system

Abstract

The system according to this embodiment aims to provide students with reference information to help them decide on their future career paths and directions. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a visualization unit, a prediction unit, and an avatar unit. The data collection unit collects data from current students and graduates. The analysis unit analyzes the data collected by the data collection unit. The visualization unit visualizes the behavioral patterns and habits of successful individuals based on the data analyzed by the analysis unit. The prediction unit predicts the future self of a student based on their current behavior, using the information visualized by the visualization unit. The avatar unit creates an avatar of the future self predicted by the prediction unit to engage in conversations and consultations.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that information for students to determine their future courses and directions is insufficient, and it is difficult to obtain guidelines for taking appropriate actions.

[0005] The system according to the embodiment aims to provide reference information for students to determine their future courses and directions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, a prediction unit, and an avatar unit. The data collection unit collects data from current students and graduates. The analysis unit analyzes the data collected by the data collection unit. The visualization unit visualizes the behavioral patterns and habits of successful individuals based on the data analyzed by the analysis unit. The prediction unit predicts the student's future self based on their current behavior, using the information visualized by the visualization unit. The avatar unit creates an avatar of the future self predicted by the prediction unit to engage in conversations and consultations. [Effects of the Invention]

[0007] The system according to this embodiment can provide students with reference information to help them decide on their future career paths and directions. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The student future planning support system utilizing an AI agent according to an embodiment of the present invention is a system that collaborates with educational institutions and government agencies to create a database of current students and graduates using generative AI. This system visualizes the actions, habits, and occupational realities of successful individuals, providing students with a reference to concretely consider their future dreams and career paths. It can also predict a student's future self based on their current actions and create an avatar for conversation and consultation. For example, it collaborates with educational institutions and government agencies to collect data on current students and graduates. The collected data includes behavioral patterns in student life, career paths after graduation, and occupational realities. This data is analyzed by generative AI to visualize the behavioral patterns and habits of successful individuals. For example, it reveals what kind of student life graduates who have taken up specific occupations had and what kind of habits they had. Next, it uses generative AI to predict a student's future self based on their current actions. For example, it predicts what kind of occupation a student is likely to take up in the future based on data such as current academic performance, achievements in club activities, hobbies, and special skills. This prediction result is created as an avatar, which can then be used to have conversations and consultations with the student. For example, when a student seeks advice about their future dreams, the avatar provides guidance based on data from past successful individuals. This system allows students to gain valuable information to help them concretely consider their future dreams and career paths. By learning about the behavioral patterns and habits of successful individuals, students can re-evaluate their own actions and work towards their future goals. Furthermore, through conversations and consultations with the avatar, students can visualize their future selves more concretely and work towards their goals. This system is also beneficial for educational institutions and government agencies. By supporting students' future planning, it can improve the quality of education. Additionally, by utilizing alumni data, it strengthens the network between current students and alumni, contributing to the revitalization of the entire community. Thus, a student future planning support system utilizing AI agents can help students plan their future and improve the quality of education.

[0029] The student future planning support system utilizing an AI agent according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, a prediction unit, and an avatar unit. The data collection unit collects data on current students and graduates. The data collection unit collects data such as behavioral patterns in student life, career paths after graduation, and actual occupations. The data collection unit can collect data on current students and graduates in cooperation with educational institutions and government agencies, for example. The data collection unit can collect data such as students' academic performance, extracurricular activities, and employment destinations. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, generative AI. The analysis unit identifies, for example, the behavioral patterns and habits of successful individuals. The analysis unit can identify the behavioral patterns and habits of successful individuals based on the collected data. Some or all of the processing described above in the analysis unit may be performed using generative AI, for example, or without generative AI. The visualization unit visualizes the behavioral patterns and habits of successful individuals based on the data analyzed by the analysis unit. The visualization unit visualizes the behavioral patterns and habits of successful individuals, for example, using generative AI. The visualization unit can visualize the behavioral patterns and habits of successful individuals as graphs or charts. Some or all of the above-described processes in the visualization unit may be performed using generative AI, for example, or without generative AI. The prediction unit predicts the student's future self based on their current behavior, using the information visualized by the visualization unit. The prediction unit predicts the student's future occupation based on their current behavior, for example, using generative AI. The prediction unit can predict the student's future occupation based on data such as their current academic performance, club activity achievements, hobbies, and special skills. Some or all of the above-described processes in the prediction unit may be performed using generative AI, for example, or without generative AI. The avatar unit creates an avatar of the future self predicted by the prediction unit and engages in conversation and consultation. The avatar unit creates an avatar of the predicted future self, for example, using generative AI.The avatar unit can, for example, create a 3D model of a predicted future self and engage in conversations and consultations with students. Some or all of the above-described processes in the avatar unit may be performed using, for example, a generative AI, or without using a generative AI. As a result, the student future planning support system utilizing the AI ​​agent according to this embodiment can support students' future planning and improve the quality of education.

[0030] The data collection department collects data on current students and graduates. Specifically, it collects data on student behavior patterns, post-graduation career paths, and actual employment situations. For example, it can collect data on students' academic performance, extracurricular activities, and employment destinations. This data is collected in cooperation with educational institutions and government agencies. Educational institutions provide data on students' grades, attendance, and participation in extracurricular activities, while government agencies provide data on graduates' employment destinations and actual employment situations. The data collection department centrally manages this data and stores it in a database. AI can be used for data collection, automating the data collection process and collecting data efficiently. For example, AI can access databases of educational institutions and government agencies and automatically extract the necessary data. AI also checks the quality of the collected data, detecting and correcting inaccurate or missing data. This allows the data collection department to efficiently collect high-quality data and improve the overall system performance. Furthermore, the data collection department regularly updates the collected data to maintain up-to-date information. For example, student performance data is updated every semester, and graduate employment data is regularly reviewed. This allows the data collection department to always perform analysis and predictions based on the latest data.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, the analysis department uses generative AI to analyze the collected data. Generative AI can analyze large amounts of data quickly and accurately, and identify patterns and trends. For example, generative AI analyzes collected data to identify the behavioral patterns and habits of successful individuals. By identifying the behavioral patterns and habits of successful individuals, it is possible to clarify what actions students should take to increase their chances of success. Generative AI automates the data analysis process and analyzes data efficiently. For example, generative AI analyzes student academic performance data and extracurricular activity data to identify commonalities among successful individuals. Generative AI also analyzes graduate employment data to identify which occupations are common among successful individuals. As a result, the analysis department can identify the behavioral patterns and habits of successful individuals based on the collected data and provide specific advice to students. Furthermore, the analysis department can also use historical data and statistical information to evaluate long-term trends and risks. For example, based on historical data, it can predict how specific behavioral patterns will affect future success. As a result, the analysis department can provide specific information to support students' future planning.

[0032] The Visualization Unit visualizes the behavioral patterns and habits of successful individuals based on data analyzed by the Analysis Unit. Specifically, the Visualization Unit uses Generative AI to visualize the behavioral patterns and habits of successful individuals as graphs and charts. Generative AI automates the data visualization process, enabling efficient data visualization. For example, the Generative AI graphs the behavioral patterns of successful individuals along a timeline, visually showing which actions lead to success. The Generative AI also charts the habits of successful individuals by category, clearly indicating which habits contribute to success. This allows the Visualization Unit to enable students to intuitively understand the behavioral patterns and habits of successful individuals. Furthermore, the Visualization Unit can make data visualization interactive. For example, students can click on graphs and charts to display detailed information or compare different datasets. This allows the Visualization Unit to help students compare their own behavior and habits with those of successful individuals and identify specific areas for improvement. Additionally, the Visualization Unit updates data visualizations in real time, providing the latest information. For example, graphs and charts are automatically updated whenever student performance data or extracurricular activity data is updated. This allows the visualization department to always provide visualizations based on the latest information, supporting students in their future planning.

