system

Abstract

The system according to this embodiment aims to help students find an appropriate career path based on their interests, strengths, and values, and to create specific goals and action plans. [Solution] The system according to the embodiment comprises a collection unit, a provision unit, a planning unit, a referral unit, and a follow-up unit. The collection unit collects students' interests, strengths, and values. The provision unit provides appropriate career information based on the information collected by the collection unit. The planning unit creates specific goals and action plans based on the information provided by the provision unit. The referral unit matches students with experts and senior students based on the action plans created by the planning unit. The follow-up unit checks the progress made with mentors introduced by the referral unit and provides support to help students achieve their goals.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for students to find an appropriate course based on their interests, concerns, strengths, and values, and it is difficult to set specific goals and create action plans.

[0005] The system according to the embodiment aims to enable students to find an appropriate course based on their interests, concerns, strengths, and values, and create specific goals and action plans.

Means for Solving the Problems

[0007] The system according to this embodiment allows students to find an appropriate career path based on their interests, strengths, and values, and to create specific goals and action plans. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that helps students to concretely envision their future careers and life plans. This system analyzes students' interests, strengths, and values, and supports them in setting appropriate career paths and goals. For example, the AI ​​agent system provides self-analysis support, career information provision, goal setting and action planning, mentor introduction, and regular follow-up. In self-analysis support, it clarifies the student's personality traits and strengths, deepening their self-understanding. The generating AI delves deeper into the student's interests and values ​​through dialogue, and provides appropriate career information and advice using natural language processing. In career information provision, it provides diverse occupation and career information to broaden the student's perspective. The generating AI provides relevant occupation information based on the student's interests and shows a concrete career path. In goal setting and action planning, it proposes specific goals and steps to achieve them. The generating AI creates an action plan tailored to the student's goals and checks their progress. In mentor introduction, it supports matching with experts and seniors as needed. The generating AI introduces appropriate mentors based on the student's needs and provides opportunities for consultation. In regular follow-up, it checks their progress and continues to provide support toward achieving their goals. The generating AI regularly checks students' progress and provides necessary support. In this way, the AI ​​agent system helps students envision their future careers and life plans concretely, enabling them to proactively shape their future through self-understanding and information provision. Thus, the AI ​​agent system can help students envision their future careers and life plans concretely.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, a provision unit, a planning unit, a referral unit, and a follow-up unit. The collection unit collects the student's interests, strengths, and values. The collection unit, for example, delves deeper into the student's interests and values ​​through dialogue with the student. The collection unit uses generative AI to reveal the student's personality traits and strengths and deepen their self-understanding. The provision unit provides appropriate career information based on the information collected by the collection unit. The provision unit, for example, provides relevant occupational information based on the student's interests. The provision unit uses generative AI to provide appropriate career information and advice through natural language processing. The planning unit creates specific goals and action plans based on the information provided by the provision unit. The planning unit, for example, creates action plans tailored to the student's goals. The planning unit uses generative AI to propose specific goals and steps toward achieving them. The referral unit matches the student with experts and mentors based on the action plans created by the planning unit. The referral unit, for example, introduces appropriate mentors based on the student's needs. The referral unit uses generative AI to provide opportunities for consultation. The follow-up department monitors the progress of students with mentors introduced by the referral department and provides support to help them achieve their goals. For example, the follow-up department regularly checks the students' progress and provides necessary support. The follow-up department uses generative AI to monitor progress and continue to provide support to help students achieve their goals. In this way, the AI ​​agent system according to the embodiment can help students to concretely envision their future careers and life plans.

[0030] The data collection department gathers information on students' interests, strengths, and values. Specifically, it utilizes generative AI to delve deeper into students' interests and values ​​through dialogue. The generative AI analyzes students' statements using natural language processing technology through dialogue, identifying areas of interest. For example, when students talk about their favorite subjects or hobbies, the content is analyzed to extract relevant keywords and clarify areas of interest. The generative AI also conducts psychological questions and personality tests to reveal students' personality traits and strengths, and analyzes the results. This allows students to deepen their self-understanding. Furthermore, the data collection department asks questions about students' values ​​and analyzes their answers to understand their values. For example, when students talk about what they value or their future goals, the content is analyzed to identify their values. In this way, the data collection department can comprehensively understand students' interests, strengths, and values ​​and collect basic information for individualized support.

[0031] The information provision department provides appropriate career information based on the information collected by the information collection department. Specifically, it utilizes generative AI to provide relevant occupational information based on students' interests. The generative AI uses natural language processing technology to search for occupations and career information related to students' interests and provides appropriate information. For example, if a student is interested in science, the generative AI will provide information on science-related occupations, university departments, and necessary qualifications. The generative AI also suggests appropriate career paths based on students' strengths and values. For example, if a student has strong leadership skills, it will suggest career paths as a manager or entrepreneur and specify the skills and experience required for them. Furthermore, the information provision department uses the generative AI to analyze past data and success stories to suggest the optimal career path for each student, providing advice that helps students choose their future paths. In this way, the information provision department can provide students with information to concretely envision their future careers and career paths, and support their decision-making.

[0032] The planning department creates specific goals and action plans based on the information provided by the provision department. Specifically, it utilizes generative AI to create action plans tailored to each student's goals. The generative AI proposes specific goals and steps to achieve them based on the student's goals and desired career path. For example, if a student aims to become a doctor, the generative AI will specifically indicate the necessary academic qualifications, certifications, and experience, and propose a learning plan and activity plan to achieve them. The generative AI also creates individually optimized action plans, taking into account the student's strengths and weaknesses. For example, if a student is good at mathematics but struggles with communication, the generative AI will propose specific activities to improve communication skills while leveraging their mathematical strengths. Furthermore, the planning department uses the generative AI to monitor the student's progress and revise the action plan as needed. This allows the planning department to provide students with concrete action plans that enable them to effectively progress towards their goals and to provide continuous support.

[0033] The placement department matches students with experts and senior colleagues based on action plans created by the planning department. Specifically, it utilizes generative AI to introduce appropriate mentors based on students' needs. The generative AI considers students' interests, goals, strengths, and weaknesses to search for and match them with the most suitable mentors. For example, if a student aims to become an engineer, the generative AI will search for successful senior colleagues and experts in the engineering field and introduce the student to the most suitable mentor from among them. The generative AI also provides opportunities for consultation to support communication between students and mentors. For example, the generative AI will coordinate the schedules of students and mentors and set up opportunities for online consultations and meetings. Furthermore, the placement department uses the generative AI to evaluate the effectiveness of student-mentor matching and to change or add mentors as needed. In this way, the placement department can ensure that students receive appropriate advice and support from experts and senior colleagues, and can support them in achieving their goals.

[0034] The follow-up department monitors the progress of students with mentors introduced by the referral department and provides support to help them achieve their goals. Specifically, it utilizes generative AI to regularly check students' progress and provide necessary support. The generative AI monitors students' progress and continuously supports them in achieving their goals. For example, the generative AI analyzes students' learning and activity status and issues early warnings if progress is behind or problems have arisen. The generative AI also provides additional support and advice according to the student's progress. For example, when a student is working on a specific assignment, the generative AI provides resources and reference materials related to that assignment to support their learning. Furthermore, the follow-up department uses the generative AI to support communication between students and mentors and to share progress. This allows the follow-up department to provide continuous support to help students effectively move towards their goals and achieve them.

