system

Abstract

The system according to this embodiment aims to suggest optimal learning and experiences based on the user's interests and aptitudes. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects the user's past experiences and interests. The analysis unit analyzes the user's interests and aptitudes based on the data collected by the data collection unit. The proposal unit proposes optimal learning and experiences based on the analysis results obtained by the analysis unit. The management unit manages the user's learning progress and goal setting.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

[0001] The technology of this 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to propose an optimal learning and experience based on the user's interests and aptitudes, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal learning and experience based on the user's interests and aptitudes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects the user's past experiences and interests. The analysis unit analyzes the user's interests and aptitudes based on the data collected by the data collection unit. The proposal unit proposes optimal learning and experiences based on the analysis results obtained by the analysis unit. The management unit manages the user's learning progress and goal setting. [Effects of the Invention]

[0007] The system according to this embodiment can suggest optimal learning and experiences based on the user's interests and aptitudes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The career path discovery support system according to an embodiment of the present invention is a system in which a generative AI agent tailored to each elementary, junior high, and high school student supports the first step in career path discovery. This career path discovery support system can broaden the possibilities of career choices by analyzing the user's interests and aptitudes and proposing optimal learning and experiences. For example, the career path discovery support system collects detailed information on the user's past experiences and interests, and the generative AI analyzes it. For example, by collecting data such as club activities the user has participated in in the past, hobbies, and academic performance, and analyzing it, the generative AI grasps the user's interests and aptitudes. Next, the generative AI proposes optimal learning and experiences based on the analysis results. For example, if the user is interested in science, the generative AI will propose science-related company experiences, internships, special lectures at universities, and research lab experience programs. Also, if the user is interested in art, the generative AI will propose art-related fieldwork and problem-solving projects. Furthermore, a personal assistant AI manages the user's learning progress and goal setting. For example, the personal assistant AI tracks the progress toward the goals set by the user and provides advice and support as needed. This allows the user to learn at their own pace and maintain motivation toward achieving their goals. This allows users to broaden their career options through learning and experiences based on their interests and aptitudes. For example, by experiencing a real work environment through company experiences, users can form a concrete image of their future career. Furthermore, through special university lectures and research lab experience programs, they can learn specialized knowledge and skills, which can be useful in future career choices. In this way, the career discovery support system utilizes generative AI and personal assistant AI to provide personalized career discovery support for each elementary, middle, and high school student, expanding their career options by offering learning and experiences based on their interests and aptitudes. Thus, the career discovery support system can broaden career options by providing learning and experiences based on users' interests and aptitudes.

[0029] The career guidance support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects the user's past experiences and interests. The user's past experiences and interests include, but are not limited to, academic performance, club activities, and hobbies. The data collection unit collects data such as club activities, hobbies, and academic performance that the user has participated in in the past. The data collection unit can also collect data such as sports clubs, music activities, and performance evaluations that the user has participated in in the past. The analysis unit analyzes the user's interests and aptitudes based on the data collected by the data collection unit. The analysis unit performs analysis using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can, for example, use data mining techniques to understand the user's interests and aptitudes. The analysis unit can also use statistical analysis techniques to evaluate the user's interests and aptitudes. The analysis unit can also use machine learning algorithms to predict the user's interests and aptitudes. The proposal unit proposes optimal learning and experiences based on the analysis results obtained by the analysis unit. The proposal department proposes curricula and experiential programs based on the user's interests. For example, if the user is interested in science, the proposal department can propose science-related corporate experience programs, internships, special university lectures, and laboratory experience programs. If the user is interested in art, the proposal department can also propose art-related fieldwork and problem-solving projects. The management department manages the user's learning progress and goal setting. For example, the management department tracks progress toward the goals set by the user and provides advice and support as needed. For example, the management department measures the user's achievement level, study time, and goal achievement rate to manage progress. The management department can also select the optimal progress management method based on data such as the user's learning records, performance evaluations, and study time. As a result, the career discovery support system according to this embodiment can provide learning and experiences based on the user's interests and aptitudes, and expand the possibilities of career choices.

[0030] The data collection unit collects information about users' past experiences and interests. This includes, but is not limited to, academic performance, club activities, and hobbies. For example, the unit collects data on club activities, hobbies, and academic performance that users have participated in. Specifically, it can collect data on sports clubs, music activities, and performance evaluations that users have participated in. The data collection unit has interfaces for acquiring data from the devices and applications used by users, such as school performance management systems, club activity record systems, and social media posts related to hobbies. This allows the data collection unit to comprehensively understand the user's diverse activities and interests. Furthermore, the data collection unit also collects information entered by users themselves, enabling users to record their interests and experiences in detail. For example, it provides forms for users to input their impressions and results from participating in specific events or projects, and stores this information in a database. This allows the data collection unit to gain a deeper understanding of users' interests and experiences, providing a foundation for the analysis and proposal units to perform more accurate analyses and make more accurate recommendations.

[0031] The analysis unit analyzes user interests and aptitudes based on data collected by the data collection unit. The analysis unit uses methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it uses data mining techniques to understand user interests and aptitudes. For example, it clusters users' past activity data to extract common interests and patterns. It can also evaluate user interests and aptitudes using statistical analysis techniques. For example, it statistically analyzes users' performance data and activity history to identify strengths and weaknesses in specific areas. Furthermore, it can predict user interests and aptitudes using machine learning algorithms. For example, it predicts users' future interests and aptitudes based on past data, providing foundational information to suggest optimal learning and experiences. By combining these techniques, the analysis unit can analyze user interests and aptitudes from multiple perspectives, obtaining more accurate results. Additionally, the analysis unit can incorporate user feedback to continuously improve its analysis models. For example, it evaluates how users reacted to suggested learning and experiences and adjusts the analysis model based on the results. This allows the analysis unit to more accurately understand the user's interests and aptitudes, providing a foundation for the proposal unit to make more appropriate suggestions.

