system

Abstract

We provide the system. [Solution] A means for receiving and integrating learning performance information, aptitude test information, and survey data, A means for analyzing an individual's interests, abilities, and personality traits based on the aforementioned integrated information, A means of providing personalized career information based on the aforementioned analysis results, A means of proposing appropriate career paths and learning plans within the region based on citizens' educational data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern educational settings, there is a need for individualized career guidance for each student. However, there are problems such as a large burden on teachers and an inability to ensure sufficient quality of individual guidance. Also, there is a problem that it is difficult to comprehensively analyze a student's interests, abilities, and personality traits and provide the most suitable career path and learning plan for the student. Furthermore, smooth information sharing with guardians during career selection is also one of the problems.

Means for Solving the Problems

[0005] This invention provides a means for integrating learning performance data, aptitude test data, and questionnaire data to analyze students' interests, abilities, and personality traits. Based on this, it supports teachers' guidance by providing students with optimal career guidance information. Furthermore, it provides a system that automatically generates learning plans according to desired career paths and shares career suggestions and learning plans with parents, thereby improving the quality of individualized instruction in educational settings and strengthening collaboration with parents.

[0006] "Learning performance data" refers to information that shows the academic achievements students have made in each subject.

[0007] "Aptitude test data" refers to information that shows the results of tests conducted to measure students' abilities and characteristics.

[0008] "Survey data" refers to information that shows the results of students' self-assessments of their own interests, preferences, and personality traits.

[0009] "Means of integration" refers to technical methods for combining data obtained from multiple sources into a single, unified format.

[0010] "Means of analysis" refer to technical methods for evaluating and judging students' interests, abilities, and personality traits based on collected data.

[0011] "Means of providing career guidance information" refers to a technical function that suggests appropriate academic and career choices based on the results of data analysis of students.

[0012] "Means for automatically generating learning plans" refers to a technical function that uses computers to formulate specific learning schedules and goals according to students' desired career paths.

[0013] "Means of sharing with parents" refers to the technical function of communicating and providing information so that parents can review the generated career guidance suggestions and study plans. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is an AI system that integrates student academic performance data, aptitude test data, and survey data to analyze student characteristics and propose the optimal career path. The system consists of a server and user terminals, with the server performing data processing and analysis, and the user terminals functioning as an interface for information input and output.

[0036] Program Processing Description

[0037] The server receives learning performance data, aptitude test data, and survey data, and integrates this data. The integrated data is stored in a database as student profiles.

[0038] The server uses machine learning algorithms to analyze students' interests, abilities, and personality traits based on their student profiles. This analysis includes using supervised learning algorithms to predict optimal career choices based on past data.

[0039] Based on the analysis results, the server searches the career guidance database for the most suitable universities, vocational schools, and job information for each student, and generates a list.

[0040] Users (students or teachers) can view these career suggestions from their devices. This information is also shared with parents.

[0041] The server automatically generates a study plan tailored to the student's desired career path, creating a list of necessary subjects and skills. This includes providing reinforcement plans for specific subjects and reference materials.

[0042] Users (teachers) can download and use materials necessary for career guidance interviews with students using their devices. These materials include a list of questions based on the student's profile and detailed career suggestions.

[0043] Specific example

[0044] The user (student) takes an aptitude test, and the results are sent to the server. Based on these results, the server suggests universities offering computer science degree programs to students interested in the IT field. It also provides a study plan to strengthen the necessary mathematical skills.

[0045] Users (parents) can view an overview of their child's career path suggestions and learning plan on their device, using it to raise awareness of career choices. This enables effective career guidance involving students, teachers, and parents working together.

[0046] This system enables detailed and personalized career guidance tailored to each student, thereby reducing the burden on teachers in busy educational settings.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The terminal (user) inputs student learning performance data, aptitude test results, and questionnaire responses, and sends them to the server. A dedicated interface is used to accurately register the data before transmission.

[0050] Step 2:

[0051] The server receives various data sent from terminals, integrates it, and builds student profiles. It checks the quality and format of the data and automatically performs data cleansing if necessary.

[0052] Step 3:

[0053] The server applies a machine learning algorithm to analyze student profiles. The algorithm extracts features to assess students' interests, abilities, and personality traits, and then performs the analysis based on these features.

[0054] Step 4:

[0055] Based on the analysis results, the server searches a career database to identify the most suitable university, vocational school, or occupation for the student. It collects relevant details and generates a list of career suggestions.

[0056] Step 5:

[0057] Users (students and teachers) view career suggestions generated by the server through their devices. They check the details of each suggestion on the interface and decide whether it matches their interests and goals.

[0058] Step 6:

[0059] The server organizes the necessary subjects and skills to create a study plan tailored to your desired career path. The study plan includes a schedule and progress goals, and also provides specialized advice for specific areas.

[0060] Step 7:

[0061] The server summarizes student career suggestions and learning plans for parents, providing them through a parent portal and email. This allows parents to share information about their child's career choices.

[0062] Step 8:

[0063] The user (teacher) downloads interview materials and question lists generated from the server to prepare for career guidance interviews. Using these as a reference, they conduct career counseling sessions with each student and provide individualized advice.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] Traditional education systems have found it difficult to provide effective career guidance based on individual students' interests, abilities, and personality traits. They can only offer generalized information, making it impossible to provide individualized career suggestions and learning plans that meet the diverse needs of students. Furthermore, there has been a lack of concrete support necessary for students to select higher education or employment opportunities, leading to increased burdens on teachers and parents.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for receiving and integrating learning performance information, aptitude test information, and survey information; means for analyzing students' interests, abilities, and personality traits; and means for providing students with optimal career guidance information. This makes it possible to formulate individualized career suggestions and learning plans for each student and share them with teachers and parents through user terminals.

[0069] "Learning performance information" refers to data that shows students' performance in classes and exams using numbers and letters.

[0070] "Aptitude test information" refers to data that includes test results used to evaluate students' interests, abilities, personality, etc.

[0071] "Survey information" refers to data obtained from surveys such as questionnaires regarding students' environment and study habits.

[0072] "Integrating" means combining multiple different types of data into a single dataset.

[0073] "Analyzing" means evaluating data and extracting useful patterns and features from it.

[0074] "Career guidance information" refers to data that includes suggestions regarding students' future academic and career choices.

[0075] A "study plan" is a comprehensive plan that outlines the subjects, skills, and resources necessary for students to achieve their goals.

[0076] A "user terminal" refers to a device such as a computer or tablet that students, teachers, or parents use to access and manipulate information.

[0077] "Downloadable" means that digital information can be saved to a device via the internet or a network.

[0078] "Automatically generating materials" means automatically creating necessary documents and reports based on student profiles using a computer program.

[0079] This invention is a system that integrates student academic performance information, aptitude test information, and survey information to analyze student characteristics and propose the optimal career path. This system mainly consists of a server and user terminals.

[0080] The server is responsible for receiving and integrating data. Specifically, it receives information sent from user terminals via the network and centralizes this data using a database management system (e.g., MySQL®). Data integration is performed efficiently by using ETL (Extract, Transform, Load) tools to organize the data.

[0081] After creating student profiles, the server analyzes student characteristics using machine learning algorithms (e.g., scikit-learn, TENSORFLOW®). The analysis utilizes supervised learning models to predict career paths based on past data.

[0082] Based on the analysis results, the server searches the career database for the most suitable career information for each student and proposes a personalized learning plan based on that information. This includes creating a list of specific subjects and skills, and setting learning goals necessary for the student's growth.

[0083] Users (students, teachers, and parents) can access career suggestions and study plans generated using their user devices. The display employs an intuitive interface using a web application framework (e.g., React, Node.js). Users can refer to this information to guide their future learning plans and career choices.

[0084] For example, a user (student) takes an aptitude test and sends the results to a server. The server analyzes the results and, based on that, recommends educational institutions that offer computer science degree programs to students interested in information technology. It also provides study plans to strengthen mathematical skills.

[0085] An example of a prompt might be: "Based on the results of an aptitude test taken by a first-year high school student and their past academic data, propose a suitable career path and study plan for the student. Please also mention any areas of particular interest or skills that need strengthening."

[0086] This system enables specific and individualized career guidance tailored to each student, reducing the workload of teachers in educational settings and improving students' learning efficiency.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] Users (students or teachers) input learning performance information, aptitude test information, and survey information using a terminal and send it to the system. This input data is sent to a server via the network. The server stores the received information in a database management system and integrates it to set up individual student data. In this process, data in different formats is unified, and duplication is eliminated to create a consistent dataset.

[0090] Step 2:

[0091] The server generates student profiles based on an integrated dataset. These profiles include learning history, abilities, interests, and personality traits. The process of building profiles based on input data involves extracting, transforming, and loading each piece of information using ETL tools.

[0092] Step 3:

[0093] The server executes machine learning algorithms to analyze the generated profiles. This includes supervised learning models that predict the optimal career path based on the student's characteristics. Based on the profiles as input, it uses past data patterns to predict career paths and generates a list of possible career paths as output.

[0094] Step 4:

[0095] The server uses the analysis results to search for the most suitable career information from the career database. Using search algorithms such as ElasticSearch (registered trademark), it quickly and effectively selects candidates and creates a list of career information. The output includes information on universities, vocational schools, and occupations that match the student's profile.

[0096] Step 5:

[0097] The server generates a learning plan for students based on their career path information. This plan includes skills and subjects that need strengthening, as well as recommended learning resources. It develops a curriculum based on the career path information as input and provides detailed learning steps as output.

[0098] Step 6:

[0099] Users (students, teachers, and parents) can view this career information and learning plan through their devices. The server visually displays the information via a web application. The user interface is intuitive, enabling quick understanding and manipulation of the information.

[0100] Step 7:

[0101] The server automatically generates and makes downloadable materials based on student profiles. Teachers and parents can use these materials to support student consultations. The server generates optimized documents using prompts. For example, it might use a prompt such as, "Based on the results of an aptitude test taken by a first-year high school student and their past academic data, please suggest a suitable career path and study plan for the student."

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] In modern society, individual students are required to analyze vast amounts of information to choose the right career path and find the best option for themselves. However, it is difficult for students and their supporters to manually integrate and analyze this information, which can lead to errors in career choices. Similarly, local communities are required to provide optimal educational support tailored to the needs of each resident, but this is also a complex task. Therefore, there is a need for a system that automates and efficiently supports optimal career choices by integrating and analyzing data from individual students and residents.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes means for receiving and integrating learning performance information, aptitude test information, and survey data; means for analyzing an individual's interests, abilities, and personality traits based on the integrated information; means for providing the individual with optimal career path information based on the analysis results; and means for proposing appropriate career paths and learning plans within the region based on citizens' educational data. This enables support for selecting the optimal career path tailored to individual needs and allows for the construction of efficient educational plans.

[0107] "Academic performance information" refers to information that shows an individual's academic performance and evaluation.

[0108] "Aptitude test information" refers to information that includes the results of tests conducted to evaluate an individual's abilities and characteristics.

[0109] "Survey data" refers to information that shows the results and responses of questionnaires collected to understand individuals' interests, personality traits, and other characteristics.

[0110] A "server" is a computer system used to process, analyze, and manage data.

[0111] "Means of integration" refers to techniques for combining and organizing data obtained from multiple sources.

[0112] "Means of analysis" refer to techniques for evaluating individual characteristics and abilities based on integrated data.

[0113] "Career guidance information" refers to information about schools and vocational training that helps individuals choose their future education and career.

[0114] "Educational data" refers to data such as an individual's learning history, academic performance, and other information related to education.

[0115] "Means of proposing appropriate career paths and learning plans within a region" refers to techniques for presenting personalized career paths and learning plans based on information about educational institutions and programs in the region.

[0116] This invention is a system that provides personalized career guidance based on educational data. In this system, the server utilizes the following technologies:

[0117] The server uses a database management system (e.g., MySQL) to receive learning performance information, aptitude test information, and survey data, and stores this information in an integrated database. After integrating the information, the server utilizes a machine learning framework (e.g., TensorFlow) to analyze individuals' interests, abilities, and personality traits based on the integrated data. This allows the server to generate optimal career guidance tailored to each individual's characteristics.

[0118] User devices (such as smartphones and personal computers) can receive information from the server through educational applications and visually confirm it. This allows students and their supporters to obtain career guidance and study plan information in real time.

[0119] For example, when a student enters their academic performance data into an application using their smartphone, all relevant data is processed on the server side, and optimal career information is presented. This includes universities and vocational training programs in a specific region, and a subsequent study plan is also suggested.

[0120] If this system is successfully implemented, residents (students and their families) can expect improvements in the overall educational level of society and the professional skills of individuals. A more precise approach is possible with a text-based prompt message that reads, "A prompt to assist in designing an AI system that suggests career paths considering students' interests and abilities based on their academic performance and aptitude test data."

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] Users input their learning performance information, aptitude test information, and survey data using a device (e.g., a smartphone app). This constitutes the input data. The input data is transmitted to the server via a secure protocol.