[0035] The data collection unit can delve deeper into students' interests and values ​​through dialogue with them. For example, the data collection unit can delve deeper into students' interests and values ​​through interviews with them. For example, the data collection unit can delve deeper into students' interests and values ​​by conducting surveys with them. This allows the data collection unit to gain a deeper understanding of students' interests and values. Some or all of the above-described processes in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input dialogue data with students into a generative AI, which can then generate questions to delve deeper into students' interests and values.

[0036] The information provider can provide relevant career information based on students' interests. For example, the information provider can provide an overview of relevant occupations based on students' interests. The information provider can also provide the skills required for relevant occupations based on students' interests. The information provider can also provide career paths for relevant occupations based on students' interests. In this way, the information provider can broaden students' perspectives by providing career information that matches their interests. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provider can input student interest data into a generative AI, and the generative AI can generate relevant career information.

[0037] The planning department can create action plans tailored to students' goals. For example, the planning department can propose specific action steps tailored to students' goals. The planning department can also propose resource allocation tailored to students' goals. The planning department can also propose a schedule tailored to students' goals. This allows the planning department to create specific action plans tailored to students' goals. Some or all of the above processes in the planning department may be performed using a generative AI, or not. For example, the planning department can input student goal data into a generative AI, which can then generate an action plan.

[0038] The referral department can introduce students to appropriate mentors based on their needs. For example, the referral department can introduce mentors with specialized knowledge based on the student's needs. For example, the referral department can introduce mentors with professional experience based on the student's needs. For example, the referral department can introduce mentors with teaching experience based on the student's needs. In this way, the referral department can provide students with appropriate advisors by introducing mentors that meet their needs. Some or all of the above processes in the referral department may be performed using a generative AI, or not. For example, the referral department can input student needs data into a generative AI, which can then select an appropriate mentor.

[0039] The follow-up department can periodically check students' progress and provide necessary support. For example, the follow-up department can check students' progress weekly. The follow-up department can also check students' progress monthly. The follow-up department can also check students' progress quarterly. This allows the follow-up department to regularly check students' progress and provide continuous support. Some or all of the above processes in the follow-up department may be performed using or without a generative AI. For example, the follow-up department can input student progress data into a generative AI, which can analyze the progress and suggest necessary support.

[0040] The data collection unit can analyze a student's past activity history and select the most suitable information collection method. For example, the data collection unit can analyze events and activities a student has participated in in the past and collect relevant information. For example, the data collection unit can also collect relevant information based on topics a student has shown interest in in the past. For example, the data collection unit can analyze resources a student has used in the past and select the most suitable information collection method. This allows the data collection unit to select the most suitable information collection method based on a student's past activity history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input student activity history data into a generative AI, which can then select the most suitable information collection method.

[0041] The data collection unit can filter information based on the student's current learning status and areas of interest during the information collection process. For example, the data collection unit can analyze the student's current learning status and filter relevant information. The data collection unit can also filter relevant information based on the student's areas of interest. The data collection unit can also filter appropriate information considering the student's learning progress. This allows the data collection unit to filter appropriate information according to the student's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input student learning status data into a generative AI, which can then filter the information.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the students' geographical location information during data collection. For example, the data collection unit can collect information related to a region based on the students' geographical location information. For example, the data collection unit can also collect information on local events and activities by considering the students' geographical location information. For example, the data collection unit can also collect information on local occupations by considering the students' geographical location information. This allows the data collection unit to prioritize the collection of highly relevant information by considering the students' geographical location information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the students' geographical location data into a generative AI, which can then prioritize the collection of highly relevant information.

[0043] The data collection unit can analyze students' social media activities and collect relevant information during the information gathering process. For example, the data collection unit can analyze students' social media activities and collect information related to topics they are interested in. For example, the data collection unit can also collect relevant information by referring to the activities of students' followers and friends on social media. For example, the data collection unit can analyze the content of students' social media posts and collect relevant information. In this way, the data collection unit can analyze students' social media activities and collect relevant information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input students' social media data into a generative AI, and the generative AI can collect relevant information.

[0044] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide highly important information in detail and less important information concisely. For example, the provider can also provide information directly related to students' goals in detail and supplementary information concisely. For example, the provider can adjust the level of detail of the information according to the students' level of interest. In this way, the provider can provide more appropriate information by adjusting the level of detail of the information according to its importance. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the provider can input information importance data into a generative AI, and the generative AI can adjust the level of detail of the information provided.

[0045] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, the provider may provide career information using an algorithm that includes detailed explanations. For example, the provider may provide learning resources using a concise and visual algorithm. For example, the provider may provide mentor introductions using individual matching algorithms. In this way, the provider can provide more appropriate information by adjusting the information provision algorithm according to the category of information. Some or all of the above processing in the information provider may be performed using a generative AI or not. For example, the provider can input information category data into a generative AI, and the generative AI can apply different information provision algorithms.

[0046] The information delivery unit can determine the priority of information delivery based on the timing of information submission. For example, the information delivery unit can prioritize the delivery of urgent information. For example, the information delivery unit can also prioritize the delivery of information directly related to the achievement of students' goals. For example, the information delivery unit can also deliver information at an appropriate time according to the students' learning progress. In this way, the information delivery unit can provide more appropriate information by determining the priority of information delivery according to the timing of information submission. Some or all of the above processing in the information delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the information delivery unit can input information submission timing data into a generative AI, and the generative AI can determine the priority of information delivery.

[0047] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can first provide information directly related to the student's goals. The provider can also prioritize providing information related to the student's interests. The provider can also prioritize providing information that is highly relevant according to the student's learning progress. In this way, the provider can provide more appropriate information by adjusting the order of delivery according to the relevance of the information. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the provider can input information relevance data into a generative AI, and the generative AI can adjust the order of delivery.

[0048] The planning department can create an optimal plan by referring to the student's past goal achievement history when creating an action plan. For example, the planning department can analyze the student's past goal achievement history and create a plan that incorporates successful methods. For example, the planning department can also consider the student's past failures and create a plan that reflects areas for improvement. For example, the planning department can create an achievable plan based on the student's past goal achievement history. In this way, the planning department can create an optimal action plan based on the student's past goal achievement history. Some or all of the above processes in the planning department may be performed using a generative AI, or not. For example, the planning department can input the student's goal achievement history data into a generative AI, which can then create an optimal plan.

[0049] The planning unit can customize the means of the plan based on the student's current learning situation when creating an action plan. For example, the planning unit can analyze the student's current learning situation and create a plan that incorporates appropriate means. The planning unit can also adjust the means of the plan according to the student's learning progress. For example, the planning unit can create a plan that incorporates achievable means, taking into account the student's learning situation. This allows the planning unit to customize the means of the plan according to the student's current learning situation. Some or all of the above processes in the planning unit may be performed using a generative AI, or not. For example, the planning unit can input student learning situation data into a generative AI, which can then customize the means of the plan.

