System

Abstract

To provide a system according to an embodiment that analyzes information on a student, and alleviates an administrative work load of a teacher by proposing an optimal course.SOLUTION: A system according to an embodiment comprises: a reception unit; an analysis unit; a prediction unit; and a providing unit. The reception unit is configured to input information on a student. The analysis unit is configured to analyze the information inputted by the reception unit. The prediction unit is configured to make a future prediction on the basis of the information analyzed by the analysis unit. The providing unit is configured to provide information proposed by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it can be too much work for teachers to suggest the best career path for each student based on their future dreams, desires, and grades.

[0005] The system according to the embodiment aims to reduce the workload of teachers by analyzing student information and suggesting optimal career paths. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a prediction unit, and a provision unit. The reception unit inputs information about students. The analysis unit analyzes the information input by the reception unit. The prediction unit predicts the future based on the information analyzed by the analysis unit. The provision unit provides the information proposed by the prediction unit. Effect of the Invention

[0007] The system according to the embodiment can reduce the workload of teachers by analyzing students' information and suggesting optimal career paths. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The career guidance system according to the embodiment of the present invention is a system that predicts the future based on the dreams, desires, and grades of each student and suggests the most suitable school and occupation as a measure against excessive work of teachers. In this career guidance system, students input information such as their future dreams, desires, and grades, and a generation AI analyzes the information to predict the future and suggests the most suitable school and occupation, thereby reducing the amount of work time that teachers spend on each student. For example, if a student inputs information such as "I want to be a doctor," "I'm interested in science," and "I have good grades in math," the generation AI will suggest a medical university or a medical occupation based on the information. This proposal is provided to the student, and the student can think specifically about his or her career path. This allows teachers to significantly reduce the time they spend on each student. In addition, students can think about specific career paths based on their dreams and desires, which expands the range of career choices. For example, students can obtain more specific information by researching the school and occupations suggested by the generation AI. This allows students to imagine their career paths more specifically and work hard toward their future goals. In this way, by using generative AI, it is possible to reduce the excessive workload of teachers and make career guidance for students more efficient. As a result, the career guidance system can reduce the excessive workload of teachers and make career guidance for students more efficient.

[0029] The career guidance system according to the embodiment includes a reception unit, an analysis unit, a prediction unit, and a provision unit. The reception unit inputs information about the student. The information about the student includes, for example, a future dream, a request, and a grade, but is not limited to such examples. The reception unit can input, for example, information about the student "I want to be a doctor," "I'm interested in science," and "I have good grades in math." The analysis unit uses a generation AI to analyze the information input by the reception unit. The analysis is performed, for example, using a statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the generation AI performs an analysis to predict the student's future career and the school to which the student will attend, based on the student's information. The prediction unit uses the generation AI to make a future prediction based on the information analyzed by the analysis unit. The future prediction is performed, for example, using a prediction model or a simulation, but is not limited to such examples. For example, the generation AI suggests a medical university or a doctor's career based on the dream of "I want to be a doctor," the request of "I'm interested in science," and the information of "I have good grades in math." The provision unit provides the student with information about the school to attend and the career proposed by the generation AI. The provision is performed based on, for example, a notification method and a format of information, but is not limited to such examples. For example, the generation AI notifies the student of the proposed information, and the student can consider his / her own career path based on the information. In this way, the career guidance system according to the embodiment can propose the most suitable educational destination or career based on the student's information, reduce the excessive workload of teachers, and make career guidance for students more efficient.

[0030] The reception unit inputs information about the students. The information about the students includes, but is not limited to, their future dreams, requests, and grades. Specifically, the students can input detailed information about their interests and concerns, future goals, current academic performance, extracurricular activity results, and even their home environment and hobbies. For example, the students can input information such as "I want to be a doctor," "I'm interested in science," and "I have good grades in math." In addition, the students can input their personality traits, learning style, and past career counseling history. The reception unit has a function to efficiently collect this information and store it in a database. Furthermore, the reception unit automatically classifies the information input by the students and organizes it so that the analysis unit can easily access it. For example, the input information is tagged by category and classified into different categories such as future dreams, requests, and grades. This allows the analysis unit to quickly obtain the necessary information and perform analysis. The reception unit also has a feedback function to check the accuracy of the information input by the students. For example, if there is an error in the information input by the students, the system automatically displays a warning and prompts them to correct it. This enables the reception unit to collect student information accurately and efficiently, thereby improving the analysis accuracy of the analysis unit.

[0031] The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. Specifically, the generation AI performs analysis to predict the future occupation and school to which the student will attend based on the student's information. The generation AI analyzes the student's input information using natural language processing technology and evaluates the relevance of each piece of information. For example, the generation AI combines and analyzes the dream of "becoming a doctor," the desire to "be interested in science," and information such as "good grades in math" to determine whether a medical university or a medical profession is suitable. Furthermore, the generation AI uses past data and statistical information to analyze what career paths students with similar backgrounds have chosen and what results they have achieved. This enables more accurate career predictions. The analysis unit creates a detailed report on the student's career path based on the analysis results performed by the generation AI. This report includes a list of recommended educational destinations and occupations, the advantages and disadvantages of each option, and information on future career paths. Furthermore, the analysis unit creates a feedback loop to continuously update the student's information and improve the accuracy of career predictions. For example, if a student enters new information, the generation AI immediately analyzes that information and updates the career prediction. This allows the analysis unit to provide highly accurate career predictions based on the student's information and support the student in choosing their career path.

[0032] The prediction unit uses the generation AI to make future predictions based on the information analyzed by the analysis unit. Future predictions are made using, for example, a prediction model or a simulation, but are not limited to such examples. Specifically, the generation AI suggests a medical university and a medical profession based on the dream of "wanting to be a doctor," the desire to "be interested in science," and the information that "you have good grades in math." The generation AI analyzes what career paths students with similar backgrounds have chosen and what results they have achieved based on past data and statistical information. For example, it analyzes the grades and post-graduation career paths of students who have entered a medical university, and evaluates their success rate and satisfaction as a doctor. In addition, the generation AI takes into account future trends in the job market and demand forecasts to suggest the optimal career path. For example, it predicts future demand and technological evolution in the medical field, and based on that, suggests a suitable educational destination and career for the student. The prediction unit makes specific career suggestions to the student based on the future predictions made by the generation AI. These suggestions include a list of recommended educational destinations and careers, the advantages and disadvantages of each option, and information on future career paths. In addition, the prediction unit creates a feedback loop to continuously update student information and improve the accuracy of future predictions. For example, when a student inputs new information, the generation AI immediately analyzes that information and updates the future prediction. This allows the prediction unit to provide highly accurate future predictions based on student information and support students in choosing their career paths.

[0033] The provision unit provides students with information on the educational destinations and careers proposed by the generation AI. The provision is performed, for example, based on a notification method and a format of information, but is not limited to such examples. Specifically, the generation AI notifies students of the information proposed by the generation AI, and the students can consider their own career paths based on the information. The provision unit provides students with a detailed report of the career path proposal. This report includes a list of recommended educational destinations and careers, the advantages and disadvantages of each option, and information on future career paths. In addition, the provision unit provides an interface that allows students to easily access detailed information on the proposed career paths. For example, through a web portal or mobile app, students can view and compare information on the proposed career paths. The provision unit also has a function that allows students to provide feedback on the career proposal. For example, if a student has doubts or concerns about the proposed career path, he or she can send a question through the system and receive a response from a professional counselor. This allows the provision unit to provide students with appropriate career information and support them in choosing their career path. In addition, the service provider collects data on students' career choices and builds a feedback loop to continuously improve the accuracy and effectiveness of the entire system. For example, it tracks students' career choices and their subsequent outcomes, and updates the generative AI's predictive model. This enables the service provider to provide students with the latest and most optimal career information, improving the efficiency and accuracy of career guidance.

[0034] The generating AI has an algorithm used by the generating AI. The algorithm used by the generating AI is important for improving the accuracy of analysis and prediction. For example, the generating AI can analyze student information and make future predictions using algorithms such as neural networks and decision trees. A neural network can analyze data using multiple layers of artificial neurons and recognize complex patterns. A decision tree can hierarchically divide data and generate rules for making predictions. This allows the generating AI to perform highly accurate analysis and predictions based on student information. The algorithm used by the generating AI can, for example, learn past data and make highly accurate predictions for new data. This allows the generating AI to suggest the most suitable educational destination or career based on the student's future dreams, requests, and grades. The algorithm used by the generating AI can, for example, use technologies such as natural language generation and image generation to analyze student information and make future predictions. This allows the generating AI to perform highly accurate analysis and predictions based on student information.

[0035] The data handling method is provided. The data handling method is important for efficient information management. For example, by clarifying the data storage method and access authority, the information management is made efficient. The data storage method includes, but is not limited to, cloud storage and local storage. Cloud storage stores data via the Internet and can be accessed from multiple devices. Local storage stores data in a device and can be accessed even offline. The access authority can ensure the security of information by, for example, setting different authority for each user. In this way, the data handling method can improve the efficiency of information management and ensure security. In the data handling method, for example, by clarifying the procedure for backing up and recovering data, the protection of information can be strengthened. In this way, the data handling method can improve the efficiency of information management and ensure security.