[0033] The prediction unit predicts a student's future based on their current behavior, using information visualized by the visualization unit. Specifically, the prediction unit uses generative AI to predict a student's future occupation based on their current behavior. The generative AI analyzes data such as academic performance, extracurricular activity achievements, hobbies, and special skills to predict future occupations. For example, the generative AI predicts what kind of occupation is suitable based on a student's academic performance data. It also predicts what skills will be useful in a future occupation based on a student's extracurricular activity data. This allows the prediction unit to reveal what kind of occupation a student is likely to pursue in the future. Furthermore, the prediction unit can also evaluate the likelihood of future success based on a student's behavior and habits. For example, the generative AI analyzes a student's behavior patterns and compares them to the behavior patterns of successful individuals to evaluate the likelihood of future success. This allows the prediction unit to specifically indicate the behaviors and habits necessary for a student to succeed in the future. In addition, the prediction unit updates the prediction results in real time, providing the latest information. For example, whenever a student's academic performance data or extracurricular activity data is updated, the prediction results are automatically updated as well. This allows the forecasting department to always provide forecasts based on the latest information, supporting students in their future planning.

[0034] The Avatar Club uses an avatar representation of a student's future self, predicted by the Prediction Club, for conversations and consultations. Specifically, the Avatar Club uses a Generative AI to create a 3D avatar model of the predicted future self. The Generative AI predicts the student's future appearance and personality based on their current data, and then creates a 3D avatar model of that future self. For example, the Generative AI analyzes the student's academic performance and extracurricular activity data to predict their future occupation and lifestyle. It also predicts their future personality and interests based on their hobbies and special skills. This allows the Avatar Club to enable students to interact with their future selves and receive specific advice. Furthermore, the Avatar Club allows for interactive conversations with avatars. For example, students can ask questions to their avatars and receive advice from them. This allows the Avatar Club to help students concretize their future plans by enabling them to interact with their future selves and receive specific advice. In addition, the Avatar Club allows for customization of the avatar's appearance and personality. For example, students can change the appearance of their avatar to their liking or adjust its personality to match their ideal. This allows the avatar club to enable students to interact with their future selves in a more familiar and relatable way.

[0035] The data collection unit can collect data on student behavior patterns, post-graduation career paths, and actual employment situations. For example, the data collection unit can collect data on student behavior patterns, such as class attendance rates, participation in club activities, and whether or not students have part-time jobs. For example, the data collection unit can collect data on post-graduation career paths, such as the type of industry students work in and the universities and faculties they attend. For example, the data collection unit can collect data on actual employment situations, such as job duties, working hours, and salary levels. By collecting data on student behavior patterns, post-graduation career paths, and actual employment situations, information that can be used as a reference for future planning can be obtained. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input student behavior patterns into AI, which can then analyze the behavior patterns and collect data.

[0036] The analysis unit can analyze the collected data and identify the behavioral patterns and habits of successful individuals. For example, the analysis unit can identify the behavioral patterns and habits of successful individuals based on the collected data. The analysis unit can analyze data such as study time, networking activities, and self-improvement methods. The analysis unit can analyze the collected data using, for example, generative AI. The analysis unit can use generative AI to identify the behavioral patterns and habits of successful individuals. This allows the analysis unit to identify the behavioral patterns and habits of successful individuals by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data to identify the behavioral patterns and habits of successful individuals.

[0037] The visualization unit can visualize the behavioral patterns and habits of successful individuals. For example, the visualization unit can visualize the behavioral patterns and habits of successful individuals as graphs or charts. For example, the visualization unit can visualize data such as study time, networking activities, and self-improvement methods. For example, the visualization unit can use generative AI to visualize the behavioral patterns and habits of successful individuals. For example, the visualization unit can use generative AI to identify the behavioral patterns and habits of successful individuals. This makes it easier for students to learn from the behavioral patterns and habits of successful individuals by visualizing them. Some or all of the above-described processes in the visualization unit may be performed using generative AI, or not. For example, the visualization unit can input data on the behavioral patterns and habits of successful individuals into a generative AI, which can then visualize the data.

[0038] The prediction unit can predict a student's future occupation from their current behavior. The prediction unit predicts a student's future occupation based on data such as their current academic performance, club activity achievements, hobbies, and special skills. The prediction unit can predict data such as job type, industry, and position. The prediction unit predicts a student's future occupation from their current behavior using, for example, generative AI. The prediction unit can predict a student's future occupation using, for example, generative AI based on data about the student's current behavior. This allows students to think concretely about their future direction by predicting their future occupation from their current behavior. Some or all of the above processing in the prediction unit may be performed using, for example, generative AI, or without generative AI. For example, the prediction unit can input data about the student's current behavior into a generative AI, and the generative AI can analyze the data to predict their future occupation.

[0039] The avatar unit can create an avatar of a predicted future self and engage in conversations and consultations with students. For example, the avatar unit can create a 3D model of a predicted future self as an avatar. For example, the avatar unit can use a generative AI to create an avatar of a predicted future self. The avatar unit can create an avatar of a predicted future self and engage in conversations and consultations with students. For example, the avatar unit can use a generative AI to create an avatar of a predicted future self and engage in conversations and consultations with students. This allows students to have a concrete dialogue with their future selves by creating an avatar of a predicted future self. Some or all of the above-described processes in the avatar unit may be performed using a generative AI, or not. For example, the avatar unit can input data of a predicted future self into a generative AI, and the generative AI can generate an avatar based on the data.

[0040] The data collection unit can analyze students' past behavioral patterns and select the optimal data collection method. For example, the data collection unit can determine the timing of data collection based on actions students have frequently performed in the past. For example, the data collection unit can select the most effective data collection method based on students' past behavioral patterns. For example, the data collection unit can analyze students' past behavioral patterns and adjust the frequency of data collection. This allows for the selection of the optimal data collection method by analyzing students' past behavioral patterns. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input students' past behavioral data into a generating AI, which can then select the optimal data collection method based on the data.

[0041] The data collection unit can filter data based on students' current academic performance and areas of interest during data collection. For example, the data collection unit can prioritize the collection of relevant data based on students' current academic performance. For example, the data collection unit can collect data of interest based on students' areas of interest. For example, the data collection unit can adjust the scope of data collection according to students' academic performance. This allows for the collection of more relevant data by filtering data based on students' academic performance 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 students' academic performance and areas of interest into a generating AI, which can then filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location information during data collection. For example, if a student is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit can collect highly relevant data based on the student's current location. For example, the data collection unit can collect relevant data by considering the student's travel history. This allows for the priority collection of highly relevant data by considering the student'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 student's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data based on that data.

[0043] The data collection unit can analyze students' social media activities and collect relevant data during data collection. For example, the data collection unit can analyze the content of students' social media posts and collect relevant data. For example, the data collection unit can consider the activities of students' social media followers and friends and collect relevant data. For example, the data collection unit can adjust the timing of data collection based on the frequency of students' social media use. This allows for the collection of relevant data by analyzing students' social media activities. 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 students' social media activities into a generating AI, which can then collect relevant data based on that 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 can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for a more detailed analysis of important data by adjusting the level of detail according to the importance of the data. Some or all of the above-described processes 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 the generative AI can adjust the level of detail of the analysis based on the data.

[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. For example, the analysis unit can 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 and special skills to hobby and special skills data. By applying different analysis algorithms depending on the data category, more appropriate data analysis becomes possible. 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 a generative AI, and the generative AI can apply different analysis algorithms based on the data.

[0046] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection period into the generative AI, and the generative AI can determine the priority of analysis based on the data.

[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. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes 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 the generative AI can adjust the order of analysis based on the data.

[0048] The visualization unit can adjust the level of detail of the visualization based on the importance of the behavioral patterns of successful individuals. For example, the visualization unit can perform detailed visualization for important behavioral patterns. For example, the visualization unit can perform simplified visualization for less important behavioral patterns. For example, the visualization unit can determine the priority of visualization according to the importance of the behavioral patterns. This allows for more detailed visualization of important behavioral patterns by adjusting the level of detail according to the importance of the behavioral patterns of successful individuals. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the importance of the behavioral patterns into the generative AI, and the generative AI can adjust the level of detail of the visualization based on the data.