[0032] The Proposal Department proposes optimal learning and experiences based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes curricula and experiential programs based on the user's interests. Specifically, if the user is interested in science, it may propose science-related corporate experiences, internships, special university lectures, or laboratory experience programs. If the user is interested in art, it may also propose art-related fieldwork or problem-solving projects. The Proposal Department provides a variety of options tailored to the user's interests and aptitudes, allowing the user to choose the learning and experiences that are best suited to them. Furthermore, the Proposal Department can continuously improve its proposals based on user feedback. For example, it can collect feedback and results from users after they participate in a proposed program and adjust the content of future proposals based on these results. This allows the Proposal Department to make flexible proposals that meet user needs and enrich the user's learning and experiences. The Proposal Department can also collaborate with external experts and educational institutions to provide information that is useful for users' career choices. For example, it may propose lectures and workshops by experts in specific fields, providing users with opportunities to interact directly with experts. This allows the Proposal Department to support users' career choices and provide information that is useful for future career development.

[0033] The management department manages users' learning progress and goal setting. For example, the management department tracks progress toward the goals set by users and provides advice and support as needed. Specifically, it measures and manages progress by measuring user achievement, study time, goal achievement rate, etc. The management department can also select the optimal progress management method based on data such as user learning records, performance evaluations, and study time. For example, it regularly checks the progress toward the goals set by users and revises or sets new goals as needed. Furthermore, the management department provides tools to visualize users' learning progress, allowing users to grasp their progress at a glance. For example, it provides a learning dashboard, allowing users to check their study time and achievement level in graphs and charts. In this way, the management department can effectively manage users' learning progress and support them in steadily moving toward their goals. The management department can also provide appropriate feedback and advice according to the user's learning progress. For example, if a user is struggling to achieve their goals, it will suggest specific improvement measures and learning methods to help the user achieve their goals. This allows the management department to comprehensively support users' learning progress and provide them with the optimal environment to achieve their goals.

[0034] The data collection unit can collect data such as club activities, hobbies, and academic performance that the user has participated in in the past. For example, the data collection unit can collect data such as sports clubs, music activities, and academic performance evaluations that the user has participated in in the past. For example, the data collection unit can collect activity records of sports clubs that the user has participated in in the past. The data collection unit can also collect records of music activities that the user has participated in in the past. The data collection unit can also collect data on the user's academic performance. This allows the data collection unit to collect detailed information about the user's past experiences and interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the user's past club activities, hobbies, and academic performance into a generating AI and have the generating AI perform the data collection.

[0035] The analysis unit can analyze the collected data and understand the user's interests and aptitudes. For example, the analysis unit can use data mining techniques to understand the user's interests and aptitudes. For example, the analysis unit can use data mining techniques to evaluate the degree of the user's interest and aptitudes. The analysis unit can also use statistical analysis techniques to evaluate the user's interests and aptitudes. For example, the analysis unit can use statistical analysis techniques to quantify the degree of the user's interest and aptitudes. The analysis unit can also use machine learning algorithms to predict the user's interests and aptitudes. For example, the analysis unit can use machine learning algorithms to predict the degree of the user's interest and aptitudes. This allows the analysis unit to accurately understand the user's interests and aptitudes. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the analysis of the user's interests and aptitudes.

[0036] The proposal unit can suggest optimal learning and experiences for the user based on the analysis results. For example, the proposal unit can suggest curricula and experience programs based on the user's interests. For example, if the user is interested in science, the proposal unit can suggest science-related company experiences or internships, or special university lectures or laboratory experience programs. Also, if the user is interested in art, the proposal unit can suggest art-related fieldwork or problem-solving projects. For example, if the user is interested in art, the proposal unit can suggest art workshops or problem-solving projects. In this way, the proposal unit can suggest optimal learning and experiences for the user. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or not using generative AI. For example, the proposal unit can input the analysis results into generative AI and have the generative AI execute suggestions for optimal learning and experiences for the user.

[0037] The management department can track progress toward the goals set by the user and provide advice and support as needed. For example, the management department can measure the user's achievement level, learning time, and goal achievement rate to manage progress. The management department can also measure the user's learning time and manage progress. For example, the management department can measure the user's learning time and manage progress. The management department can also measure the user's goal achievement rate and manage progress. For example, the management department can measure the user's goal achievement rate and manage progress. In this way, the management department can manage the user's learning progress and support goal achievement. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input data such as the user's achievement level, learning time, and goal achievement rate into a generating AI and have the generating AI perform progress management.

[0038] The proposal department can propose science-related corporate experiences and internships, as well as special university lectures and laboratory experience programs. For example, if the user is interested in science, the proposal department can propose science-related corporate experiences and internships. The proposal department can also propose science-related internships if the user is interested in science. For example, if the proposal department is interested in science, the proposal department can propose science-related internships. The proposal department can also propose special university lectures and laboratory experience programs if the user is interested in science. For example, if the proposal department is interested in science, the proposal department can propose special university lectures. The proposal department can also propose university laboratory experience programs if the user is interested in science. This allows the proposal department to propose the most suitable learning and experiences for users interested in science. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input the analysis results into a generative AI and have the generative AI make suggestions for science-related learning and experiences.

[0039] The proposal unit can propose art-related fieldwork and practical problem-solving projects. For example, if the user is interested in art, the proposal unit can propose art-related fieldwork. The proposal unit can also propose practical problem-solving projects if the user is interested in art. For example, if the proposal unit is interested in art, the proposal unit can propose practical problem-solving projects. This allows the proposal unit to propose optimal learning and experiences for users interested in art. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the analysis results into a generative AI and have the generative AI propose art-related learning and experiences.