[0050] The planning department can create optimal action plans by considering students' geographical location information. For example, the planning department can create plans that utilize local resources based on students' geographical location information. For example, the planning department can also create plans that consider the convenience of commuting to school or work by considering students' geographical location information. For example, the planning department can create plans that incorporate local events and activities based on students' geographical location information. In this way, the planning department can create optimal action plans by considering students' geographical location information. Some or all of the above processes in the planning department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the planning department can input students' geographical location data into a generative AI, and the generative AI can create an optimal plan.

[0051] The planning department can analyze students' social media activities and propose methods for creating action plans. For example, the planning department can analyze students' social media activities and propose methods related to topics they are interested in. For example, the planning department can also propose methods for creating plans by referring to the activities of students' followers and friends on social media. For example, the planning department can analyze the content of students' social media posts and propose relevant methods. In this way, the planning department can analyze students' social media activities and propose methods for creating plans. Some or all of the above processes in the planning department may be performed using generative AI, or not. For example, the planning department can input students' social media data into a generative AI, and the generative AI can propose methods for creating plans.

[0052] The referral department can select the most suitable mentor by referring to the student's past consultation history when referring mentors. For example, the referral department can analyze the student's past consultation history and select a mentor based on the content of successful consultations. For example, the referral department can also select a mentor that reflects areas for improvement, taking into account the student's past consultation history. For example, the referral department can select the most suitable mentor based on the student's past consultation history. In this way, the referral department can select the most suitable mentor based on the student's past consultation history. Some or all of the above processing in the referral department may be performed using a generative AI, or it may be performed without using a generative AI. For example, the referral department can input the student's consultation history data into a generative AI, and the generative AI can select the most suitable mentor.

[0053] The referral system can customize mentor selection based on the student's current learning situation when referring mentors. For example, the referral system can analyze the student's current learning situation and select an appropriate mentor. The referral system can also adjust mentor selection according to the student's learning progress. The referral system can also select the most suitable mentor considering the student's learning situation. This allows the referral system to customize mentor selection according to the student's current learning situation. Some or all of the above processes in the referral system may be performed using a generative AI, or not. For example, the referral system can input student learning situation data into a generative AI, which can then customize mentor selection.

[0054] The referral department can select the most suitable mentor when referring students, taking into account the student's geographical location. For example, the referral department can select a local mentor based on the student's geographical location. For example, the referral department can also select a mentor that is convenient for commuting to school or work, taking into account the student's geographical location. For example, the referral department can select a mentor that participates in local events or activities, taking into account the student's geographical location. In this way, the referral department can select the most suitable mentor, taking into account the student's geographical location. Some or all of the above processing in the referral department may be performed using a generative AI, or it may be performed without a generative AI. For example, the referral department can input the student's geographical location data into a generative AI, and the generative AI can select the most suitable mentor.

[0055] The referral department can select mentors by analyzing students' social media activity when referring them. For example, the referral department can analyze students' social media activity and select mentors related to topics they are interested in. For example, the referral department can also select mentors by referring to the activities of students' followers and friends on social media. For example, the referral department can analyze the content of students' social media posts and select relevant mentors. In this way, the referral department can select mentors by analyzing students' social media activity. Some or all of the above processing in the referral department may be performed using generative AI, or it may be performed without generative AI. For example, the referral department can input students' social media data into generative AI, and the generative AI can select mentors.

[0056] The follow-up unit can provide optimal support during follow-up by referring to the student's past progress. For example, the follow-up unit can analyze the student's past progress and provide support that incorporates successful methods. For example, the follow-up unit can also consider the student's past failures and provide support that reflects areas for improvement. For example, the follow-up unit can provide achievable support based on the student's past progress. In this way, the follow-up unit can provide optimal support based on the student's past progress. Some or all of the above processes in the follow-up unit may be performed using a generative AI, or not. For example, the follow-up unit can input student progress data into a generative AI, which can then provide optimal support.

[0057] The follow-up unit can customize the means of support based on the student's current learning situation during follow-up. For example, the follow-up unit can analyze the student's current learning situation and provide support incorporating appropriate means. For example, the follow-up unit can also adjust the means of support according to the student's learning progress. For example, the follow-up unit can consider the student's learning situation and provide support incorporating achievable means. In this way, the follow-up unit can customize the means of support according to the student's current learning situation. Some or all of the above processing in the follow-up unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the follow-up unit can input the student's learning situation data into a generative AI, and the generative AI can customize the means of support.

[0058] The follow-up unit can provide optimal support during follow-up, taking into account the student's geographical location. For example, the follow-up unit can provide support that utilizes local resources based on the student's geographical location. For example, the follow-up unit can also provide support that takes into account the convenience of commuting to school or work, taking into account the student's geographical location. For example, the follow-up unit can also provide support that incorporates local events and activities based on the student's geographical location. In this way, the follow-up unit can provide optimal support, taking into account the student's geographical location. Some or all of the above processing in the follow-up unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the follow-up unit can input the student's geographical location data into a generative AI, which can then provide optimal support.

[0059] The follow-up unit can analyze a student's social media activity during follow-up and suggest ways to support them. For example, the follow-up unit can analyze a student's social media activity and suggest ways related to topics they are interested in. For example, the follow-up unit can also suggest ways to support them by referring to the activities of the student's followers and friends on social media. For example, the follow-up unit can analyze the content of a student's social media posts and suggest relevant ways. In this way, the follow-up unit can analyze a student's social media activity and suggest ways to support them. Some or all of the above processing in the follow-up unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the follow-up unit can input the student's social media data into a generative AI, which can then suggest ways to support them.

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

[0061] The information provider can customize how information is presented based on students' learning styles. For example, visual learners can be provided with information using graphs and charts. Auditory learners can be provided with audio explanations or podcast-style information. Furthermore, experiential learners can be provided with real-life experiences or interactive simulations. This allows the information provider to deliver information that is optimally suited to each student's learning style.

[0062] The referral department can analyze a student's past interactions with mentors and select the most suitable mentor. For example, it can select a mentor with similar characteristics by referring to past successful interactions with mentors. It can also consider past interactions with problematic mentors and select a mentor who reflects those improvements. Furthermore, based on past interactions, it can select a mentor who best suits the student's needs. In this way, the referral department can select the most suitable mentor based on a student's past interactions.

[0063] The data collection unit can analyze students' learning history and select the most suitable information collection method. For example, it can analyze learning resources that students have used in the past and prioritize the collection of similar resources. It can also collect relevant information based on topics that students have shown interest in in the past. Furthermore, it can select the most suitable information collection method based on the student's learning history. In this way, the data collection unit can select the most suitable information collection method based on the student's learning history.

[0064] The planning department can customize the content of action plans based on students' learning styles. For example, visual learners can be provided with action plans that use graphs and charts. Auditory learners can be provided with audio explanations or action plans in podcast format. Furthermore, experiential learners can be provided with action plans that incorporate real-life experiences and interactive simulations. This allows the planning department to create optimal action plans tailored to each student's learning style.