[0049] The visualization unit can apply different visualization methods depending on the occupational category of successful individuals during visualization. For example, the visualization unit can apply a visualization method specialized for a particular occupational category. For example, the visualization unit can apply different visualization methods for each occupational category. For example, the visualization unit can select the optimal visualization method according to the occupational category. This makes it possible to perform more appropriate visualizations by applying different visualization methods according to the occupational category of successful individuals. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input occupational categories into a generative AI, and the generative AI can apply different visualization methods based on the data.

[0050] The visualization unit can determine the visualization priority based on the timing of collecting successful individuals' behavioral patterns. For example, the visualization unit may prioritize the visualization of the most recent behavioral patterns. For example, the visualization unit can visualize the most recent behavioral patterns while referring to past behavioral patterns. For example, the visualization unit can adjust the visualization priority according to the timing of collecting behavioral patterns. This allows for the prioritization of visualization of the most recent behavioral patterns by determining the visualization priority based on the timing of collecting successful individuals' behavioral patterns. Some or all of the above processing in the visualization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the visualization unit can input the timing of collecting behavioral patterns into the generating AI, and the generating AI can determine the visualization priority based on the data.

[0051] The visualization unit can adjust the order of visualization based on the relevance of the behavioral patterns of successful individuals during visualization. For example, the visualization unit can prioritize the visualization of highly relevant behavioral patterns. For example, the visualization unit can postpone the visualization of less relevant behavioral patterns. For example, the visualization unit can adjust the order of visualization according to the relevance of the behavioral patterns. This allows for the prioritization of visualization of highly relevant behavioral patterns by adjusting the order of visualization based on the relevance of the behavioral patterns of successful individuals. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the relevance of behavioral patterns into a generative AI, and the generative AI can adjust the order of visualization based on the data.

[0052] The prediction unit can adjust the level of detail of its predictions based on the importance of the students' current behavioral patterns. For example, the prediction unit can make detailed predictions for important behavioral patterns. For example, it can make simplified predictions for less important behavioral patterns. The prediction unit can determine the priority of predictions according to the importance of the behavioral patterns. This allows for more detailed predictions of important behavioral patterns by adjusting the level of detail according to the importance of the students' current behavioral patterns. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the importance of behavioral patterns into the generative AI, which can then adjust the level of detail of the predictions based on the data.

[0053] The prediction unit can apply different prediction algorithms depending on the student's behavior category during prediction. For example, the prediction unit can apply a prediction algorithm specialized for academic performance to academic performance data. For example, the prediction unit can apply a prediction algorithm specialized for club activities to club activity data. For example, the prediction unit can apply a prediction algorithm specialized for hobbies and special skills to hobby and special skills data. By applying different prediction algorithms depending on the student's behavior category, more appropriate predictions become possible. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input behavior categories into a generative AI, and the generative AI can apply different prediction algorithms based on the data.

[0054] The prediction unit can determine the priority of predictions based on when the students' behavior patterns are collected. For example, the prediction unit may prioritize predicting the most recent behavior patterns. For example, the prediction unit can predict the most recent behavior patterns by referring to past behavior patterns. For example, the prediction unit may adjust the priority of predictions according to when the behavior patterns are collected. This allows the prediction of the most recent behavior patterns to be prioritized by determining the priority of predictions based on when the students' behavior patterns are collected. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the timing of behavior pattern collection into a generative AI, and the generative AI can determine the priority of predictions based on the data.

[0055] The prediction unit can adjust the order of predictions based on the relevance of students' behavior patterns during the prediction process. For example, the prediction unit may prioritize predicting highly relevant behavior patterns. For example, the prediction unit may postpone predicting less relevant behavior patterns. For example, the prediction unit can adjust the order of predictions according to the relevance of behavior patterns. This allows for prioritizing the prediction of highly relevant behavior patterns by adjusting the order of predictions based on the relevance of students' behavior patterns. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the relevance of behavior patterns into a generative AI, which can then adjust the order of predictions based on the data.

[0056] The avatar unit can adjust the level of detail of the avatar based on the predicted importance of the future self during the avatar generation process. For example, the avatar unit can generate a detailed avatar for an important future self. For example, the avatar unit can generate a simplified avatar for a less important future self. The avatar unit can adjust the level of detail of the avatar according to the importance of the future self. This allows for a more detailed avatar of a more important future self by adjusting the level of detail of the avatar according to the predicted importance of the future self. Some or all of the above processing in the avatar unit may be performed using a generation AI, for example, or without a generation AI. For example, the avatar unit can input the importance of the future self into a generation AI, and the generation AI can adjust the level of detail of the avatar based on the data.

[0057] The avatar unit can apply different avatar representation techniques depending on the predicted future occupational category of the user during avatar creation. For example, the avatar unit can apply an avatar representation technique specialized for a particular occupational category. For example, the avatar unit can apply different avatar representation techniques for each occupational category. For example, the avatar unit can select the optimal avatar representation technique depending on the occupational category. This makes it possible to create a more appropriate avatar representation by applying different avatar representation techniques depending on the predicted future occupational category of the user. Some or all of the above processing in the avatar unit may be performed using a generative AI, for example, or without a generative AI. For example, the avatar unit can input the occupational category into a generative AI, and the generative AI can apply different avatar representation techniques based on the data.

[0058] The avatar unit can determine the priority of avatars based on predicted future self collection times during avatar creation. For example, the avatar unit can prioritize creating an avatar of the most recent future self. For example, the avatar unit can create an avatar of the most recent future self while referring to past future selves. For example, the avatar unit can adjust the priority of avatars according to the collection times of future selves. This allows the avatar unit to prioritize creating an avatar of the most recent future self by determining the priority of avatars based on predicted future self collection times. Some or all of the above processing in the avatar unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar unit can input the collection times of future selves into a generation AI, and the generation AI can determine the priority of avatars based on the data.

[0059] The avatar unit can adjust the order of avatars based on the predicted relevance of the future self during the avatar creation process. For example, the avatar unit can prioritize creating avatars of highly relevant future selves. For example, the avatar unit can postpone creating avatars of less relevant future selves. For example, the avatar unit can adjust the order of avatars according to the relevance of the future self. This allows for prioritizing the creation of avatars of highly relevant future selves by adjusting the order of avatars based on the predicted relevance of the future self. Some or all of the above processing in the avatar unit may be performed using, for example, a generative AI, or without a generative AI. For example, the avatar unit can input the relevance of the future self into a generative AI, and the generative AI can adjust the order of avatars based on the data.

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

[0061] The data collection unit can collect not only students' academic performance and extracurricular activity results, but also their health data. For example, by collecting data such as students' sleep duration, diet, and exercise habits, it is possible to understand students' health status and consider it as a factor influencing their future career choices. The analysis unit can analyze the collected health data and identify the impact of health status on career choices. For example, it can reveal what kinds of jobs students with healthy lifestyles tend to pursue. The visualization unit can visualize health data as graphs and charts, allowing students to visually understand their own health status. The prediction unit can predict students' future health status based on health data and show the impact of health status on career choices. The avatar unit can create an avatar of a future version of the student with healthy lifestyle habits based on health data, and can converse and consult with the student.

[0062] The data collection unit can also collect data on students' hobbies and interests. For example, by collecting data on what books students read, what movies they watch, and what hobbies they have, it is possible to understand students' interests and concerns. The analysis unit can analyze the collected data on hobbies and interests and identify the influence of those interests on career choices. For example, it can reveal what kinds of jobs students with certain hobbies tend to pursue. The visualization unit can visualize the data on hobbies and interests as graphs and charts, allowing students to visually understand their own interests and concerns. The prediction unit can predict students' future careers based on the data on hobbies and interests and show the influence of those interests on career choices. The avatar unit can create an avatar of the student's future self with those interests based on the data on hobbies and interests, and can converse and consult with the student.

