision unit may be performed using AI, for example, or without using AI. For example, the experience provision unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of virtual work experiences.

[0087] The experience provision unit can analyze the user's social media activity and provide relevant experiences when providing virtual career experiences. For example, the experience provision unit can provide experiences based on the occupations the user has shown interest in on social media. For example, the experience provision unit can provide career experiences related to the user's areas of interest based on the user's social media activity. The experience provision unit can also analyze the user's social media activity and provide career experiences based on trends. In this way, the experience provision unit can analyze the user's social media activity and provide relevant virtual career experiences. Some or all of the above processing in the experience provision unit may be performed using AI, for example, or without AI. For example, the experience provision unit can input the user's social media data into a generating AI and have the generating AI perform the provision of virtual career experiences.

[0088] The progress visualization unit can estimate the user's emotions and adjust the progress display method based on the estimated user emotions. For example, if the user is feeling stressed, the progress visualization unit can provide a simple and highly visible display method. For example, if the user wants to increase their motivation, the progress visualization unit can provide a display method that includes detailed information. Furthermore, if the user is feeling anxious, the progress visualization unit can provide a display method that provides reassurance. In this way, the progress visualization unit can adjust the progress display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without AI. For example, the progress visualization unit can input user emotion data into the generative AI and have the generative AI adjust the progress display method.

[0089] The progress visualization unit can provide the optimal display method by referring to the user's past progress data when visualizing progress. For example, the progress visualization unit can provide the optimal display method based on the user's past progress data. For example, the progress visualization unit can provide a display method that increases motivation based on the user's past progress data. The progress visualization unit can also analyze the user's past progress data and provide an efficient display method. As a result, the progress visualization unit can provide the optimal progress display by referring to the user's past progress data. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without using AI. For example, the progress visualization unit can input the user's past progress data into a generating AI and have the generating AI perform the task of providing a progress display method.

[0090] The progress visualization unit can adjust the level of detail displayed based on the user's current goals when visualizing progress. For example, if the user's current goals are advanced, the progress visualization unit can provide a detailed progress display. For example, if the user's current goals are at a beginner level, the progress visualization unit can provide a basic progress display. The progress visualization unit can also provide a step-by-step progress display depending on the user's current goals. This allows the progress visualization unit to adjust the level of detail displayed based on the user's current goals. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without AI. For example, the progress visualization unit can input user goal data into a generating AI and have the generating AI adjust the method of displaying progress.

[0091] The progress visualization unit can estimate the user's emotions and determine the priority of progress based on the estimated user emotions. For example, if the user is feeling stressed, the progress visualization unit can prioritize relaxing progress. For example, if the user wants to increase their motivation, the progress visualization unit can prioritize challenging progress. Also, if the user is feeling anxious, the progress visualization unit can prioritize reassuring progress. In this way, the progress visualization unit can determine the priority of progress 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 progress visualization unit may be performed using AI, for example, or without AI. For example, the progress visualization unit can input user emotion data into a generative AI and have the generative AI determine the priority of progress.

[0092] The progress visualization unit can prioritize displaying highly relevant progress while considering the user's geographical location information when visualizing progress. For example, if the user lives in an urban area, the progress visualization unit can display progress that can be completed in an urban area. For example, if the user lives in a rural area, the progress visualization unit can display progress that can be completed in a rural area. Furthermore, the progress visualization unit can also display progress that takes regional characteristics into account based on the user's geographical location information. In this way, the progress visualization unit can provide highly relevant progress while considering the user's geographical location information. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without using AI. For example, the progress visualization unit can input the user's geographical location information data into a generating AI and have the generating AI execute the display of progress.

[0093] The progress visualization unit can analyze the user's social media activity and display relevant progress when visualizing progress. For example, the progress visualization unit can display progress based on topics the user has shown interest in on social media. For example, the progress visualization unit can display progress related to areas of interest from the user's social media activity. The progress visualization unit can also analyze the user's social media activity and display progress based on trends. In this way, the progress visualization unit can analyze the user's social media activity and provide relevant progress. Some or all of the above processing in the progress visualization unit may be performed using AI, for example, or without AI. For example, the progress visualization unit can input the user's social media data into a generating AI and have the generating AI perform the display of progress.