[0036] The present invention provides a method for evaluating the reliability and accuracy of the proposed information. The method for evaluating the reliability and accuracy of the proposed information is important for ensuring the accuracy of the information. For example, by clarifying the criteria for evaluating the accuracy and reliability of the data, the reliability of the proposed information can be improved. The accuracy of the data can be confirmed, for example, by evaluating the consistency and completeness of the data. The reliability can be confirmed, for example, by evaluating the source of the data or data from a reliable information source. This can improve the reliability and accuracy of the proposed information. The accuracy of the proposed information can be confirmed, for example, by evaluating the accuracy and error rate of the prediction. The accuracy of the prediction can be confirmed, for example, by evaluating the degree of agreement between the actual result and the predicted result. The error rate can be confirmed, for example, by evaluating the difference between the predicted result and the actual result. This can improve the reliability and accuracy of the proposed information. The method for evaluating the reliability and accuracy of the proposed information can maintain the accuracy of the information by, for example, periodically evaluating the information and making improvements based on the evaluation results. This can improve the reliability and accuracy of the proposed information.

[0037] The reception unit can input information on the student's future dreams, requests, and grades. The reception unit can input information such as the student's future dreams, requests, and grades, for example. The future dreams and requests include, but are not limited to, information such as "I want to be a doctor" and "I'm interested in science." The grades include, but are not limited to, information such as "good grades in math" and "poor grades in English." This allows individual career guidance to be provided by inputting information such as the student's future dreams, requests, and grades. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the information input by the student to the generation AI, and the generation AI can analyze the information.

[0038] The analysis unit can analyze the input information and predict the future based on the student's future dreams, desires, and grades. The analysis unit can, for example, analyze the input information and predict the future based on the student's future dreams, desires, and grades. The future prediction is performed, for example, using a prediction model or a simulation, but is not limited to such examples. For example, the generation AI suggests a medical university or a medical career based on the dream of "wanting to be a doctor," the desire of "being interested in science," and information such as "good grades in mathematics." This makes it possible to provide appropriate career guidance by predicting the future based on the student's future dreams, desires, and grades. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the student's information into the generation AI, and the generation AI can analyze the information.

[0039] The prediction unit can predict the student's future occupation and the school to which the student will attend based on the student's information. The prediction unit predicts the student's future occupation and the school to which the student will attend based on the student's information, for example. The future occupation includes, but is not limited to, a doctor, an engineer, a teacher, etc. The school to which the student will attend includes, but is not limited to, a university, a vocational school, a high school, etc. This widens the range of career choices by predicting the student's future occupation and the school to which the student will attend based on the student's information. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit inputs the student's information into the generation AI, and the generation AI can make a prediction based on the information.

[0040] The provision unit can provide the student with information on the educational destination and occupation proposed by the generation AI. The provision unit, for example, provides the student with information on the educational destination and occupation proposed by the generation AI. The information on the educational destination and occupation includes, for example, a school curriculum and the job content of the occupation, but is not limited to such examples. This allows the student to specifically consider his / her career path based on the information proposed by the generation AI. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit notifies the student of the information proposed by the generation AI, and the student can consider his / her career path based on the information.

[0041] The reception unit can analyze the student's past input history and select the optimal input method. The reception unit, for example, analyzes the student's past input history and selects the optimal input method. The past input history includes, for example, past input contents, input date and time, etc., but is not limited to such examples. The appropriate input method includes, for example, keyboard input, voice input, etc., but is not limited to such examples. For example, an input method (voice, text, etc.) that the student has frequently used in the past can be preferentially suggested. In addition, a pattern of information that the student has input in the past can be analyzed and an optimal input method can be suggested. Furthermore, an input method to be used in a specific time period can be predicted and suggested from the student's past input history. In this way, the optimal input method can be selected by analyzing the student's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs the student's past input history into a generation AI, and the generation AI can select the optimal input method based on the data.

[0042] The reception unit can filter the information based on the student's current learning situation and field of interest when inputting information. The reception unit, for example, filters the information based on the student's current learning situation and field of interest when inputting information. The current learning situation includes, for example, grades, attendance, progress on assignments, etc., but is not limited to such examples. The field of interest includes, for example, science, literature, sports, etc., but is not limited to such examples. For example, based on the student's current learning situation, only related information can be displayed on the input screen. Also, based on the student's field of interest, information that attracts interest can be preferentially displayed. Furthermore, appropriate information can be filtered and displayed according to the student's learning progress. As a result, appropriate information can be input by filtering based on the student's current learning situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the student's learning situation and field of interest to the generation AI, and the generation AI can perform filtering based on the data.

[0043] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the student when inputting information. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the student when inputting information. The geographical location information includes, for example, an address, a GPS coordinate, and the like, but is not limited to such examples. The highly relevant information includes, for example, nearby school information, local occupation information, and the like, but is not limited to such examples. For example, if a student lives in a specific area, information related to the area can be preferentially input. In addition, if a student attends a specific school, information related to the school can be preferentially input. Furthermore, if a student is interested in a specific area, information related to the area can be preferentially input. As a result, by preferentially inputting highly relevant information in consideration of the geographical location information of the student, appropriate information input is possible. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the student to the generation AI, and the generation AI can select highly relevant information based on the data.

[0044] The reception unit can analyze the social media activity of the student at the time of inputting information and input the related information. The reception unit can, for example, analyze the social media activity of the student at the time of inputting information and input the related information. The social media activity includes, for example, the content of posts, accounts followed, and the like, but is not limited to such examples. The related information includes, for example, articles related to interests, event information, and the like, but is not limited to such examples. For example, the related information can be input based on information shared by the student on social media. Also, the related information can be input based on information of accounts followed by the student on social media. Furthermore, the related information can be input based on topics in which the student showed interest on social media. In this way, the related information can be input by analyzing the student's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the student's social media activity data to the generation AI, and the generation AI can select related information based on the data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the information during the analysis. The importance of the information includes, for example, the impact level and the urgency level, but is not limited to such examples. The level of detail of the analysis includes, for example, the depth of the analysis and the number of analysis items, but is not limited to such examples. For example, a detailed analysis can be performed for important information. In addition, a concise analysis can be performed for less important information. Furthermore, the depth of the analysis can be adjusted according to the importance of the information. As a result, appropriate analysis can be performed by adjusting the level of detail of the analysis based on the importance of the information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the information to the generation AI, and the generation AI can adjust the level of detail of the analysis based on the data.

[0046] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of information during analysis. The categories of information include, for example, academic information, hobby information, health information, etc., but are not limited to such examples. The analysis algorithms include, for example, regression analysis, clustering, etc., but are not limited to such examples. For example, an analysis algorithm dedicated to academic performance can be applied to information regarding academic performance. In addition, an analysis algorithm dedicated to future dreams can be applied to information regarding future dreams. Furthermore, an analysis algorithm dedicated to requests can be applied to information regarding requests. This makes it possible to perform appropriate analysis by applying different analysis algorithms depending on the category of information. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs category data of information to the generation AI, and the generation AI can select an appropriate analysis algorithm based on the data.

[0047] The prediction unit can improve the accuracy of prediction by considering the interrelationship of information at the time of prediction. The prediction unit, for example, improves the accuracy of prediction by considering the interrelationship of information at the time of prediction. The interrelationship of information includes, for example, causal relationship, correlation, etc., but is not limited to such examples. The accuracy of prediction includes, for example, error rate, accuracy, etc., but is not limited to such examples. For example, prediction can be made by considering the interrelationship between a student's grades and future dreams. In addition, prediction can be made by considering the interrelationship between a student's requests and grades. Furthermore, prediction can be made by considering the interrelationship between a student's future dreams and requests. As a result, the accuracy of prediction is improved by considering the interrelationship of information. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit inputs interrelationship data of information to the generation AI, and the generation AI can improve the accuracy of prediction based on the data.

[0048] The prediction unit can make predictions taking into account the attribute information of the students at the time of prediction. The prediction unit, for example, makes predictions taking into account the attribute information of the students at the time of prediction. The attribute information of the students includes, for example, age, gender, grade, etc., but is not limited to such examples. The prediction includes, for example, a prediction model, a simulation, etc., but is not limited to such examples. For example, the prediction can be made taking into account the age of the students. In addition, the prediction can be made taking into account the gender of the students. Furthermore, the prediction can be made taking into account the place of residence of the students. As a result, more appropriate predictions can be made by taking into account the attribute information of the students. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit inputs attribute information data of the students to the generation AI, and the generation AI can make predictions based on the data.

[0049] The providing unit can adjust the level of detail of the information provided based on the importance of the information at the time of providing. The providing unit, for example, adjusts the level of detail of the information provided based on the importance of the information at the time of providing. The importance of the information includes, for example, the impact level, the urgency level, and the like, but is not limited to such examples. The level of detail of the information provided includes, for example, the depth of the information, the number of items provided, and the like, but is not limited to such examples. For example, a detailed explanation can be provided for important information. In addition, a concise explanation can be provided for less important information. Furthermore, the level of detail of the information provided can be adjusted according to the importance of the information. As a result, by adjusting the level of detail of the information provided based on the importance of the information, appropriate information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the information to the generating AI, and the generating AI can adjust the level of detail of the information provided based on the data.