[0063] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location. For example, if a student is in a specific region, it can prioritize the collection of data related to that region. It can also collect highly relevant data based on the student's current location. It can collect relevant data by considering the student's travel history. This allows for the priority collection of highly relevant data by considering the student's geographical location. 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 student's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data based on that information.

[0064] The data collection unit can analyze students' social media activities and collect relevant data. For example, it can analyze the content of students' social media posts and collect relevant data. It can also collect relevant data considering the activities of students' social media followers and friends. It can adjust the timing of data collection based on students' frequency of social media use. This allows for the collection of relevant data by analyzing students' social media activities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on students' social media activities into a generating AI, which can then collect relevant data based on that data.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data, and a simplified analysis on less important data. It can also determine the priority of the analysis according to the importance of the data. This allows for more detailed analysis of important data by adjusting the level of detail according to the importance of the data. Some or all of the above-described processes 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, which can then adjust the level of detail of the analysis based on the data.

[0066] The prediction unit can determine the priority of predictions based on when the students' behavior patterns are collected. For example, it can prioritize predicting the most recent behavior patterns. It can also predict the most recent behavior patterns while referring to past behavior patterns. The prediction priority can be adjusted according to when the behavior patterns are collected. This allows for prioritizing the prediction of the most recent behavior patterns by determining the priority of predictions based on when the students' behavior patterns are collected. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the timing of behavior pattern collection into a generative AI, and the generative AI can determine the priority of predictions based on the data.

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

[0068] Step 1: The data collection department collects data on current students and graduates. For example, it collects data on student behavior patterns, post-graduation career paths, and actual employment situations. The data collection department can collect data on current students and graduates in cooperation with educational institutions and government agencies. It can also collect data on students' academic performance, extracurricular activities, and employment destinations. These processes may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses a generative AI to analyze the collected data and identify the behavioral patterns and habits of successful individuals. This makes it possible to identify the behavioral patterns and habits of successful individuals based on the collected data. These processes may be performed using a generative AI or without one. Step 3: The visualization unit visualizes the behavioral patterns and habits of successful individuals based on the data analyzed by the analysis unit. For example, the behavioral patterns and habits of successful individuals can be visualized as graphs or charts using generative AI. These processes may be performed using generative AI or without it. Step 4: The prediction unit predicts the student's future self based on their current behavior, using the information visualized by the visualization unit. For example, it can use generative AI to predict a student's future occupation based on their current behavior. Based on data such as the student's current academic performance, club activity achievements, hobbies, and special skills, it can predict their future occupation. These processes may or may not be performed using generative AI. Step 5: The avatar unit creates an avatar of the future self predicted by the prediction unit and engages in conversation and consultation. For example, using a generative AI, the predicted future self can be created as a 3D model avatar and used to have conversations and consultations with students. These processes may be performed using a generative AI or not.

[0069] (Example of form 2) The student future planning support system utilizing an AI agent according to an embodiment of the present invention is a system that collaborates with educational institutions and government agencies to create a database of current students and graduates using generative AI. This system visualizes the actions, habits, and occupational realities of successful individuals, providing students with a reference to concretely consider their future dreams and career paths. It can also predict a student's future self based on their current actions and create an avatar for conversation and consultation. For example, it collaborates with educational institutions and government agencies to collect data on current students and graduates. The collected data includes behavioral patterns in student life, career paths after graduation, and occupational realities. This data is analyzed by generative AI to visualize the behavioral patterns and habits of successful individuals. For example, it reveals what kind of student life graduates who have taken up specific occupations had and what kind of habits they had. Next, it uses generative AI to predict a student's future self based on their current actions. For example, it predicts what kind of occupation a student is likely to take up in the future based on data such as current academic performance, achievements in club activities, hobbies, and special skills. This prediction result is created as an avatar, which can then be used to have conversations and consultations with the student. For example, when a student seeks advice about their future dreams, the avatar provides guidance based on data from past successful individuals. This system allows students to gain valuable information to help them concretely consider their future dreams and career paths. By learning about the behavioral patterns and habits of successful individuals, students can re-evaluate their own actions and work towards their future goals. Furthermore, through conversations and consultations with the avatar, students can visualize their future selves more concretely and work towards their goals. This system is also beneficial for educational institutions and government agencies. By supporting students' future planning, it can improve the quality of education. Additionally, by utilizing alumni data, it strengthens the network between current students and alumni, contributing to the revitalization of the entire community. Thus, a student future planning support system utilizing AI agents can help students plan their future and improve the quality of education.

[0070] The student future planning support system utilizing an AI agent according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, a prediction unit, and an avatar unit. The data collection unit collects data on current students and graduates. The data collection unit collects data such as behavioral patterns in student life, career paths after graduation, and actual occupations. The data collection unit can collect data on current students and graduates in cooperation with educational institutions and government agencies, for example. The data collection unit can collect data such as students' academic performance, extracurricular activities, and employment destinations. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, generative AI. The analysis unit identifies, for example, the behavioral patterns and habits of successful individuals. The analysis unit can identify the behavioral patterns and habits of successful individuals based on the collected data. Some or all of the processing described above in the analysis unit may be performed using generative AI, for example, or without generative AI. The visualization unit visualizes the behavioral patterns and habits of successful individuals based on the data analyzed by the analysis unit. The visualization unit visualizes the behavioral patterns and habits of successful individuals, for example, using generative AI. The visualization unit can visualize the behavioral patterns and habits of successful individuals as graphs or charts. Some or all of the above-described processes in the visualization unit may be performed using generative AI, for example, or without generative AI. The prediction unit predicts the student's future self based on their current behavior, using the information visualized by the visualization unit. The prediction unit predicts the student's future occupation based on their current behavior, for example, using generative AI. The prediction unit can predict the student's future occupation based on data such as their current academic performance, club activity achievements, hobbies, and special skills. Some or all of the above-described processes in the prediction unit may be performed using generative AI, for example, or without generative AI. The avatar unit creates an avatar of the future self predicted by the prediction unit and engages in conversation and consultation. The avatar unit creates an avatar of the predicted future self, for example, using generative AI.The avatar unit can, for example, create a 3D model of a predicted future self and engage in conversations and consultations with students. Some or all of the above-described processes in the avatar unit may be performed using, for example, a generative AI, or without using a generative AI. As a result, the student future planning support system utilizing the AI ​​agent according to this embodiment can support students' future planning and improve the quality of education.

[0071] The data collection department collects data on current students and graduates. Specifically, it collects data on student behavior patterns, post-graduation career paths, and actual employment situations. For example, it can collect data on students' academic performance, extracurricular activities, and employment destinations. This data is collected in cooperation with educational institutions and government agencies. Educational institutions provide data on students' grades, attendance, and participation in extracurricular activities, while government agencies provide data on graduates' employment destinations and actual employment situations. The data collection department centrally manages this data and stores it in a database. AI can be used for data collection, automating the data collection process and collecting data efficiently. For example, AI can access databases of educational institutions and government agencies and automatically extract the necessary data. AI also checks the quality of the collected data, detecting and correcting inaccurate or missing data. This allows the data collection department to efficiently collect high-quality data and improve the overall system performance. Furthermore, the data collection department regularly updates the collected data to maintain up-to-date information. For example, student performance data is updated every semester, and graduate employment data is regularly reviewed. This allows the data collection department to always perform analysis and predictions based on the latest data.

[0072] The analysis department analyzes the data collected by the data collection department. Specifically, the analysis department uses generative AI to analyze the collected data. Generative AI can analyze large amounts of data quickly and accurately, and identify patterns and trends. For example, generative AI analyzes collected data to identify the behavioral patterns and habits of successful individuals. By identifying the behavioral patterns and habits of successful individuals, it is possible to clarify what actions students should take to increase their chances of success. Generative AI automates the data analysis process and analyzes data efficiently. For example, generative AI analyzes student academic performance data and extracurricular activity data to identify commonalities among successful individuals. Generative AI also analyzes graduate employment data to identify which occupations are common among successful individuals. As a result, the analysis department can identify the behavioral patterns and habits of successful individuals based on the collected data and provide specific advice to students. Furthermore, the analysis department can also use historical data and statistical information to evaluate long-term trends and risks. For example, based on historical data, it can predict how specific behavioral patterns will affect future success. As a result, the analysis department can provide specific information to support students' future planning.