[0040] The data collection unit can analyze the user's past data submission history and select the optimal collection method. For example, the data collection unit can prioritize collecting data in formats that the user has frequently submitted in the past. The data collection unit can also analyze the time periods when the user previously submitted data and select the optimal collection timing. The data collection unit can also analyze the content of the data the user previously submitted and prioritize collecting relevant data. This allows the data collection unit to collect data in the most optimal way based on the user's past data submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data submission history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect relevant data based on the user's areas of interest. For example, the data collection unit can filter and collect relevant data based on the user's areas of interest. The data collection unit can also collect data related to areas the user has recently shown interest in. For example, the data collection unit can collect data related to areas the user has recently shown interest in. This allows the data collection unit to prioritize collecting data related to the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of company experience and internship information in the area where the user is currently located. The data collection unit can also collect special lectures and research lab experience programs at nearby universities based on the user's geographical location. For example, the data collection unit can collect special lectures and research lab experience programs at nearby universities based on the user's geographical location. The data collection unit can also collect local event and fieldwork information based on the user's geographical location information. For example, the data collection unit can collect local event and fieldwork information based on the user's geographical location information. This allows the data collection unit to prioritize the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on the user's interests and passions shared on social media. The data collection unit can also analyze the user's social media activity history and collect relevant learning and experience data. For example, the data collection unit can analyze the user's social media activity history and collect relevant learning and experience data. The data collection unit can also collect relevant data based on the accounts and groups the user follows. For example, the data collection unit can collect relevant data based on the accounts and groups the user follows. This allows the data collection unit to collect relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm specialized for academic performance to academic performance data. The analysis unit can also apply an analysis algorithm specialized for club activities to club activity data. For example, the analysis unit can apply an analysis algorithm specialized for hobbies to hobby data. This allows the analysis unit to apply the most suitable analysis algorithm depending on the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted data. The analysis unit may also postpone the analysis of older data. The analysis unit may also adjust the analysis schedule based on the submission date. This allows the analysis unit to determine the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into a generative AI and have the generative AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0048] The proposal unit can adjust the level of detail of its proposals based on the importance of the learning or experience. For example, the proposal unit will provide detailed proposals for important learning or experiences. The proposal unit can also provide concise proposals for less important learning or experiences. The proposal unit can also prioritize proposals according to the importance of the learning or experience. This allows the proposal unit to adjust the level of detail of its proposals according to the importance of the learning or experience. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not. For example, the proposal unit can input the importance of the learning or experience into the generative AI and have the generative AI adjust the level of detail of the proposals.

[0049] The proposal unit can apply different proposal algorithms depending on the category of learning or experience when making a proposal. For example, the proposal unit can apply a science-specific proposal algorithm to science-related learning or experiences. The proposal unit can also apply an art-specific proposal algorithm to art-related learning or experiences. For example, the proposal unit can apply an art-specific proposal algorithm to art-related learning or experiences. The proposal unit can also apply a sports-specific proposal algorithm to sports-related learning or experiences. This allows the proposal unit to apply the most suitable proposal algorithm depending on the category of learning or experience. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category of learning or experience into a generative AI and have the generative AI apply different proposal algorithms.

[0050] The proposal department can determine the priority of proposals based on the submission date of learnings and experiences. For example, the proposal department may prioritize proposals for learnings and experiences that have been submitted recently. The proposal department may also postpone proposals for learnings and experiences that have been submitted earlier. The proposal department may also adjust the proposal schedule based on the submission date. This allows the proposal department to determine the priority of proposals based on the submission date of learnings and experiences. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department may input the submission dates of learnings and experiences into a generative AI and have the generative AI determine the priority of proposals.

[0051] The proposal unit can adjust the order of proposals based on the relevance of learning and experiences. For example, the proposal unit can prioritize proposing highly relevant learning and experiences. The proposal unit can also postpone proposing less relevant learning and experiences. The proposal unit can also adjust the schedule of proposals based on the relevance of learning and experiences. This allows the proposal unit to adjust the order of proposals based on the relevance of learning and experiences. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the relevance of learning and experiences into a generative AI and have the generative AI adjust the order of proposals.

[0052] The management department can select the optimal management method by referring to the user's past learning history when managing progress. For example, the management department can select the optimal progress management method based on the user's past learning history. The management department can also perform progress management by referring to learning methods that the user has succeeded with in the past. For example, the management department can perform progress management by referring to learning methods that the user has succeeded with in the past. The management department can also analyze the user's past learning history and create an optimal progress management schedule. For example, the management department can analyze the user's past learning history and create an optimal progress management schedule. This allows the management department to select the optimal progress management method based on the user's past learning history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's past learning history into a generating AI and have the generating AI select the optimal management method.

[0053] The management unit can customize the management methods based on the user's current living situation when managing progress. For example, if the user is busy, the management unit can provide a simple progress management method. The management unit can also provide a detailed progress management method if the user has ample time. The management unit can also adjust the frequency of progress management according to the user's living situation. This allows the management unit to customize the progress management methods according to the user's current living situation. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the management methods.

[0054] The management department can select the optimal management method when managing progress, taking into account the user's geographical location information. For example, the management department can provide the optimal progress management method according to the situation in the user's current location. The management department can also prioritize the management of relevant tasks based on the user's geographical location. For example, the management department prioritizes the management of relevant tasks based on the user's geographical location. The management department can also adjust the progress management schedule based on the user's geographical location information. For example, the management department adjusts the progress management schedule based on the user's geographical location information. This allows the management department to select the optimal progress management method based on the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0055] The management department can analyze users' social media activity and propose management methods when managing progress. For example, the management department can analyze users' social media activity and prioritize the management of related tasks. The management department can also propose the optimal progress management method based on users' social media activity history. For example, the management department can propose the optimal progress management method based on users' social media activity history. The management department can also manage related tasks based on accounts and groups that users follow. For example, the management department can manage related tasks based on accounts and groups that users follow. This allows the management department to propose the optimal progress management method based on users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input users' social media activity data into a generating AI and have the generating AI execute the proposal of management methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The career guidance support system can suggest learning opportunities and experiences tailored to a user's geographical location, taking their location into consideration. For example, if a user lives in an urban area, it can suggest urban corporate experience programs and internships. Specifically, if a user lives in an urban area, it can suggest urban corporate experience programs, internships, and special university lectures. If a user lives in a rural area, it can suggest rural fieldwork and unique regional experience programs. For example, if a user lives in a rural area, it can suggest rural fieldwork and unique regional experience programs. Furthermore, if a user lives overseas, it can suggest local cultural experiences and international internships. For example, if a user lives overseas, it can suggest local cultural experiences and international internships. In this way, the career guidance support system can suggest the most suitable learning opportunities and experiences based on the user's geographical location.

[0058] The career guidance support system can analyze a user's social media activity and suggest relevant learning opportunities and experiences. For example, it can make suggestions based on the interests and passions the user has shared on social media. Specifically, it suggests relevant learning opportunities and experiences based on the interests and passions the user has shared on social media. It can also analyze a user's social media activity history and suggest relevant learning opportunities and experiences. For example, it analyzes a user's social media activity history and suggests relevant learning opportunities and experiences. It can also suggest relevant learning opportunities and experiences based on the accounts and groups the user follows. For example, it suggests relevant learning opportunities and experiences based on the accounts and groups the user follows. In this way, the career guidance support system can suggest optimal learning opportunities and experiences based on the user's social media activity.