[0124] Step 2:

[0125] The server stores the received data in a database management system (e.g., MySQL) and processes it as integrated data. The data integration process involves standardizing the format and correcting inconsistent data. The output is an integrated dataset.

[0126] Step 3:

[0127] The server analyzes the integrated dataset using a machine learning framework (e.g., TensorFlow). Specifically, it uses supervised learning algorithms to identify individuals' interests, abilities, and personality traits by comparing them with historical trend data. The output of this step is an individual trait profile.

[0128] Step 4:

[0129] The server generates career information based on the user's profile. This process involves searching databases of educational institutions and vocational training programs to identify the most suitable career options. The output is a list of career paths and study plans.

[0130] Step 5:

[0131] The device displays route information transmitted from the server on its user interface. Users can view this information and check for further details as needed. Furthermore, this information can be shared with parents and educators.

[0132] Step 6:

[0133] Based on user feedback and suggested changes, the server updates and re-proposes the learning plan. This assumes that course selection is a dynamic process and optimizes it to meet individual needs.

[0134] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0135] This invention combines an AI system that integrates student learning performance data, aptitude test data, and survey data to analyze characteristics and propose optimal career paths with an emotion engine that recognizes user emotions. This system consists of a server, a terminal, and an emotion engine.

[0136] Program Processing Description

[0137] The terminal (user) inputs and transmits student academic performance data, aptitude test results, and questionnaire responses. The terminal is equipped with a camera and microphone, which collects the user's (student or teacher's) facial expressions and voice as emotional data.

[0138] The server integrates learning, test, and survey data received from terminals to build student profiles. It also adds emotional data acquired by the emotion engine to the profiles.

[0139] The emotion engine analyzes the user's facial expressions and voice to evaluate their emotional state in real time. Based on these results, it reflects the student's current psychological state in their profile.

[0140] The server generates optimal career suggestions based on student profiles and emotional data. The results of the emotional engine are used to adjust the content and presentation of the suggestions. For example, if a user is feeling anxious, the suggestions may be explained more thoroughly or encouraging messages may be added.

[0141] Users (students and teachers) view career suggestions on their devices and provide feedback through the interface. The emotion engine then analyzes this feedback, and the server makes further adjustments to the career paths.

[0142] The server creates a learning plan tailored to the user's desired career path. Based on the emotional engine's evaluation, it can incorporate suggestions for improving motivation as the plan progresses.

[0143] Users (parents) can check career guidance suggestions and learning plans on the parent portal and provide support and consultation as needed. The emotion engine also provides support to facilitate smooth communication with parents.

[0144] Specific example

[0145] As soon as the user (student) submits their test results to the server, the emotion engine analyzes their facial expressions and detects their level of tension. Based on this information, the server generates career guidance suggestions, including counseling services and relaxation techniques to alleviate excessive stress.

[0146] Users (parents) can view career suggestions for their children on their devices and learn how to facilitate conversations with their children and provide reassurance based on insights provided by the emotion engine.

[0147] Thus, the present invention provides a system that enables more personalized and effective career guidance by combining it with emotion recognition functionality.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] The terminal (user) inputs student learning performance data, aptitude test results, and questionnaire responses and sends them to the server. The interface incorporates a camera and microphone for emotion recognition, recording students' facial expressions and voices in real time.

[0151] Step 2:

[0152] The server centrally manages learning performance data, aptitude test data, and survey data received from terminals, and integrates this data to create student profiles. It verifies the accuracy of the data and adds it to the profiles.

[0153] Step 3:

[0154] The emotion engine analyzes facial expressions and voice data acquired from the device to recognize the student's emotional state. This uses facial recognition and voice tone analysis technologies. The recognized emotion data is sent to the server and reflected in the student profile.

[0155] Step 4:

[0156] Based on student profiles, the server uses machine learning algorithms to analyze individual interests, abilities, and personality traits. Emotional data is also considered, and all of this information is used to search for and select the most suitable career path information.

[0157] Step 5:

[0158] The server creates career suggestions based on the selected career information and makes adjustments according to the student's emotional state. For example, it adds a reassuring message to students who are feeling anxious. The career information is sent to the student's and teacher's devices.

[0159] Step 6:

[0160] Users (students and teachers) review career guidance suggestions using their devices. They can provide emotionally sensitive feedback and reactions, and this information is returned to the server.

[0161] Step 7:

[0162] The server automatically generates a study plan tailored to the student's desired career path, based on career suggestions and emotional analysis results. This plan includes activities to reinforce specific subjects and manage emotional states.

[0163] Step 8:

[0164] Users (parents) review career suggestions and learning plans along with the emotional analysis results. This allows them to understand their child's current emotional state and provide appropriate support.

[0165] Step 9:

[0166] The emotion engine continuously receives feedback and learns to improve analysis accuracy and help the server optimize suggestions.

[0167] (Example 2)

[0168] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0169] In recent years, there has been a growing need for individually optimized career guidance that takes into account the characteristics and emotional states of learners. However, existing systems have difficulty integrating characteristic data and emotional data, resulting in insufficient personalization of career recommendations. Therefore, there is a demand for systems that can provide more effective and individualized career guidance.

[0170] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0171] In this invention, the server includes means for receiving and integrating learning performance data, aptitude test data, and questionnaire data; means for analyzing individual characteristics based on the integrated data; and means for analyzing emotional data and evaluating the user's emotional state. This enables career guidance optimized for each individual learner.

[0172] "Learning performance data" refers to information used to evaluate the knowledge and skills acquired by learners in educational activities.

[0173] "Aptitude test data" refers to information showing the results of tests used to evaluate learners' vocational aptitudes and abilities.

[0174] "Survey data" refers to research information collected with the aim of understanding learners' interests, values, and personality traits.

[0175] "Means of integration" refers to methods that combine multiple datasets and process them as unified information.

[0176] "Means of analyzing characteristics" refers to methods of evaluating and understanding learners' interests, abilities, and personality traits based on integrated data.

[0177] "Emotional data" refers to information that indicates psychological and emotional responses extracted from the learner's facial expressions and voice.

[0178] "Methods for evaluating emotional states" refer to methods that analyze emotional data to identify the learner's current psychological state.

[0179] "Means of providing career guidance information" refers to methods of presenting the optimal career path to learners based on analysis results and emotional states.

[0180] "Methods for automatically generating learning plans" refer to automated methods for designing educational programs and activities that are tailored to the learner's wishes and aptitudes.

[0181] "Methods for adjusting career paths based on evaluations" refer to methods that optimize career information and learning plans by considering feedback and reactions from users.

[0182] This invention constitutes an educational support system, comprising a server, a terminal, and an emotion analysis device. The following describes specific implementations of this system.

[0183] The server manages databases and computing resources, and runs software to receive and integrate academic performance data, aptitude test data, and survey data. This data is used to build profiles that provide a detailed understanding of each student's characteristics. The server processes the integrated data and utilizes generative AI models to provide individually optimized career guidance and learning plans. This enables instruction tailored to the individual educational needs of each student.

[0184] The terminal is a device operated by students and teachers, and functions as a data input and output interface. The terminal is equipped with a camera and microphone, and includes the ability to transmit facial expression and voice data in real time to an emotion analysis device. This allows for the collection of data to determine the user's emotional state.

[0185] Users are categorized as students, teachers, and parents, each with a specific role. Students input their learning data and impressions and send feedback to the server. Teachers provide generated career information as part of their educational support and use information from the server to select teaching materials and methods as needed. Parents view their child's career suggestions and learning plans through the parent portal and provide support at home.

[0186] The emotion analysis device analyzes emotional data and determines students' emotional states in real time. This enables personalized educational proposals that take into account psychological characteristics. For example, if a student shows signs of anxiety, the content and presentation method of career guidance can be adjusted.

[0187] For example, if a user (student) shows signs of anxiety when submitting test results via their device, the server can use this information to generate career guidance that includes counseling services tailored to reduce stress.

[0188] An example of a prompt used with the generating AI model is, "Please suggest information to include in the current career path suggestions based on the emotions read from the user's facial expressions." By inputting such text, it becomes possible to generate more detailed and effective career path information.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The terminal (user) inputs learning performance data, aptitude test data, and questionnaire responses. This data is registered on the terminal through the input interface and sent to the server. The terminal also captures the user's facial expressions and voice via the camera and microphone, collecting emotional data. Inputs are in various forms of numerical and text data, and the output is a data bundle sent to the server.

[0192] Step 2:

[0193] The server receives learning performance data, aptitude test data, and survey data sent from terminals. This data is integrated to generate individual student profiles. Database software is used for integration, storing the data as centralized information while maintaining data integrity. The input is a dataset for each student, and the output is the integrated student profile.

[0194] Step 3:

[0195] The emotion analysis device analyzes facial expression and voice data received from a terminal. Using a machine learning algorithm, it evaluates the emotional state in real time and adds the results to a profile. The input is the student's emotional data, and the output is the emotional evaluation result.

[0196] Step 4:

[0197] The server uses a generative AI model to create optimal career path suggestions based on student profiles and emotion assessment results. This process involves inputting prompts into the generative AI model to adjust the content and presentation of the suggestions, taking the emotion analysis results into account. The input consists of integrated data and emotion information, while the output is personalized career path suggestions.

[0198] Step 5:

[0199] Users (students and teachers) review career suggestions provided through their terminals. They then send feedback on the suggestions back to the server via the interface, allowing the emotion analysis device to analyze their response again. The input consists of career suggestions and feedback, while the output is adjusted career information based on the feedback.

[0200] Step 6:

[0201] The server automatically generates a personalized learning plan based on the feedback. The generated learning plan incorporates progress-based motivational support and additional resources. Input is feedback and profile data, and output is the adjusted learning plan.

[0202] Step 7:

[0203] Users (parents) view career suggestions and learning plans for their children through the parent portal. They utilize insights into career suggestions and use them as a means of effective communication. Input is data accessed by parents, and output is support policies for the home.

[0204] (Application Example 2)

[0205] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0206] Traditional education systems fail to adequately consider each student's individual academic performance and aptitude when providing career guidance. Furthermore, there is a lack of educational support tailored to students' psychological states and emotions, making it difficult to provide appropriate guidance and suggestions for each individual student. Moreover, there is a lack of means to alleviate the anxiety and stress experienced by students and their parents as a result of these challenges.

[0207] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0208] In this invention, the server includes means for receiving and integrating learning performance data, aptitude test data, and questionnaire data; means for recognizing emotions from the user's voice and visual information and evaluating their emotional state; and means for providing students with optimal career information based on the analysis results and emotional evaluation. This makes it possible to create personalized career suggestions and learning plans that take into account the student's individual data and emotional state.

[0209] "Learning performance data" refers to information that shows students' learning progress and achievements, and includes data such as test scores and assignment evaluation results.

[0210] "Aptitude test data" refers to the results of tests conducted to measure students' strengths and interests, and it is data that indicates what kind of career path a student is suited for.

[0211] "Survey data" refers to information from questionnaires answered by students and their guardians, and it is data that shows students' interests, desires, attitudes towards learning, etc.

[0212] "Auditory and visual information" refers to audio data collected from students and image and video data acquired by cameras, and is used for emotion recognition.

[0213] "Emotional state" refers to a student's psychological state, and includes an assessment of the type and intensity of emotions such as joy and anxiety.

[0214] "Career guidance information" refers to information about future educational options and career choices for students, and is presented based on the student's interests and abilities.

[0215] "Optimal career information for students" refers to information about the most suitable career path, suggested by taking into account each student's individual data and emotional state.

[0216] To realize this invention, the following system configuration is necessary.

[0217] First, the device collects student academic performance data, aptitude test data, and survey data. The device is equipped with a camera and microphone, which capture the user's voice and visual information as emotional data. This enables emotion recognition.

[0218] The server integrates learning performance data, aptitude test data, and survey data received from terminals to analyze students' interests, abilities, and attributes. Furthermore, it uses an emotion engine to assess the user's emotional state from their voice and visual information and adds this assessment to the integrated data. Based on this information, the server provides students with the most suitable career guidance.

[0219] During this process, data analysis is performed using the AI ​​framework "TensorFlow Lite" and the cloud AI service "Google Cloud AI (registered trademark)." This enables detailed career guidance tailored to each user's individual learning performance and psychological state.

[0220] As a concrete example, when a student enters their test results into a terminal, the server analyzes that information and provides career guidance that corresponds to their emotional state. For instance, if a student is showing anxiety, it provides career guidance that includes relaxation suggestions and encouraging messages.

[0221] An example of a prompt is, "What kind of encouraging message should be provided to alleviate students' learning stress?" By inputting this prompt into the AI ​​model, a message appropriate for the student will be generated.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The terminal receives learning performance data, aptitude test data, and survey data from students. This data is collected through the terminal's interface, and the camera and microphone simultaneously capture the students' voice and visual information. This data is then transmitted from the terminal to the server.