[0073] The Visualization Unit visualizes the behavioral patterns and habits of successful individuals based on data analyzed by the Analysis Unit. Specifically, the Visualization Unit uses Generative AI to visualize the behavioral patterns and habits of successful individuals as graphs and charts. Generative AI automates the data visualization process, enabling efficient data visualization. For example, the Generative AI graphs the behavioral patterns of successful individuals along a timeline, visually showing which actions lead to success. The Generative AI also charts the habits of successful individuals by category, clearly indicating which habits contribute to success. This allows the Visualization Unit to enable students to intuitively understand the behavioral patterns and habits of successful individuals. Furthermore, the Visualization Unit can make data visualization interactive. For example, students can click on graphs and charts to display detailed information or compare different datasets. This allows the Visualization Unit to help students compare their own behavior and habits with those of successful individuals and identify specific areas for improvement. Additionally, the Visualization Unit updates data visualizations in real time, providing the latest information. For example, graphs and charts are automatically updated whenever student performance data or extracurricular activity data is updated. This allows the visualization department to always provide visualizations based on the latest information, supporting students in their future planning.

[0074] The prediction unit predicts a student's future based on their current behavior, using information visualized by the visualization unit. Specifically, the prediction unit uses generative AI to predict a student's future occupation based on their current behavior. The generative AI analyzes data such as academic performance, extracurricular activity achievements, hobbies, and special skills to predict future occupations. For example, the generative AI predicts what kind of occupation is suitable based on a student's academic performance data. It also predicts what skills will be useful in a future occupation based on a student's extracurricular activity data. This allows the prediction unit to reveal what kind of occupation a student is likely to pursue in the future. Furthermore, the prediction unit can also evaluate the likelihood of future success based on a student's behavior and habits. For example, the generative AI analyzes a student's behavior patterns and compares them to the behavior patterns of successful individuals to evaluate the likelihood of future success. This allows the prediction unit to specifically indicate the behaviors and habits necessary for a student to succeed in the future. In addition, the prediction unit updates the prediction results in real time, providing the latest information. For example, whenever a student's academic performance data or extracurricular activity data is updated, the prediction results are automatically updated as well. This allows the forecasting department to always provide forecasts based on the latest information, supporting students in their future planning.

[0075] The Avatar Club uses an avatar representation of a student's future self, predicted by the Prediction Club, for conversations and consultations. Specifically, the Avatar Club uses a Generative AI to create a 3D avatar model of the predicted future self. The Generative AI predicts the student's future appearance and personality based on their current data, and then creates a 3D avatar model of that future self. For example, the Generative AI analyzes the student's academic performance and extracurricular activity data to predict their future occupation and lifestyle. It also predicts their future personality and interests based on their hobbies and special skills. This allows the Avatar Club to enable students to interact with their future selves and receive specific advice. Furthermore, the Avatar Club allows for interactive conversations with avatars. For example, students can ask questions to their avatars and receive advice from them. This allows the Avatar Club to help students concretize their future plans by enabling them to interact with their future selves and receive specific advice. In addition, the Avatar Club allows for customization of the avatar's appearance and personality. For example, students can change the appearance of their avatar to their liking or adjust its personality to match their ideal. This allows the avatar club to enable students to interact with their future selves in a more familiar and relatable way.

[0076] The data collection unit can collect data on student behavior patterns, post-graduation career paths, and actual employment situations. For example, the data collection unit can collect data on student behavior patterns, such as class attendance rates, participation in club activities, and whether or not students have part-time jobs. For example, the data collection unit can collect data on post-graduation career paths, such as the type of industry students work in and the universities and faculties they attend. For example, the data collection unit can collect data on actual employment situations, such as job duties, working hours, and salary levels. By collecting data on student behavior patterns, post-graduation career paths, and actual employment situations, information that can be used as a reference for future planning can be obtained. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input student behavior patterns into AI, which can then analyze the behavior patterns and collect data.

[0077] The analysis unit can analyze the collected data and identify the behavioral patterns and habits of successful individuals. For example, the analysis unit can identify the behavioral patterns and habits of successful individuals based on the collected data. The analysis unit can analyze data such as study time, networking activities, and self-improvement methods. The analysis unit can analyze the collected data using, for example, generative AI. The analysis unit can use generative AI to identify the behavioral patterns and habits of successful individuals. This allows the analysis unit to identify the behavioral patterns and habits of successful individuals by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data to identify the behavioral patterns and habits of successful individuals.

[0078] The visualization unit can visualize the behavioral patterns and habits of successful individuals. For example, the visualization unit can visualize the behavioral patterns and habits of successful individuals as graphs or charts. For example, the visualization unit can visualize data such as study time, networking activities, and self-improvement methods. For example, the visualization unit can use generative AI to visualize the behavioral patterns and habits of successful individuals. For example, the visualization unit can use generative AI to identify the behavioral patterns and habits of successful individuals. This makes it easier for students to learn from the behavioral patterns and habits of successful individuals by visualizing them. Some or all of the above-described processes in the visualization unit may be performed using generative AI, or not. For example, the visualization unit can input data on the behavioral patterns and habits of successful individuals into a generative AI, which can then visualize the data.

[0079] The prediction unit can predict a student's future occupation from their current behavior. The prediction unit predicts a student's future occupation based on data such as their current academic performance, club activity achievements, hobbies, and special skills. The prediction unit can predict data such as job type, industry, and position. The prediction unit predicts a student's future occupation from their current behavior using, for example, generative AI. The prediction unit can predict a student's future occupation using, for example, generative AI based on data about the student's current behavior. This allows students to think concretely about their future direction by predicting their future occupation from their current behavior. Some or all of the above processing in the prediction unit may be performed using, for example, generative AI, or without generative AI. For example, the prediction unit can input data about the student's current behavior into a generative AI, and the generative AI can analyze the data to predict their future occupation.

[0080] The avatar unit can create an avatar of a predicted future self and engage in conversations and consultations with students. For example, the avatar unit can create a 3D model of a predicted future self as an avatar. For example, the avatar unit can use a generative AI to create an avatar of a predicted future self. The avatar unit can create an avatar of a predicted future self and engage in conversations and consultations with students. For example, the avatar unit can use a generative AI to create an avatar of a predicted future self and engage in conversations and consultations with students. This allows students to have a concrete dialogue with their future selves by creating an avatar of a predicted future self. Some or all of the above-described processes in the avatar unit may be performed using a generative AI, or not. For example, the avatar unit can input data of a predicted future self into a generative AI, and the generative AI can generate an avatar based on the data.

[0081] The data collection unit can estimate students' emotions and adjust the timing of data collection based on the estimated emotions. For example, if a student is stressed, the data collection unit can temporarily suspend data collection and resume it when the student is relaxed. For example, if a student is focused, the data collection unit can collect detailed data at that time. For example, if a student is tired, the data collection unit can adjust the timing to complete data collection in a shorter time. This allows for more appropriate data collection by adjusting the timing of data collection according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 or not using AI. For example, the data collection unit can input student emotion data into a generative AI, which can estimate emotions based on the data and adjust the timing of data collection.

[0082] The data collection unit can analyze students' past behavioral patterns and select the optimal data collection method. For example, the data collection unit can determine the timing of data collection based on actions students have frequently performed in the past. For example, the data collection unit can select the most effective data collection method based on students' past behavioral patterns. For example, the data collection unit can analyze students' past behavioral patterns and adjust the frequency of data collection. This allows for the selection of the optimal data collection method by analyzing students' past behavioral patterns. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input students' past behavioral data into a generating AI, which can then select the optimal data collection method based on the data.