[0065] The follow-up department can analyze students' learning history and select the most suitable follow-up method. For example, it can analyze follow-up methods students have used in the past and prioritize providing similar methods. It can also provide relevant support based on follow-up methods that have been successful for students in the past. Furthermore, it can select the most suitable follow-up method based on the student's learning history. In this way, the follow-up department can select the most suitable follow-up method based on the student's learning history.

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

[0067] Step 1: The data collection department gathers information on students' interests, strengths, and values. For example, it delves deeper into students' interests and values ​​through dialogue, uses generative AI to reveal their personality traits and strengths, and deepens their self-understanding. Step 2: The provisioning unit provides appropriate career information based on the information collected by the collection unit. For example, it provides relevant occupational information based on students' interests and uses generative AI to provide appropriate career information and advice through natural language processing. Step 3: The planning department creates specific goals and action plans based on the information provided by the provision department. For example, they create action plans tailored to the students' goals and use a generation AI to suggest specific goals and the steps to achieve them. Step 4: The referral department matches students with experts and mentors based on the action plan created by the planning department. For example, it introduces appropriate mentors based on the students' needs and provides opportunities for consultation using AI-generated resources. Step 5: The follow-up team monitors the progress of students with mentors introduced by the referral team and provides support to help them achieve their goals. For example, they regularly check the students' progress, provide necessary support, use AI to monitor progress, and continue to provide support to help them achieve their goals.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that helps students to concretely envision their future careers and life plans. This system analyzes students' interests, strengths, and values, and supports them in setting appropriate career paths and goals. For example, the AI ​​agent system provides self-analysis support, career information provision, goal setting and action planning, mentor introduction, and regular follow-up. In self-analysis support, it clarifies the student's personality traits and strengths, deepening their self-understanding. The generating AI delves deeper into the student's interests and values ​​through dialogue, and provides appropriate career information and advice using natural language processing. In career information provision, it provides diverse occupation and career information to broaden the student's perspective. The generating AI provides relevant occupation information based on the student's interests and shows a concrete career path. In goal setting and action planning, it proposes specific goals and steps to achieve them. The generating AI creates an action plan tailored to the student's goals and checks their progress. In mentor introduction, it supports matching with experts and seniors as needed. The generating AI introduces appropriate mentors based on the student's needs and provides opportunities for consultation. In regular follow-up, it checks their progress and continues to provide support toward achieving their goals. The generating AI regularly checks students' progress and provides necessary support. In this way, the AI ​​agent system helps students envision their future careers and life plans concretely, enabling them to proactively shape their future through self-understanding and information provision. Thus, the AI ​​agent system can help students envision their future careers and life plans concretely.

[0069] The AI ​​agent system according to this embodiment comprises a collection unit, a provision unit, a planning unit, a referral unit, and a follow-up unit. The collection unit collects the student's interests, strengths, and values. The collection unit, for example, delves deeper into the student's interests and values ​​through dialogue with the student. The collection unit uses generative AI to reveal the student's personality traits and strengths and deepen their self-understanding. The provision unit provides appropriate career information based on the information collected by the collection unit. The provision unit, for example, provides relevant occupational information based on the student's interests. The provision unit uses generative AI to provide appropriate career information and advice through natural language processing. The planning unit creates specific goals and action plans based on the information provided by the provision unit. The planning unit, for example, creates action plans tailored to the student's goals. The planning unit uses generative AI to propose specific goals and steps toward achieving them. The referral unit matches the student with experts and mentors based on the action plans created by the planning unit. The referral unit, for example, introduces appropriate mentors based on the student's needs. The referral unit uses generative AI to provide opportunities for consultation. The follow-up department monitors the progress of students with mentors introduced by the referral department and provides support to help them achieve their goals. For example, the follow-up department regularly checks the students' progress and provides necessary support. The follow-up department uses generative AI to monitor progress and continue to provide support to help students achieve their goals. In this way, the AI ​​agent system according to the embodiment can help students to concretely envision their future careers and life plans.

[0070] The data collection department gathers information on students' interests, strengths, and values. Specifically, it utilizes generative AI to delve deeper into students' interests and values ​​through dialogue. The generative AI analyzes students' statements using natural language processing technology through dialogue, identifying areas of interest. For example, when students talk about their favorite subjects or hobbies, the content is analyzed to extract relevant keywords and clarify areas of interest. The generative AI also conducts psychological questions and personality tests to reveal students' personality traits and strengths, and analyzes the results. This allows students to deepen their self-understanding. Furthermore, the data collection department asks questions about students' values ​​and analyzes their answers to understand their values. For example, when students talk about what they value or their future goals, the content is analyzed to identify their values. In this way, the data collection department can comprehensively understand students' interests, strengths, and values ​​and collect basic information for individualized support.

[0071] The information provision department provides appropriate career information based on the information collected by the information collection department. Specifically, it utilizes generative AI to provide relevant occupational information based on students' interests. The generative AI uses natural language processing technology to search for occupations and career information related to students' interests and provides appropriate information. For example, if a student is interested in science, the generative AI will provide information on science-related occupations, university departments, and necessary qualifications. The generative AI also suggests appropriate career paths based on students' strengths and values. For example, if a student has strong leadership skills, it will suggest career paths as a manager or entrepreneur and specify the skills and experience required for them. Furthermore, the information provision department uses the generative AI to analyze past data and success stories to suggest the optimal career path for each student, providing advice that helps students choose their future paths. In this way, the information provision department can provide students with information to concretely envision their future careers and career paths, and support their decision-making.

[0072] The planning department creates specific goals and action plans based on the information provided by the provision department. Specifically, it utilizes generative AI to create action plans tailored to each student's goals. The generative AI proposes specific goals and steps to achieve them based on the student's goals and desired career path. For example, if a student aims to become a doctor, the generative AI will specifically indicate the necessary academic qualifications, certifications, and experience, and propose a learning plan and activity plan to achieve them. The generative AI also creates individually optimized action plans, taking into account the student's strengths and weaknesses. For example, if a student is good at mathematics but struggles with communication, the generative AI will propose specific activities to improve communication skills while leveraging their mathematical strengths. Furthermore, the planning department uses the generative AI to monitor the student's progress and revise the action plan as needed. This allows the planning department to provide students with concrete action plans that enable them to effectively progress towards their goals and to provide continuous support.

[0073] The placement department matches students with experts and senior colleagues based on action plans created by the planning department. Specifically, it utilizes generative AI to introduce appropriate mentors based on students' needs. The generative AI considers students' interests, goals, strengths, and weaknesses to search for and match them with the most suitable mentors. For example, if a student aims to become an engineer, the generative AI will search for successful senior colleagues and experts in the engineering field and introduce the student to the most suitable mentor from among them. The generative AI also provides opportunities for consultation to support communication between students and mentors. For example, the generative AI will coordinate the schedules of students and mentors and set up opportunities for online consultations and meetings. Furthermore, the placement department uses the generative AI to evaluate the effectiveness of student-mentor matching and to change or add mentors as needed. In this way, the placement department can ensure that students receive appropriate advice and support from experts and senior colleagues, and can support them in achieving their goals.