[0225] Step 2:

[0226] The server integrates learning performance data, aptitude test data, and survey data received from terminals. This creates a basic profile of students regarding their interests, abilities, and attributes. The input data is stored in a database and prepared for analysis.

[0227] Step 3:

[0228] The server uses an emotion engine to assess the student's emotional state from their voice and visual information. Using an AI framework such as TensorFlow Lite, it performs real-time emotion recognition and adds the results to the student's profile. The evaluation results of the emotion data are sent to the server and integrated into the profile.

[0229] Step 4:

[0230] The server utilizes an AI model based on integrated profiles to generate optimal career guidance information for students. It takes profiles and emotional assessment results as input, performs calculations and data processing related to career suggestions, and outputs the results in a format usable in the next step.

[0231] Step 5:

[0232] The user checks career path information on their device. Based on the information displayed on the device, the user evaluates the career path options. They also receive a prompt message from a generated AI model, "What kind of encouraging message should be provided to alleviate students' learning stress?", and use the message displayed on the device.

[0233] Step 6:

[0234] Users input feedback on the provided career information and messages using a terminal. The terminal collects the feedback information and sends it to the server. The server records the received feedback in a database and uses it to make future suggestions and adjust the educational plan.

[0235] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0236] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0237] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0238] [Second Embodiment]

[0239] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0240] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0241] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0242] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0243] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0244] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0245] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0246] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0247] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0248] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0249] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0250] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0251] This invention is an AI system that integrates student academic performance data, aptitude test data, and survey data to analyze student characteristics and propose the optimal career path. The system consists of a server and user terminals, with the server performing data processing and analysis, and the user terminals functioning as an interface for information input and output.

[0252] Program Processing Description

[0253] The server receives learning performance data, aptitude test data, and survey data, and integrates this data. The integrated data is stored in a database as student profiles.

[0254] The server uses machine learning algorithms to analyze students' interests, abilities, and personality traits based on their student profiles. This analysis includes using supervised learning algorithms to predict optimal career choices based on past data.

[0255] Based on the analysis results, the server searches the career guidance database for the most suitable universities, vocational schools, and job information for each student, and generates a list.

[0256] Users (students or teachers) can view these career suggestions from their devices. This information is also shared with parents.

[0257] The server automatically generates a study plan tailored to the student's desired career path, creating a list of necessary subjects and skills. This includes providing reinforcement plans for specific subjects and reference materials.

[0258] Users (teachers) can download and use materials necessary for career guidance interviews with students using their devices. These materials include a list of questions based on the student's profile and detailed career suggestions.

[0259] Specific example

[0260] The user (student) takes an aptitude test, and the results are sent to the server. Based on these results, the server suggests universities offering computer science degree programs to students interested in the IT field. It also provides a study plan to strengthen the necessary mathematical skills.

[0261] Users (parents) can view an overview of their child's career path suggestions and learning plan on their device, using it to raise awareness of career choices. This enables effective career guidance involving students, teachers, and parents working together.

[0262] This system enables detailed and personalized career guidance tailored to each student, thereby reducing the burden on teachers in busy educational settings.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] The terminal (user) inputs student learning performance data, aptitude test results, and questionnaire responses, and sends them to the server. A dedicated interface is used to accurately register the data before transmission.

[0266] Step 2:

[0267] The server receives various data sent from terminals, integrates it, and builds student profiles. It checks the quality and format of the data and automatically performs data cleansing if necessary.

[0268] Step 3:

[0269] The server applies a machine learning algorithm to analyze student profiles. The algorithm extracts features to assess students' interests, abilities, and personality traits, and then performs the analysis based on these features.

[0270] Step 4:

[0271] Based on the analysis results, the server searches a career database to identify the most suitable university, vocational school, or occupation for the student. It collects relevant details and generates a list of career suggestions.

[0272] Step 5:

[0273] Users (students and teachers) view career suggestions generated by the server through their devices. They check the details of each suggestion on the interface and decide whether it matches their interests and goals.

[0274] Step 6:

[0275] The server organizes the necessary subjects and skills to create a study plan tailored to your desired career path. The study plan includes a schedule and progress goals, and also provides specialized advice for specific areas.

[0276] Step 7:

[0277] The server summarizes student career suggestions and learning plans for parents, providing them through a parent portal and email. This allows parents to share information about their child's career choices.

[0278] Step 8:

[0279] The user (teacher) downloads interview materials and question lists generated from the server to prepare for career guidance interviews. Using these as a reference, they conduct career counseling sessions with each student and provide individualized advice.

[0280] (Example 1)

[0281] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0282] Traditional education systems have found it difficult to provide effective career guidance based on individual students' interests, abilities, and personality traits. They can only offer generalized information, making it impossible to provide individualized career suggestions and learning plans that meet the diverse needs of students. Furthermore, there has been a lack of concrete support necessary for students to select higher education or employment opportunities, leading to increased burdens on teachers and parents.

[0283] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0284] In this invention, the server includes means for receiving and integrating learning achievement information, aptitude test information, and survey information, means for analyzing the interests, abilities, and personality characteristics of students, and means for providing optimal course information for students. As a result, it becomes possible to formulate individualized course proposals and learning plans for each student and share them with teachers and guardians through the user terminal.

[0285] "Learning achievement information" refers to data that indicates the grades of students in classes and tests in numerical or textual form.

[0286] "Aptitude test information" refers to data including the test results for evaluating the interests, abilities, personality, etc. of students.

[0287] "Survey information" refers to data obtained from questionnaire surveys regarding the environment and learning habits of students.

[0288] "Integrate" means to gather multiple different types of data as one dataset.

[0289] "Analyze" means to evaluate data and extract useful patterns and features from it.

[0290] "Course information" refers to data including proposals regarding the future academic and career choices of students.

[0291] "Learning plan" refers to a plan comprehensively showing the subjects, skills, and resources necessary for students to achieve their goals.

[0292] "User terminal" refers to devices such as computers and tablets through which students, teachers, and guardians access and operate information.

[0293] "Downloadable" means that digital information can be saved on the terminal online or via a network.

[0294] "Automatically generating materials" means automatically creating necessary documents and reports based on student profiles using a computer program.

[0295] This invention is a system that integrates student academic performance information, aptitude test information, and survey information to analyze student characteristics and propose the optimal career path. This system mainly consists of a server and user terminals.

[0296] The server is responsible for receiving and integrating data. Specifically, it receives information sent from user terminals over the network and centralizes this data using a database management system (e.g., MySQL). Data integration is performed efficiently by using ETL (Extract, Transform, Load) tools to organize the data.

[0297] After creating student profiles, the server analyzes student characteristics using machine learning algorithms (e.g., scikit-learn, TensorFlow). The analysis utilizes supervised learning models to predict future career paths based on historical data.

[0298] Based on the analysis results, the server searches the career database for the most suitable career information for each student and proposes a personalized learning plan based on that information. This includes creating a list of specific subjects and skills, and setting learning goals necessary for the student's growth.

[0299] Users (students, teachers, and parents) can access career suggestions and study plans generated using their user devices. The display employs an intuitive interface using a web application framework (e.g., React, Node.js). Users can refer to this information to guide their future learning plans and career choices.

[0300] For example, a user (student) takes an aptitude test and sends the results to the server. The server analyzes the results and recommends educational institutions that offer a degree program in computer science to students who are interested in the field of information technology. It also presents a study plan to strengthen math skills.

[0301] As an example of a prompt sentence, "Based on the results of the aptitude test taken by a first-year high school student and past academic performance data, please propose a suitable career path and learning plan for the student. Also, mention fields that the student is likely to be interested in and skills that need to be strengthened." can be considered.

[0302] This system enables specific and individualized career support tailored to each student, making it possible to reduce the workload of teachers in educational settings and improve the learning efficiency of students.

[0303] The flow of the specific process in Example 1 will be described using FIG. 11.

[0304] Step 1:

[0305] The user (student or teacher) uses a terminal to input learning achievement information, aptitude test information, and survey information and sends it to the system. This input data is sent to the server through the network. The server saves the received information in a database management system, integrates it, and sets up individual student data. In this process, data in different formats is unified, and a consistent dataset is created while eliminating duplicates.

[0306] Step 2:

[0307] Based on the integrated dataset, the server generates a student profile. The profile includes learning history, abilities, interests, personality traits, etc. The process of constructing a profile based on the input data is carried out by using an ETL tool to extract, transform, and load each piece of information.

[0308] Step 3:

[0309] The server executes machine learning algorithms to analyze the generated profiles. This includes supervised learning models that predict the optimal career path based on the student's characteristics. Based on the profiles as input, it uses past data patterns to predict career paths and generates a list of possible career paths as output.

[0310] Step 4:

[0311] The server uses the analysis results to search for the most suitable career information from the career database. Using search algorithms such as Elasticsearch, it quickly and effectively selects candidates and creates a list of career information. The output includes information on universities, vocational schools, and occupations that match the student's profile.

[0312] Step 5:

[0313] The server generates a learning plan for students based on their career path information. This plan includes skills and subjects that need strengthening, as well as recommended learning resources. It develops a curriculum based on the career path information as input and provides detailed learning steps as output.

[0314] Step 6:

[0315] Users (students, teachers, and parents) can view this career information and learning plan through their devices. The server visually displays the information via a web application. The user interface is intuitive, enabling quick understanding and manipulation of the information.

[0316] Step 7:

[0317] The server automatically generates and makes downloadable materials based on student profiles. Teachers and parents can use these materials to support student consultations. The server generates optimized documents using prompts. For example, it might use a prompt such as, "Based on the results of an aptitude test taken by a first-year high school student and their past academic data, please suggest a suitable career path and study plan for the student."

[0318] (Application Example 1)

[0319] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0320] In modern society, individual students are required to analyze vast amounts of information to choose the right career path and find the best option for themselves. However, it is difficult for students and their supporters to manually integrate and analyze this information, which can lead to errors in career choices. Similarly, local communities are required to provide optimal educational support tailored to the needs of each resident, but this is also a complex task. Therefore, there is a need for a system that automates and efficiently supports optimal career choices by integrating and analyzing data from individual students and residents.

[0321] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0322] In this invention, the server includes means for receiving and integrating learning performance information, aptitude test information, and survey data; means for analyzing an individual's interests, abilities, and personality traits based on the integrated information; means for providing the individual with optimal career path information based on the analysis results; and means for proposing appropriate career paths and learning plans within the region based on citizens' educational data. This enables support for selecting the optimal career path tailored to individual needs and allows for the construction of efficient educational plans.

[0323] "Academic performance information" refers to information that shows an individual's academic performance and evaluation.

[0324] "Aptitude test information" refers to information that includes the results of tests conducted to evaluate an individual's abilities and characteristics.

[0325] "Survey data" refers to information that shows the results and responses of questionnaires collected to understand individuals' interests, personality traits, and other characteristics.

[0326] A "server" is a computer system used to process, analyze, and manage data.

[0327] "Means of integration" refers to techniques for combining and organizing data obtained from multiple sources.

[0328] "Means of analysis" refer to techniques for evaluating individual characteristics and abilities based on integrated data.

[0329] "Career guidance information" refers to information about schools and vocational training that helps individuals choose their future education and career.

[0330] "Educational data" refers to data such as an individual's learning history, academic performance, and other information related to education.

[0331] "Means of proposing appropriate career paths and learning plans within a region" refers to techniques for presenting personalized career paths and learning plans based on information about educational institutions and programs in the region.

[0332] This invention is a system that provides personalized career guidance based on educational data. In this system, the server utilizes the following technologies:

[0333] The server uses a database management system (e.g., MySQL) to receive learning performance information, aptitude test information, and survey data, and stores this information in an integrated database. After integrating the information, the server utilizes a machine learning framework (e.g., TensorFlow) to analyze individuals' interests, abilities, and personality traits based on the integrated data. This allows the server to generate optimal career guidance tailored to each individual's characteristics.

[0334] User devices (such as smartphones and personal computers) can receive information from the server through educational applications and visually confirm it. This allows students and their supporters to obtain career guidance and study plan information in real time.

[0335] For example, when a student enters their academic performance data into an application using their smartphone, all relevant data is processed on the server side, and optimal career information is presented. This includes universities and vocational training programs in a specific region, and a subsequent study plan is also suggested.

[0336] If this system is successfully implemented, residents (students and their families) can expect improvements in the overall educational level of society and the professional skills of individuals. A more precise approach is possible with a text-based prompt message that reads, "A prompt to assist in designing an AI system that suggests career paths considering students' interests and abilities based on their academic performance and aptitude test data."

[0337] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0338] Step 1:

[0339] Users input their learning performance information, aptitude test information, and survey data using a device (e.g., a smartphone app). This constitutes the input data. The input data is transmitted to the server via a secure protocol.

[0340] Step 2:

[0341] The server stores the received data in a database management system (e.g., MySQL) and processes it as integrated data. The data integration process involves standardizing the format and correcting inconsistent data. The output is an integrated dataset.