[0083] The data collection unit can filter data based on students' current academic performance and areas of interest during data collection. For example, the data collection unit can prioritize the collection of relevant data based on students' current academic performance. For example, the data collection unit can collect data of interest based on students' areas of interest. For example, the data collection unit can adjust the scope of data collection according to students' academic performance. This allows for the collection of more relevant data by filtering data based on students' academic performance 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 students' academic performance and areas of interest into a generating AI, which can then filter the data.

[0084] The data collection unit can estimate a student's emotions and determine the priority of data to collect based on the estimated emotions. For example, if a student is excited, the data collection unit can prioritize collecting data that is of interest. For example, if a student is relaxed, the data collection unit can prioritize collecting detailed data. For example, if a student is stressed, the data collection unit can prioritize collecting important data. This allows for the collection of more important data by prioritizing data collection according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 or not. For example, the data collection unit can input student emotion data into a generative AI, which can estimate emotions based on the data and determine the priority of data to collect.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location information during data collection. For example, if a student is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit can collect highly relevant data based on the student's current location. For example, the data collection unit can collect relevant data by considering the student's travel history. This allows for the priority collection of highly relevant data by considering the student'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 student's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data based on that data.

[0086] The data collection unit can analyze students' social media activities and collect relevant data during data collection. For example, the data collection unit can analyze the content of students' social media posts and collect relevant data. For example, the data collection unit can consider the activities of students' social media followers and friends and collect relevant data. For example, the data collection unit can adjust the timing of data collection based on the frequency of students' social media use. This allows for the collection of relevant data by analyzing students' social media activities. 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 students' social media activities into a generating AI, which can then collect relevant data based on that data.

[0087] The analysis unit can estimate the student's emotions and adjust the data analysis method based on the estimated emotions. For example, if the student is relaxed, the analysis unit can perform a detailed data analysis. If the student is stressed, the analysis unit can perform a simplified data analysis. If the student is excited, the analysis unit can perform a visually easy-to-understand data analysis. By adjusting the data analysis method according to the student's emotions, more appropriate data analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input student emotion data into a generative AI, which can estimate emotions based on the data and adjust the data analysis method.

[0088] 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 can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for a more detailed analysis of important data by adjusting the level of detail according to the importance of the data. Some or all of the above-described processes 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 the generative AI can adjust the level of detail of the analysis based on the data.

[0089] 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. For example, the analysis unit can 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 and special skills to hobby and special skills data. By applying different analysis algorithms depending on the data category, more appropriate data analysis becomes possible. 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 a generative AI, and the generative AI can apply different analysis algorithms based on the data.

[0090] The analysis unit can estimate the student's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the student is relaxed, the analysis unit can display detailed analysis results. For example, if the student is stressed, the analysis unit can display simplified analysis results. For example, if the student is excited, the analysis unit can display visually easy-to-understand analysis results. This allows for more appropriate analysis results to be provided by adjusting the display method of the analysis results according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input student emotion data into a generative AI, which can estimate emotions based on the data and adjust the display method of the analysis results.

[0091] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection period into the generative AI, and the generative AI can determine the priority of analysis based on the data.

[0092] 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. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes 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 the generative AI can adjust the order of analysis based on the data.

[0093] The visualization unit can estimate the student's emotions and adjust the visualization's presentation based on the estimated emotions. For example, if the student is relaxed, the visualization unit can provide a detailed visualization. If the student is stressed, the visualization unit can provide a simplified visualization. If the student is excited, the visualization unit can provide a visually easy-to-understand visualization. By adjusting the visualization's presentation according to the student's emotions, more appropriate visualization becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using a generative AI, or not. For example, the visualization unit can input student emotion data into a generative AI, which can estimate emotions based on the data and adjust the visualization's presentation.

[0094] The visualization unit can adjust the level of detail of the visualization based on the importance of the behavioral patterns of successful individuals. For example, the visualization unit can perform detailed visualization for important behavioral patterns. For example, the visualization unit can perform simplified visualization for less important behavioral patterns. For example, the visualization unit can determine the priority of visualization according to the importance of the behavioral patterns. This allows for more detailed visualization of important behavioral patterns by adjusting the level of detail according to the importance of the behavioral patterns of successful individuals. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the importance of the behavioral patterns into the generative AI, and the generative AI can adjust the level of detail of the visualization based on the data.

[0095] The visualization unit can apply different visualization methods depending on the occupational category of successful individuals during visualization. For example, the visualization unit can apply a visualization method specialized for a particular occupational category. For example, the visualization unit can apply different visualization methods for each occupational category. For example, the visualization unit can select the optimal visualization method according to the occupational category. This makes it possible to perform more appropriate visualizations by applying different visualization methods according to the occupational category of successful individuals. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input occupational categories into a generative AI, and the generative AI can apply different visualization methods based on the data.

[0096] The visualization unit can estimate the student's emotions and adjust the length of the visualization based on the estimated emotions. For example, if the student is relaxed, the visualization unit can provide a detailed visualization. If the student is stressed, the visualization unit can provide a simplified visualization. If the student is excited, the visualization unit can provide a visually easy-to-understand visualization. By adjusting the length of the visualization according to the student's emotions, more appropriate visualization becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using a generative AI, or not. For example, the visualization unit can input student emotion data into a generative AI, which can estimate the emotions based on the data and adjust the length of the visualization.

[0097] The visualization unit can determine the visualization priority based on the timing of collecting successful individuals' behavioral patterns. For example, the visualization unit may prioritize the visualization of the most recent behavioral patterns. For example, the visualization unit can visualize the most recent behavioral patterns while referring to past behavioral patterns. For example, the visualization unit can adjust the visualization priority according to the timing of collecting behavioral patterns. This allows for the prioritization of visualization of the most recent behavioral patterns by determining the visualization priority based on the timing of collecting successful individuals' behavioral patterns. Some or all of the above processing in the visualization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the visualization unit can input the timing of collecting behavioral patterns into the generating AI, and the generating AI can determine the visualization priority based on the data.

[0098] The visualization unit can adjust the order of visualization based on the relevance of the behavioral patterns of successful individuals during visualization. For example, the visualization unit can prioritize the visualization of highly relevant behavioral patterns. For example, the visualization unit can postpone the visualization of less relevant behavioral patterns. For example, the visualization unit can adjust the order of visualization according to the relevance of the behavioral patterns. This allows for the prioritization of visualization of highly relevant behavioral patterns by adjusting the order of visualization based on the relevance of the behavioral patterns of successful individuals. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the relevance of behavioral patterns into a generative AI, and the generative AI can adjust the order of visualization based on the data.

[0099] The prediction unit can estimate the student's emotions and adjust the prediction method based on the estimated emotions. For example, if the student is relaxed, the prediction unit can make a detailed prediction. If the student is stressed, the prediction unit can make a simplified prediction. If the student is excited, the prediction unit can make a visually easy-to-understand prediction. By adjusting the prediction method according to the student's emotions, more appropriate predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using a generative AI, or not using a generative AI. For example, the prediction unit can input student emotion data into a generative AI, which can estimate emotions based on the data and adjust the prediction method.

[0100] The prediction unit can adjust the level of detail of its predictions based on the importance of the students' current behavioral patterns. For example, the prediction unit can make detailed predictions for important behavioral patterns. For example, it can make simplified predictions for less important behavioral patterns. The prediction unit can determine the priority of predictions according to the importance of the behavioral patterns. This allows for more detailed predictions of important behavioral patterns by adjusting the level of detail according to the importance of the students' current behavioral patterns. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the importance of behavioral patterns into the generative AI, which can then adjust the level of detail of the predictions based on the data.

[0101] The prediction unit can apply different prediction algorithms depending on the student's behavior category during prediction. For example, the prediction unit can apply a prediction algorithm specialized for academic performance to academic performance data. For example, the prediction unit can apply a prediction algorithm specialized for club activities to club activity data. For example, the prediction unit can apply a prediction algorithm specialized for hobbies and special skills to hobby and special skills data. By applying different prediction algorithms depending on the student's behavior category, more appropriate predictions become possible. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input behavior categories into a generative AI, and the generative AI can apply different prediction algorithms based on the data.