[0050] The providing unit can apply different providing algorithms depending on the category of information at the time of providing. The providing unit applies different providing algorithms depending on the category of information at the time of providing, for example. The categories of information include, for example, academic information, hobby information, health information, etc., but are not limited to such examples. The providing algorithm includes, for example, a recommendation system, filtering, etc., but are not limited to such examples. For example, a providing algorithm dedicated to academic performance can be applied to information regarding academic performance. In addition, a providing algorithm dedicated to future dreams can be applied to information regarding future dreams. Furthermore, a providing algorithm dedicated to requests can be applied to information regarding requests. As a result, by applying different providing algorithms depending on the category of information, appropriate information can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs category data of information to the generating AI, and the generating AI can select an appropriate providing algorithm based on the data.

[0051] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The providing unit, for example, determines the priority of provision based on the time of submission of information at the time of provision. The time of submission of information includes, for example, a submission deadline, a submission date and time, etc., but is not limited to such examples. The priority of provision includes, for example, importance, urgency, etc., but is not limited to such examples. For example, recently submitted information can be provided preferentially. Information submitted earlier can also be postponed. Furthermore, the priority of provision can be adjusted according to the submission time. As a result, by determining the priority of provision based on the time of submission of information, appropriate information can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input information submission time data to the generating AI, and the generating AI can determine the priority of provision based on the data.

[0052] The providing unit can adjust the order of provision based on the relevance of information at the time of provision. For example, the providing unit adjusts the order of provision based on the relevance of information at the time of provision. The relevance of information includes, for example, causal relationship, correlation, etc., but is not limited to such examples. The order of provision includes, for example, importance, relevance, etc., but is not limited to such examples. For example, highly relevant information can be provided preferentially. In addition, information with low relevance can be postponed. Furthermore, the order of provision can be adjusted according to the relevance of information. As a result, by adjusting the order of provision based on the relevance of information, appropriate information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance data of information to the generating AI, and the generating AI can adjust the order of provision based on the data.

[0053] The algorithm used by the generation AI can select an appropriate algorithm by referring to past data when selecting an algorithm. The algorithm used by the generation AI can select an appropriate algorithm by referring to past data when selecting an algorithm. Past data includes, for example, past input contents, analysis results, etc., but is not limited to such examples. The selection of an algorithm includes, for example, performance evaluation, application range, etc., but is not limited to such examples. For example, the most accurate algorithm can be selected based on past data. In addition, the most efficient algorithm can be selected based on past data. Furthermore, the most reliable algorithm can be selected based on past data. As a result, the accuracy of analysis is improved by selecting the optimal algorithm by referring to past data. Part or all of the above-mentioned processing in the algorithm used by the generation AI may be performed, for example, using AI, or may be performed without using AI. For example, the generation AI can select an algorithm based on past data.

[0054] The algorithm used by the generation AI can apply different algorithms depending on the category of information when selecting the algorithm. The algorithm used by the generation AI can apply different algorithms depending on the category of information when selecting the algorithm. The categories of information include, but are not limited to, academic information, hobby information, health information, etc., for example. The selection of the algorithm includes, but is not limited to, performance evaluation, scope of application, etc., for example. For example, an algorithm dedicated to academic performance can be applied to information on academic performance. In addition, an algorithm dedicated to future dreams can be applied to information on future dreams. Furthermore, an algorithm dedicated to requests can be applied to information on requests. This makes it possible to perform appropriate analysis by applying different algorithms depending on the category of information. Part or all of the above-mentioned processing in the algorithm used by the generation AI may be performed, for example, using AI, or may be performed without using AI. For example, the generation AI can select an algorithm based on category data of information.

[0055] The algorithm used by the generation AI can weight the algorithm based on the time of information submission when selecting the algorithm. The algorithm used by the generation AI can weight the algorithm based on the time of information submission when selecting the algorithm. The time of information submission includes, for example, a submission deadline, a submission date and time, etc., but is not limited to such examples. The weighting of the algorithm includes, for example, a performance evaluation, a scope of application, etc., but is not limited to such examples. For example, an algorithm that preferentially analyzes recently submitted information can be selected. In addition, for information submitted earlier, an algorithm that is to be postponed can be selected. Furthermore, the weighting of the algorithm can be adjusted according to the submission time. As a result, by weighting the algorithm based on the time of information submission, appropriate analysis is possible. Part or all of the above-mentioned processing in the algorithm used by the generation AI may be performed, for example, using AI, or may be performed without using AI. For example, the generation AI can weight the algorithm based on the data on the time of information submission.

[0056] The data handling method can select an appropriate handling method by referring to past data when handling data. The data handling method can select an appropriate handling method by referring to past data when handling data, for example. Past data includes, for example, past input contents, analysis results, etc., but is not limited to such examples. The handling method can include, for example, data storage methods, access rights, etc., but is not limited to such examples. For example, the most accurate handling method can be selected based on past data. In addition, the most efficient handling method can be selected based on past data. Furthermore, the most reliable handling method can be selected based on past data. As a result, the accuracy of data management is improved by selecting the optimal handling method by referring to past data. Part or all of the above-mentioned processing in the data handling method can be performed, for example, using AI, or can be performed without using AI. For example, the data handling method can select a handling method based on past data.

[0057] The data handling method can weight data based on the time of information submission when handling data. The data handling method, for example, weights data based on the time of information submission when handling data. The time of information submission includes, for example, a submission deadline, a submission date and time, etc., but is not limited to such examples. The weighting of data includes, for example, performance evaluation, scope of application, etc., but is not limited to such examples. For example, recently submitted data can be handled preferentially. In addition, data submitted earlier can be postponed. Furthermore, the weighting of data can be adjusted according to the time of submission. As a result, by weighting data based on the time of information submission, appropriate data management is possible. Part or all of the above-mentioned processing in the data handling method may be performed using, for example, AI, or may be performed without using AI. For example, the data handling method can weight data based on the time of information submission data.

[0058] The reliability and accuracy of the proposed information can be evaluated by referring to past evaluation data at the time of evaluation and selecting an appropriate evaluation method. The reliability and accuracy of the proposed information can be evaluated by, for example, referring to past evaluation data at the time of evaluation and selecting an appropriate evaluation method. The past evaluation data includes, for example, past input contents, evaluation results, etc., but is not limited to such examples. The evaluation method includes, for example, data accuracy, reliability, etc., but is not limited to such examples. For example, the most accurate evaluation method can be selected based on the past evaluation data. In addition, the most efficient evaluation method can be selected based on the past evaluation data. Furthermore, the most reliable evaluation method can be selected based on the past evaluation data. As a result, the accuracy of the evaluation is improved by selecting the optimal evaluation method by referring to the past evaluation data. Part or all of the above-mentioned processing in the evaluation method of the reliability and accuracy of the proposed information may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation method can be selected based on the past evaluation data.

[0059] The method for evaluating the reliability and accuracy of the proposed information may weight the evaluation data based on the time of submission of the information at the time of evaluation. The method for evaluating the reliability and accuracy of the proposed information may weight the evaluation data based on the time of submission of the information at the time of evaluation. The time of submission of the information may include, but is not limited to, a deadline for submission, a submission date and time, etc. The weighting of the evaluation data may include, but is not limited to, a performance evaluation, a scope of application, etc. For example, the most recently submitted information may be preferentially evaluated. Information submitted earlier may be postponed. Furthermore, the weighting of the evaluation data may be adjusted according to the time of submission. As a result, the evaluation data may be weighted based on the time of submission of the information, making it possible to perform an appropriate evaluation. A part or all of the above-mentioned processing in the method for evaluating the reliability and accuracy of the proposed information may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation method may perform weighting based on the data on the time of submission of the information.

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

[0061] The career guidance system can also suggest career paths based on the student's hobbies and special skills. For example, if a student is interested in music and is good at playing musical instruments, a music college or a music-related career can be suggested. If a student is interested in sports and has a good record in a particular sport, a sports-related college or career can be suggested. Furthermore, if a student is interested in art and has a talent for painting and design, an art college or a design-related career can be suggested. This allows for more personalized career suggestions based on the student's hobbies and special skills.

[0062] Generative AI can also suggest optimal career paths based on a student's learning style. For example, if a student has a visual learning style, it can suggest universities and occupations that offer visual materials and curricula. If a student has an auditory learning style, it can suggest universities and occupations that offer many lecture-style classes. Furthermore, if a student has an experiential learning style, it can suggest universities and occupations that offer plenty of practical training and internships. This allows for more effective career suggestions based on a student's learning style.

[0063] The data handling method can further include a function for strengthening the protection of students' privacy. For example, by encrypting and storing student data, data leakage can be prevented. Also, by introducing two-factor authentication when accessing student data, security can be strengthened. Furthermore, by anonymizing student data, personal information can be protected. This makes it possible to handle data while protecting student privacy.

[0064] The method of evaluating the reliability and accuracy of the proposed information can further include evaluation by a third party. For example, the reliability of the proposed information can be improved by having the information evaluated by an educational institution or an expert. Furthermore, the accuracy of the information can be maintained by periodically having an audit by a third party. Furthermore, transparency can be ensured by making the results of the evaluation by the third party public. This can increase the reliability and accuracy of the proposed information.