[0342] Step 3:

[0343] The server analyzes the integrated dataset using a machine learning framework (e.g., TensorFlow). Specifically, it uses supervised learning algorithms to identify individuals' interests, abilities, and personality traits by comparing them with historical trend data. The output of this step is an individual trait profile.

[0344] Step 4:

[0345] The server generates career information based on the user's profile. This process involves searching databases of educational institutions and vocational training programs to identify the most suitable career options. The output is a list of career paths and study plans.

[0346] Step 5:

[0347] The device displays route information transmitted from the server on its user interface. Users can view this information and check for further details as needed. Furthermore, this information can be shared with parents and educators.

[0348] Step 6:

[0349] Based on user feedback and suggested changes, the server updates and re-proposes the learning plan. This assumes that course selection is a dynamic process and optimizes it to meet individual needs.

[0350] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0351] This invention combines an AI system that integrates student learning performance data, aptitude test data, and survey data to analyze characteristics and propose optimal career paths with an emotion engine that recognizes user emotions. This system consists of a server, a terminal, and an emotion engine.

[0352] Program Processing Description

[0353] The terminal (user) inputs and transmits student academic performance data, aptitude test results, and questionnaire responses. The terminal is equipped with a camera and microphone, which collects the user's (student or teacher's) facial expressions and voice as emotional data.

[0354] The server integrates learning, test, and survey data received from terminals to build student profiles. It also adds emotional data acquired by the emotion engine to the profiles.

[0355] The emotion engine analyzes the user's facial expressions and voice to evaluate their emotional state in real time. Based on these results, it reflects the student's current psychological state in their profile.

[0356] The server generates optimal career suggestions based on student profiles and emotional data. The results of the emotional engine are used to adjust the content and presentation of the suggestions. For example, if a user is feeling anxious, the suggestions may be explained more thoroughly or encouraging messages may be added.

[0357] Users (students and teachers) view career suggestions on their devices and provide feedback through the interface. The emotion engine then analyzes this feedback, and the server makes further adjustments to the career paths.

[0358] The server creates a learning plan tailored to the user's desired career path. Based on the emotional engine's evaluation, it can incorporate suggestions for improving motivation as the plan progresses.

[0359] Users (parents) can check career guidance suggestions and learning plans on the parent portal and provide support and consultation as needed. The emotion engine also provides support to facilitate smooth communication with parents.

[0360] Specific example

[0361] As soon as the user (student) submits their test results to the server, the emotion engine analyzes their facial expressions and detects their level of tension. Based on this information, the server generates career guidance suggestions, including counseling services and relaxation techniques to alleviate excessive stress.

[0362] Users (parents) can view career suggestions for their children on their devices and learn how to facilitate conversations with their children and provide reassurance based on insights provided by the emotion engine.

[0363] Thus, the present invention provides a system that enables more personalized and effective career guidance by combining it with emotion recognition functionality.

[0364] The following describes the processing flow.

[0365] Step 1:

[0366] The terminal (user) inputs student learning performance data, aptitude test results, and questionnaire responses and sends them to the server. The interface incorporates a camera and microphone for emotion recognition, recording students' facial expressions and voices in real time.

[0367] Step 2:

[0368] The server centrally manages learning performance data, aptitude test data, and survey data received from terminals, and integrates this data to create student profiles. It verifies the accuracy of the data and adds it to the profiles.

[0369] Step 3:

[0370] The emotion engine analyzes facial expressions and voice data acquired from the device to recognize the student's emotional state. This uses facial recognition and voice tone analysis technologies. The recognized emotion data is sent to the server and reflected in the student profile.

[0371] Step 4:

[0372] Based on student profiles, the server uses machine learning algorithms to analyze individual interests, abilities, and personality traits. Emotional data is also considered, and all of this information is used to search for and select the most suitable career path information.

[0373] Step 5:

[0374] The server creates career suggestions based on the selected career information and makes adjustments according to the student's emotional state. For example, it adds a reassuring message to students who are feeling anxious. The career information is sent to the student's and teacher's devices.

[0375] Step 6:

[0376] Users (students and teachers) review career guidance suggestions using their devices. They can provide emotionally sensitive feedback and reactions, and this information is returned to the server.

[0377] Step 7:

[0378] The server automatically generates a study plan tailored to the student's desired career path, based on career suggestions and emotional analysis results. This plan includes activities to reinforce specific subjects and manage emotional states.

[0379] Step 8:

[0380] Users (parents) review career suggestions and learning plans along with the emotional analysis results. This allows them to understand their child's current emotional state and provide appropriate support.

[0381] Step 9:

[0382] The emotion engine continuously receives feedback and learns to improve analysis accuracy and help the server optimize suggestions.

[0383] (Example 2)

[0384] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0385] In recent years, there has been a growing need for individually optimized career guidance that takes into account the characteristics and emotional states of learners. However, existing systems have difficulty integrating characteristic data and emotional data, resulting in insufficient personalization of career recommendations. Therefore, there is a demand for systems that can provide more effective and individualized career guidance.

[0386] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0387] In this invention, the server includes means for receiving and integrating learning performance data, aptitude test data, and questionnaire data; means for analyzing individual characteristics based on the integrated data; and means for analyzing emotional data and evaluating the user's emotional state. This enables career guidance optimized for each individual learner.

[0388] "Learning performance data" refers to information used to evaluate the knowledge and skills acquired by learners in educational activities.

[0389] "Aptitude test data" refers to information showing the results of tests used to evaluate learners' vocational aptitudes and abilities.

[0390] "Survey data" refers to research information collected with the aim of understanding learners' interests, values, and personality traits.

[0391] "Means of integration" refers to methods that combine multiple datasets and process them as unified information.

[0392] "Means of analyzing characteristics" refers to methods of evaluating and understanding learners' interests, abilities, and personality traits based on integrated data.

[0393] "Emotional data" refers to information that indicates psychological and emotional responses extracted from the learner's facial expressions and voice.

[0394] "Methods for evaluating emotional states" refer to methods that analyze emotional data to identify the learner's current psychological state.

[0395] "Means of providing career guidance information" refers to methods of presenting the optimal career path to learners based on analysis results and emotional states.

[0396] "Methods for automatically generating learning plans" refer to automated methods for designing educational programs and activities that are tailored to the learner's wishes and aptitudes.

[0397] "Methods for adjusting career paths based on evaluations" refer to methods that optimize career information and learning plans by considering feedback and reactions from users.

[0398] This invention constitutes an educational support system, comprising a server, a terminal, and an emotion analysis device. The following describes specific implementations of this system.

[0399] The server manages databases and computing resources, and runs software to receive and integrate academic performance data, aptitude test data, and survey data. This data is used to build profiles that provide a detailed understanding of each student's characteristics. The server processes the integrated data and utilizes generative AI models to provide individually optimized career guidance and learning plans. This enables instruction tailored to the individual educational needs of each student.

[0400] The terminal is a device operated by students and teachers, and functions as a data input and output interface. The terminal is equipped with a camera and microphone, and includes the ability to transmit facial expression and voice data in real time to an emotion analysis device. This allows for the collection of data to determine the user's emotional state.

[0401] Users are categorized as students, teachers, and parents, each with a specific role. Students input their learning data and impressions and send feedback to the server. Teachers provide generated career information as part of their educational support and use information from the server to select teaching materials and methods as needed. Parents view their child's career suggestions and learning plans through the parent portal and provide support at home.

[0402] The emotion analysis device analyzes emotional data and determines students' emotional states in real time. This enables personalized educational proposals that take into account psychological characteristics. For example, if a student shows signs of anxiety, the content and presentation method of career guidance can be adjusted.

[0403] For example, if a user (student) shows signs of anxiety when submitting test results via their device, the server can use this information to generate career guidance that includes counseling services tailored to reduce stress.

[0404] An example of a prompt used with the generating AI model is, "Please suggest information to include in the current career path suggestions based on the emotions read from the user's facial expressions." By inputting such text, it becomes possible to generate more detailed and effective career path information.

[0405] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0406] Step 1:

[0407] The terminal (user) inputs learning performance data, aptitude test data, and questionnaire responses. This data is registered on the terminal through the input interface and sent to the server. The terminal also captures the user's facial expressions and voice via the camera and microphone, collecting emotional data. Inputs are in various forms of numerical and text data, and the output is a data bundle sent to the server.

[0408] Step 2:

[0409] The server receives learning performance data, aptitude test data, and survey data sent from terminals. This data is integrated to generate individual student profiles. Database software is used for integration, storing the data as centralized information while maintaining data integrity. The input is a dataset for each student, and the output is the integrated student profile.

[0410] Step 3:

[0411] The emotion analysis device analyzes facial expression and voice data received from a terminal. Using a machine learning algorithm, it evaluates the emotional state in real time and adds the results to a profile. The input is the student's emotional data, and the output is the emotional evaluation result.

[0412] Step 4:

[0413] The server uses a generative AI model to create optimal career path suggestions based on student profiles and emotion assessment results. This process involves inputting prompts into the generative AI model to adjust the content and presentation of the suggestions, taking the emotion analysis results into account. The input consists of integrated data and emotion information, while the output is personalized career path suggestions.

[0414] Step 5:

[0415] Users (students and teachers) review career suggestions provided through their terminals. They then send feedback on the suggestions back to the server via the interface, allowing the emotion analysis device to analyze their response again. The input consists of career suggestions and feedback, while the output is adjusted career information based on the feedback.

[0416] Step 6:

[0417] The server automatically generates a personalized learning plan based on the feedback. The generated learning plan incorporates progress-based motivational support and additional resources. Input is feedback and profile data, and output is the adjusted learning plan.

[0418] Step 7:

[0419] Users (parents) view career suggestions and learning plans for their children through the parent portal. They utilize insights into career suggestions and use them as a means of effective communication. Input is data accessed by parents, and output is support policies for the home.

[0420] (Application Example 2)

[0421] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0422] Traditional education systems fail to adequately consider each student's individual academic performance and aptitude when providing career guidance. Furthermore, there is a lack of educational support tailored to students' psychological states and emotions, making it difficult to provide appropriate guidance and suggestions for each individual student. Moreover, there is a lack of means to alleviate the anxiety and stress experienced by students and their parents as a result of these challenges.

[0423] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0424] In this invention, the server includes means for receiving and integrating learning performance data, aptitude test data, and questionnaire data; means for recognizing emotions from the user's voice and visual information and evaluating their emotional state; and means for providing students with optimal career information based on the analysis results and emotional evaluation. This makes it possible to create personalized career suggestions and learning plans that take into account the student's individual data and emotional state.

[0425] "Learning performance data" refers to information that shows students' learning progress and achievements, and includes data such as test scores and assignment evaluation results.

[0426] "Aptitude test data" refers to the results of tests conducted to measure students' strengths and interests, and it is data that indicates what kind of career path a student is suited for.

[0427] "Survey data" refers to information from questionnaires answered by students and their guardians, and it is data that shows students' interests, desires, attitudes towards learning, etc.

[0428] "Auditory and visual information" refers to audio data collected from students and image and video data acquired by cameras, and is used for emotion recognition.

[0429] "Emotional state" refers to a student's psychological state, and includes an assessment of the type and intensity of emotions such as joy and anxiety.

[0430] "Career guidance information" refers to information about future educational options and career choices for students, and is presented based on the student's interests and abilities.

[0431] "Optimal career information for students" refers to information about the most suitable career path, suggested by taking into account each student's individual data and emotional state.

[0432] To realize this invention, the following system configuration is necessary.

[0433] First, the device collects student academic performance data, aptitude test data, and survey data. The device is equipped with a camera and microphone, which capture the user's voice and visual information as emotional data. This enables emotion recognition.

[0434] The server integrates learning performance data, aptitude test data, and survey data received from terminals to analyze students' interests, abilities, and attributes. Furthermore, it uses an emotion engine to assess the user's emotional state from their voice and visual information and adds this assessment to the integrated data. Based on this information, the server provides students with the most suitable career guidance.

[0435] During this process, data analysis is performed using the AI ​​framework "TensorFlow Lite" and the cloud AI service "Google Cloud AI." This enables detailed career guidance tailored to each user's individual learning performance and psychological state.

[0436] As a concrete example, when a student enters their test results into a terminal, the server analyzes that information and provides career guidance that corresponds to their emotional state. For instance, if a student is showing anxiety, it provides career guidance that includes relaxation suggestions and encouraging messages.

[0437] An example of a prompt is, "What kind of encouraging message should be provided to alleviate students' learning stress?" By inputting this prompt into the AI ​​model, a message appropriate for the student will be generated.

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] The terminal receives learning performance data, aptitude test data, and survey data from students. This data is collected through the terminal's interface, and the camera and microphone simultaneously capture the students' voice and visual information. This data is then transmitted from the terminal to the server.

[0441] Step 2:

[0442] The server integrates learning performance data, aptitude test data, and survey data received from terminals. This creates a basic profile of students regarding their interests, abilities, and attributes. The input data is stored in a database and prepared for analysis.