[0102] The prediction unit can estimate the student's emotions and adjust how the prediction results are displayed based on the estimated emotions. For example, if the student is relaxed, the prediction unit can display detailed prediction results. If the student is stressed, the prediction unit can display simplified prediction results. If the student is excited, the prediction unit can display visually easy-to-understand prediction results. This allows for more appropriate prediction results by adjusting how the prediction results are displayed according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the prediction unit may be performed using a generative AI, or not. For example, the prediction unit can input student emotion data into a generative AI, which can estimate emotions based on the data and adjust how the prediction results are displayed.

[0103] The prediction unit can determine the priority of predictions based on when the students' behavior patterns are collected. For example, the prediction unit may prioritize predicting the most recent behavior patterns. For example, the prediction unit can predict the most recent behavior patterns by referring to past behavior patterns. For example, the prediction unit may adjust the priority of predictions according to when the behavior patterns are collected. This allows the prediction of the most recent behavior patterns to be prioritized by determining the priority of predictions based on when the students' behavior patterns are collected. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the timing of behavior pattern collection into a generative AI, and the generative AI can determine the priority of predictions based on the data.

[0104] The prediction unit can adjust the order of predictions based on the relevance of students' behavior patterns during the prediction process. For example, the prediction unit may prioritize predicting highly relevant behavior patterns. For example, the prediction unit may postpone predicting less relevant behavior patterns. For example, the prediction unit can adjust the order of predictions according to the relevance of behavior patterns. This allows for prioritizing the prediction of highly relevant behavior patterns by adjusting the order of predictions based on the relevance of students' behavior patterns. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the relevance of behavior patterns into a generative AI, which can then adjust the order of predictions based on the data.

[0105] The avatar unit can estimate the student's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the student is relaxed, the avatar unit can display an avatar with a calm expression. If the student is stressed, the avatar unit can display an avatar with an encouraging expression. If the student is excited, the avatar unit can display an avatar with an energetic expression. By adjusting the avatar's expression according to the student's emotions, a more appropriate avatar expression becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the avatar unit may be performed using a generative AI, or not. For example, the avatar unit can input student emotion data into a generative AI, which can estimate the emotions based on the data and adjust the avatar's expression.

[0106] The avatar unit can adjust the level of detail of the avatar based on the predicted importance of the future self during the avatar generation process. For example, the avatar unit can generate a detailed avatar for an important future self. For example, the avatar unit can generate a simplified avatar for a less important future self. The avatar unit can adjust the level of detail of the avatar according to the importance of the future self. This allows for a more detailed avatar of a more important future self by adjusting the level of detail of the avatar according to the predicted importance of the future self. Some or all of the above processing in the avatar unit may be performed using a generation AI, for example, or without a generation AI. For example, the avatar unit can input the importance of the future self into a generation AI, and the generation AI can adjust the level of detail of the avatar based on the data.

[0107] The avatar unit can apply different avatar representation techniques depending on the predicted future occupational category of the user during avatar creation. For example, the avatar unit can apply an avatar representation technique specialized for a particular occupational category. For example, the avatar unit can apply different avatar representation techniques for each occupational category. For example, the avatar unit can select the optimal avatar representation technique depending on the occupational category. This makes it possible to create a more appropriate avatar representation by applying different avatar representation techniques depending on the predicted future occupational category of the user. Some or all of the above processing in the avatar unit may be performed using a generative AI, for example, or without a generative AI. For example, the avatar unit can input the occupational category into a generative AI, and the generative AI can apply different avatar representation techniques based on the data.

[0108] The avatar unit can estimate the student's emotions and adjust the avatar's conversation content based on the estimated emotions. For example, if the student is relaxed, the avatar unit can provide calm conversation content. For example, if the student is stressed, the avatar unit can provide encouraging conversation content. For example, if the student is excited, the avatar unit can provide energetic conversation content. In this way, by adjusting the avatar's conversation content according to the student's emotions, more appropriate conversation content can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the avatar unit may be performed using a generative AI, or not using a generative AI. For example, the avatar unit can input student emotion data into a generative AI, the generative AI can estimate the emotions based on the data, and adjust the avatar's conversation content.

[0109] The avatar unit can determine the priority of avatars based on predicted future self collection times during avatar creation. For example, the avatar unit can prioritize creating an avatar of the most recent future self. For example, the avatar unit can create an avatar of the most recent future self while referring to past future selves. For example, the avatar unit can adjust the priority of avatars according to the collection times of future selves. This allows the avatar unit to prioritize creating an avatar of the most recent future self by determining the priority of avatars based on predicted future self collection times. Some or all of the above processing in the avatar unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar unit can input the collection times of future selves into a generation AI, and the generation AI can determine the priority of avatars based on the data.

[0110] The avatar unit can adjust the order of avatars based on the predicted relevance of the future self during the avatar creation process. For example, the avatar unit can prioritize creating avatars of highly relevant future selves. For example, the avatar unit can postpone creating avatars of less relevant future selves. For example, the avatar unit can adjust the order of avatars according to the relevance of the future self. This allows for prioritizing the creation of avatars of highly relevant future selves by adjusting the order of avatars based on the predicted relevance of the future self. Some or all of the above processing in the avatar unit may be performed using, for example, a generative AI, or without a generative AI. For example, the avatar unit can input the relevance of the future self into a generative AI, and the generative AI can adjust the order of avatars based on the data.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The data collection unit can collect not only students' academic performance and extracurricular activity results, but also their health data. For example, by collecting data such as students' sleep duration, diet, and exercise habits, it is possible to understand students' health status and consider it as a factor influencing their future career choices. The analysis unit can analyze the collected health data and identify the impact of health status on career choices. For example, it can reveal what kinds of jobs students with healthy lifestyles tend to pursue. The visualization unit can visualize health data as graphs and charts, allowing students to visually understand their own health status. The prediction unit can predict students' future health status based on health data and show the impact of health status on career choices. The avatar unit can create an avatar of a future version of the student with healthy lifestyle habits based on health data, and can converse and consult with the student.

[0113] The data collection unit can also collect data on students' hobbies and interests. For example, by collecting data on what books students read, what movies they watch, and what hobbies they have, it is possible to understand students' interests and concerns. The analysis unit can analyze the collected data on hobbies and interests and identify the influence of those interests on career choices. For example, it can reveal what kinds of jobs students with certain hobbies tend to pursue. The visualization unit can visualize the data on hobbies and interests as graphs and charts, allowing students to visually understand their own interests and concerns. The prediction unit can predict students' future careers based on the data on hobbies and interests and show the influence of those interests on career choices. The avatar unit can create an avatar of the student's future self with those interests based on the data on hobbies and interests, and can converse and consult with the student.

[0114] The analysis unit can estimate the student's emotions and adjust the data analysis method based on the estimated emotions. For example, if the student is relaxed, a detailed data analysis can be performed. If the student is stressed, a simplified data analysis can be performed. If the student is excited, a visually easy-to-understand data analysis can be performed. By adjusting the data analysis method according to the student's emotions, more appropriate data analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input student emotion data into a generative AI, which can estimate emotions based on the data and adjust the data analysis method.

[0115] The visualization unit can estimate the student's emotions when visualizing the behavioral patterns and habits of successful individuals, and adjust the visualization's presentation based on the estimated emotions. For example, if a student is relaxed, a detailed visualization can be performed. If a student is stressed, a simplified visualization can be performed. If a student is excited, a visually easy-to-understand visualization can be performed. This allows for more appropriate visualization by adjusting the visualization's presentation according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using a generative AI, or not. For example, the visualization unit can input student emotion data into a generative AI, which can then estimate the emotions based on the data and adjust the visualization's presentation.

[0116] The prediction unit can estimate the student's emotions and adjust the prediction method based on the estimated emotions. For example, if the student is relaxed, a detailed prediction can be made. If the student is stressed, a simplified prediction can be made. If the student is excited, a visually easy-to-understand prediction can be made. By adjusting the prediction method according to the student's emotions, more accurate predictions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using a generative AI, or not using a generative AI. For example, the prediction unit can input student emotion data into a generative AI, which can estimate emotions based on the data and adjust the prediction method.