[0065] The reception department can also suggest career paths based on the student's family structure and home environment. For example, if a student grew up in a family of doctors, a medical career path can be suggested. If a student grew up in a family of teachers, a career path in education can be suggested. Furthermore, if a student grew up in a multicultural family, a career path related to international relations and multicultural understanding can be suggested. This allows for more appropriate career suggestions based on the student's home environment.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The reception unit inputs student information. Student information includes, for example, future dreams, requests, grades, etc. Students can input information such as "I want to be a doctor," "I'm interested in science," and "I have good grades in math." Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms. The generation AI performs analysis to predict the student's future career and future school based on the student's information. Step 3: The prediction unit uses the generation AI to make future predictions based on the information analyzed by the analysis unit. Future predictions are made using, for example, predictive models and simulations. Based on the dream of "becoming a doctor," the desire to "be interested in science," and the information that "you have good grades in math," the generation AI suggests a medical university and a career as a doctor. Step 4: The provision unit provides the students with the information on further education and careers suggested by the generation AI. The provision is based on, for example, the notification method and the format of the information. The generation AI notifies the students of the suggested information, and the students can use that information to consider their own career paths.

[0068] (Example 2) The career guidance system according to the embodiment of the present invention is a system that predicts the future based on the dreams, desires, and grades of each student and suggests the most suitable school and occupation as a measure against excessive work of teachers. In this career guidance system, students input information such as their future dreams, desires, and grades, and a generation AI analyzes the information to predict the future and suggests the most suitable school and occupation, thereby reducing the amount of work time that teachers spend on each student. For example, if a student inputs information such as "I want to be a doctor," "I'm interested in science," and "I have good grades in math," the generation AI will suggest a medical university or a medical occupation based on the information. This proposal is provided to the student, and the student can think specifically about his or her career path. This allows teachers to significantly reduce the time they spend on each student. In addition, students can think about specific career paths based on their dreams and desires, which expands the range of career choices. For example, students can obtain more specific information by researching the school and occupations suggested by the generation AI. This allows students to imagine their career paths more specifically and work hard toward their future goals. In this way, by using generative AI, it is possible to reduce the excessive workload of teachers and make career guidance for students more efficient. As a result, the career guidance system can reduce the excessive workload of teachers and make career guidance for students more efficient.

[0069] The career guidance system according to the embodiment includes a reception unit, an analysis unit, a prediction unit, and a provision unit. The reception unit inputs information about the student. The information about the student includes, for example, a future dream, a request, and a grade, but is not limited to such examples. The reception unit can input, for example, information about the student "I want to be a doctor," "I'm interested in science," and "I have good grades in math." The analysis unit uses a generation AI to analyze the information input by the reception unit. The analysis is performed, for example, using a statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the generation AI performs an analysis to predict the student's future career and the school to which the student will attend, based on the student's information. The prediction unit uses the generation AI to make a future prediction based on the information analyzed by the analysis unit. The future prediction is performed, for example, using a prediction model or a simulation, but is not limited to such examples. For example, the generation AI suggests a medical university or a doctor's career based on the dream of "I want to be a doctor," the request of "I'm interested in science," and the information of "I have good grades in math." The provision unit provides the student with information about the school to attend and the career proposed by the generation AI. The provision is performed based on, for example, a notification method and a format of information, but is not limited to such examples. For example, the generation AI notifies the student of the proposed information, and the student can consider his / her own career path based on the information. In this way, the career guidance system according to the embodiment can propose the most suitable educational destination or career based on the student's information, reduce the excessive workload of teachers, and make career guidance for students more efficient.

[0070] The reception unit inputs information about the students. The information about the students includes, but is not limited to, their future dreams, requests, and grades. Specifically, the students can input detailed information about their interests and concerns, future goals, current academic performance, extracurricular activity results, and even their home environment and hobbies. For example, the students can input information such as "I want to be a doctor," "I'm interested in science," and "I have good grades in math." In addition, the students can input their personality traits, learning style, and past career counseling history. The reception unit has a function to efficiently collect this information and store it in a database. Furthermore, the reception unit automatically classifies the information input by the students and organizes it so that the analysis unit can easily access it. For example, the input information is tagged by category and classified into different categories such as future dreams, requests, and grades. This allows the analysis unit to quickly obtain the necessary information and perform analysis. The reception unit also has a feedback function to check the accuracy of the information input by the students. For example, if there is an error in the information input by the students, the system automatically displays a warning and prompts them to correct it. This enables the reception unit to collect student information accurately and efficiently, thereby improving the analysis accuracy of the analysis unit.

[0071] The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. Specifically, the generation AI performs analysis to predict the future occupation and school to which the student will attend based on the student's information. The generation AI analyzes the student's input information using natural language processing technology and evaluates the relevance of each piece of information. For example, the generation AI combines and analyzes the dream of "becoming a doctor," the desire to "be interested in science," and information such as "good grades in math" to determine whether a medical university or a medical profession is suitable. Furthermore, the generation AI uses past data and statistical information to analyze what career paths students with similar backgrounds have chosen and what results they have achieved. This enables more accurate career predictions. The analysis unit creates a detailed report on the student's career path based on the analysis results performed by the generation AI. This report includes a list of recommended educational destinations and occupations, the advantages and disadvantages of each option, and information on future career paths. Furthermore, the analysis unit creates a feedback loop to continuously update the student's information and improve the accuracy of career predictions. For example, if a student enters new information, the generation AI immediately analyzes that information and updates the career prediction. This allows the analysis unit to provide highly accurate career predictions based on the student's information and support the student in choosing their career path.

[0072] The prediction unit uses the generation AI to make future predictions based on the information analyzed by the analysis unit. Future predictions are made using, for example, a prediction model or a simulation, but are not limited to such examples. Specifically, the generation AI suggests a medical university and a medical profession based on the dream of "wanting to be a doctor," the desire to "be interested in science," and the information that "you have good grades in math." The generation AI analyzes what career paths students with similar backgrounds have chosen and what results they have achieved based on past data and statistical information. For example, it analyzes the grades and post-graduation career paths of students who have entered a medical university, and evaluates their success rate and satisfaction as a doctor. In addition, the generation AI takes into account future trends in the job market and demand forecasts to suggest the optimal career path. For example, it predicts future demand and technological evolution in the medical field, and based on that, suggests a suitable educational destination and career for the student. The prediction unit makes specific career suggestions to the student based on the future predictions made by the generation AI. These suggestions include a list of recommended educational destinations and careers, the advantages and disadvantages of each option, and information on future career paths. In addition, the prediction unit creates a feedback loop to continuously update student information and improve the accuracy of future predictions. For example, when a student inputs new information, the generation AI immediately analyzes that information and updates the future prediction. This allows the prediction unit to provide highly accurate future predictions based on student information and support students in choosing their career paths.

[0073] The provision unit provides students with information on the educational destinations and careers proposed by the generation AI. The provision is performed, for example, based on a notification method and a format of information, but is not limited to such examples. Specifically, the generation AI notifies students of the information proposed by the generation AI, and the students can consider their own career paths based on the information. The provision unit provides students with a detailed report of the career path proposal. This report includes a list of recommended educational destinations and careers, the advantages and disadvantages of each option, and information on future career paths. In addition, the provision unit provides an interface that allows students to easily access detailed information on the proposed career paths. For example, through a web portal or mobile app, students can view and compare information on the proposed career paths. The provision unit also has a function that allows students to provide feedback on the career proposal. For example, if a student has doubts or concerns about the proposed career path, he or she can send a question through the system and receive a response from a professional counselor. This allows the provision unit to provide students with appropriate career information and support them in choosing their career path. In addition, the service provider collects data on students' career choices and builds a feedback loop to continuously improve the accuracy and effectiveness of the entire system. For example, it tracks students' career choices and their subsequent outcomes, and updates the generative AI's predictive model. This enables the service provider to provide students with the latest and most optimal career information, improving the efficiency and accuracy of career guidance.

[0074] The generating AI has an algorithm used by the generating AI. The algorithm used by the generating AI is important for improving the accuracy of analysis and prediction. For example, the generating AI can analyze student information and make future predictions using algorithms such as neural networks and decision trees. A neural network can analyze data using multiple layers of artificial neurons and recognize complex patterns. A decision tree can hierarchically divide data and generate rules for making predictions. This allows the generating AI to perform highly accurate analysis and predictions based on student information. The algorithm used by the generating AI can, for example, learn past data and make highly accurate predictions for new data. This allows the generating AI to suggest the most suitable educational destination or career based on the student's future dreams, requests, and grades. The algorithm used by the generating AI can, for example, use technologies such as natural language generation and image generation to analyze student information and make future predictions. This allows the generating AI to perform highly accurate analysis and predictions based on student information.

[0075] The data handling method is provided. The data handling method is important for efficient information management. For example, by clarifying the data storage method and access authority, the information management is made efficient. The data storage method includes, but is not limited to, cloud storage and local storage. Cloud storage stores data via the Internet and can be accessed from multiple devices. Local storage stores data in a device and can be accessed even offline. The access authority can ensure the security of information by, for example, setting different authority for each user. In this way, the data handling method can improve the efficiency of information management and ensure security. In the data handling method, for example, by clarifying the procedure for backing up and recovering data, the protection of information can be strengthened. In this way, the data handling method can improve the efficiency of information management and ensure security.

[0076] The present invention provides a method for evaluating the reliability and accuracy of the proposed information. The method for evaluating the reliability and accuracy of the proposed information is important for ensuring the accuracy of the information. For example, by clarifying the criteria for evaluating the accuracy and reliability of the data, the reliability of the proposed information can be improved. The accuracy of the data can be confirmed, for example, by evaluating the consistency and completeness of the data. The reliability can be confirmed, for example, by evaluating the source of the data or data from a reliable information source. This can improve the reliability and accuracy of the proposed information. The accuracy of the proposed information can be confirmed, for example, by evaluating the accuracy and error rate of the prediction. The accuracy of the prediction can be confirmed, for example, by evaluating the degree of agreement between the actual result and the predicted result. The error rate can be confirmed, for example, by evaluating the difference between the predicted result and the actual result. This can improve the reliability and accuracy of the proposed information. The method for evaluating the reliability and accuracy of the proposed information can maintain the accuracy of the information by, for example, periodically evaluating the information and making improvements based on the evaluation results. This can improve the reliability and accuracy of the proposed information.