[0443] Step 3:

[0444] The server uses an emotion engine to assess the student's emotional state from their voice and visual information. Using an AI framework such as TensorFlow Lite, it performs real-time emotion recognition and adds the results to the student's profile. The evaluation results of the emotion data are sent to the server and integrated into the profile.

[0445] Step 4:

[0446] The server utilizes an AI model based on integrated profiles to generate optimal career guidance information for students. It takes profiles and emotional assessment results as input, performs calculations and data processing related to career suggestions, and outputs the results in a format usable in the next step.

[0447] Step 5:

[0448] The user checks career path information on their device. Based on the information displayed on the device, the user evaluates the career path options. They also receive a prompt message from a generated AI model, "What kind of encouraging message should be provided to alleviate students' learning stress?", and use the message displayed on the device.

[0449] Step 6:

[0450] Users input feedback on the provided career information and messages using a terminal. The terminal collects the feedback information and sends it to the server. The server records the received feedback in a database and uses it to make future suggestions and adjust the educational plan.

[0451] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0452] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0453] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0454] [Third Embodiment]

[0455] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0456] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0457] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0458] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0459] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0460] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0461] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0462] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0463] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0464] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0465] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0466] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0467] This invention is an AI system that integrates student academic performance data, aptitude test data, and survey data to analyze student characteristics and propose the optimal career path. The system consists of a server and user terminals, with the server performing data processing and analysis, and the user terminals functioning as an interface for information input and output.

[0468] Program Processing Description

[0469] The server receives learning performance data, aptitude test data, and survey data, and integrates this data. The integrated data is stored in a database as student profiles.

[0470] The server uses machine learning algorithms to analyze students' interests, abilities, and personality traits based on their student profiles. This analysis includes using supervised learning algorithms to predict optimal career choices based on past data.

[0471] Based on the analysis results, the server searches the career guidance database for the most suitable universities, vocational schools, and job information for each student, and generates a list.

[0472] Users (students or teachers) can view these career suggestions from their devices. This information is also shared with parents.

[0473] The server automatically generates a study plan tailored to the student's desired career path, creating a list of necessary subjects and skills. This includes providing reinforcement plans for specific subjects and reference materials.

[0474] Users (teachers) can download and use materials necessary for career guidance interviews with students using their devices. These materials include a list of questions based on the student's profile and detailed career suggestions.

[0475] Specific example

[0476] The user (student) takes an aptitude test, and the results are sent to the server. Based on these results, the server suggests universities offering computer science degree programs to students interested in the IT field. It also provides a study plan to strengthen the necessary mathematical skills.

[0477] Users (parents) can view an overview of their child's career path suggestions and learning plan on their device, using it to raise awareness of career choices. This enables effective career guidance involving students, teachers, and parents working together.

[0478] This system enables detailed and personalized career guidance tailored to each student, thereby reducing the burden on teachers in busy educational settings.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] The terminal (user) inputs student learning performance data, aptitude test results, and questionnaire responses, and sends them to the server. A dedicated interface is used to accurately register the data before transmission.

[0482] Step 2:

[0483] The server receives various data sent from terminals, integrates it, and builds student profiles. It checks the quality and format of the data and automatically performs data cleansing if necessary.

[0484] Step 3:

[0485] The server applies a machine learning algorithm to analyze student profiles. The algorithm extracts features to assess students' interests, abilities, and personality traits, and then performs the analysis based on these features.

[0486] Step 4:

[0487] Based on the analysis results, the server searches a career database to identify the most suitable university, vocational school, or occupation for the student. It collects relevant details and generates a list of career suggestions.

[0488] Step 5:

[0489] Users (students and teachers) view career suggestions generated by the server through their devices. They check the details of each suggestion on the interface and decide whether it matches their interests and goals.

[0490] Step 6:

[0491] The server organizes the necessary subjects and skills to create a study plan tailored to your desired career path. The study plan includes a schedule and progress goals, and also provides specialized advice for specific areas.

[0492] Step 7:

[0493] The server summarizes student career suggestions and learning plans for parents, providing them through a parent portal and email. This allows parents to share information about their child's career choices.

[0494] Step 8:

[0495] The user (teacher) downloads interview materials and question lists generated from the server to prepare for career guidance interviews. Using these as a reference, they conduct career counseling sessions with each student and provide individualized advice.

[0496] (Example 1)

[0497] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] Traditional education systems have found it difficult to provide effective career guidance based on individual students' interests, abilities, and personality traits. They can only offer generalized information, making it impossible to provide individualized career suggestions and learning plans that meet the diverse needs of students. Furthermore, there has been a lack of concrete support necessary for students to select higher education or employment opportunities, leading to increased burdens on teachers and parents.

[0499] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0500] In this invention, the server includes means for receiving and integrating learning performance information, aptitude test information, and survey information; means for analyzing students' interests, abilities, and personality traits; and means for providing students with optimal career guidance information. This makes it possible to formulate individualized career suggestions and learning plans for each student and share them with teachers and parents through user terminals.

[0501] "Learning performance information" refers to data that shows students' performance in classes and exams using numbers and letters.

[0502] "Aptitude test information" refers to data that includes test results used to evaluate students' interests, abilities, personality, etc.

[0503] "Survey information" refers to data obtained from surveys such as questionnaires regarding students' environment and study habits.

[0504] "Integrating" means combining multiple different types of data into a single dataset.

[0505] "Analyzing" means evaluating data and extracting useful patterns and features from it.

[0506] "Career guidance information" refers to data that includes suggestions regarding students' future academic and career choices.

[0507] A "study plan" is a comprehensive plan that outlines the subjects, skills, and resources necessary for students to achieve their goals.

[0508] A "user terminal" refers to a device such as a computer or tablet that students, teachers, or parents use to access and manipulate information.

[0509] "Downloadable" means that digital information can be saved to a device via the internet or a network.

[0510] "Automatically generating materials" means automatically creating necessary documents and reports based on student profiles using a computer program.

[0511] This invention is a system that integrates student academic performance information, aptitude test information, and survey information to analyze student characteristics and propose the optimal career path. This system mainly consists of a server and user terminals.

[0512] The server is responsible for receiving and integrating data. Specifically, it receives information sent from user terminals over the network and centralizes this data using a database management system (e.g., MySQL). Data integration is performed efficiently by using ETL (Extract, Transform, Load) tools to organize the data.

[0513] After creating student profiles, the server analyzes student characteristics using machine learning algorithms (e.g., scikit-learn, TensorFlow). The analysis utilizes supervised learning models to predict future career paths based on historical data.

[0514] Based on the analysis results, the server searches the career database for the most suitable career information for each student and proposes a personalized learning plan based on that information. This includes creating a list of specific subjects and skills, and setting learning goals necessary for the student's growth.

[0515] Users (students, teachers, and parents) can access career suggestions and study plans generated using their user devices. The display employs an intuitive interface using a web application framework (e.g., React, Node.js). Users can refer to this information to guide their future learning plans and career choices.

[0516] For example, a user (student) takes an aptitude test and sends the results to a server. The server analyzes the results and, based on that, recommends educational institutions that offer computer science degree programs to students interested in information technology. It also provides study plans to strengthen mathematical skills.

[0517] An example of a prompt might be: "Based on the results of an aptitude test taken by a first-year high school student and their past academic data, propose a suitable career path and study plan for the student. Please also mention any areas of particular interest or skills that need strengthening."

[0518] This system enables specific and individualized career guidance tailored to each student, reducing the workload of teachers in educational settings and improving students' learning efficiency.

[0519] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0520] Step 1:

[0521] Users (students or teachers) input learning performance information, aptitude test information, and survey information using a terminal and send it to the system. This input data is sent to a server via the network. The server stores the received information in a database management system and integrates it to set up individual student data. In this process, data in different formats is unified, and duplication is eliminated to create a consistent dataset.

[0522] Step 2:

[0523] The server generates student profiles based on an integrated dataset. These profiles include learning history, abilities, interests, and personality traits. The process of building profiles based on input data involves extracting, transforming, and loading each piece of information using ETL tools.

[0524] Step 3:

[0525] The server executes machine learning algorithms to analyze the generated profiles. This includes supervised learning models that predict the optimal career path based on the student's characteristics. Based on the profiles as input, it uses past data patterns to predict career paths and generates a list of possible career paths as output.

[0526] Step 4:

[0527] The server uses the analysis results to search for the most suitable career information from the career database. Using search algorithms such as Elasticsearch, it quickly and effectively selects candidates and creates a list of career information. The output includes information on universities, vocational schools, and occupations that match the student's profile.

[0528] Step 5:

[0529] The server generates a learning plan for students based on their career path information. This plan includes skills and subjects that need strengthening, as well as recommended learning resources. It develops a curriculum based on the career path information as input and provides detailed learning steps as output.

[0530] Step 6:

[0531] Users (students, teachers, and parents) can view this career information and learning plan through their devices. The server visually displays the information via a web application. The user interface is intuitive, enabling quick understanding and manipulation of the information.

[0532] Step 7:

[0533] The server automatically generates and makes downloadable materials based on student profiles. Teachers and parents can use these materials to support student consultations. The server generates optimized documents using prompts. For example, it might use a prompt such as, "Based on the results of an aptitude test taken by a first-year high school student and their past academic data, please suggest a suitable career path and study plan for the student."

[0534] (Application Example 1)

[0535] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0536] In modern society, individual students are required to analyze vast amounts of information to choose the right career path and find the best option for themselves. However, it is difficult for students and their supporters to manually integrate and analyze this information, which can lead to errors in career choices. Similarly, local communities are required to provide optimal educational support tailored to the needs of each resident, but this is also a complex task. Therefore, there is a need for a system that automates and efficiently supports optimal career choices by integrating and analyzing data from individual students and residents.

[0537] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0538] In this invention, the server includes means for receiving and integrating learning performance information, aptitude test information, and survey data; means for analyzing an individual's interests, abilities, and personality traits based on the integrated information; means for providing the individual with optimal career path information based on the analysis results; and means for proposing appropriate career paths and learning plans within the region based on citizens' educational data. This enables support for selecting the optimal career path tailored to individual needs and allows for the construction of efficient educational plans.

[0539] "Academic performance information" refers to information that shows an individual's academic performance and evaluation.

[0540] "Aptitude test information" refers to information that includes the results of tests conducted to evaluate an individual's abilities and characteristics.

[0541] "Survey data" refers to information that shows the results and responses of questionnaires collected to understand individuals' interests, personality traits, and other characteristics.

[0542] A "server" is a computer system used to process, analyze, and manage data.

[0543] "Means of integration" refers to techniques for combining and organizing data obtained from multiple sources.

[0544] "Means of analysis" refer to techniques for evaluating individual characteristics and abilities based on integrated data.

[0545] "Career guidance information" refers to information about schools and vocational training that helps individuals choose their future education and career.

[0546] "Educational data" refers to data such as an individual's learning history, academic performance, and other information related to education.

[0547] "Means of proposing appropriate career paths and learning plans within a region" refers to techniques for presenting personalized career paths and learning plans based on information about educational institutions and programs in the region.

[0548] This invention is a system that provides personalized career guidance based on educational data. In this system, the server utilizes the following technologies:

[0549] The server uses a database management system (e.g., MySQL) to receive learning performance information, aptitude test information, and survey data, and stores this information in an integrated database. After integrating the information, the server utilizes a machine learning framework (e.g., TensorFlow) to analyze individuals' interests, abilities, and personality traits based on the integrated data. This allows the server to generate optimal career guidance tailored to each individual's characteristics.

[0550] User devices (such as smartphones and personal computers) can receive information from the server through educational applications and visually confirm it. This allows students and their supporters to obtain career guidance and study plan information in real time.

[0551] For example, when a student enters their academic performance data into an application using their smartphone, all relevant data is processed on the server side, and optimal career information is presented. This includes universities and vocational training programs in a specific region, and a subsequent study plan is also suggested.

[0552] If this system is successfully implemented, residents (students and their families) can expect improvements in the overall educational level of society and the professional skills of individuals. A more precise approach is possible with a text-based prompt message that reads, "A prompt to assist in designing an AI system that suggests career paths considering students' interests and abilities based on their academic performance and aptitude test data."

[0553] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0554] Step 1:

[0555] Users input their learning performance information, aptitude test information, and survey data using a device (e.g., a smartphone app). This constitutes the input data. The input data is transmitted to the server via a secure protocol.

[0556] Step 2:

[0557] The server stores the received data in a database management system (e.g., MySQL) and processes it as integrated data. The data integration process involves standardizing the format and correcting inconsistent data. The output is an integrated dataset.

[0558] Step 3:

[0559] The server analyzes the integrated dataset using a machine learning framework (e.g., TensorFlow). Specifically, it uses supervised learning algorithms to identify individuals' interests, abilities, and personality traits by comparing them with historical trend data. The output of this step is an individual trait profile.