[0059] The career guidance support system can suggest optimal learning and experiences by referring to the user's past learning history. For example, it can suggest relevant learning and experiences based on the user's past learning history. Specifically, it suggests relevant learning and experiences based on the user's past learning history. It can also suggest optimal learning and experiences by referring to learning methods that the user has succeeded with in the past. For example, it suggests optimal learning and experiences by referring to learning methods that the user has succeeded with in the past. It can also analyze the user's past learning history and create an optimal learning and experience schedule. For example, it analyzes the user's past learning history and creates an optimal learning and experience schedule. In this way, the career guidance support system can suggest optimal learning and experiences based on the user's past learning history.

[0060] The career guidance support system can customize learning and experience suggestions based on the user's current lifestyle. For example, if the user is busy, it can suggest effective learning and experiences that can be completed in a short amount of time. Specifically, if the user is busy, it can suggest effective online courses or workshops that can be completed in a short amount of time. If the user has more free time, it can also suggest long-term projects or internships. For example, if the user has more free time, it can suggest long-term projects or internships. Furthermore, the system can adjust the frequency of learning and experiences according to the user's lifestyle. For example, it can adjust the frequency of learning and experiences according to the user's lifestyle and suggest a manageable schedule. In this way, the career guidance support system can suggest the most suitable learning and experiences based on the user's current lifestyle.

[0061] The career guidance support system can provide region-specific progress management methods by taking into account the user's geographical location information. For example, if the user lives in an urban area, it can provide progress management methods tailored to the urban environment. Specifically, if the user lives in an urban area, it can provide progress management methods tailored to the urban environment to efficiently advance tasks. Similarly, if the user lives in a rural area, it can provide progress management methods tailored to the rural environment. For example, if the user lives in a rural area, it can provide progress management methods tailored to the rural environment to manage tasks in a way that leverages the characteristics of the region. Furthermore, if the user lives overseas, it can provide progress management methods tailored to the local environment to manage tasks from an international perspective. In this way, the career guidance support system can provide the optimal progress management method based on the user's geographical location information.

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

[0063] Step 1: The data collection unit gathers information about the user's past experiences and interests. For example, it collects data such as club activities the user has participated in in the past, hobbies, and academic performance. This allows for comprehensive acquisition of information about the user's interests and aptitudes. Step 2: The analysis unit analyzes the user's interests and aptitudes based on the data collected by the data collection unit. For example, it uses methods such as data mining, statistical analysis, and machine learning algorithms to perform the analysis. This allows for an accurate understanding of the user's interests and aptitudes. Step 3: The proposal unit proposes optimal learning and experiences based on the analysis results obtained by the analysis unit. For example, it proposes curricula and experience programs based on the user's interests. This allows users to obtain learning and experiences that match their interests and aptitudes. Step 4: The administration department manages users' learning progress and goal setting. For example, it tracks progress toward the goals set by users and provides advice and support as needed. This allows users to effectively manage their own learning progress.

[0064] (Example of form 2) The career path discovery support system according to an embodiment of the present invention is a system in which a generative AI agent tailored to each elementary, junior high, and high school student supports the first step in career path discovery. This career path discovery support system can broaden the possibilities of career choices by analyzing the user's interests and aptitudes and proposing optimal learning and experiences. For example, the career path discovery support system collects detailed information on the user's past experiences and interests, and the generative AI analyzes it. For example, by collecting data such as club activities the user has participated in in the past, hobbies, and academic performance, and analyzing it, the generative AI grasps the user's interests and aptitudes. Next, the generative AI proposes optimal learning and experiences based on the analysis results. For example, if the user is interested in science, the generative AI will propose science-related company experiences, internships, special lectures at universities, and research lab experience programs. Also, if the user is interested in art, the generative AI will propose art-related fieldwork and problem-solving projects. Furthermore, a personal assistant AI manages the user's learning progress and goal setting. For example, the personal assistant AI tracks the progress toward the goals set by the user and provides advice and support as needed. This allows the user to learn at their own pace and maintain motivation toward achieving their goals. This allows users to broaden their career options through learning and experiences based on their interests and aptitudes. For example, by experiencing a real work environment through company experiences, users can form a concrete image of their future career. Furthermore, through special university lectures and research lab experience programs, they can learn specialized knowledge and skills, which can be useful in future career choices. In this way, the career discovery support system utilizes generative AI and personal assistant AI to provide personalized career discovery support for each elementary, middle, and high school student, expanding their career options by offering learning and experiences based on their interests and aptitudes. Thus, the career discovery support system can broaden career options by providing learning and experiences based on users' interests and aptitudes.

[0065] The career guidance support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects the user's past experiences and interests. The user's past experiences and interests include, but are not limited to, academic performance, club activities, and hobbies. The data collection unit collects data such as club activities, hobbies, and academic performance that the user has participated in in the past. The data collection unit can also collect data such as sports clubs, music activities, and performance evaluations that the user has participated in in the past. The analysis unit analyzes the user's interests and aptitudes based on the data collected by the data collection unit. The analysis unit performs analysis using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can, for example, use data mining techniques to understand the user's interests and aptitudes. The analysis unit can also use statistical analysis techniques to evaluate the user's interests and aptitudes. The analysis unit can also use machine learning algorithms to predict the user's interests and aptitudes. The proposal unit proposes optimal learning and experiences based on the analysis results obtained by the analysis unit. The proposal department proposes curricula and experiential programs based on the user's interests. For example, if the user is interested in science, the proposal department can propose science-related corporate experience programs, internships, special university lectures, and laboratory experience programs. If the user is interested in art, the proposal department can also propose art-related fieldwork and problem-solving projects. The management department manages the user's learning progress and goal setting. For example, the management department tracks progress toward the goals set by the user and provides advice and support as needed. For example, the management department measures the user's achievement level, study time, and goal achievement rate to manage progress. The management department can also select the optimal progress management method based on data such as the user's learning records, performance evaluations, and study time. As a result, the career discovery support system according to this embodiment can provide learning and experiences based on the user's interests and aptitudes, and expand the possibilities of career choices.