[0074] The follow-up department monitors the progress of students with mentors introduced by the referral department and provides support to help them achieve their goals. Specifically, it utilizes generative AI to regularly check students' progress and provide necessary support. The generative AI monitors students' progress and continuously supports them in achieving their goals. For example, the generative AI analyzes students' learning and activity status and issues early warnings if progress is behind or problems have arisen. The generative AI also provides additional support and advice according to the student's progress. For example, when a student is working on a specific assignment, the generative AI provides resources and reference materials related to that assignment to support their learning. Furthermore, the follow-up department uses the generative AI to support communication between students and mentors and to share progress. This allows the follow-up department to provide continuous support to help students effectively move towards their goals and achieve them.

[0075] The data collection unit can delve deeper into students' interests and values ​​through dialogue with them. For example, the data collection unit can delve deeper into students' interests and values ​​through interviews with them. For example, the data collection unit can delve deeper into students' interests and values ​​by conducting surveys with them. This allows the data collection unit to gain a deeper understanding of students' interests and values. Some or all of the above-described processes in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input dialogue data with students into a generative AI, which can then generate questions to delve deeper into students' interests and values.

[0076] The information provider can provide relevant career information based on students' interests. For example, the information provider can provide an overview of relevant occupations based on students' interests. The information provider can also provide the skills required for relevant occupations based on students' interests. The information provider can also provide career paths for relevant occupations based on students' interests. In this way, the information provider can broaden students' perspectives by providing career information that matches their interests. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provider can input student interest data into a generative AI, and the generative AI can generate relevant career information.

[0077] The planning department can create action plans tailored to students' goals. For example, the planning department can propose specific action steps tailored to students' goals. The planning department can also propose resource allocation tailored to students' goals. The planning department can also propose a schedule tailored to students' goals. This allows the planning department to create specific action plans tailored to students' goals. Some or all of the above processes in the planning department may be performed using a generative AI, or not. For example, the planning department can input student goal data into a generative AI, which can then generate an action plan.

[0078] The referral department can introduce students to appropriate mentors based on their needs. For example, the referral department can introduce mentors with specialized knowledge based on the student's needs. For example, the referral department can introduce mentors with professional experience based on the student's needs. For example, the referral department can introduce mentors with teaching experience based on the student's needs. In this way, the referral department can provide students with appropriate advisors by introducing mentors that meet their needs. Some or all of the above processes in the referral department may be performed using a generative AI, or not. For example, the referral department can input student needs data into a generative AI, which can then select an appropriate mentor.

[0079] The follow-up department can periodically check students' progress and provide necessary support. For example, the follow-up department can check students' progress weekly. The follow-up department can also check students' progress monthly. The follow-up department can also check students' progress quarterly. This allows the follow-up department to regularly check students' progress and provide continuous support. Some or all of the above processes in the follow-up department may be performed using or without a generative AI. For example, the follow-up department can input student progress data into a generative AI, which can analyze the progress and suggest necessary support.

[0080] The data collection unit can estimate a student's emotions and determine the priority of information to collect based on the estimated emotions. For example, if a student is feeling anxious, the data collection unit may prioritize collecting information that provides reassurance. If a student is excited, the data collection unit may also prioritize collecting information that is interesting. If a student is calm, the data collection unit may also prioritize collecting detailed information. In this way, the data collection unit can collect more appropriate information by adjusting the priority of information according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing described above in the data collection unit may be performed using the generative AI or not. For example, the data collection unit can input student emotion data into the generative AI, which can then determine the priority of information.

[0081] The data collection unit can analyze a student's past activity history and select the most suitable information collection method. For example, the data collection unit can analyze events and activities a student has participated in in the past and collect relevant information. For example, the data collection unit can also collect relevant information based on topics a student has shown interest in in the past. For example, the data collection unit can analyze resources a student has used in the past and select the most suitable information collection method. This allows the data collection unit to select the most suitable information collection method based on a student's past activity history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input student activity history data into a generative AI, which can then select the most suitable information collection method.

[0082] The data collection unit can filter information based on the student's current learning status and areas of interest during the information collection process. For example, the data collection unit can analyze the student's current learning status and filter relevant information. The data collection unit can also filter relevant information based on the student's areas of interest. The data collection unit can also filter appropriate information considering the student's learning progress. This allows the data collection unit to filter appropriate information according to the student's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input student learning status data into a generative AI, which can then filter the information.

[0083] The data collection unit can estimate a student's emotions and adjust the depth of information collected based on the estimated emotions. For example, if a student is feeling anxious, the data collection unit will collect concise and easy-to-understand information. If a student is excited, for example, the data collection unit may also collect detailed and in-depth information. If a student is calm, for example, the data collection unit may also collect balanced information. This allows the data collection unit to collect more appropriate information by adjusting the depth of information according to the student'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 processing described above in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input student emotion data into a generative AI, which can then adjust the depth of information.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the students' geographical location information during data collection. For example, the data collection unit can collect information related to a region based on the students' geographical location information. For example, the data collection unit can also collect information on local events and activities by considering the students' geographical location information. For example, the data collection unit can also collect information on local occupations by considering the students' geographical location information. This allows the data collection unit to prioritize the collection of highly relevant information by considering the students' geographical location information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the students' geographical location data into a generative AI, which can then prioritize the collection of highly relevant information.

[0085] The data collection unit can analyze students' social media activities and collect relevant information during the information gathering process. For example, the data collection unit can analyze students' social media activities and collect information related to topics they are interested in. For example, the data collection unit can also collect relevant information by referring to the activities of students' followers and friends on social media. For example, the data collection unit can analyze the content of students' social media posts and collect relevant information. In this way, the data collection unit can analyze students' social media activities and collect relevant information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input students' social media data into a generative AI, and the generative AI can collect relevant information.

[0086] The information provider can estimate a student's emotions and adjust the way the information is presented based on the estimated emotions. For example, if a student is feeling anxious, the information provider can present it in a reassuring way. If a student is excited, the information provider can present it in an engaging way. If a student is calm, the information provider can present it in a detailed and easy-to-understand way. In this way, the information provider can provide more appropriate information by adjusting the way the information is presented according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 information provider may be performed using the generative AI or not. For example, the information provider can input student emotion data into the generative AI, which can then adjust the way the information is presented.

[0087] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide highly important information in detail and less important information concisely. For example, the provider can also provide information directly related to students' goals in detail and supplementary information concisely. For example, the provider can adjust the level of detail of the information according to the students' level of interest. In this way, the provider can provide more appropriate information by adjusting the level of detail of the information according to its importance. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the provider can input information importance data into a generative AI, and the generative AI can adjust the level of detail of the information provided.

[0088] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, the provider may provide career information using an algorithm that includes detailed explanations. For example, the provider may provide learning resources using a concise and visual algorithm. For example, the provider may provide mentor introductions using individual matching algorithms. In this way, the provider can provide more appropriate information by adjusting the information provision algorithm according to the category of information. Some or all of the above processing in the information provider may be performed using a generative AI or not. For example, the provider can input information category data into a generative AI, and the generative AI can apply different information provision algorithms.

[0089] The information provider can estimate a student's emotions and adjust the length of the information provided based on the estimated emotions. For example, if a student is feeling anxious, the provider can provide short, concise information. If a student is excited, the provider can also provide more detailed and longer information. If a student is calm, the provider can also provide balanced information. This allows the provider to provide more appropriate information by adjusting the length of the information according to the student'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 processing described above in the information provider may be performed using or without a generative AI. For example, the provider can input student emotion data into a generative AI, which can then adjust the length of the information.