[0560] Step 4:

[0561] The server generates career information based on the user's profile. This process involves searching databases of educational institutions and vocational training programs to identify the most suitable career options. The output is a list of career paths and study plans.

[0562] Step 5:

[0563] The device displays route information transmitted from the server on its user interface. Users can view this information and check for further details as needed. Furthermore, this information can be shared with parents and educators.

[0564] Step 6:

[0565] Based on user feedback and suggested changes, the server updates and re-proposes the learning plan. This assumes that course selection is a dynamic process and optimizes it to meet individual needs.

[0566] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0567] This invention combines an AI system that integrates student learning performance data, aptitude test data, and survey data to analyze characteristics and propose optimal career paths with an emotion engine that recognizes user emotions. This system consists of a server, a terminal, and an emotion engine.

[0568] Program Processing Description

[0569] The terminal (user) inputs and transmits student academic performance data, aptitude test results, and questionnaire responses. The terminal is equipped with a camera and microphone, which collects the user's (student or teacher's) facial expressions and voice as emotional data.

[0570] The server integrates learning, test, and survey data received from terminals to build student profiles. It also adds emotional data acquired by the emotion engine to the profiles.

[0571] The emotion engine analyzes the user's facial expressions and voice to evaluate their emotional state in real time. Based on these results, it reflects the student's current psychological state in their profile.

[0572] The server generates optimal career suggestions based on student profiles and emotional data. The results of the emotional engine are used to adjust the content and presentation of the suggestions. For example, if a user is feeling anxious, the suggestions may be explained more thoroughly or encouraging messages may be added.

[0573] Users (students and teachers) view career suggestions on their devices and provide feedback through the interface. The emotion engine then analyzes this feedback, and the server makes further adjustments to the career paths.

[0574] The server creates a learning plan tailored to the user's desired career path. Based on the emotional engine's evaluation, it can incorporate suggestions for improving motivation as the plan progresses.

[0575] Users (parents) can check career guidance suggestions and learning plans on the parent portal and provide support and consultation as needed. The emotion engine also provides support to facilitate smooth communication with parents.

[0576] Specific example

[0577] As soon as the user (student) submits their test results to the server, the emotion engine analyzes their facial expressions and detects their level of tension. Based on this information, the server generates career guidance suggestions, including counseling services and relaxation techniques to alleviate excessive stress.

[0578] Users (parents) can view career suggestions for their children on their devices and learn how to facilitate conversations with their children and provide reassurance based on insights provided by the emotion engine.

[0579] Thus, the present invention provides a system that enables more personalized and effective career guidance by combining it with emotion recognition functionality.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] The terminal (user) inputs student learning performance data, aptitude test results, and questionnaire responses and sends them to the server. The interface incorporates a camera and microphone for emotion recognition, recording students' facial expressions and voices in real time.

[0583] Step 2:

[0584] The server centrally manages learning performance data, aptitude test data, and survey data received from terminals, and integrates this data to create student profiles. It verifies the accuracy of the data and adds it to the profiles.

[0585] Step 3:

[0586] The emotion engine analyzes facial expressions and voice data acquired from the device to recognize the student's emotional state. This uses facial recognition and voice tone analysis technologies. The recognized emotion data is sent to the server and reflected in the student profile.

[0587] Step 4:

[0588] Based on student profiles, the server uses machine learning algorithms to analyze individual interests, abilities, and personality traits. Emotional data is also considered, and all of this information is used to search for and select the most suitable career path information.

[0589] Step 5:

[0590] The server creates career suggestions based on the selected career information and makes adjustments according to the student's emotional state. For example, it adds a reassuring message to students who are feeling anxious. The career information is sent to the student's and teacher's devices.

[0591] Step 6:

[0592] Users (students and teachers) review career guidance suggestions using their devices. They can provide emotionally sensitive feedback and reactions, and this information is returned to the server.

[0593] Step 7:

[0594] The server automatically generates a study plan tailored to the student's desired career path, based on career suggestions and emotional analysis results. This plan includes activities to reinforce specific subjects and manage emotional states.

[0595] Step 8:

[0596] Users (parents) review career suggestions and learning plans along with the emotional analysis results. This allows them to understand their child's current emotional state and provide appropriate support.

[0597] Step 9:

[0598] The emotion engine continuously receives feedback and learns to improve analysis accuracy and help the server optimize suggestions.

[0599] (Example 2)

[0600] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0601] In recent years, there has been a growing need for individually optimized career guidance that takes into account the characteristics and emotional states of learners. However, existing systems have difficulty integrating characteristic data and emotional data, resulting in insufficient personalization of career recommendations. Therefore, there is a demand for systems that can provide more effective and individualized career guidance.

[0602] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0603] In this invention, the server includes means for receiving and integrating learning performance data, aptitude test data, and questionnaire data; means for analyzing individual characteristics based on the integrated data; and means for analyzing emotional data and evaluating the user's emotional state. This enables career guidance optimized for each individual learner.

[0604] "Learning performance data" refers to information used to evaluate the knowledge and skills acquired by learners in educational activities.

[0605] "Aptitude test data" refers to information showing the results of tests used to evaluate learners' vocational aptitudes and abilities.

[0606] "Survey data" refers to research information collected with the aim of understanding learners' interests, values, and personality traits.

[0607] "Means of integration" refers to methods that combine multiple datasets and process them as unified information.

[0608] "Means of analyzing characteristics" refers to methods of evaluating and understanding learners' interests, abilities, and personality traits based on integrated data.

[0609] "Emotional data" refers to information that indicates psychological and emotional responses extracted from the learner's facial expressions and voice.

[0610] "Methods for evaluating emotional states" refer to methods that analyze emotional data to identify the learner's current psychological state.

[0611] "Means of providing career guidance information" refers to methods of presenting the optimal career path to learners based on analysis results and emotional states.

[0612] "Methods for automatically generating learning plans" refer to automated methods for designing educational programs and activities that are tailored to the learner's wishes and aptitudes.

[0613] "Methods for adjusting career paths based on evaluations" refer to methods that optimize career information and learning plans by considering feedback and reactions from users.

[0614] This invention constitutes an educational support system, comprising a server, a terminal, and an emotion analysis device. The following describes specific implementations of this system.

[0615] The server manages databases and computing resources, and runs software to receive and integrate academic performance data, aptitude test data, and survey data. This data is used to build profiles that provide a detailed understanding of each student's characteristics. The server processes the integrated data and utilizes generative AI models to provide individually optimized career guidance and learning plans. This enables instruction tailored to the individual educational needs of each student.

[0616] The terminal is a device operated by students and teachers, and functions as a data input and output interface. The terminal is equipped with a camera and microphone, and includes the ability to transmit facial expression and voice data in real time to an emotion analysis device. This allows for the collection of data to determine the user's emotional state.

[0617] Users are categorized as students, teachers, and parents, each with a specific role. Students input their learning data and impressions and send feedback to the server. Teachers provide generated career information as part of their educational support and use information from the server to select teaching materials and methods as needed. Parents view their child's career suggestions and learning plans through the parent portal and provide support at home.

[0618] The emotion analysis device analyzes emotional data and determines students' emotional states in real time. This enables personalized educational proposals that take into account psychological characteristics. For example, if a student shows signs of anxiety, the content and presentation method of career guidance can be adjusted.

[0619] For example, if a user (student) shows signs of anxiety when submitting test results via their device, the server can use this information to generate career guidance that includes counseling services tailored to reduce stress.

[0620] An example of a prompt used with the generating AI model is, "Please suggest information to include in the current career path suggestions based on the emotions read from the user's facial expressions." By inputting such text, it becomes possible to generate more detailed and effective career path information.

[0621] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0622] Step 1:

[0623] The terminal (user) inputs learning performance data, aptitude test data, and questionnaire responses. This data is registered on the terminal through the input interface and sent to the server. The terminal also captures the user's facial expressions and voice via the camera and microphone, collecting emotional data. Inputs are in various forms of numerical and text data, and the output is a data bundle sent to the server.

[0624] Step 2:

[0625] The server receives learning performance data, aptitude test data, and survey data sent from terminals. This data is integrated to generate individual student profiles. Database software is used for integration, storing the data as centralized information while maintaining data integrity. The input is a dataset for each student, and the output is the integrated student profile.

[0626] Step 3:

[0627] The emotion analysis device analyzes facial expression and voice data received from a terminal. Using a machine learning algorithm, it evaluates the emotional state in real time and adds the results to a profile. The input is the student's emotional data, and the output is the emotional evaluation result.

[0628] Step 4:

[0629] The server uses a generative AI model to create optimal career path suggestions based on student profiles and emotion assessment results. This process involves inputting prompts into the generative AI model to adjust the content and presentation of the suggestions, taking the emotion analysis results into account. The input consists of integrated data and emotion information, while the output is personalized career path suggestions.

[0630] Step 5:

[0631] Users (students and teachers) review career suggestions provided through their terminals. They then send feedback on the suggestions back to the server via the interface, allowing the emotion analysis device to analyze their response again. The input consists of career suggestions and feedback, while the output is adjusted career information based on the feedback.

[0632] Step 6:

[0633] The server automatically generates a personalized learning plan based on the feedback. The generated learning plan incorporates progress-based motivational support and additional resources. Input is feedback and profile data, and output is the adjusted learning plan.

[0634] Step 7:

[0635] Users (parents) view career suggestions and learning plans for their children through the parent portal. They utilize insights into career suggestions and use them as a means of effective communication. Input is data accessed by parents, and output is support policies for the home.

[0636] (Application Example 2)

[0637] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0638] Traditional education systems fail to adequately consider each student's individual academic performance and aptitude when providing career guidance. Furthermore, there is a lack of educational support tailored to students' psychological states and emotions, making it difficult to provide appropriate guidance and suggestions for each individual student. Moreover, there is a lack of means to alleviate the anxiety and stress experienced by students and their parents as a result of these challenges.

[0639] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0640] In this invention, the server includes means for receiving and integrating learning performance data, aptitude test data, and questionnaire data; means for recognizing emotions from the user's voice and visual information and evaluating their emotional state; and means for providing students with optimal career information based on the analysis results and emotional evaluation. This makes it possible to create personalized career suggestions and learning plans that take into account the student's individual data and emotional state.

[0641] "Learning performance data" refers to information that shows students' learning progress and achievements, and includes data such as test scores and assignment evaluation results.

[0642] "Aptitude test data" refers to the results of tests conducted to measure students' strengths and interests, and it is data that indicates what kind of career path a student is suited for.

[0643] "Survey data" refers to information from questionnaires answered by students and their guardians, and it is data that shows students' interests, desires, attitudes towards learning, etc.

[0644] "Auditory and visual information" refers to audio data collected from students and image and video data acquired by cameras, and is used for emotion recognition.

[0645] "Emotional state" refers to a student's psychological state, and includes an assessment of the type and intensity of emotions such as joy and anxiety.

[0646] "Career guidance information" refers to information about future educational options and career choices for students, and is presented based on the student's interests and abilities.

[0647] "Optimal career information for students" refers to information about the most suitable career path, suggested by taking into account each student's individual data and emotional state.

[0648] To realize this invention, the following system configuration is necessary.

[0649] First, the device collects student academic performance data, aptitude test data, and survey data. The device is equipped with a camera and microphone, which capture the user's voice and visual information as emotional data. This enables emotion recognition.

[0650] The server integrates learning performance data, aptitude test data, and survey data received from terminals to analyze students' interests, abilities, and attributes. Furthermore, it uses an emotion engine to assess the user's emotional state from their voice and visual information and adds this assessment to the integrated data. Based on this information, the server provides students with the most suitable career guidance.

[0651] During this process, data analysis is performed using the AI ​​framework "TensorFlow Lite" and the cloud AI service "Google Cloud AI." This enables detailed career guidance tailored to each user's individual learning performance and psychological state.

[0652] As a concrete example, when a student enters their test results into a terminal, the server analyzes that information and provides career guidance that corresponds to their emotional state. For instance, if a student is showing anxiety, it provides career guidance that includes relaxation suggestions and encouraging messages.

[0653] An example of a prompt is, "What kind of encouraging message should be provided to alleviate students' learning stress?" By inputting this prompt into the AI ​​model, a message appropriate for the student will be generated.

[0654] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0655] Step 1:

[0656] The terminal receives learning performance data, aptitude test data, and survey data from students. This data is collected through the terminal's interface, and the camera and microphone simultaneously capture the students' voice and visual information. This data is then transmitted from the terminal to the server.

[0657] Step 2:

[0658] The server integrates learning performance data, aptitude test data, and survey data received from terminals. This creates a basic profile of students regarding their interests, abilities, and attributes. The input data is stored in a database and prepared for analysis.

[0659] Step 3:

[0660] The server uses an emotion engine to assess the student's emotional state from their voice and visual information. Using an AI framework such as TensorFlow Lite, it performs real-time emotion recognition and adds the results to the student's profile. The evaluation results of the emotion data are sent to the server and integrated into the profile.