[0077] The reception unit can input information on the student's future dreams, requests, and grades. The reception unit can input information such as the student's future dreams, requests, and grades, for example. The future dreams and requests include, but are not limited to, information such as "I want to be a doctor" and "I'm interested in science." The grades include, but are not limited to, information such as "good grades in math" and "poor grades in English." This allows individual career guidance to be provided by inputting information such as the student's future dreams, requests, and grades. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the information input by the student to the generation AI, and the generation AI can analyze the information.

[0078] The analysis unit can analyze the input information and predict the future based on the student's future dreams, desires, and grades. The analysis unit can, for example, analyze the input information and predict the future based on the student's future dreams, desires, and grades. The future prediction is performed, for example, using a prediction model or a simulation, but is not limited to such examples. For example, the generation AI suggests a medical university or a medical career based on the dream of "wanting to be a doctor," the desire of "being interested in science," and information such as "good grades in mathematics." This makes it possible to provide appropriate career guidance by predicting the future based on the student's future dreams, desires, and grades. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the student's information into the generation AI, and the generation AI can analyze the information.

[0079] The prediction unit can predict the student's future occupation and the school to which the student will attend based on the student's information. The prediction unit predicts the student's future occupation and the school to which the student will attend based on the student's information, for example. The future occupation includes, but is not limited to, a doctor, an engineer, a teacher, etc. The school to which the student will attend includes, but is not limited to, a university, a vocational school, a high school, etc. This widens the range of career choices by predicting the student's future occupation and the school to which the student will attend based on the student's information. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit inputs the student's information into the generation AI, and the generation AI can make a prediction based on the information.

[0080] The provision unit can provide the student with information on the educational destination and occupation proposed by the generation AI. The provision unit, for example, provides the student with information on the educational destination and occupation proposed by the generation AI. The information on the educational destination and occupation includes, for example, a school curriculum and the job content of the occupation, but is not limited to such examples. This allows the student to specifically consider his / her career path based on the information proposed by the generation AI. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit notifies the student of the information proposed by the generation AI, and the student can consider his / her career path based on the information.

[0081] The reception unit can estimate the emotion of the student and adjust the timing of information input based on the estimated emotion of the student. The reception unit, for example, estimates the emotion of the student and adjusts the timing of information input based on the estimated emotion of the student. The emotion of the student includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The timing of information input includes, for example, during class, during break, after school, etc., but is not limited to such examples. For example, if the student is feeling stressed, the student can be prompted to input information at a time when the student can relax. In addition, if the student is concentrating, the student can be prompted to input information at that timing. Furthermore, if the student is tired, the student can be prompted to input information after a break. This allows more appropriate information input by adjusting the timing of information input according to the emotion of the student. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception department can input a student's emotional data into the generation AI, which can then infer the student's emotions based on that data.

[0082] The reception unit can analyze the student's past input history and select the optimal input method. The reception unit, for example, analyzes the student's past input history and selects the optimal input method. The past input history includes, for example, past input contents, input date and time, etc., but is not limited to such examples. The appropriate input method includes, for example, keyboard input, voice input, etc., but is not limited to such examples. For example, an input method (voice, text, etc.) that the student has frequently used in the past can be preferentially suggested. In addition, a pattern of information that the student has input in the past can be analyzed and an optimal input method can be suggested. Furthermore, an input method to be used in a specific time period can be predicted and suggested from the student's past input history. In this way, the optimal input method can be selected by analyzing the student's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs the student's past input history into a generation AI, and the generation AI can select the optimal input method based on the data.

[0083] The reception unit can filter the information based on the student's current learning situation and field of interest when inputting information. The reception unit, for example, filters the information based on the student's current learning situation and field of interest when inputting information. The current learning situation includes, for example, grades, attendance, progress on assignments, etc., but is not limited to such examples. The field of interest includes, for example, science, literature, sports, etc., but is not limited to such examples. For example, based on the student's current learning situation, only related information can be displayed on the input screen. Also, based on the student's field of interest, information that attracts interest can be preferentially displayed. Furthermore, appropriate information can be filtered and displayed according to the student's learning progress. As a result, appropriate information can be input by filtering based on the student's current learning situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the student's learning situation and field of interest to the generation AI, and the generation AI can perform filtering based on the data.

[0084] The reception unit can estimate the emotion of the student and determine the priority of the information to be input based on the estimated emotion of the student. The reception unit, for example, estimates the emotion of the student and determines the priority of the information to be input based on the estimated emotion of the student. The emotion of the student includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The priority of the information includes, for example, importance, urgency, etc., but is not limited to such examples. For example, when the student is excited, important information can be input preferentially. Furthermore, when the student is relaxed, detailed information can be input. Furthermore, when the student is tired, brief information can be input preferentially. In this way, by determining the priority of the information to be input according to the emotion of the student, more appropriate information input is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception department can input students' emotional data into the generation AI, which can then determine the priority of information based on that data.

[0085] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the student when inputting information. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the student when inputting information. The geographical location information includes, for example, an address, a GPS coordinate, and the like, but is not limited to such examples. The highly relevant information includes, for example, nearby school information, local occupation information, and the like, but is not limited to such examples. For example, if a student lives in a specific area, information related to the area can be preferentially input. In addition, if a student attends a specific school, information related to the school can be preferentially input. Furthermore, if a student is interested in a specific area, information related to the area can be preferentially input. As a result, by preferentially inputting highly relevant information in consideration of the geographical location information of the student, appropriate information input is possible. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the student to the generation AI, and the generation AI can select highly relevant information based on the data.

[0086] The reception unit can analyze the social media activity of the student at the time of inputting information and input the related information. The reception unit can, for example, analyze the social media activity of the student at the time of inputting information and input the related information. The social media activity includes, for example, the content of posts, accounts followed, and the like, but is not limited to such examples. The related information includes, for example, articles related to interests, event information, and the like, but is not limited to such examples. For example, the related information can be input based on information shared by the student on social media. Also, the related information can be input based on information of accounts followed by the student on social media. Furthermore, the related information can be input based on topics in which the student showed interest on social media. In this way, the related information can be input by analyzing the student's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the student's social media activity data to the generation AI, and the generation AI can select related information based on the data.

[0087] The analysis unit can estimate the student's emotion and adjust the expression method of the analysis based on the estimated student's emotion. The analysis unit, for example, estimates the student's emotion and adjusts the expression method of the analysis based on the estimated student's emotion. The student's emotion includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The expression method of the analysis includes, for example, graph, text, voice, etc., but is not limited to such examples. For example, when the student is relaxed, a detailed analysis result can be provided. In addition, when the student is nervous, a concise and to the point analysis result can be provided. Furthermore, when the student is excited, a visually attractive analysis result can be provided. In this way, by adjusting the expression method of the analysis according to the student's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input students' emotional data into the generation AI, which can then adjust the way the analysis is expressed based on that data.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the information during the analysis. The importance of the information includes, for example, the impact level and the urgency level, but is not limited to such examples. The level of detail of the analysis includes, for example, the depth of the analysis and the number of analysis items, but is not limited to such examples. For example, a detailed analysis can be performed for important information. In addition, a concise analysis can be performed for less important information. Furthermore, the depth of the analysis can be adjusted according to the importance of the information. As a result, appropriate analysis can be performed by adjusting the level of detail of the analysis based on the importance of the information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the information to the generation AI, and the generation 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 category of information during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of information during analysis. The categories of information include, for example, academic information, hobby information, health information, etc., but are not limited to such examples. The analysis algorithms include, for example, regression analysis, clustering, etc., but are not limited to such examples. For example, an analysis algorithm dedicated to academic performance can be applied to information regarding academic performance. In addition, an analysis algorithm dedicated to future dreams can be applied to information regarding future dreams. Furthermore, an analysis algorithm dedicated to requests can be applied to information regarding requests. This makes it possible to perform appropriate analysis by applying different analysis algorithms depending on the category of information. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs category data of information to the generation AI, and the generation AI can select an appropriate analysis algorithm based on the data.

[0090] The prediction unit can estimate the student's emotion and adjust the prediction criteria based on the estimated student's emotion. The prediction unit, for example, estimates the student's emotion and adjusts the prediction criteria based on the estimated student's emotion. The student's emotion includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The prediction criteria includes, for example, past data, statistical models, etc., but is not limited to such examples. For example, when the student is relaxed, a detailed prediction can be made. In addition, when the student is nervous, a concise and to the point prediction can be made. Furthermore, when the student is excited, a visually appealing prediction can be made. This allows for more appropriate prediction by adjusting the prediction criteria according to the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input students' emotional data into the generation AI, which can then adjust its prediction criteria based on that data.