[0094] The mental support unit can estimate the user's emotions and adjust the content of the mental support based on the estimated emotions. For example, if the user is feeling stressed, the mental support unit can provide relaxing mental support. For example, if the user wants to increase their motivation, the mental support unit can provide encouraging messages. Furthermore, if the user is feeling anxious, the mental support unit can provide reassuring mental support. In this way, the mental support unit can adjust the content of the mental support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the mental support.

[0095] The mental support unit can provide optimal support by referring to the user's past mental state when providing mental support. For example, the mental support unit can provide optimal mental support based on the user's past mental state. For example, the mental support unit can provide support that is effective in reducing stress based on the user's past mental state. The mental support unit can also analyze the user's past mental state and provide support that helps improve mental health. In this way, the mental support unit can provide optimal mental support by referring to the user's past mental state. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without using AI. For example, the mental support unit can input the user's past mental state data into a generating AI and have the generating AI perform the provision of mental support.

[0096] The mental support unit can estimate the user's emotions and determine the priority of mental support based on the estimated emotions. For example, if the user is feeling stressed, the mental support unit will prioritize relaxation support. For example, if the user wants to increase their motivation, the mental support unit can prioritize encouraging messages. Also, if the user is feeling anxious, the mental support unit can prioritize reassuring support. In this way, the mental support unit can determine the priority of mental support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the mental support unit may be performed using AI, for example, or not using AI. For example, the mental support unit can input user emotion data into the generative AI and have the generative AI determine the priority of mental support.

[0097] The mental support unit can prioritize providing highly relevant support by considering the user's geographical location when providing mental support. For example, if the user lives in an urban area, the mental support unit can provide mental support that can be performed in an urban area. For example, if the user lives in a rural area, the mental support unit can provide mental support that can be performed in a rural area. In addition, the mental support unit can provide mental support that takes regional characteristics into account based on the user's geographical location. This allows the mental support unit to provide highly relevant mental support by considering the user's geographical location. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of mental support.

[0098] The community provider can estimate the user's emotions and adjust how they participate in the community based on those estimated emotions. For example, if a user is feeling stressed, the community provider can offer relaxing community activities. If a user wants to increase their motivation, the community provider can offer challenging community activities. Furthermore, if a user is feeling anxious, the community provider can offer reassuring community activities. This allows the community provider to adjust how users participate in the community 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-described processes in the community provider may be performed using AI, or not. For example, the community provider can input user emotion data into a generative AI and have the generative AI adjust how users participate in the community.

[0099] The community provider unit can provide the optimal way for a user to participate in a community by referring to the user's past community participation history. For example, the community provider unit can provide the optimal way to participate based on the user's past community participation history. For example, the community provider unit can provide activities that the user is interested in based on the user's past community participation history. The community provider unit can also analyze the user's past community participation history and provide methods that make participation easier. In this way, the community provider unit can provide the optimal way to participate by referring to the user's past community participation history. Some or all of the above processing in the community provider unit may be performed using AI, for example, or without AI. For example, the community provider unit can input the user's past community participation history data into a generating AI and have the generating AI perform the community provision.

[0100] The community provider can estimate the user's emotions and determine community priorities based on those estimated emotions. For example, if the user is feeling stressed, the community provider can prioritize relaxing community activities. If the user wants to increase their motivation, the community provider can prioritize challenging community activities. Furthermore, if the user is feeling anxious, the community provider can prioritize reassuring community activities. This allows the community provider to determine community priorities 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 community provider may be performed using AI, or not. For example, the community provider can input user emotion data into a generative AI and have the generative AI determine community priorities.

[0101] The community provider unit can prioritize providing highly relevant communities by considering the user's geographical location information when providing communities. For example, if the user lives in an urban area, the community provider unit can provide community activities that can be performed in urban areas. For example, if the user lives in a rural area, the community provider unit can provide community activities that can be performed in rural areas. Furthermore, the community provider unit can also provide community activities that take regional characteristics into account based on the user's geographical location information. This allows the community provider unit to provide highly relevant community activities by considering the user's geographical location information. Some or all of the above processing in the community provider unit may be performed using AI, for example, or without AI. For example, the community provider unit can input the user's geographical location information data into a generating AI and have the generating AI perform the community provision.

[0102] The learning delivery unit can estimate the user's emotions and adjust the content of the learning materials based on those emotions. For example, if the user is feeling stressed, the learning delivery unit can provide relaxing learning materials. For example, if the user wants to increase their motivation, the learning delivery unit can provide challenging learning materials. Furthermore, if the user is feeling anxious, the learning delivery unit can provide reassuring learning materials. In this way, the learning delivery unit can adjust the content of the learning materials based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or without AI. For example, the learning delivery unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the learning materials.