[0066] The data collection unit collects information about users' past experiences and interests. This includes, but is not limited to, academic performance, club activities, and hobbies. For example, the unit collects data on club activities, hobbies, and academic performance that users have participated in. Specifically, it can collect data on sports clubs, music activities, and performance evaluations that users have participated in. The data collection unit has interfaces for acquiring data from the devices and applications used by users, such as school performance management systems, club activity record systems, and social media posts related to hobbies. This allows the data collection unit to comprehensively understand the user's diverse activities and interests. Furthermore, the data collection unit also collects information entered by users themselves, enabling users to record their interests and experiences in detail. For example, it provides forms for users to input their impressions and results from participating in specific events or projects, and stores this information in a database. This allows the data collection unit to gain a deeper understanding of users' interests and experiences, providing a foundation for the analysis and proposal units to perform more accurate analyses and make more accurate recommendations.

[0067] The analysis unit analyzes user interests and aptitudes based on data collected by the data collection unit. The analysis unit uses methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it uses data mining techniques to understand user interests and aptitudes. For example, it clusters users' past activity data to extract common interests and patterns. It can also evaluate user interests and aptitudes using statistical analysis techniques. For example, it statistically analyzes users' performance data and activity history to identify strengths and weaknesses in specific areas. Furthermore, it can predict user interests and aptitudes using machine learning algorithms. For example, it predicts users' future interests and aptitudes based on past data, providing foundational information to suggest optimal learning and experiences. By combining these techniques, the analysis unit can analyze user interests and aptitudes from multiple perspectives, obtaining more accurate results. Additionally, the analysis unit can incorporate user feedback to continuously improve its analysis models. For example, it evaluates how users reacted to suggested learning and experiences and adjusts the analysis model based on the results. This allows the analysis unit to more accurately understand the user's interests and aptitudes, providing a foundation for the proposal unit to make more appropriate suggestions.

[0068] The Proposal Department proposes optimal learning and experiences based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes curricula and experiential programs based on the user's interests. Specifically, if the user is interested in science, it may propose science-related corporate experiences, internships, special university lectures, or laboratory experience programs. If the user is interested in art, it may also propose art-related fieldwork or problem-solving projects. The Proposal Department provides a variety of options tailored to the user's interests and aptitudes, allowing the user to choose the learning and experiences that are best suited to them. Furthermore, the Proposal Department can continuously improve its proposals based on user feedback. For example, it can collect feedback and results from users after they participate in a proposed program and adjust the content of future proposals based on these results. This allows the Proposal Department to make flexible proposals that meet user needs and enrich the user's learning and experiences. The Proposal Department can also collaborate with external experts and educational institutions to provide information that is useful for users' career choices. For example, it may propose lectures and workshops by experts in specific fields, providing users with opportunities to interact directly with experts. This allows the Proposal Department to support users' career choices and provide information that is useful for future career development.

[0069] The management department manages users' learning progress and goal setting. For example, the management department tracks progress toward the goals set by users and provides advice and support as needed. Specifically, it measures and manages progress by measuring user achievement, study time, goal achievement rate, etc. The management department can also select the optimal progress management method based on data such as user learning records, performance evaluations, and study time. For example, it regularly checks the progress toward the goals set by users and revises or sets new goals as needed. Furthermore, the management department provides tools to visualize users' learning progress, allowing users to grasp their progress at a glance. For example, it provides a learning dashboard, allowing users to check their study time and achievement level in graphs and charts. In this way, the management department can effectively manage users' learning progress and support them in steadily moving toward their goals. The management department can also provide appropriate feedback and advice according to the user's learning progress. For example, if a user is struggling to achieve their goals, it will suggest specific improvement measures and learning methods to help the user achieve their goals. This allows the management department to comprehensively support users' learning progress and provide them with the optimal environment to achieve their goals.

[0070] The data collection unit can collect data such as club activities, hobbies, and academic performance that the user has participated in in the past. For example, the data collection unit can collect data such as sports clubs, music activities, and academic performance evaluations that the user has participated in in the past. For example, the data collection unit can collect activity records of sports clubs that the user has participated in in the past. The data collection unit can also collect records of music activities that the user has participated in in the past. The data collection unit can also collect data on the user's academic performance. This allows the data collection unit to collect detailed information about the user's past experiences and interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the user's past club activities, hobbies, and academic performance into a generating AI and have the generating AI perform the data collection.

[0071] The analysis unit can analyze the collected data and understand the user's interests and aptitudes. For example, the analysis unit can use data mining techniques to understand the user's interests and aptitudes. For example, the analysis unit can use data mining techniques to evaluate the degree of the user's interest and aptitudes. The analysis unit can also use statistical analysis techniques to evaluate the user's interests and aptitudes. For example, the analysis unit can use statistical analysis techniques to quantify the degree of the user's interest and aptitudes. The analysis unit can also use machine learning algorithms to predict the user's interests and aptitudes. For example, the analysis unit can use machine learning algorithms to predict the degree of the user's interest and aptitudes. This allows the analysis unit to accurately understand the user's interests and aptitudes. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the analysis of the user's interests and aptitudes.

[0072] The proposal unit can suggest optimal learning and experiences for the user based on the analysis results. For example, the proposal unit can suggest curricula and experience programs based on the user's interests. For example, if the user is interested in science, the proposal unit can suggest science-related company experiences or internships, or special university lectures or laboratory experience programs. Also, if the user is interested in art, the proposal unit can suggest art-related fieldwork or problem-solving projects. For example, if the user is interested in art, the proposal unit can suggest art workshops or problem-solving projects. In this way, the proposal unit can suggest optimal learning and experiences for the user. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or not using generative AI. For example, the proposal unit can input the analysis results into generative AI and have the generative AI execute suggestions for optimal learning and experiences for the user.

[0073] The management department can track progress toward the goals set by the user and provide advice and support as needed. For example, the management department can measure the user's achievement level, learning time, and goal achievement rate to manage progress. The management department can also measure the user's learning time and manage progress. For example, the management department can measure the user's learning time and manage progress. The management department can also measure the user's goal achievement rate and manage progress. For example, the management department can measure the user's goal achievement rate and manage progress. In this way, the management department can manage the user's learning progress and support goal achievement. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input data such as the user's achievement level, learning time, and goal achievement rate into a generating AI and have the generating AI perform progress management.