[0090] The information delivery unit can determine the priority of information delivery based on the timing of information submission. For example, the information delivery unit can prioritize the delivery of urgent information. For example, the information delivery unit can also prioritize the delivery of information directly related to the achievement of students' goals. For example, the information delivery unit can also deliver information at an appropriate time according to the students' learning progress. In this way, the information delivery unit can provide more appropriate information by determining the priority of information delivery according to the timing of information submission. Some or all of the above processing in the information delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the information delivery unit can input information submission timing data into a generative AI, and the generative AI can determine the priority of information delivery.

[0091] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can first provide information directly related to the student's goals. The provider can also prioritize providing information related to the student's interests. The provider can also prioritize providing information that is highly relevant according to the student's learning progress. In this way, the provider can provide more appropriate information by adjusting the order of delivery according to the relevance of the information. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the provider can input information relevance data into a generative AI, and the generative AI can adjust the order of delivery.

[0092] The planning unit can estimate a student's emotions and adjust the content of the action plan based on the estimated emotions. For example, if a student is feeling anxious, the planning unit may suggest a simple and easily achievable action plan. For example, if a student is excited, the planning unit may suggest a challenging action plan. For example, if a student is calm, the planning unit may suggest a balanced action plan. In this way, the planning unit can create a more appropriate action plan by adjusting its content according to the student'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 planning unit may be performed using or without generative AI. For example, the planning unit can input student emotion data into a generative AI, which can then adjust the content of the action plan.

[0093] The planning department can create an optimal plan by referring to the student's past goal achievement history when creating an action plan. For example, the planning department can analyze the student's past goal achievement history and create a plan that incorporates successful methods. For example, the planning department can also consider the student's past failures and create a plan that reflects areas for improvement. For example, the planning department can create an achievable plan based on the student's past goal achievement history. In this way, the planning department can create an optimal action plan based on the student's past goal achievement history. Some or all of the above processes in the planning department may be performed using a generative AI, or not. For example, the planning department can input the student's goal achievement history data into a generative AI, which can then create an optimal plan.

[0094] The planning unit can customize the means of the plan based on the student's current learning situation when creating an action plan. For example, the planning unit can analyze the student's current learning situation and create a plan that incorporates appropriate means. The planning unit can also adjust the means of the plan according to the student's learning progress. For example, the planning unit can create a plan that incorporates achievable means, taking into account the student's learning situation. This allows the planning unit to customize the means of the plan according to the student's current learning situation. Some or all of the above processes in the planning unit may be performed using a generative AI, or not. For example, the planning unit can input student learning situation data into a generative AI, which can then customize the means of the plan.

[0095] The planning unit can estimate students' emotions and prioritize action plans based on those estimated emotions. For example, if a student is feeling anxious, the planning unit might prioritize easy and achievable tasks. If a student is excited, the planning unit might prioritize challenging tasks. If a student is calm, the planning unit might prioritize balanced tasks. This allows the planning unit to create more appropriate action plans by prioritizing them according to students' 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 planning unit may be performed using or without generative AI. For example, the planning unit can input student emotion data into a generative AI, which can then determine the priority of action plans.

[0096] The planning department can create optimal action plans by considering students' geographical location information. For example, the planning department can create plans that utilize local resources based on students' geographical location information. For example, the planning department can also create plans that consider the convenience of commuting to school or work by considering students' geographical location information. For example, the planning department can create plans that incorporate local events and activities based on students' geographical location information. In this way, the planning department can create optimal action plans by considering students' geographical location information. Some or all of the above processes in the planning department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the planning department can input students' geographical location data into a generative AI, and the generative AI can create an optimal plan.

[0097] The planning department can analyze students' social media activities and propose methods for creating action plans. For example, the planning department can analyze students' social media activities and propose methods related to topics they are interested in. For example, the planning department can also propose methods for creating plans by referring to the activities of students' followers and friends on social media. For example, the planning department can analyze the content of students' social media posts and propose relevant methods. In this way, the planning department can analyze students' social media activities and propose methods for creating plans. Some or all of the above processes in the planning department may be performed using generative AI, or not. For example, the planning department can input students' social media data into a generative AI, and the generative AI can propose methods for creating plans.

[0098] The referral system can estimate a student's emotions and select a mentor based on those emotions. For example, if a student is feeling anxious, the referral system can select a mentor who provides reassurance. If a student is excited, the referral system can select a mentor who provides stimulation. If a student is calm, the referral system can select a balanced mentor. This allows the referral system to recommend a more appropriate mentor by selecting one according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the referral system may be performed using or without a generative AI. For example, the referral system can input student emotion data into a generative AI, which can then select a mentor.

[0099] The referral department can select the most suitable mentor by referring to the student's past consultation history when referring mentors. For example, the referral department can analyze the student's past consultation history and select a mentor based on the content of successful consultations. For example, the referral department can also select a mentor that reflects areas for improvement, taking into account the student's past consultation history. For example, the referral department can select the most suitable mentor based on the student's past consultation history. In this way, the referral department can select the most suitable mentor based on the student's past consultation history. Some or all of the above processing in the referral department may be performed using a generative AI, or it may be performed without using a generative AI. For example, the referral department can input the student's consultation history data into a generative AI, and the generative AI can select the most suitable mentor.

[0100] The referral system can customize mentor selection based on the student's current learning situation when referring mentors. For example, the referral system can analyze the student's current learning situation and select an appropriate mentor. The referral system can also adjust mentor selection according to the student's learning progress. The referral system can also select the most suitable mentor considering the student's learning situation. This allows the referral system to customize mentor selection according to the student's current learning situation. Some or all of the above processes in the referral system may be performed using a generative AI, or not. For example, the referral system can input student learning situation data into a generative AI, which can then customize mentor selection.

[0101] The referral system can estimate a student's emotions and determine the priority of mentor recommendations based on those emotions. For example, if a student is feeling anxious, the referral system will prioritize recommending a mentor who provides reassurance. If a student is excited, the referral system may also prioritize recommending a stimulating mentor. If a student is calm, the referral system may also prioritize recommending a balanced mentor. In this way, the referral system can recommend a more appropriate mentor by prioritizing mentor recommendations according to the student'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 referral system may be performed using or without generative AI. For example, the referral system can input student emotion data into a generative AI, which can then determine the priority of mentor recommendations.

[0102] The referral department can select the most suitable mentor when referring students, taking into account the student's geographical location. For example, the referral department can select a local mentor based on the student's geographical location. For example, the referral department can also select a mentor that is convenient for commuting to school or work, taking into account the student's geographical location. For example, the referral department can select a mentor that participates in local events or activities, taking into account the student's geographical location. In this way, the referral department can select the most suitable mentor, taking into account the student's geographical location. Some or all of the above processing in the referral department may be performed using a generative AI, or it may be performed without a generative AI. For example, the referral department can input the student's geographical location data into a generative AI, and the generative AI can select the most suitable mentor.