[0091] The prediction unit can improve the accuracy of prediction by considering the interrelationship of information at the time of prediction. The prediction unit, for example, improves the accuracy of prediction by considering the interrelationship of information at the time of prediction. The interrelationship of information includes, for example, causal relationship, correlation, etc., but is not limited to such examples. The accuracy of prediction includes, for example, error rate, accuracy, etc., but is not limited to such examples. For example, prediction can be made by considering the interrelationship between a student's grades and future dreams. In addition, prediction can be made by considering the interrelationship between a student's requests and grades. Furthermore, prediction can be made by considering the interrelationship between a student's future dreams and requests. As a result, the accuracy of prediction is improved by considering the interrelationship of information. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit inputs interrelationship data of information to the generation AI, and the generation AI can improve the accuracy of prediction based on the data.

[0092] The prediction unit can make predictions taking into account the attribute information of the students at the time of prediction. The prediction unit, for example, makes predictions taking into account the attribute information of the students at the time of prediction. The attribute information of the students includes, for example, age, gender, grade, etc., but is not limited to such examples. The prediction includes, for example, a prediction model, a simulation, etc., but is not limited to such examples. For example, the prediction can be made taking into account the age of the students. In addition, the prediction can be made taking into account the gender of the students. Furthermore, the prediction can be made taking into account the place of residence of the students. As a result, more appropriate predictions can be made by taking into account the attribute information of the students. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit inputs attribute information data of the students to the generation AI, and the generation AI can make predictions based on the data.

[0093] The providing unit can estimate the emotion of the student and adjust the expression method of the information to be provided based on the estimated emotion of the student. The providing unit, for example, estimates the emotion of the student and adjusts the expression method of the information to be provided based on the estimated emotion of the student. The emotion of the student includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The expression method of the information includes, for example, graph, text, voice, etc., but is not limited to such examples. For example, when the student is relaxed, detailed information can be provided. In addition, when the student is nervous, concise and to the point information can be provided. Furthermore, when the student is excited, visually attractive information can be provided. As a result, by adjusting the expression method of the information to be provided according to the emotion of the student, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the provider can input students' emotional data into the generation AI, which can then adjust the way information is presented based on that data.

[0094] The providing unit can adjust the level of detail of the information provided based on the importance of the information at the time of providing. The providing unit, for example, adjusts the level of detail of the information provided based on the importance of the information at the time of providing. The importance of the information includes, for example, the impact level, the urgency level, and the like, but is not limited to such examples. The level of detail of the information provided includes, for example, the depth of the information, the number of items provided, and the like, but is not limited to such examples. For example, a detailed explanation can be provided for important information. In addition, a concise explanation can be provided for less important information. Furthermore, the level of detail of the information provided can be adjusted according to the importance of the information. As a result, by adjusting the level of detail of the information provided based on the importance of the information, appropriate information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the information to the generating AI, and the generating AI can adjust the level of detail of the information provided based on the data.

[0095] The providing unit can apply different providing algorithms depending on the category of information at the time of providing. The providing unit applies different providing algorithms depending on the category of information at the time of providing, for example. The categories of information include, for example, academic information, hobby information, health information, etc., but are not limited to such examples. The providing algorithm includes, for example, a recommendation system, filtering, etc., but are not limited to such examples. For example, a providing algorithm dedicated to academic performance can be applied to information regarding academic performance. In addition, a providing algorithm dedicated to future dreams can be applied to information regarding future dreams. Furthermore, a providing algorithm dedicated to requests can be applied to information regarding requests. As a result, by applying different providing algorithms depending on the category of information, appropriate information can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs category data of information to the generating AI, and the generating AI can select an appropriate providing algorithm based on the data.

[0096] The providing unit can estimate the emotion of the student and adjust the length of the information to be provided based on the estimated emotion of the student. The providing unit, for example, estimates the emotion of the student and adjusts the length of the information to be provided based on the estimated emotion of the student. The emotion of the student includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The length of the information includes, for example, the number of characters, time, etc., but is not limited to such examples. For example, when the student is in a hurry, short and to the point information can be provided. In addition, when the student is relaxed, detailed information can be provided. Furthermore, when the student is excited, visually attractive information can be provided. In this way, by adjusting the length of the information to be provided according to the emotion of the student, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input students' emotional data into the generating AI, which can then adjust the length of the information based on that data.

[0097] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The providing unit, for example, determines the priority of provision based on the time of submission of information at the time of provision. The time of submission of information includes, for example, a submission deadline, a submission date and time, etc., but is not limited to such examples. The priority of provision includes, for example, importance, urgency, etc., but is not limited to such examples. For example, recently submitted information can be provided preferentially. Information submitted earlier can also be postponed. Furthermore, the priority of provision can be adjusted according to the submission time. As a result, by determining the priority of provision based on the time of submission of information, appropriate information can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input information submission time data to the generating AI, and the generating AI can determine the priority of provision based on the data.

[0098] The providing unit can adjust the order of provision based on the relevance of information at the time of provision. For example, the providing unit adjusts the order of provision based on the relevance of information at the time of provision. The relevance of information includes, for example, causal relationship, correlation, etc., but is not limited to such examples. The order of provision includes, for example, importance, relevance, etc., but is not limited to such examples. For example, highly relevant information can be provided preferentially. In addition, information with low relevance can be postponed. Furthermore, the order of provision can be adjusted according to the relevance of information. As a result, by adjusting the order of provision based on the relevance of information, appropriate information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance data of information to the generating AI, and the generating AI can adjust the order of provision based on the data.

[0099] The algorithm used by the generation AI can estimate the student's emotion and select an algorithm based on the estimated student's emotion. The algorithm used by the generation AI can estimate the student's emotion and select an algorithm based on the estimated student's emotion. The student's emotion includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The selection of the algorithm includes, for example, performance evaluation, scope of application, etc., but is not limited to such examples. For example, when the student is relaxed, an algorithm that performs detailed analysis can be selected. In addition, when the student is nervous, an algorithm that performs a concise and to the point analysis can be selected. Furthermore, when the student is excited, an algorithm that performs a visually attractive analysis can be selected. In this way, by selecting an algorithm based on the student's emotion, more appropriate analysis is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the algorithm used by the generation AI may be performed using, for example, an AI, or may be performed without using an AI. For example, generative AI can select algorithms based on students' emotional data.

[0100] The algorithm used by the generation AI can select an appropriate algorithm by referring to past data when selecting an algorithm. The algorithm used by the generation AI can select an appropriate algorithm by referring to past data when selecting an algorithm. Past data includes, for example, past input contents, analysis results, etc., but is not limited to such examples. The selection of an algorithm includes, for example, performance evaluation, application range, etc., but is not limited to such examples. For example, the most accurate algorithm can be selected based on past data. In addition, the most efficient algorithm can be selected based on past data. Furthermore, the most reliable algorithm can be selected based on past data. As a result, the accuracy of analysis is improved by selecting the optimal algorithm by referring to past data. Part or all of the above-mentioned processing in the algorithm used by the generation AI may be performed, for example, using AI, or may be performed without using AI. For example, the generation AI can select an algorithm based on past data.

[0101] The algorithm used by the generation AI can apply different algorithms depending on the category of information when selecting the algorithm. The algorithm used by the generation AI can apply different algorithms depending on the category of information when selecting the algorithm. The categories of information include, but are not limited to, academic information, hobby information, health information, etc., for example. The selection of the algorithm includes, but is not limited to, performance evaluation, scope of application, etc., for example. For example, an algorithm dedicated to academic performance can be applied to information on academic performance. In addition, an algorithm dedicated to future dreams can be applied to information on future dreams. Furthermore, an algorithm dedicated to requests can be applied to information on requests. This makes it possible to perform appropriate analysis by applying different algorithms depending on the category of information. Part or all of the above-mentioned processing in the algorithm used by the generation AI may be performed, for example, using AI, or may be performed without using AI. For example, the generation AI can select an algorithm based on category data of information.

[0102] The algorithm used by the generation AI can estimate the student's emotion and determine the priority of the algorithm based on the estimated student's emotion. The algorithm used by the generation AI can estimate the student's emotion and determine the priority of the algorithm based on the estimated student's emotion. The student's emotion includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The priority of the algorithm includes, for example, performance evaluation, scope of application, etc., but is not limited to such examples. For example, if the student is relaxed, an algorithm that performs detailed analysis can be prioritized. In addition, if the student is nervous, an algorithm that performs a concise and to the point analysis can be prioritized. Furthermore, if the student is excited, an algorithm that performs a visually attractive analysis can be prioritized. In this way, by determining the priority of the algorithm based on the student's emotion, more appropriate analysis is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the algorithm used by the generation AI may be performed, for example, using an AI, or may be performed without using an AI. For example, generative AI can prioritize algorithms based on students' emotional data.

[0103] The algorithm used by the generation AI can weight the algorithm based on the time of information submission when selecting the algorithm. The algorithm used by the generation AI can weight the algorithm based on the time of information submission when selecting the algorithm. The time of information submission includes, for example, a submission deadline, a submission date and time, etc., but is not limited to such examples. The weighting of the algorithm includes, for example, a performance evaluation, a scope of application, etc., but is not limited to such examples. For example, an algorithm that preferentially analyzes recently submitted information can be selected. In addition, for information submitted earlier, an algorithm that is to be postponed can be selected. Furthermore, the weighting of the algorithm can be adjusted according to the submission time. As a result, by weighting the algorithm based on the time of information submission, appropriate analysis is possible. Part or all of the above-mentioned processing in the algorithm used by the generation AI may be performed, for example, using AI, or may be performed without using AI. For example, the generation AI can weight the algorithm based on the data on the time of information submission.