[0117] The avatar unit can estimate the student's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the student is relaxed, an avatar with a calm expression can be displayed. If the student is stressed, an avatar with an encouraging expression can be displayed. If the student is excited, an avatar with an energetic expression can be displayed. This allows for more appropriate avatar representation by adjusting the avatar's expression according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the avatar unit may be performed using a generative AI, or not. For example, the avatar unit can input student emotion data into a generative AI, which can estimate the emotions based on the data and adjust the avatar's expression.

[0118] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location. For example, if a student is in a specific region, it can prioritize the collection of data related to that region. It can also collect highly relevant data based on the student's current location. It can collect relevant data by considering the student's travel history. This allows for the priority collection of highly relevant data by considering the student's geographical location. 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 student's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data based on that information.

[0119] The data collection unit can analyze students' social media activities and collect relevant data. For example, it can analyze the content of students' social media posts and collect relevant data. It can also collect relevant data considering the activities of students' social media followers and friends. It can adjust the timing of data collection based on students' frequency of social media use. This allows for the collection of relevant data by analyzing students' social media activities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on students' social media activities into a generating AI, which can then collect relevant data based on that data.

[0120] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data, and a simplified analysis on less important data. It can also determine the priority of the analysis according to the importance of the data. This allows for more detailed analysis of important data by adjusting the level of detail according to the importance of the data. Some or all of the above-described processes 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, which can then adjust the level of detail of the analysis based on the data.

[0121] The prediction unit can determine the priority of predictions based on when the students' behavior patterns are collected. For example, it can prioritize predicting the most recent behavior patterns. It can also predict the most recent behavior patterns while referring to past behavior patterns. The prediction priority can be adjusted according to when the behavior patterns are collected. This allows for prioritizing the prediction of the most recent behavior patterns by determining the priority of predictions based on when the students' behavior patterns are collected. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the timing of behavior pattern collection into a generative AI, and the generative AI can determine the priority of predictions based on the data.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection department collects data on current students and graduates. For example, it collects data on student behavior patterns, post-graduation career paths, and actual employment situations. The data collection department can collect data on current students and graduates in cooperation with educational institutions and government agencies. It can also collect data on students' academic performance, extracurricular activities, and employment destinations. These processes may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses a generative AI to analyze the collected data and identify the behavioral patterns and habits of successful individuals. This makes it possible to identify the behavioral patterns and habits of successful individuals based on the collected data. These processes may be performed using a generative AI or without one. Step 3: The visualization unit visualizes the behavioral patterns and habits of successful individuals based on the data analyzed by the analysis unit. For example, the behavioral patterns and habits of successful individuals can be visualized as graphs or charts using generative AI. These processes may be performed using generative AI or without it. Step 4: The prediction unit predicts the student's future self based on their current behavior, using the information visualized by the visualization unit. For example, it can use generative AI to predict a student's future occupation based on their current behavior. Based on data such as the student's current academic performance, club activity achievements, hobbies, and special skills, it can predict their future occupation. These processes may or may not be performed using generative AI. Step 5: The avatar unit creates an avatar of the future self predicted by the prediction unit and engages in conversation and consultation. For example, using a generative AI, the predicted future self can be created as a 3D model avatar and used to have conversations and consultations with students. These processes may be performed using a generative AI or not.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, prediction unit, and avatar unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects student behavior patterns and career path data using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12 and visualizes the analyzed data as graphs and charts. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future occupations from the student's current behavior. The avatar unit is implemented in the specific processing unit 46A of the smart device 14 and can create an avatar of the predicted future self to converse and consult with the student. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, visualization unit, prediction unit, and avatar unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects student behavior patterns and career path data using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The visualization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and visualizes the analyzed data as graphs and charts. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts future occupations from the student's current behavior. The avatar unit is implemented, for example, by the control unit 46A of the smart glasses 214 and can create an avatar of the predicted future self to converse and consult with the student. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, prediction unit, and avatar unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects student behavior patterns and career path data using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The visualization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and visualizes the analyzed data as graphs and charts. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts future occupations from the student's current behavior. The avatar unit is implemented, for example, by the control unit 46A of the headset terminal 314 and can create an avatar of the predicted future self to converse and consult with the student. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, visualization unit, prediction unit, and avatar unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects student behavior patterns and career path data using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The visualization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and visualizes the analyzed data as graphs and charts. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts future occupations from the students' current behavior. The avatar unit is implemented, for example, by the control unit 46A of the robot 414 and can create an avatar of the predicted future self to converse and consult with students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The data collection department collects data on current students and graduates, An analysis unit analyzes the data collected by the aforementioned collection unit, A visualization unit visualizes the behavioral patterns and habits of successful individuals based on the data analyzed by the aforementioned analysis unit. A prediction unit predicts the student's future self based on the information visualized by the aforementioned visualization unit, and The system includes an avatar unit that creates an avatar of the future self predicted by the prediction unit and engages in conversation and consultation with that avatar. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on student behavior patterns, post-graduation career paths, and actual employment situations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the collected data, we identify the behavioral patterns and habits of successful individuals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, Visualizing the behavioral patterns and habits of successful people. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, Predicting future careers based on students' current behavior. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned avatar section is, I create an avatar of my predicted future self and engage in conversations and consultations with students. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate students' emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze students' past behavioral patterns and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on students' current academic status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate students' emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze students' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate students' emotions and adjust the data analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) 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 16) The aforementioned analysis unit, The system estimates students' emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned visualization unit, The system estimates students' emotions and adjusts the visualization method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, When visualizing, adjust the level of detail based on the importance of the behavioral patterns of successful individuals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, When visualizing the results, different visualization methods are applied depending on the occupational category of the successful individuals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, The system estimates the students' emotions and adjusts the length of the visualization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, When visualizing data, prioritize visualizations based on when the behavioral patterns of successful individuals were collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, When visualizing data, adjust the order of visualizations based on the relevance of successful individuals' behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, We estimate students' emotions and adjust the prediction method based on the estimated emotions of the students. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, When making predictions, adjust the level of detail based on the importance of students' current behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, different prediction algorithms are applied depending on the student's behavior category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, The system estimates students' emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, When making predictions, the priority of predictions is determined based on when student behavior patterns were collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, During prediction, the order of predictions is adjusted based on the relevance of students' behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned avatar section is, The system estimates the students' emotions and adjusts the avatar's representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned avatar section is, When creating an avatar, the level of detail of the avatar is adjusted based on the predicted future importance of the user. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned avatar section is, When creating an avatar, different avatar representation techniques are applied depending on the predicted future occupational category of the user. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned avatar section is, The system estimates the student's emotions and adjusts the avatar's conversation content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned avatar section is, When creating an avatar, prioritize your avatar based on your predicted future collection timing. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned avatar section is, When creating an avatar, the order of avatars is adjusted based on the predicted future relevance of the user. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data on current students and graduates, An analysis unit analyzes the data collected by the aforementioned collection unit, A visualization unit visualizes the behavioral patterns and habits of successful individuals based on the data analyzed by the aforementioned analysis unit. A prediction unit predicts the student's future self based on the information visualized by the aforementioned visualization unit, and The system includes an avatar unit that creates an avatar of the future self predicted by the prediction unit and engages in conversation and consultation with that avatar. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on student behavior patterns, post-graduation career paths, and actual employment situations. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the collected data, we identify the behavioral patterns and habits of successful individuals. The system according to feature 1.

4. The aforementioned visualization unit, Visualizing the behavioral patterns and habits of successful people. The system according to feature 1.

5. The prediction unit, Predicting future careers based on students' current behavior. The system according to feature 1.

6. The aforementioned avatar section is, I create an avatar of my predicted future self and engage in conversations and consultations with students. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze students' past behavioral patterns and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on students' current academic status and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is We estimate students' emotions and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

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