[0103] The referral department can select mentors by analyzing students' social media activity when referring them. For example, the referral department can analyze students' social media activity and select mentors related to topics they are interested in. For example, the referral department can also select mentors by referring to the activities of students' followers and friends on social media. For example, the referral department can analyze the content of students' social media posts and select relevant mentors. In this way, the referral department can select mentors by analyzing students' social media activity. Some or all of the above processing in the referral department may be performed using generative AI, or it may be performed without generative AI. For example, the referral department can input students' social media data into generative AI, and the generative AI can select mentors.

[0104] The follow-up unit can estimate the student's emotions and adjust the content of the follow-up based on the estimated emotions. For example, if the student is feeling anxious, the follow-up unit can provide reassuring support. If the student is excited, the follow-up unit can also provide stimulating support. If the student is calm, the follow-up unit can also provide balanced support. In this way, the follow-up unit can provide more appropriate support by adjusting the content of the follow-up according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 follow-up unit may be performed using the generative AI or not. For example, the follow-up unit can input the student's emotion data into the generative AI, and the generative AI can adjust the content of the follow-up.

[0105] The follow-up unit can provide optimal support during follow-up by referring to the student's past progress. For example, the follow-up unit can analyze the student's past progress and provide support that incorporates successful methods. For example, the follow-up unit can also consider the student's past failures and provide support that reflects areas for improvement. For example, the follow-up unit can provide achievable support based on the student's past progress. In this way, the follow-up unit can provide optimal support based on the student's past progress. Some or all of the above processes in the follow-up unit may be performed using a generative AI, or not. For example, the follow-up unit can input student progress data into a generative AI, which can then provide optimal support.

[0106] The follow-up unit can customize the means of support based on the student's current learning situation during follow-up. For example, the follow-up unit can analyze the student's current learning situation and provide support incorporating appropriate means. For example, the follow-up unit can also adjust the means of support according to the student's learning progress. For example, the follow-up unit can consider the student's learning situation and provide support incorporating achievable means. In this way, the follow-up unit can customize the means of support according to the student's current learning situation. Some or all of the above processing in the follow-up unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the follow-up unit can input the student's learning situation data into a generative AI, and the generative AI can customize the means of support.

[0107] The follow-up unit can estimate the student's emotions and determine the priority of follow-up based on the estimated emotions. For example, if the student is feeling anxious, the follow-up unit may prioritize reassuring follow-up. For example, if the student is excited, the follow-up unit may prioritize stimulating follow-up. For example, if the student is calm, the follow-up unit may prioritize balanced follow-up. In this way, the follow-up unit can provide more appropriate support by determining the priority of follow-up according to the student'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 follow-up unit may be performed using or without a generative AI. For example, the follow-up unit can input student emotion data into a generative AI, which can then determine the priority of follow-up.

[0108] The follow-up unit can provide optimal support during follow-up, taking into account the student's geographical location. For example, the follow-up unit can provide support that utilizes local resources based on the student's geographical location. For example, the follow-up unit can also provide support that takes into account the convenience of commuting to school or work, taking into account the student's geographical location. For example, the follow-up unit can also provide support that incorporates local events and activities based on the student's geographical location. In this way, the follow-up unit can provide optimal support, taking into account the student's geographical location. Some or all of the above processing in the follow-up unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the follow-up unit can input the student's geographical location data into a generative AI, which can then provide optimal support.

[0109] The follow-up unit can analyze a student's social media activity during follow-up and suggest ways to support them. For example, the follow-up unit can analyze a student's social media activity and suggest ways related to topics they are interested in. For example, the follow-up unit can also suggest ways to support them by referring to the activities of the student's followers and friends on social media. For example, the follow-up unit can analyze the content of a student's social media posts and suggest relevant ways. In this way, the follow-up unit can analyze a student's social media activity and suggest ways to support them. Some or all of the above processing in the follow-up unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the follow-up unit can input the student's social media data into a generative AI, which can then suggest ways to support them.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can estimate the student's emotions and adjust the tone of the conversation based on those estimates. For example, if a student is feeling anxious, the unit can conduct the conversation in a calm, reassuring tone. If a student is excited, the unit can maintain that excitement while conducting the conversation in an engaging, lively tone. Furthermore, if a student is calm, the unit can conduct the conversation in a calm, logical tone to provide detailed information. In this way, the data collection unit can conduct the conversation in an optimal manner, tailored to the student's emotions.

[0112] The information provider can customize how information is presented based on students' learning styles. For example, visual learners can be provided with information using graphs and charts. Auditory learners can be provided with audio explanations or podcast-style information. Furthermore, experiential learners can be provided with real-life experiences or interactive simulations. This allows the information provider to deliver information that is optimally suited to each student's learning style.

[0113] The planning department can estimate students' emotions and adjust the feedback methods for the action plan based on those estimated emotions. For example, if a student is feeling anxious, the planning department can emphasize positive feedback and provide reassuring feedback. If a student is excited, the planning department can maintain that excitement while providing challenging feedback. Furthermore, if a student is calm, the planning department can provide detailed and specific feedback. In this way, the planning department can provide optimal feedback tailored to the student's emotions.

[0114] The referral department can analyze a student's past interactions with mentors and select the most suitable mentor. For example, it can select a mentor with similar characteristics by referring to past successful interactions with mentors. It can also consider past interactions with problematic mentors and select a mentor who reflects those improvements. Furthermore, based on past interactions, it can select a mentor who best suits the student's needs. In this way, the referral department can select the most suitable mentor based on a student's past interactions.

[0115] The follow-up unit can estimate a student's emotions and adjust the timing of follow-ups based on those estimates. For example, if a student is feeling anxious, the follow-up unit can provide support early to reassure them. If a student is excited, the follow-up unit can provide support before their excitement subsides to maintain their motivation. Furthermore, if a student is calm, the follow-up unit can provide support at the appropriate time to check on the progress of the plan. This allows the follow-up unit to provide optimal support tailored to the student's emotions.

[0116] The data collection unit can analyze students' learning history and select the most suitable information collection method. For example, it can analyze learning resources that students have used in the past and prioritize the collection of similar resources. It can also collect relevant information based on topics that students have shown interest in in the past. Furthermore, it can select the most suitable information collection method based on the student's learning history. In this way, the data collection unit can select the most suitable information collection method based on the student's learning history.

[0117] The information provider can estimate the student's emotions and adjust the frequency of information delivery based on those estimates. For example, if a student is feeling anxious, the provider can provide information frequently to reassure them. If a student is excited, the provider can provide information at an appropriate frequency to maintain that excitement. Furthermore, if a student is calm, the provider can provide information at the necessary times to support the progress of the plan. In this way, the provider can adjust the optimal frequency of information delivery according to the student's emotions.

[0118] The planning department can customize the content of action plans based on students' learning styles. For example, visual learners can be provided with action plans that use graphs and charts. Auditory learners can be provided with audio explanations or action plans in podcast format. Furthermore, experiential learners can be provided with action plans that incorporate real-life experiences and interactive simulations. This allows the planning department to create optimal action plans tailored to each student's learning style.