[0104] The data handling method can estimate the student's emotion and adjust the data handling method based on the estimated student's emotion. The data handling method can estimate the student's emotion and adjust the data handling method based on the estimated student's emotion. The student's emotion includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The data handling method can include, for example, data storage method, access authority, etc., but is not limited to such examples. For example, when the student is relaxed, detailed data can be handled. In addition, when the student is nervous, concise data can be handled. Furthermore, when the student is excited, visually attractive data can be handled. This allows more appropriate data management by adjusting the data handling method based on the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data handling method may be performed using, for example, an AI, or may be performed without using an AI. For example, the data handling method can be adjusted based on the student's emotion data.

[0105] The data handling method can select an appropriate handling method by referring to past data when handling data. The data handling method can select an appropriate handling method by referring to past data when handling data, for example. Past data includes, for example, past input contents, analysis results, etc., but is not limited to such examples. The handling method can include, for example, data storage methods, access rights, etc., but is not limited to such examples. For example, the most accurate handling method can be selected based on past data. In addition, the most efficient handling method can be selected based on past data. Furthermore, the most reliable handling method can be selected based on past data. As a result, the accuracy of data management is improved by selecting the optimal handling method by referring to past data. Part or all of the above-mentioned processing in the data handling method can be performed, for example, using AI, or can be performed without using AI. For example, the data handling method can select a handling method based on past data.

[0106] The data handling method can estimate the emotion of the student and determine the priority of data handling based on the estimated emotion of the student. The data handling method can, for example, estimate the emotion of the student and determine the priority of data handling based on the estimated emotion of the student. The emotion of the student includes, for example, joy, sadness, excitement, etc., but is not limited to such examples. The priority of data handling can include, for example, importance, urgency, etc., but is not limited to such examples. For example, when the student is relaxed, detailed data can be preferentially handled. In addition, when the student is nervous, concise data can be preferentially handled. Furthermore, when the student is excited, visually attractive data can be preferentially handled. This enables more appropriate data management by determining the priority of data handling based on the emotion of the student. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data handling method may be performed using, for example, an AI, or may be performed without using an AI. For example, data handling methods can determine priorities based on students' emotional data.

[0107] The data handling method can weight data based on the time of information submission when handling data. The data handling method, for example, weights data based on the time of information submission when handling data. The time of information submission includes, for example, a submission deadline, a submission date and time, etc., but is not limited to such examples. The weighting of data includes, for example, performance evaluation, scope of application, etc., but is not limited to such examples. For example, recently submitted data can be handled preferentially. In addition, data submitted earlier can be postponed. Furthermore, the weighting of data can be adjusted according to the time of submission. As a result, by weighting data based on the time of information submission, appropriate data management is possible. Part or all of the above-mentioned processing in the data handling method may be performed using, for example, AI, or may be performed without using AI. For example, the data handling method can weight data based on the time of information submission data.

[0108] The method for evaluating the reliability and accuracy of the proposed information can estimate the emotions of the students and adjust the evaluation method based on the estimated emotions of the students. The method for evaluating the reliability and accuracy of the proposed information can, for example, estimate the emotions of the students and adjust the evaluation method based on the estimated emotions of the students. The emotions of the students include, for example, joy, sadness, excitement, etc., but are not limited to such examples. The evaluation method includes, for example, accuracy and reliability of data, but are not limited to such examples. For example, if the student is relaxed, a detailed evaluation can be performed. In addition, if the student is nervous, a concise evaluation can be performed. Furthermore, if the student is excited, a visually appealing evaluation can be performed. This allows for a more appropriate evaluation by adjusting the evaluation method based on the emotions of the students. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but are not limited to such examples. A part or all of the above-mentioned processing in the method for evaluating the reliability and accuracy of the proposed information may be performed, for example, using an AI, or may be performed without using an AI. For example, assessment methods can be adjusted based on students' emotional data.

[0109] The reliability and accuracy of the proposed information can be evaluated by referring to past evaluation data at the time of evaluation and selecting an appropriate evaluation method. The reliability and accuracy of the proposed information can be evaluated by, for example, referring to past evaluation data at the time of evaluation and selecting an appropriate evaluation method. The past evaluation data includes, for example, past input contents, evaluation results, etc., but is not limited to such examples. The evaluation method includes, for example, data accuracy, reliability, etc., but is not limited to such examples. For example, the most accurate evaluation method can be selected based on the past evaluation data. In addition, the most efficient evaluation method can be selected based on the past evaluation data. Furthermore, the most reliable evaluation method can be selected based on the past evaluation data. As a result, the accuracy of the evaluation is improved by selecting the optimal evaluation method by referring to the past evaluation data. Part or all of the above-mentioned processing in the evaluation method of the reliability and accuracy of the proposed information may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation method can be selected based on the past evaluation data.

[0110] The method for evaluating the reliability and accuracy of the proposed information can estimate the emotions of the students and determine the priority of the evaluation based on the estimated emotions of the students. The method for evaluating the reliability and accuracy of the proposed information can estimate the emotions of the students and determine the priority of the evaluation based on the estimated emotions of the students. The emotions of the students include, for example, joy, sadness, excitement, etc., but are not limited to such examples. The priority of the evaluation can include, for example, importance, urgency, etc., but are not limited to such examples. For example, if the student is relaxed, a detailed evaluation can be given priority. In addition, if the student is nervous, a concise evaluation can be given priority. Furthermore, if the student is excited, a visually attractive evaluation can be given priority. This enables more appropriate evaluation by determining the priority of the evaluation based on the emotions of the students. The estimation of the emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but are not limited to such examples. Some or all of the above-mentioned processes in the method for evaluating the reliability and accuracy of the proposed information may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation method may determine priorities based on the emotion data of students.

[0111] The method for evaluating the reliability and accuracy of the proposed information may weight the evaluation data based on the time of submission of the information at the time of evaluation. The method for evaluating the reliability and accuracy of the proposed information may weight the evaluation data based on the time of submission of the information at the time of evaluation. The time of submission of the information may include, but is not limited to, a deadline for submission, a submission date and time, etc. The weighting of the evaluation data may include, but is not limited to, a performance evaluation, a scope of application, etc. For example, the most recently submitted information may be preferentially evaluated. Information submitted earlier may be postponed. Furthermore, the weighting of the evaluation data may be adjusted according to the time of submission. As a result, the evaluation data may be weighted based on the time of submission of the information, making it possible to perform an appropriate evaluation. A part or all of the above-mentioned processing in the method for evaluating the reliability and accuracy of the proposed information may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation method may perform weighting based on the data on the time of submission of the information.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The career guidance system can also suggest career paths based on the student's hobbies and special skills. For example, if a student is interested in music and is good at playing musical instruments, a music college or a music-related career can be suggested. If a student is interested in sports and has a good record in a particular sport, a sports-related college or career can be suggested. Furthermore, if a student is interested in art and has a talent for painting and design, an art college or a design-related career can be suggested. This allows for more personalized career suggestions based on the student's hobbies and special skills.

[0114] Generative AI can also suggest optimal career paths based on a student's learning style. For example, if a student has a visual learning style, it can suggest universities and occupations that offer visual materials and curricula. If a student has an auditory learning style, it can suggest universities and occupations that offer many lecture-style classes. Furthermore, if a student has an experiential learning style, it can suggest universities and occupations that offer plenty of practical training and internships. This allows for more effective career suggestions based on a student's learning style.

[0115] The data handling method can further include a function for strengthening the protection of students' privacy. For example, by encrypting and storing student data, data leakage can be prevented. Also, by introducing two-factor authentication when accessing student data, security can be strengthened. Furthermore, by anonymizing student data, personal information can be protected. This makes it possible to handle data while protecting student privacy.

[0116] The method of evaluating the reliability and accuracy of the proposed information can further include evaluation by a third party. For example, the reliability of the proposed information can be improved by having the information evaluated by an educational institution or an expert. Furthermore, the accuracy of the information can be maintained by periodically having an audit by a third party. Furthermore, transparency can be ensured by making the results of the evaluation by the third party public. This can increase the reliability and accuracy of the proposed information.

[0117] The reception department can also suggest career paths based on the student's family structure and home environment. For example, if a student grew up in a family of doctors, a medical career path can be suggested. If a student grew up in a family of teachers, a career path in education can be suggested. Furthermore, if a student grew up in a multicultural family, a career path related to international relations and multicultural understanding can be suggested. This allows for more appropriate career suggestions based on the student's home environment.

[0118] The analysis unit can estimate the student's emotions and adjust the timing of the analysis based on the estimated student's emotions. For example, if a student is feeling stressed, the analysis can be performed during a time when the student is able to relax. Also, if a student is concentrating, the analysis can be performed at that time. Furthermore, if a student is tired, the analysis can be performed after a break. In this way, more appropriate analysis can be performed by adjusting the timing of the analysis according to the student's emotions.

[0119] The prediction unit can estimate the student's emotion and adjust the accuracy of the prediction based on the estimated student's emotion. For example, if the student is relaxed, a detailed prediction can be made. If the student is nervous, a concise and to-the-point prediction can be made. If the student is excited, a visually appealing prediction can be made. This allows for more appropriate prediction by adjusting the accuracy of the prediction according to the student's emotion.