[0661] Step 4:

[0662] The server utilizes an AI model based on integrated profiles to generate optimal career guidance information for students. It takes profiles and emotional assessment results as input, performs calculations and data processing related to career suggestions, and outputs the results in a format usable in the next step.

[0663] Step 5:

[0664] The user checks career path information on their device. Based on the information displayed on the device, the user evaluates the career path options. They also receive a prompt message from a generated AI model, "What kind of encouraging message should be provided to alleviate students' learning stress?", and use the message displayed on the device.

[0665] Step 6:

[0666] Users input feedback on the provided career information and messages using a terminal. The terminal collects the feedback information and sends it to the server. The server records the received feedback in a database and uses it to make future suggestions and adjust the educational plan.

[0667] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0668] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0669] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0670] [Fourth Embodiment]

[0671] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0672] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0673] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0674] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0675] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0676] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0677] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0678] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0679] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0680] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0681] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0682] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0683] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0684] This invention is an AI system that integrates student academic performance data, aptitude test data, and survey data to analyze student characteristics and propose the optimal career path. The system consists of a server and user terminals, with the server performing data processing and analysis, and the user terminals functioning as an interface for information input and output.

[0685] Program Processing Description

[0686] The server receives learning performance data, aptitude test data, and survey data, and integrates this data. The integrated data is stored in a database as student profiles.

[0687] The server uses machine learning algorithms to analyze students' interests, abilities, and personality traits based on their student profiles. This analysis includes using supervised learning algorithms to predict optimal career choices based on past data.

[0688] Based on the analysis results, the server searches the career guidance database for the most suitable universities, vocational schools, and job information for each student, and generates a list.

[0689] Users (students or teachers) can view these career suggestions from their devices. This information is also shared with parents.

[0690] The server automatically generates a study plan tailored to the student's desired career path, creating a list of necessary subjects and skills. This includes providing reinforcement plans for specific subjects and reference materials.

[0691] Users (teachers) can download and use materials necessary for career guidance interviews with students using their devices. These materials include a list of questions based on the student's profile and detailed career suggestions.

[0692] Specific example

[0693] The user (student) takes an aptitude test, and the results are sent to the server. Based on these results, the server suggests universities offering computer science degree programs to students interested in the IT field. It also provides a study plan to strengthen the necessary mathematical skills.

[0694] Users (parents) can view an overview of their child's career path suggestions and learning plan on their device, using it to raise awareness of career choices. This enables effective career guidance involving students, teachers, and parents working together.

[0695] This system enables detailed and personalized career guidance tailored to each student, thereby reducing the burden on teachers in busy educational settings.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The terminal (user) inputs student learning performance data, aptitude test results, and questionnaire responses, and sends them to the server. A dedicated interface is used to accurately register the data before transmission.

[0699] Step 2:

[0700] The server receives various data sent from terminals, integrates it, and builds student profiles. It checks the quality and format of the data and automatically performs data cleansing if necessary.

[0701] Step 3:

[0702] The server applies a machine learning algorithm to analyze student profiles. The algorithm extracts features to assess students' interests, abilities, and personality traits, and then performs the analysis based on these features.

[0703] Step 4:

[0704] Based on the analysis results, the server searches a career database to identify the most suitable university, vocational school, or occupation for the student. It collects relevant details and generates a list of career suggestions.

[0705] Step 5:

[0706] Users (students and teachers) view career suggestions generated by the server through their devices. They check the details of each suggestion on the interface and decide whether it matches their interests and goals.

[0707] Step 6:

[0708] The server organizes the necessary subjects and skills to create a study plan tailored to your desired career path. The study plan includes a schedule and progress goals, and also provides specialized advice for specific areas.

[0709] Step 7:

[0710] The server summarizes student career suggestions and learning plans for parents, providing them through a parent portal and email. This allows parents to share information about their child's career choices.

[0711] Step 8:

[0712] The user (teacher) downloads interview materials and question lists generated from the server to prepare for career guidance interviews. Using these as a reference, they conduct career counseling sessions with each student and provide individualized advice.

[0713] (Example 1)

[0714] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0715] Traditional education systems have found it difficult to provide effective career guidance based on individual students' interests, abilities, and personality traits. They can only offer generalized information, making it impossible to provide individualized career suggestions and learning plans that meet the diverse needs of students. Furthermore, there has been a lack of concrete support necessary for students to select higher education or employment opportunities, leading to increased burdens on teachers and parents.

[0716] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0717] In this invention, the server includes means for receiving and integrating learning performance information, aptitude test information, and survey information; means for analyzing students' interests, abilities, and personality traits; and means for providing students with optimal career guidance information. This makes it possible to formulate individualized career suggestions and learning plans for each student and share them with teachers and parents through user terminals.

[0718] "Learning performance information" refers to data that shows students' performance in classes and exams using numbers and letters.

[0719] "Aptitude test information" refers to data that includes test results used to evaluate students' interests, abilities, personality, etc.

[0720] "Survey information" refers to data obtained from surveys such as questionnaires regarding students' environment and study habits.

[0721] "Integrating" means combining multiple different types of data into a single dataset.

[0722] "Analyzing" means evaluating data and extracting useful patterns and features from it.

[0723] "Career guidance information" refers to data that includes suggestions regarding students' future academic and career choices.

[0724] A "study plan" is a comprehensive plan that outlines the subjects, skills, and resources necessary for students to achieve their goals.

[0725] A "user terminal" refers to a device such as a computer or tablet that students, teachers, or parents use to access and manipulate information.

[0726] "Downloadable" means that digital information can be saved to a device via the internet or a network.

[0727] "Automatically generating materials" means automatically creating necessary documents and reports based on student profiles using a computer program.

[0728] This invention is a system that integrates student academic performance information, aptitude test information, and survey information to analyze student characteristics and propose the optimal career path. This system mainly consists of a server and user terminals.

[0729] The server is responsible for receiving and integrating data. Specifically, it receives information sent from user terminals over the network and centralizes this data using a database management system (e.g., MySQL). Data integration is performed efficiently by using ETL (Extract, Transform, Load) tools to organize the data.

[0730] After creating student profiles, the server analyzes student characteristics using machine learning algorithms (e.g., scikit-learn, TensorFlow). The analysis utilizes supervised learning models to predict future career paths based on historical data.

[0731] Based on the analysis results, the server searches the career database for the most suitable career information for each student and proposes a personalized learning plan based on that information. This includes creating a list of specific subjects and skills, and setting learning goals necessary for the student's growth.

[0732] Users (students, teachers, and parents) can access career suggestions and study plans generated using their user devices. The display employs an intuitive interface using a web application framework (e.g., React, Node.js). Users can refer to this information to guide their future learning plans and career choices.

[0733] For example, a user (student) takes an aptitude test and sends the results to a server. The server analyzes the results and, based on that, recommends educational institutions that offer computer science degree programs to students interested in information technology. It also provides study plans to strengthen mathematical skills.

[0734] An example of a prompt might be: "Based on the results of an aptitude test taken by a first-year high school student and their past academic data, propose a suitable career path and study plan for the student. Please also mention any areas of particular interest or skills that need strengthening."

[0735] This system enables specific and individualized career guidance tailored to each student, reducing the workload of teachers in educational settings and improving students' learning efficiency.

[0736] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0737] Step 1:

[0738] Users (students or teachers) input learning performance information, aptitude test information, and survey information using a terminal and send it to the system. This input data is sent to a server via the network. The server stores the received information in a database management system and integrates it to set up individual student data. In this process, data in different formats is unified, and duplication is eliminated to create a consistent dataset.

[0739] Step 2:

[0740] The server generates student profiles based on an integrated dataset. These profiles include learning history, abilities, interests, and personality traits. The process of building profiles based on input data involves extracting, transforming, and loading each piece of information using ETL tools.

[0741] Step 3:

[0742] The server executes machine learning algorithms to analyze the generated profiles. This includes supervised learning models that predict the optimal career path based on the student's characteristics. Based on the profiles as input, it uses past data patterns to predict career paths and generates a list of possible career paths as output.

[0743] Step 4:

[0744] The server uses the analysis results to search for the most suitable career information from the career database. Using search algorithms such as Elasticsearch, it quickly and effectively selects candidates and creates a list of career information. The output includes information on universities, vocational schools, and occupations that match the student's profile.

[0745] Step 5:

[0746] The server generates a learning plan for students based on their career path information. This plan includes skills and subjects that need strengthening, as well as recommended learning resources. It develops a curriculum based on the career path information as input and provides detailed learning steps as output.

[0747] Step 6:

[0748] Users (students, teachers, and parents) can view this career information and learning plan through their devices. The server visually displays the information via a web application. The user interface is intuitive, enabling quick understanding and manipulation of the information.

[0749] Step 7:

[0750] The server automatically generates and makes downloadable materials based on student profiles. Teachers and parents can use these materials to support student consultations. The server generates optimized documents using prompts. For example, it might use a prompt such as, "Based on the results of an aptitude test taken by a first-year high school student and their past academic data, please suggest a suitable career path and study plan for the student."

[0751] (Application Example 1)

[0752] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0753] In modern society, individual students are required to analyze vast amounts of information to choose the right career path and find the best option for themselves. However, it is difficult for students and their supporters to manually integrate and analyze this information, which can lead to errors in career choices. Similarly, local communities are required to provide optimal educational support tailored to the needs of each resident, but this is also a complex task. Therefore, there is a need for a system that automates and efficiently supports optimal career choices by integrating and analyzing data from individual students and residents.

[0754] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0755] In this invention, the server includes means for receiving and integrating learning performance information, aptitude test information, and survey data; means for analyzing an individual's interests, abilities, and personality traits based on the integrated information; means for providing the individual with optimal career path information based on the analysis results; and means for proposing appropriate career paths and learning plans within the region based on citizens' educational data. This enables support for selecting the optimal career path tailored to individual needs and allows for the construction of efficient educational plans.

[0756] "Academic performance information" refers to information that shows an individual's academic performance and evaluation.

[0757] "Aptitude test information" refers to information that includes the results of tests conducted to evaluate an individual's abilities and characteristics.

[0758] "Survey data" refers to information that shows the results and responses of questionnaires collected to understand individuals' interests, personality traits, and other characteristics.

[0759] A "server" is a computer system used to process, analyze, and manage data.

[0760] "Means of integration" refers to techniques for combining and organizing data obtained from multiple sources.

[0761] "Means of analysis" refer to techniques for evaluating individual characteristics and abilities based on integrated data.

[0762] "Career guidance information" refers to information about schools and vocational training that helps individuals choose their future education and career.

[0763] "Educational data" refers to data such as an individual's learning history, academic performance, and other information related to education.

[0764] "Means of proposing appropriate career paths and learning plans within a region" refers to techniques for presenting personalized career paths and learning plans based on information about educational institutions and programs in the region.

[0765] This invention is a system that provides personalized career guidance based on educational data. In this system, the server utilizes the following technologies:

[0766] The server uses a database management system (e.g., MySQL) to receive learning performance information, aptitude test information, and survey data, and stores this information in an integrated database. After integrating the information, the server utilizes a machine learning framework (e.g., TensorFlow) to analyze individuals' interests, abilities, and personality traits based on the integrated data. This allows the server to generate optimal career guidance tailored to each individual's characteristics.

[0767] User devices (such as smartphones and personal computers) can receive information from the server through educational applications and visually confirm it. This allows students and their supporters to obtain career guidance and study plan information in real time.

[0768] For example, when a student enters their academic performance data into an application using their smartphone, all relevant data is processed on the server side, and optimal career information is presented. This includes universities and vocational training programs in a specific region, and a subsequent study plan is also suggested.

[0769] If this system is successfully implemented, residents (students and their families) can expect improvements in the overall educational level of society and the professional skills of individuals. A more precise approach is possible with a text-based prompt message that reads, "A prompt to assist in designing an AI system that suggests career paths considering students' interests and abilities based on their academic performance and aptitude test data."

[0770] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0771] Step 1:

[0772] Users input their learning performance information, aptitude test information, and survey data using a device (e.g., a smartphone app). This constitutes the input data. The input data is transmitted to the server via a secure protocol.

[0773] Step 2:

[0774] The server stores the received data in a database management system (e.g., MySQL) and processes it as integrated data. The data integration process involves standardizing the format and correcting inconsistent data. The output is an integrated dataset.

[0775] Step 3:

[0776] The server analyzes the integrated dataset using a machine learning framework (e.g., TensorFlow). Specifically, it uses supervised learning algorithms to identify individuals' interests, abilities, and personality traits by comparing them with historical trend data. The output of this step is an individual trait profile.

[0777] Step 4:

[0778] The server generates career information based on the user's profile. This process involves searching databases of educational institutions and vocational training programs to identify the most suitable career options. The output is a list of career paths and study plans.

[0779] Step 5:

[0780] The device displays route information transmitted from the server on its user interface. Users can view this information and check for further details as needed. Furthermore, this information can be shared with parents and educators.