[0119] The referral department can estimate a student's emotions and adjust the content of the initial meeting with a mentor based on those estimates. For example, if a student is feeling anxious, the referral department can set up the initial meeting to provide reassurance. If a student is excited, the referral department can set up the initial meeting to maintain that excitement. Furthermore, if a student is calm, the referral department can set up the initial meeting to be detailed and specific. In this way, the referral department can set up the optimal initial meeting tailored to the student's emotions.

[0120] The follow-up department can analyze students' learning history and select the most suitable follow-up method. For example, it can analyze follow-up methods students have used in the past and prioritize providing similar methods. It can also provide relevant support based on follow-up methods that have been successful for students in the past. Furthermore, it can select the most suitable follow-up method based on the student's learning history. In this way, the follow-up department can select the most suitable follow-up method based on the student's learning history.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection department gathers information on students' interests, strengths, and values. For example, it delves deeper into students' interests and values ​​through dialogue, uses generative AI to reveal their personality traits and strengths, and deepens their self-understanding. Step 2: The provisioning unit provides appropriate career information based on the information collected by the collection unit. For example, it provides relevant occupational information based on students' interests and uses generative AI to provide appropriate career information and advice through natural language processing. Step 3: The planning department creates specific goals and action plans based on the information provided by the provision department. For example, they create action plans tailored to the students' goals and use a generation AI to suggest specific goals and the steps to achieve them. Step 4: The referral department matches students with experts and mentors based on the action plan created by the planning department. For example, it introduces appropriate mentors based on the students' needs and provides opportunities for consultation using AI-generated resources. Step 5: The follow-up team monitors the progress of students with mentors introduced by the referral team and provides support to help them achieve their goals. For example, they regularly check the students' progress, provide necessary support, use AI to monitor progress, and continue to provide support to help them achieve their goals.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, provision unit, planning unit, introduction unit, and follow-up unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14, which delves into the student's interests and values ​​through dialogue. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which provides appropriate career information based on the information collected by the collection unit. The planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which creates specific goals and action plans based on the information provided by the provision unit. The introduction unit is implemented, for example, by the control unit 46A of the smart device 14, which matches the student with experts and senior students based on the action plan created by the planning unit. The follow-up unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which checks the progress with the mentor introduced by the introduction unit and provides support towards achieving the goals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, provision unit, planning unit, introduction unit, and follow-up unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214, which delves into the student's interests and values ​​through dialogue. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides appropriate career information based on the information collected by the collection unit. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates specific goals and action plans based on the information provided by the provision unit. The introduction unit is implemented by, for example, the control unit 46A of the smart glasses 214, which matches the student with experts and mentors based on the action plan created by the planning unit. The follow-up unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks the progress with the mentor introduced by the introduction unit and provides support towards achieving the goals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, provision unit, planning unit, introduction unit, and follow-up unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314, which delves into the student's interests and values ​​through dialogue. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides appropriate career information based on the information collected by the collection unit. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates specific goals and action plans based on the information provided by the provision unit. The introduction unit is implemented by, for example, the control unit 46A of the headset terminal 314, which matches the student with experts and senior students based on the action plan created by the planning unit. The follow-up unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks the progress with the mentor introduced by the introduction unit and provides support for achieving goals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, provision unit, planning unit, introduction unit, and follow-up unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414, which delves into the interests and values ​​of students through dialogue. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides appropriate career information based on the information collected by the collection unit. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates specific goals and action plans based on the information provided by the provision unit. The introduction unit is implemented by, for example, the control unit 46A of the robot 414, which matches students with experts and senior students based on the action plan created by the planning unit. The follow-up unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks the progress with mentors introduced by the introduction unit and provides support for achieving goals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The collection department gathers information on students' interests, strengths, and values, A providing unit that provides appropriate route information based on the information collected by the aforementioned collection unit, A planning department creates specific goals and action plans based on the information provided by the aforementioned provisioning department. Based on the action plan created by the aforementioned planning department, the introduction department will match the user with experts and senior colleagues. The system includes a follow-up unit that checks the progress made with the mentor introduced by the aforementioned introduction unit and provides support to help achieve the goal. A system characterized by the following features. (Note 2) The aforementioned collection unit is Through dialogue with students, we delve deeper into their interests and values. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide relevant career information based on students' interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, Create an action plan tailored to the student's goals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned introduction section is, We introduce appropriate mentors based on the students' needs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned follow-up section is Regularly check students' progress and provide the necessary support. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates students' emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze students' past activity records and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filter it based on the students' current learning situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate students' emotions and adjust the depth of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information, taking into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, analyze students' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, We estimate students' emotions and adjust the way we present information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, The system estimates students' emotions and adjusts the length of information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned planning department, The system estimates the students' 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 20) The aforementioned planning department, When creating an action plan, refer to the student's past goal achievement history to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, When creating an action plan, customize the planning methods based on the students' current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, Estimate students' emotions and prioritize action plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, When creating an action plan, take into account the students' geographical location to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning department, When creating action plans, we analyze students' social media activity and propose methods for planning. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned introduction section is, The system estimates the students' emotions and selects mentors to introduce based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned introduction section is, When introducing mentors, the most suitable mentor is selected by referring to the student's past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned introduction section is, When introducing mentors, the selection of mentors is customized based on the student's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned introduction section is, The system estimates students' emotions and prioritizes mentor recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned introduction section is, When introducing mentors, the most suitable mentor will be selected considering the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned introduction section is, When introducing mentors, we analyze students' social media activity to select the appropriate mentor. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned follow-up section is We estimate the students' emotions and adjust the follow-up content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned follow-up section is During follow-up sessions, we refer to the student's past progress to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned follow-up section is During follow-up sessions, customize the support methods based on the student's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned follow-up section is Estimate students' emotions and determine follow-up priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned follow-up section is During follow-up, we provide optimal support while taking into account the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned follow-up section is During follow-up sessions, we analyze students' social media activity and propose ways to support them. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 collection department gathers information on students' interests, strengths, and values, A providing unit that provides appropriate route information based on the information collected by the aforementioned collection unit, A planning department creates specific goals and action plans based on the information provided by the aforementioned provisioning department. Based on the action plan created by the aforementioned planning department, the introduction department will match the user with experts and senior colleagues. The system includes a follow-up unit that checks the progress made with the mentor introduced by the aforementioned introduction unit and provides support to help achieve the goal. A system characterized by the following features.

2. The aforementioned collection unit is Through dialogue with students, we delve deeper into their interests and values. The system according to feature 1.

3. The aforementioned supply unit is, Provide relevant career information based on students' interests. The system according to feature 1.

4. The aforementioned planning department, Create an action plan tailored to the student's goals. The system according to feature 1.

5. The aforementioned introduction section is, We introduce appropriate mentors based on the students' needs. The system according to feature 1.

6. The aforementioned follow-up section is Regularly check students' progress and provide the necessary support. The system according to feature 1.

7. The aforementioned collection unit is The system estimates students' emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze students' past activity records and select the most suitable information gathering method. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, filter it based on the students' current learning situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is We estimate students' emotions and adjust the depth of information collected based on those estimated emotions. The system according to feature 1.

Images