[0074] The proposal department can propose science-related corporate experiences and internships, as well as special university lectures and laboratory experience programs. For example, if the user is interested in science, the proposal department can propose science-related corporate experiences and internships. The proposal department can also propose science-related internships if the user is interested in science. For example, if the proposal department is interested in science, the proposal department can propose science-related internships. The proposal department can also propose special university lectures and laboratory experience programs if the user is interested in science. For example, if the proposal department is interested in science, the proposal department can propose special university lectures. The proposal department can also propose university laboratory experience programs if the user is interested in science. This allows the proposal department to propose the most suitable learning and experiences for users interested in science. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input the analysis results into a generative AI and have the generative AI make suggestions for science-related learning and experiences.

[0075] The proposal unit can propose art-related fieldwork and practical problem-solving projects. For example, if the user is interested in art, the proposal unit can propose art-related fieldwork. The proposal unit can also propose practical problem-solving projects if the user is interested in art. For example, if the proposal unit is interested in art, the proposal unit can propose practical problem-solving projects. This allows the proposal unit to propose optimal learning and experiences for users interested in art. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the analysis results into a generative AI and have the generative AI propose art-related learning and experiences.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data on past club activities and hobbies when the user is relaxed. The data collection unit can also collect data on academic performance when the user is focused. For example, the data collection unit can collect data on recent interests when the user is excited. For example, the data collection unit can collect data on recent interests when the user is excited. This allows the data collection unit to collect data at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0077] The data collection unit can analyze the user's past data submission history and select the optimal collection method. For example, the data collection unit can prioritize collecting data in formats that the user has frequently submitted in the past. The data collection unit can also analyze the time periods when the user previously submitted data and select the optimal collection timing. The data collection unit can also analyze the content of the data the user previously submitted and prioritize collecting relevant data. This allows the data collection unit to collect data in the most optimal way based on the user's past data submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data submission history into a generating AI and have the generating AI select the optimal collection method.

[0078] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect relevant data based on the user's areas of interest. For example, the data collection unit can filter and collect relevant data based on the user's areas of interest. The data collection unit can also collect data related to areas the user has recently shown interest in. For example, the data collection unit can collect data related to areas the user has recently shown interest in. This allows the data collection unit to prioritize collecting data related to the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform data filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data related to relaxing hobbies. The data collection unit may also prioritize collecting data related to challenging projects if the user is excited. For example, if the user is excited, the data collection unit may prioritize collecting data related to challenging projects. The data collection unit may also prioritize collecting data related to academic performance if the user is focused. For example, if the user is focused, the data collection unit may prioritize collecting data related to academic performance. In this way, the data collection unit can determine the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of company experience and internship information in the area where the user is currently located. The data collection unit can also collect special lectures and research lab experience programs at nearby universities based on the user's geographical location. For example, the data collection unit can collect special lectures and research lab experience programs at nearby universities based on the user's geographical location. The data collection unit can also collect local event and fieldwork information based on the user's geographical location information. For example, the data collection unit can collect local event and fieldwork information based on the user's geographical location information. This allows the data collection unit to prioritize the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0081] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on the user's interests and passions shared on social media. The data collection unit can also analyze the user's social media activity history and collect relevant learning and experience data. For example, the data collection unit can analyze the user's social media activity history and collect relevant learning and experience data. The data collection unit can also collect relevant data based on the accounts and groups the user follows. For example, the data collection unit can collect relevant data based on the accounts and groups the user follows. This allows the data collection unit to collect relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. The analysis unit can also provide concise analysis results that get straight to the point if the user is in a hurry. The analysis unit can also provide visually appealing analysis results if the user is excited. This allows the analysis unit to adjust the presentation of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the analysis results are presented.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm specialized for academic performance to academic performance data. The analysis unit can also apply an analysis algorithm specialized for club activities to club activity data. For example, the analysis unit can apply an analysis algorithm specialized for hobbies to hobby data. This allows the analysis unit to apply the most suitable analysis algorithm depending on the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. The analysis unit can also provide a visually appealing analysis result if the user is excited. For example, if the user is excited, the analysis unit can provide a visually appealing analysis result. This allows the analysis unit to adjust the length of the analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis results.

[0086] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted data. The analysis unit may also postpone the analysis of older data. The analysis unit may also adjust the analysis schedule based on the submission date. This allows the analysis unit to determine the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into a generative AI and have the generative AI determine the priority of analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0088] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. It can also provide concise, to the point, suggestions if the user is in a hurry. For example, if the user is in a hurry, the suggestion unit can provide concise, to the point, suggestions. It can also provide visually appealing suggestions if the user is excited. For example, if the user is excited, the suggestion unit can provide visually appealing suggestions. In this way, the suggestion unit can adjust the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, for example, or without a generative AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way the proposal is expressed.

[0089] The proposal unit can adjust the level of detail of its proposals based on the importance of the learning or experience. For example, the proposal unit will provide detailed proposals for important learning or experiences. The proposal unit can also provide concise proposals for less important learning or experiences. The proposal unit can also prioritize proposals according to the importance of the learning or experience. This allows the proposal unit to adjust the level of detail of its proposals according to the importance of the learning or experience. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not. For example, the proposal unit can input the importance of the learning or experience into the generative AI and have the generative AI adjust the level of detail of the proposals.

[0090] The proposal unit can apply different proposal algorithms depending on the category of learning or experience when making a proposal. For example, the proposal unit can apply a science-specific proposal algorithm to science-related learning or experiences. The proposal unit can also apply an art-specific proposal algorithm to art-related learning or experiences. For example, the proposal unit can apply an art-specific proposal algorithm to art-related learning or experiences. The proposal unit can also apply a sports-specific proposal algorithm to sports-related learning or experiences. This allows the proposal unit to apply the most suitable proposal algorithm depending on the category of learning or experience. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category of learning or experience into a generative AI and have the generative AI apply different proposal algorithms.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. The suggestion unit can also provide visually appealing suggestions if the user is excited. This allows the suggestion unit to adjust the length of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, 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 suggestion unit may be performed using a generative AI or not. For example, the suggestion unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the suggestion.

[0092] The proposal department can determine the priority of proposals based on the submission date of learnings and experiences. For example, the proposal department may prioritize proposals for learnings and experiences that have been submitted recently. The proposal department may also postpone proposals for learnings and experiences that have been submitted earlier. The proposal department may also adjust the proposal schedule based on the submission date. This allows the proposal department to determine the priority of proposals based on the submission date of learnings and experiences. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department may input the submission dates of learnings and experiences into a generative AI and have the generative AI determine the priority of proposals.