[0103] The learning delivery unit can provide optimal content by referring to the user's past learning history when delivering learning content. For example, the learning delivery unit can provide optimal learning content based on the user's past learning history. For example, the learning delivery unit can provide learning content necessary for skill improvement based on the user's past learning history. The learning delivery unit can also analyze the user's past learning history and provide efficient learning content. In this way, the learning delivery unit can provide optimal learning content by referring to the user's past learning history. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or without using AI. For example, the learning delivery unit can input the user's past learning history data into a generating AI and have the generating AI execute the provision of learning content.

[0104] The learning delivery unit can estimate the user's emotions and prioritize learning content based on those emotions. For example, if the user is feeling stressed, the learning delivery unit will prioritize relaxing learning content. For example, if the user wants to increase their motivation, the learning delivery unit can prioritize challenging learning content. Also, if the user is feeling anxious, the learning delivery unit can prioritize reassuring learning content. In this way, the learning delivery unit can prioritize learning content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or not using AI. For example, the learning delivery unit can input user emotion data into the generative AI and have the generative AI determine the priority of learning content.

[0105] The learning delivery unit can prioritize providing highly relevant content by considering the user's geographical location when delivering learning content. For example, if the user lives in an urban area, the learning delivery unit can provide learning content that can be executed in an urban area. For example, if the user lives in a rural area, the learning delivery unit can provide learning content that can be executed in a rural area. The learning delivery unit can also provide learning content that takes regional characteristics into account based on the user's geographical location. This allows the learning delivery unit to provide highly relevant learning content by considering the user's geographical location. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or without AI. For example, the learning delivery unit can input the user's geographical location data into a generating AI and have the generating AI execute the provision of learning content.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The planning unit can adjust action plans considering the user's health condition. For example, if a user inputs their health checkup results, the planning unit can provide an action plan based on those results to maintain or improve their health. Furthermore, if a user sets a specific health goal, the unit can provide concrete steps to achieve that goal. In addition, the planning unit can provide an action plan that includes exercise and dietary advice, tailored to the user's health condition. This allows the planning unit to provide action plans that take the user's health into consideration.

[0108] The experience provision department can customize the content of virtual career experiences based on the user's hobbies and interests. For example, if a user is interested in music, they can be offered a music-related career experience. Similarly, if a user is interested in sports, they can be offered a sports-related career experience. Furthermore, if a user is interested in art, they can be offered an art-related career experience. This allows the experience provision department to customize the content of virtual career experiences based on the user's hobbies and interests.

[0109] The progress visualization unit can adjust the display method of progress according to the user's learning style. For example, users with a visual learning style can be provided with progress displays using graphs and charts. Users with an auditory learning style can be provided with a function to report progress via voice. Furthermore, users with a tactile learning style can be provided with interactive progress displays. In this way, the progress visualization unit can provide progress displays that are tailored to the user's learning style.

[0110] The Mental Support Department can analyze users' sleep data and provide appropriate mental support. For example, if a user is sleep-deprived, it can provide relaxing mental support. Conversely, if a user is getting good quality sleep, it can provide mental support to boost motivation. Furthermore, it can suggest stress management and refreshing methods based on the user's sleep patterns. In this way, the Mental Support Department can provide mental support that takes the user's sleep data into consideration.

[0111] The community provider can offer communities based on users' occupations and areas of expertise. For example, it can provide a community for healthcare professionals to share medical information and provide support. It can also provide a community for engineers to facilitate technical information exchange and collaboration. Furthermore, it can provide a community for educators to share knowledge and experience related to education. In this way, the community provider can offer communities tailored to users' occupations and areas of expertise.

[0112] The planning department can estimate the user's emotions and adjust the content of the action plan based on those emotions. For example, if the user is feeling stressed, it can provide an action plan that helps them relax. If the user wants to increase their motivation, it can provide an action plan that includes challenging goals. Furthermore, if the user is feeling anxious, it can provide an action plan that provides a sense of security. In this way, the planning department can adjust the content of the action plan based on the user's emotions.

[0113] The experience delivery unit can estimate the user's emotions and adjust the content of the virtual work experience based on those emotions. For example, if the user is feeling stressed, it can provide a relaxing work experience. If the user wants to increase their motivation, it can provide a challenging work experience. Furthermore, if the user is feeling anxious, it can provide a work experience that provides a sense of security. In this way, the experience delivery unit can adjust the content of the virtual work experience based on the user's emotions.