[0120] The providing unit can estimate the emotion of the student and adjust the format of the information to be provided based on the estimated emotion of the student. For example, if the student is relaxed, detailed information can be provided. If the student is nervous, concise information that focuses on the main points can be provided. Furthermore, if the student is excited, visually appealing information can be provided. In this way, by adjusting the format of the information to be provided according to the emotion of the student, more appropriate information can be provided.

[0121] The providing unit can estimate the student's emotions and adjust the timing of the information to be provided based on the estimated student's emotions. For example, if the student is relaxed, detailed information can be provided. If the student is nervous, concise information that focuses on the main points can be provided. Furthermore, if the student is excited, visually appealing information can be provided. In this way, by adjusting the timing of the information to be provided according to the student's emotions, more appropriate information can be provided.

[0122] The providing unit can estimate the emotion of the student and adjust the content of the information to be provided based on the estimated emotion of the student. For example, if the student is relaxed, detailed information can be provided. If the student is nervous, concise information that focuses on the main points can be provided. Furthermore, if the student is excited, visually appealing information can be provided. In this way, by adjusting the content of the information to be provided according to the emotion of the student, more appropriate information can be provided.

[0123] The process flow of the second embodiment will be briefly described below.

[0124] Step 1: The reception unit inputs student information. Student information includes, for example, future dreams, requests, grades, etc. Students can input information such as "I want to be a doctor," "I'm interested in science," and "I have good grades in math." Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms. The generation AI performs analysis to predict the student's future career and future school based on the student's information. Step 3: The prediction unit uses the generation AI to make future predictions based on the information analyzed by the analysis unit. Future predictions are made using, for example, predictive models and simulations. Based on the dream of "becoming a doctor," the desire to "be interested in science," and the information that "you have good grades in math," the generation AI suggests a medical university and a career as a doctor. Step 4: The provision unit provides the students with the information on further education and careers suggested by the generation AI. The provision is based on, for example, the notification method and the format of the information. The generation AI notifies the students of the suggested information, and the students can use that information to consider their own career paths.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0127] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned reception unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and inputs information about the student. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information using the generation AI. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and performs future prediction based on the analyzed information. The provision unit is realized by the control unit 46A of the smart device 14, and provides the student with information on the educational destination and occupation proposed by the generation AI. The algorithm used by the generation AI is realized by the specific processing unit 290 of the data processing device 12. The method of handling data is realized by the specific processing unit 290 of the data processing device 12. The method of evaluating the reliability and accuracy of the proposed information is realized by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0136] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned reception unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and inputs information about the student. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information using the generation AI. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and performs future prediction based on the analyzed information. The provision unit is realized by the control unit 46A of the smart glasses 214, and provides the student with information on the educational destination and occupation proposed by the generation AI. The algorithm used by the generation AI is realized by the specific processing unit 290 of the data processing device 12. The method of handling data is realized by the specific processing unit 290 of the data processing device 12. The method of evaluating the reliability and accuracy of the proposed information is realized by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0147] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements including the above-mentioned reception unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and inputs information on the student. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information using the generation AI. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and performs future prediction based on the analyzed information. The provision unit is realized by the control unit 46A of the headset type terminal 314, and provides the student with information on the educational destination and occupation proposed by the generation AI. The algorithm used by the generation AI is realized by the specific processing unit 290 of the data processing device 12. The method of handling data is realized by the specific processing unit 290 of the data processing device 12. The method of evaluating the reliability and accuracy of the proposed information is realized by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a 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.

[0163] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0172] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0174] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0176] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] Each of the multiple elements including the above-mentioned reception unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and inputs information about the student. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information using the generation AI. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and performs future prediction based on the analyzed information. The provision unit is realized by the control unit 46A of the robot 414, and provides the student with information on the educational destination and occupation proposed by the generation AI. The algorithm used by the generation AI is realized by the specific processing unit 290 of the data processing device 12. The method of handling data is realized by the specific processing unit 290 of the data processing device 12. The method of evaluating the reliability and accuracy of the proposed information is realized by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0179] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0183] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] In addition, 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 upon request from the data processing device 12.

[0188] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0190] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0194] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0196] (Appendix 1) A reception desk where students' information is entered; an analysis unit that analyzes the information input by the reception unit; A prediction unit that performs future prediction based on the information analyzed by the analysis unit; A providing unit that provides information suggested by the prediction unit. A system characterized by: (Appendix 2) Equipped with algorithms used by generative AI 2. The system of claim 1, (Appendix 3) Have a method for handling data 2. The system of claim 1, (Appendix 4) Have a method for evaluating the reliability and accuracy of proposed information 2. The system of claim 1, (Appendix 5) The reception unit is Enter information about students' future dreams, requests, and grades 2. The system of claim 1, (Appendix 6) The analysis unit is Analyze the input information and make predictions based on the student's future dreams, desires, and grades 2. The system of claim 1, (Appendix 7) The prediction unit is Predicting future careers and schools based on student information 2. The system of claim 1, (Appendix 8) The providing unit is Providing students with information on further education and career options suggested by generative AI 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the student's emotions and adjust the timing of information input based on the estimated student's emotions. 2. The system of claim 1, (Appendix 10) The reception unit is Analyze the student's past input history and select the appropriate input method 2. The system of claim 1, (Appendix 11) The reception unit is As information is entered, it is filtered based on the student's current learning status and interests 2. The system of claim 1, (Appendix 12) The reception unit is Estimate the student's emotions and prioritize the information to be entered based on the estimated student's emotions. 2. The system of claim 1, (Appendix 13) The reception unit is When entering information, the system takes into account the student's geographic location to prioritize the most relevant information 2. The system of claim 1, (Appendix 14) The reception unit is When entering information, analyze students' social media activity and enter relevant information 2. The system of claim 1, (Appendix 15) The analysis unit is Estimate the student's emotions and adjust the way the analysis is presented based on the estimated student emotions. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the level of detail based on the importance of the information 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of information 2. The system of claim 1, (Appendix 18) The prediction unit is Estimate student emotions and adjust prediction criteria based on the estimated student emotions 2. The system of claim 1, (Appendix 19) The prediction unit is When making predictions, the accuracy of the predictions is improved by considering the correlation of information. 2. The system of claim 1, (Appendix 20) The prediction unit is When making predictions, the attributes of students are taken into account. 2. The system of claim 1, (Appendix 21) The providing unit is Estimate the student's emotions and adjust the way information is presented based on the estimated student's emotions 2. The system of claim 1, (Appendix 22) The providing unit is When providing information, adjust the level of detail provided based on the importance of the information 2. The system of claim 1, (Appendix 23) The providing unit is When providing, different provisioning algorithms are applied depending on the category of information 2. The system of claim 1, (Appendix 24) The providing unit is Estimate the student's emotions and adjust the length of information provided based on the estimated student emotions 2. The system of claim 1, (Appendix 25) The providing unit is When provided, the information will be prioritized based on when it was submitted 2. The system of claim 1, (Appendix 26) The providing unit is When serving, adjust the order of serving based on the relevance of the information 2. The system of claim 1, (Appendix 27) The algorithm used by the generative AI is Estimate the student's emotions and select an algorithm based on the estimated student emotions. 3. The system of claim 2, (Appendix 28) The algorithm used by the generative AI is When selecting an algorithm, refer to past data to select the appropriate algorithm. 3. The system of claim 2, (Appendix 29) The algorithm used by the generative AI is When selecting algorithms, apply different algorithms depending on the category of information 3. The system of claim 2, (Appendix 30) The algorithm used by the generative AI is Estimate student emotions and prioritize algorithms based on the estimated student emotions 3. The system of claim 2, (Appendix 31) The algorithm used by the generative AI is When selecting algorithms, we weight them based on when information was submitted. 3. The system of claim 2, (Appendix 32) How we handle your data: Infer student sentiment and adjust how data is handled based on the inferred student sentiment 4. The system of claim 3. (Appendix 33) How we handle your data: When handling data, refer to past data to select appropriate handling methods 4. The system of claim 3. (Appendix 34) How we handle your data: Estimate student emotions and prioritize data handling based on the estimated student emotions 4. The system of claim 3. (Appendix 35) How we handle your data: When processing data, we weight the data based on when the information was submitted 4. The system of claim 3. (Appendix 36) The method for assessing the reliability and accuracy of the proposed information is as follows: Estimate student emotions and adjust assessment methods based on estimated student emotions 5. The system of claim 4. (Appendix 37) The method for assessing the reliability and accuracy of the proposed information is as follows: When evaluating, select the appropriate evaluation method by referring to past evaluation data. 5. The system of claim 4. (Appendix 38) The method for assessing the reliability and accuracy of the proposed information is as follows: Estimate students' emotions and prioritize assessments based on the estimated emotions 5. The system of claim 4. (Appendix 39) The method for assessing the reliability and accuracy of the proposed information is as follows: During evaluation, weighting of evaluation data will be based on when the information was submitted 5. The system of claim 4. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where students' information is entered; an analysis unit that analyzes the information input by the reception unit; A prediction unit that performs future prediction based on the information analyzed by the analysis unit; A providing unit that provides information suggested by the prediction unit. A system characterized by:

2. The reception unit is Enter information about students' future dreams, requests, and grades 2. The system of claim 1 .

3. The analysis unit is Analyze the input information and make predictions based on the student's future dreams, desires, and grades 2. The system of claim 1 .

4. The prediction unit is Predicting future careers and schools based on student information 2. The system of claim 1 .

5. The providing unit is Providing students with information on further education and careers suggested by generative AI 2. The system of claim 1 .

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