[0093] The proposal unit can adjust the order of proposals based on the relevance of learning and experiences. For example, the proposal unit can prioritize proposing highly relevant learning and experiences. The proposal unit can also postpone proposing less relevant learning and experiences. The proposal unit can also adjust the schedule of proposals based on the relevance of learning and experiences. This allows the proposal unit to adjust the order of proposals based on the relevance of learning and experiences. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the relevance of learning and experiences into a generative AI and have the generative AI adjust the order of proposals.

[0094] The management unit can estimate the user's emotions and adjust the progress management method based on the estimated user emotions. For example, if the user is relaxed, the management unit can perform detailed progress management. For example, if the user is relaxed, the management unit can perform detailed progress management. The management unit can also perform concise progress management that gets straight to the point if the user is in a hurry. For example, if the user is excited, the management unit can perform progress management that is visually appealing if the user is excited. For example, if the management unit is excited, the management unit can perform progress management that is visually appealing if the user is excited. In this way, the management unit can adjust the progress management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI and have the AI ​​adjust the progress management method.

[0095] The management department can select the optimal management method by referring to the user's past learning history when managing progress. For example, the management department can select the optimal progress management method based on the user's past learning history. The management department can also perform progress management by referring to learning methods that the user has succeeded with in the past. For example, the management department can perform progress management by referring to learning methods that the user has succeeded with in the past. The management department can also analyze the user's past learning history and create an optimal progress management schedule. For example, the management department can analyze the user's past learning history and create an optimal progress management schedule. This allows the management department to select the optimal progress management method based on the user's past learning history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's past learning history into a generating AI and have the generating AI select the optimal management method.

[0096] The management unit can customize the management methods based on the user's current living situation when managing progress. For example, if the user is busy, the management unit can provide a simple progress management method. The management unit can also provide a detailed progress management method if the user has ample time. The management unit can also adjust the frequency of progress management according to the user's living situation. This allows the management unit to customize the progress management methods according to the user's current living situation. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the management methods.

[0097] The management department can estimate the user's emotions and determine the priority of progress management based on the estimated emotions. For example, if the user is stressed, the management department can prioritize tasks that promote relaxation. The management department can also prioritize challenging tasks if the user is excited. For example, if the user is excited, the management department can prioritize challenging tasks. The management department can also prioritize important tasks if the user is focused. For example, if the user is focused, the management department can prioritize important tasks. This allows the management department to determine the priority of progress management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI and have the AI ​​determine the priorities for progress management.

[0098] The management department can select the optimal management method when managing progress, taking into account the user's geographical location information. For example, the management department can provide the optimal progress management method according to the situation in the user's current location. The management department can also prioritize the management of relevant tasks based on the user's geographical location. For example, the management department prioritizes the management of relevant tasks based on the user's geographical location. The management department can also adjust the progress management schedule based on the user's geographical location information. For example, the management department adjusts the progress management schedule based on the user's geographical location information. This allows the management department to select the optimal progress management method based on the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0099] The management department can analyze users' social media activity and propose management methods when managing progress. For example, the management department can analyze users' social media activity and prioritize the management of related tasks. The management department can also propose the optimal progress management method based on users' social media activity history. For example, the management department can propose the optimal progress management method based on users' social media activity history. The management department can also manage related tasks based on accounts and groups that users follow. For example, the management department can manage related tasks based on accounts and groups that users follow. This allows the management department to propose the optimal progress management method based on users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input users' social media activity data into a generating AI and have the generating AI execute the proposal of management methods.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The career guidance support system can estimate the user's emotions and customize its suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing learning experiences. Specifically, if the user is stressed, it might suggest relaxing art workshops or nature experience programs. If the user is excited, it can suggest challenging projects or internships. For example, if the user is excited, it might suggest challenging science experiments or corporate experience programs. If the user is relaxed, it can suggest detailed learning experiences. For example, if the user is relaxed, it might suggest detailed curricula or experience programs. In this way, the career guidance support system can suggest the most suitable learning experiences according to the user's emotions.

[0102] The career guidance support system can suggest learning opportunities and experiences tailored to a user's geographical location, taking their location into consideration. For example, if a user lives in an urban area, it can suggest urban corporate experience programs and internships. Specifically, if a user lives in an urban area, it can suggest urban corporate experience programs, internships, and special university lectures. If a user lives in a rural area, it can suggest rural fieldwork and unique regional experience programs. For example, if a user lives in a rural area, it can suggest rural fieldwork and unique regional experience programs. Furthermore, if a user lives overseas, it can suggest local cultural experiences and international internships. For example, if a user lives overseas, it can suggest local cultural experiences and international internships. In this way, the career guidance support system can suggest the most suitable learning opportunities and experiences based on the user's geographical location.

[0103] The career guidance support system can analyze a user's social media activity and suggest relevant learning opportunities and experiences. For example, it can make suggestions based on the interests and passions the user has shared on social media. Specifically, it suggests relevant learning opportunities and experiences based on the interests and passions the user has shared on social media. It can also analyze a user's social media activity history and suggest relevant learning opportunities and experiences. For example, it analyzes a user's social media activity history and suggests relevant learning opportunities and experiences. It can also suggest relevant learning opportunities and experiences based on the accounts and groups the user follows. For example, it suggests relevant learning opportunities and experiences based on the accounts and groups the user follows. In this way, the career guidance support system can suggest optimal learning opportunities and experiences based on the user's social media activity.

[0104] The career guidance support system can estimate the user's emotions and adjust its progress management methods based on those emotions. For example, if the user is relaxed, it can provide detailed progress management. Specifically, when the user is relaxed, it provides detailed progress management, closely tracking learning progress and goal achievement. If the user is in a hurry, it can provide concise progress management that focuses on the essentials. For example, if the user is in a hurry, it provides concise progress management that focuses on the essentials and quickly provides the necessary support. If the user is excited, it can provide visually appealing progress management. For example, if the user is excited, it provides visually appealing progress management to boost motivation. In this way, the career guidance support system can provide the optimal progress management method according to the user's emotions.

[0105] The career guidance support system can suggest optimal learning and experiences by referring to the user's past learning history. For example, it can suggest relevant learning and experiences based on the user's past learning history. Specifically, it suggests relevant learning and experiences based on the user's past learning history. It can also suggest optimal learning and experiences by referring to learning methods that the user has succeeded with in the past. For example, it suggests optimal learning and experiences by referring to learning methods that the user has succeeded with in the past. It can also analyze the user's past learning history and create an optimal learning and experience schedule. For example, it analyzes the user's past learning history and creates an optimal learning and experience schedule. In this way, the career guidance support system can suggest optimal learning and experiences based on the user's past learning history.