[0114] The progress visualization unit can estimate the user's emotions and adjust the progress display method based on those emotions. For example, if the user is feeling stressed, it can provide a simple and highly visible display method. If the user wants to increase their motivation, it can provide a display method that includes detailed information. Furthermore, if the user is feeling anxious, it can provide a display method that provides reassurance. In this way, the progress visualization unit can adjust the progress display method based on the user's emotions.

[0115] The mental support department can estimate the user's emotions and adjust the content of the mental support based on those estimates. For example, if a user is feeling stressed, it can provide relaxing mental support. If a user wants to boost their motivation, it can provide encouraging messages. Furthermore, if a user is feeling anxious, it can provide reassuring mental support. In this way, the mental support department can adjust the content of the mental support based on the user's emotions.

[0116] The community provider can estimate users' emotions and adjust how they participate in the community based on those estimates. For example, if a user is feeling stressed, it can provide relaxing community activities. If a user wants to increase their motivation, it can provide challenging community activities. Furthermore, if a user is feeling anxious, it can provide community activities that provide a sense of security. In this way, the community provider can adjust how users participate in the community based on their emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The planning department provides action plans and mindsets to clarify the user's future career. When the user inputs what kind of person they want to become in the future, the department provides specific action plans and mindsets modeled after the career paths and thinking of successful individuals. For example, it can provide step-by-step instructions, goal-setting methods, and methods for improving positive thinking and self-efficacy. Step 2: The Experience Provision Department will provide virtual job experiences utilizing VR technology. The goal is to enable users to form a concrete image of their future careers through these virtual job experiences. For example, they can provide a variety of jobs to experience and scenarios for those experiences, and then use VR technology to provide a realistic job experience. Step 3: The progress visualization section visualizes progress toward achieving the goal. It displays the user's progress toward achieving the goal using graphs and charts, and provides evaluation criteria and visualization formats for progress. It can also allow users to check their progress in real time.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the planning unit, experience unit, progress visualization unit, mental support unit, community unit, and learning unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the smart device 14 and provides action plans and mindsets to clarify the user's future career. The experience unit is implemented by the control unit 46A of the smart device 14 and provides a virtual work experience utilizing VR technology. The progress visualization unit is implemented by the control unit 46A of the smart device 14 and visualizes progress toward achieving goals. The mental support unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotions and motivation and provides mental support at the appropriate time. The community unit is implemented by the control unit 46A of the smart device 14 and provides an online community where users can interact with each other and support each other's growth through information sharing and cooperation. The learning provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides learning content that complements career education and economic / financial literacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the planning unit, experience unit, progress visualization unit, mental support unit, community unit, and learning unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the smart glasses 214 and provides an action plan and mindset to clarify the user's future career. The experience unit is implemented by the control unit 46A of the smart glasses 214 and provides a virtual work experience utilizing VR technology. The progress visualization unit is implemented by the control unit 46A of the smart glasses 214 and visualizes progress toward achieving goals. The mental support unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotions and motivation and provides mental support at the appropriate time. The community unit is implemented by the control unit 46A of the smart glasses 214 and provides an online community where users can interact with each other and support each other's growth through information sharing and cooperation. The learning provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides learning content that complements career education and economic / financial literacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the planning unit, experience unit, progress visualization unit, mental support unit, community unit, and learning unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the headset terminal 314 and provides action plans and mindsets to clarify the user's future career. The experience unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides a virtual work experience utilizing VR technology. The progress visualization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and visualizes progress toward achieving goals. The mental support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotions and motivation and provides mental support at the appropriate time. The community unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides an online community where users can interact with each other and support each other's growth through information sharing and cooperation. The learning provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides learning content that complements career education and economic / financial literacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the planning unit, experience unit, progress visualization unit, mental support unit, community unit, and learning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the robot 414 and provides an action plan and mindset to clarify the user's future career. The experience unit is implemented by, for example, the control unit 46A of the robot 414 and provides a virtual work experience utilizing VR technology. The progress visualization unit is implemented by, for example, the control unit 46A of the robot 414 and visualizes progress toward achieving goals. The mental support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotions and motivation and provides mental support at the appropriate time. The community unit is implemented by, for example, the control unit 46A of the robot 414 and provides an online community where users can interact with each other and support each other's growth through information sharing and cooperation. The learning provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides learning content that complements career education and economic / financial literacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The Planning Department provides action plans and mindsets to clarify users' future careers, The Experience Provision Department provides virtual job experiences utilizing VR technology, It includes a progress visualization unit that visualizes progress, A system characterized by the following features. (Note 2) It has a mental support department that provides mental support. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a community provision department that provides online communities. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a learning department that provides learning content to complement career education and economic / financial literacy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned plan provision unit, We provide concrete action plans and mindsets based on the career paths and thinking of successful individuals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned experience provision unit is We provide virtual job experiences using VR technology. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned progress visualization unit, Visualize progress toward achieving the goal. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned plan provision unit, The system estimates the user's emotions and adjusts the content of the action plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned plan provision unit, We analyze the user's past career history and provide an optimal action plan. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned plan provision unit, When providing an action plan, adjust the level of detail in the plan based on the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned plan provision unit, It estimates the user's emotions and prioritizes the action plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned plan provision unit, When providing action plans, we prioritize providing highly relevant plans by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned plan provision unit, When providing action plans, we analyze users' social media activity and provide relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned experience provision unit is The system estimates the user's emotions and adjusts the content of the virtual job experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned experience provision unit is When providing virtual job experiences, we refer to the user's past job experiences to provide the most optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned experience provision unit is When providing a virtual job experience, adjust the level of detail of the experience based on the user's current occupation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned experience provision unit is It estimates the user's emotions and prioritizes virtual job experiences based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned experience provision unit is When providing virtual job experiences, we prioritize providing highly relevant experiences by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned experience provision unit is When providing virtual job experiences, we analyze users' social media activity and deliver relevant experiences. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned progress visualization unit, It estimates the user's emotions and adjusts how progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned progress visualization unit, When visualizing progress, the system provides the optimal display method by referencing the user's past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned progress visualization unit, When visualizing progress, adjust the level of detail displayed based on the user's current goals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned progress visualization unit, It estimates the user's emotions and prioritizes progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress visualization unit, When visualizing progress, the system prioritizes displaying the most relevant progress, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned progress visualization unit, When visualizing progress, the system analyzes users' social media activity and displays relevant progress. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned mental support unit is The system estimates the user's emotions and adjusts the content of mental support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned mental support unit is When providing mental support, we refer to the user's past mental state to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned mental support unit is It estimates the user's emotions and determines the priority of mental support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned mental support unit is When providing mental support, we prioritize providing highly relevant support by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned community provision unit is It estimates user sentiment and adjusts how users participate in the community based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned community provision unit is When providing a community, we refer to the user's past community participation history to provide the most suitable way for them to participate. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned community provision unit is It estimates user sentiment and determines community priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned community provision unit is When providing communities, we prioritize providing highly relevant communities by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning provision unit, It estimates the user's emotions and adjusts the content of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning provision unit, When providing learning content, we refer to the user's past learning history to provide the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning provision unit, It estimates the user's emotions and prioritizes learning content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned learning provision unit, When providing learning content, we prioritize providing highly relevant content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Planning Department provides action plans and mindsets to clarify users' future careers, The Experience Provision Department provides virtual job experiences utilizing VR technology, It includes a progress visualization unit that visualizes progress, A system characterized by the following features.

2. It has a mental support department that provides mental support. The system according to feature 1.

3. It has a community provision department that provides online communities. The system according to feature 1.

4. It includes a learning department that provides learning content to complement career education and economic and financial literacy. The system according to feature 1.

5. The aforementioned plan provision unit, We provide concrete action plans and mindsets based on the career paths and thinking of successful individuals. The system according to feature 1.

6. The aforementioned experience provision unit is We provide virtual job experiences using VR technology. The system according to feature 1.

7. The aforementioned progress visualization unit, Visualize progress toward achieving the goal. The system according to feature 1.

8. The aforementioned plan provision unit, The system estimates the user's emotions and adjusts the content of the action plan based on those estimated emotions. The system according to feature 1.

9. The aforementioned plan provision unit, We analyze the user's past career history and provide an optimal action plan. The system according to feature 1.

10. The aforementioned plan provision unit, When providing an action plan, adjust the level of detail in the plan based on the user's current skill level. The system according to feature 1.

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