[0106] The career guidance support system can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. Specifically, if the user is relaxed, it will provide detailed suggestions and explain the content of learning and experiences in detail. If the user is in a hurry, it can provide concise suggestions that get straight to the point. For example, if the user is in a hurry, it will provide concise suggestions that get straight to the point and quickly convey the necessary information. If the user is excited, it can provide visually appealing suggestions. For example, if the user is excited, it will provide visually appealing suggestions and suggest content that will pique their interest. In this way, the career guidance support system can provide the most appropriate way of presenting suggestions according to the user's emotions.

[0107] The career guidance support system can customize learning and experience suggestions based on the user's current lifestyle. For example, if the user is busy, it can suggest effective learning and experiences that can be completed in a short amount of time. Specifically, if the user is busy, it can suggest effective online courses or workshops that can be completed in a short amount of time. If the user has more free time, it can also suggest long-term projects or internships. For example, if the user has more free time, it can suggest long-term projects or internships. Furthermore, the system can adjust the frequency of learning and experiences according to the user's lifestyle. For example, it can adjust the frequency of learning and experiences according to the user's lifestyle and suggest a manageable schedule. In this way, the career guidance support system can suggest the most suitable learning and experiences based on the user's current lifestyle.

[0108] The career guidance support system can estimate the user's emotions and determine the priority of progress management based on those emotions. For example, if the user is feeling stressed, it can prioritize tasks that promote relaxation. Specifically, if the user is feeling stressed, it prioritizes tasks that promote relaxation to reduce stress. Similarly, if the user is excited, it can prioritize challenging tasks to increase motivation. Furthermore, if the user is focused, it can prioritize important tasks to ensure efficient progress. In this way, the career guidance support system can provide optimal progress management priorities according to the user's emotions.

[0109] The career guidance support system can provide region-specific progress management methods by taking into account the user's geographical location information. For example, if the user lives in an urban area, it can provide progress management methods tailored to the urban environment. Specifically, if the user lives in an urban area, it can provide progress management methods tailored to the urban environment to efficiently advance tasks. Similarly, if the user lives in a rural area, it can provide progress management methods tailored to the rural environment. For example, if the user lives in a rural area, it can provide progress management methods tailored to the rural environment to manage tasks in a way that leverages the characteristics of the region. Furthermore, if the user lives overseas, it can provide progress management methods tailored to the local environment to manage tasks from an international perspective. In this way, the career guidance support system can provide the optimal progress management method based on the user's geographical location information.

[0110] The career guidance support system can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is in a hurry, it can provide short, concise suggestions. Specifically, if the user is in a hurry, it will provide short, concise suggestions to quickly convey the necessary information. If the user is relaxed, it can provide detailed suggestions. For example, if the user is relaxed, it will provide detailed suggestions that explain the content of learning and experiences in detail. If the user is excited, it can provide visually appealing suggestions. For example, if the user is excited, it will provide visually appealing suggestions that will pique their interest. In this way, the career guidance support system can provide the optimal suggestion length according to the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit gathers information about the user's past experiences and interests. For example, it collects data such as club activities the user has participated in in the past, hobbies, and academic performance. This allows for comprehensive acquisition of information about the user's interests and aptitudes. Step 2: The analysis unit analyzes the user's interests and aptitudes based on the data collected by the data collection unit. For example, it uses methods such as data mining, statistical analysis, and machine learning algorithms to perform the analysis. This allows for an accurate understanding of the user's interests and aptitudes. Step 3: The proposal unit proposes optimal learning and experiences based on the analysis results obtained by the analysis unit. For example, it proposes curricula and experience programs based on the user's interests. This allows users to obtain learning and experiences that match their interests and aptitudes. Step 4: The administration department manages users' learning progress and goal setting. For example, it tracks progress toward the goals set by users and provides advice and support as needed. This allows users to effectively manage their own learning progress.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect the user's past experiences and interests, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes optimal learning and experiences based on the analysis results. The management unit is implemented in the control unit 46A of the smart device 14 and manages the user's learning progress and goal setting. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the user's past experiences and interests, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes optimal learning and experiences based on the analysis results. The management unit is implemented in the control unit 46A of the smart glasses 214 and manages the user's learning progress and goal setting. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the user's past experiences and interests, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes optimal learning and experiences based on the analysis results. The management unit is implemented in the control unit 46A of the headset terminal 314 and manages the user's learning progress and goal setting. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the user's past experiences and interests, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes optimal learning and experiences based on the analysis results. The management unit is implemented in the control unit 46A of the robot 414 and manages the user's learning progress and goal setting. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A data collection unit that gathers users' past experiences and interests, An analysis unit analyzes the user's interests and aptitudes based on the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal learning and experience, It includes a management unit that manages the user's learning progress and goal setting. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data on users' past club activities, hobbies, academic performance, and other relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to understand the user's interests and aptitudes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose the most suitable learning and experience for the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Track progress toward user-defined goals and provide advice and support as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose science-related corporate experience programs, internships, special university lectures, and research lab experience programs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose art-related fieldwork and practical problem-solving projects. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past data submission history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the learning and experience. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of learning or experience. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the learning and experience reports are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of learning and experiences. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, We estimate the user's emotions and adjust the progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When managing progress, the system selects the optimal management method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing progress, customize the management methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, Estimate user emotions and prioritize progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When managing progress, select the optimal management method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, During progress management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that gathers users' past experiences and interests, An analysis unit analyzes the user's interests and aptitudes based on the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal learning and experience, It includes a management unit that manages the user's learning progress and goal setting. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data on users' past club activities, hobbies, academic performance, and other relevant information. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to understand the user's interests and aptitudes. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose the most suitable learning and experience for the user. The system according to feature 1.

5. The aforementioned management department, Track progress toward user-defined goals and provide advice and support as needed. The system according to feature 1.

6. The aforementioned proposal section is, We propose science-related corporate experience programs, internships, special university lectures, and research lab experience programs. The system according to feature 1.

7. The aforementioned proposal section is, We propose art-related fieldwork and practical problem-solving projects. The system according to feature 1.

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

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