[0781] Step 6:

[0782] Based on user feedback and suggested changes, the server updates and re-proposes the learning plan. This assumes that course selection is a dynamic process and optimizes it to meet individual needs.

[0783] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0784] This invention combines an AI system that integrates student learning performance data, aptitude test data, and survey data to analyze characteristics and propose optimal career paths with an emotion engine that recognizes user emotions. This system consists of a server, a terminal, and an emotion engine.

[0785] Program Processing Description

[0786] The terminal (user) inputs and transmits student academic performance data, aptitude test results, and questionnaire responses. The terminal is equipped with a camera and microphone, which collects the user's (student or teacher's) facial expressions and voice as emotional data.

[0787] The server integrates learning, test, and survey data received from terminals to build student profiles. It also adds emotional data acquired by the emotion engine to the profiles.

[0788] The emotion engine analyzes the user's facial expressions and voice to evaluate their emotional state in real time. Based on these results, it reflects the student's current psychological state in their profile.

[0789] The server generates optimal career suggestions based on student profiles and emotional data. The results of the emotional engine are used to adjust the content and presentation of the suggestions. For example, if a user is feeling anxious, the suggestions may be explained more thoroughly or encouraging messages may be added.

[0790] Users (students and teachers) view career suggestions on their devices and provide feedback through the interface. The emotion engine then analyzes this feedback, and the server makes further adjustments to the career paths.

[0791] The server creates a learning plan tailored to the user's desired career path. Based on the emotional engine's evaluation, it can incorporate suggestions for improving motivation as the plan progresses.

[0792] Users (parents) can check career guidance suggestions and learning plans on the parent portal and provide support and consultation as needed. The emotion engine also provides support to facilitate smooth communication with parents.

[0793] Specific example

[0794] As soon as the user (student) submits their test results to the server, the emotion engine analyzes their facial expressions and detects their level of tension. Based on this information, the server generates career guidance suggestions, including counseling services and relaxation techniques to alleviate excessive stress.

[0795] Users (parents) can view career suggestions for their children on their devices and learn how to facilitate conversations with their children and provide reassurance based on insights provided by the emotion engine.

[0796] Thus, the present invention provides a system that enables more personalized and effective career guidance by combining it with emotion recognition functionality.

[0797] The following describes the processing flow.

[0798] Step 1:

[0799] The terminal (user) inputs student learning performance data, aptitude test results, and questionnaire responses and sends them to the server. The interface incorporates a camera and microphone for emotion recognition, recording students' facial expressions and voices in real time.

[0800] Step 2:

[0801] The server centrally manages learning performance data, aptitude test data, and survey data received from terminals, and integrates this data to create student profiles. It verifies the accuracy of the data and adds it to the profiles.

[0802] Step 3:

[0803] The emotion engine analyzes facial expressions and voice data acquired from the device to recognize the student's emotional state. This uses facial recognition and voice tone analysis technologies. The recognized emotion data is sent to the server and reflected in the student profile.

[0804] Step 4:

[0805] Based on student profiles, the server uses machine learning algorithms to analyze individual interests, abilities, and personality traits. Emotional data is also considered, and all of this information is used to search for and select the most suitable career path information.

[0806] Step 5:

[0807] The server creates career suggestions based on the selected career information and makes adjustments according to the student's emotional state. For example, it adds a reassuring message to students who are feeling anxious. The career information is sent to the student's and teacher's devices.

[0808] Step 6:

[0809] Users (students and teachers) review career guidance suggestions using their devices. They can provide emotionally sensitive feedback and reactions, and this information is returned to the server.

[0810] Step 7:

[0811] The server automatically generates a study plan tailored to the student's desired career path, based on career suggestions and emotional analysis results. This plan includes activities to reinforce specific subjects and manage emotional states.

[0812] Step 8:

[0813] Users (parents) review career suggestions and learning plans along with the emotional analysis results. This allows them to understand their child's current emotional state and provide appropriate support.

[0814] Step 9:

[0815] The emotion engine continuously receives feedback and learns to improve analysis accuracy and help the server optimize suggestions.

[0816] (Example 2)

[0817] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0818] In recent years, there has been a growing need for individually optimized career guidance that takes into account the characteristics and emotional states of learners. However, existing systems have difficulty integrating characteristic data and emotional data, resulting in insufficient personalization of career recommendations. Therefore, there is a demand for systems that can provide more effective and individualized career guidance.

[0819] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0820] In this invention, the server includes means for receiving and integrating learning performance data, aptitude test data, and questionnaire data; means for analyzing individual characteristics based on the integrated data; and means for analyzing emotional data and evaluating the user's emotional state. This enables career guidance optimized for each individual learner.

[0821] "Learning performance data" refers to information used to evaluate the knowledge and skills acquired by learners in educational activities.

[0822] "Aptitude test data" refers to information showing the results of tests used to evaluate learners' vocational aptitudes and abilities.

[0823] "Survey data" refers to research information collected with the aim of understanding learners' interests, values, and personality traits.

[0824] "Means of integration" refers to methods that combine multiple datasets and process them as unified information.

[0825] "Means of analyzing characteristics" refers to methods of evaluating and understanding learners' interests, abilities, and personality traits based on integrated data.

[0826] "Emotional data" refers to information that indicates psychological and emotional responses extracted from the learner's facial expressions and voice.

[0827] "Methods for evaluating emotional states" refer to methods that analyze emotional data to identify the learner's current psychological state.

[0828] "Means of providing career guidance information" refers to methods of presenting the optimal career path to learners based on analysis results and emotional states.

[0829] "Methods for automatically generating learning plans" refer to automated methods for designing educational programs and activities that are tailored to the learner's wishes and aptitudes.

[0830] "Methods for adjusting career paths based on evaluations" refer to methods that optimize career information and learning plans by considering feedback and reactions from users.

[0831] This invention constitutes an educational support system, comprising a server, a terminal, and an emotion analysis device. The following describes specific implementations of this system.

[0832] The server manages databases and computing resources, and runs software to receive and integrate academic performance data, aptitude test data, and survey data. This data is used to build profiles that provide a detailed understanding of each student's characteristics. The server processes the integrated data and utilizes generative AI models to provide individually optimized career guidance and learning plans. This enables instruction tailored to the individual educational needs of each student.

[0833] The terminal is a device operated by students and teachers, and functions as a data input and output interface. The terminal is equipped with a camera and microphone, and includes the ability to transmit facial expression and voice data in real time to an emotion analysis device. This allows for the collection of data to determine the user's emotional state.

[0834] Users are categorized as students, teachers, and parents, each with a specific role. Students input their learning data and impressions and send feedback to the server. Teachers provide generated career information as part of their educational support and use information from the server to select teaching materials and methods as needed. Parents view their child's career suggestions and learning plans through the parent portal and provide support at home.

[0835] The emotion analysis device analyzes emotional data and determines students' emotional states in real time. This enables personalized educational proposals that take into account psychological characteristics. For example, if a student shows signs of anxiety, the content and presentation method of career guidance can be adjusted.

[0836] For example, if a user (student) shows signs of anxiety when submitting test results via their device, the server can use this information to generate career guidance that includes counseling services tailored to reduce stress.

[0837] An example of a prompt used with the generating AI model is, "Please suggest information to include in the current career path suggestions based on the emotions read from the user's facial expressions." By inputting such text, it becomes possible to generate more detailed and effective career path information.

[0838] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0839] Step 1:

[0840] The terminal (user) inputs learning performance data, aptitude test data, and questionnaire responses. This data is registered on the terminal through the input interface and sent to the server. The terminal also captures the user's facial expressions and voice via the camera and microphone, collecting emotional data. Inputs are in various forms of numerical and text data, and the output is a data bundle sent to the server.

[0841] Step 2:

[0842] The server receives learning performance data, aptitude test data, and survey data sent from terminals. This data is integrated to generate individual student profiles. Database software is used for integration, storing the data as centralized information while maintaining data integrity. The input is a dataset for each student, and the output is the integrated student profile.

[0843] Step 3:

[0844] The emotion analysis device analyzes facial expression and voice data received from a terminal. Using a machine learning algorithm, it evaluates the emotional state in real time and adds the results to a profile. The input is the student's emotional data, and the output is the emotional evaluation result.

[0845] Step 4:

[0846] The server uses a generative AI model to create optimal career path suggestions based on student profiles and emotion assessment results. This process involves inputting prompts into the generative AI model to adjust the content and presentation of the suggestions, taking the emotion analysis results into account. The input consists of integrated data and emotion information, while the output is personalized career path suggestions.

[0847] Step 5:

[0848] Users (students and teachers) review career suggestions provided through their terminals. They then send feedback on the suggestions back to the server via the interface, allowing the emotion analysis device to analyze their response again. The input consists of career suggestions and feedback, while the output is adjusted career information based on the feedback.

[0849] Step 6:

[0850] The server automatically generates a personalized learning plan based on the feedback. The generated learning plan incorporates progress-based motivational support and additional resources. Input is feedback and profile data, and output is the adjusted learning plan.

[0851] Step 7:

[0852] Users (parents) view career suggestions and learning plans for their children through the parent portal. They utilize insights into career suggestions and use them as a means of effective communication. Input is data accessed by parents, and output is support policies for the home.

[0853] (Application Example 2)

[0854] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0855] Traditional education systems fail to adequately consider each student's individual academic performance and aptitude when providing career guidance. Furthermore, there is a lack of educational support tailored to students' psychological states and emotions, making it difficult to provide appropriate guidance and suggestions for each individual student. Moreover, there is a lack of means to alleviate the anxiety and stress experienced by students and their parents as a result of these challenges.

[0856] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0857] In this invention, the server includes means for receiving and integrating learning performance data, aptitude test data, and questionnaire data; means for recognizing emotions from the user's voice and visual information and evaluating their emotional state; and means for providing students with optimal career information based on the analysis results and emotional evaluation. This makes it possible to create personalized career suggestions and learning plans that take into account the student's individual data and emotional state.

[0858] "Learning performance data" refers to information that shows students' learning progress and achievements, and includes data such as test scores and assignment evaluation results.

[0859] "Aptitude test data" refers to the results of tests conducted to measure students' strengths and interests, and it is data that indicates what kind of career path a student is suited for.

[0860] "Survey data" refers to information from questionnaires answered by students and their guardians, and it is data that shows students' interests, desires, attitudes towards learning, etc.

[0861] "Auditory and visual information" refers to audio data collected from students and image and video data acquired by cameras, and is used for emotion recognition.

[0862] "Emotional state" refers to a student's psychological state, and includes an assessment of the type and intensity of emotions such as joy and anxiety.

[0863] "Career guidance information" refers to information about future educational options and career choices for students, and is presented based on the student's interests and abilities.

[0864] "Optimal career information for students" refers to information about the most suitable career path, suggested by taking into account each student's individual data and emotional state.

[0865] To realize this invention, the following system configuration is necessary.

[0866] First, the device collects student academic performance data, aptitude test data, and survey data. The device is equipped with a camera and microphone, which capture the user's voice and visual information as emotional data. This enables emotion recognition.

[0867] The server integrates learning performance data, aptitude test data, and survey data received from terminals to analyze students' interests, abilities, and attributes. Furthermore, it uses an emotion engine to assess the user's emotional state from their voice and visual information and adds this assessment to the integrated data. Based on this information, the server provides students with the most suitable career guidance.

[0868] During this process, data analysis is performed using the AI ​​framework "TensorFlow Lite" and the cloud AI service "Google Cloud AI." This enables detailed career guidance tailored to each user's individual learning performance and psychological state.

[0869] As a concrete example, when a student enters their test results into a terminal, the server analyzes that information and provides career guidance that corresponds to their emotional state. For instance, if a student is showing anxiety, it provides career guidance that includes relaxation suggestions and encouraging messages.

[0870] An example of a prompt is, "What kind of encouraging message should be provided to alleviate students' learning stress?" By inputting this prompt into the AI ​​model, a message appropriate for the student will be generated.

[0871] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0872] Step 1:

[0873] The terminal receives learning performance data, aptitude test data, and survey data from students. This data is collected through the terminal's interface, and the camera and microphone simultaneously capture the students' voice and visual information. This data is then transmitted from the terminal to the server.

[0874] Step 2:

[0875] The server integrates learning performance data, aptitude test data, and survey data received from terminals. This creates a basic profile of students regarding their interests, abilities, and attributes. The input data is stored in a database and prepared for analysis.

[0876] Step 3:

[0877] The server uses an emotion engine to assess the student's emotional state from their voice and visual information. Using an AI framework such as TensorFlow Lite, it performs real-time emotion recognition and adds the results to the student's profile. The evaluation results of the emotion data are sent to the server and integrated into the profile.

[0878] Step 4:

[0879] The server utilizes an AI model based on integrated profiles to generate optimal career guidance information for students. It takes profiles and emotional assessment results as input, performs calculations and data processing related to career suggestions, and outputs the results in a format usable in the next step.

[0880] Step 5:

[0881] The user checks career path information on their device. Based on the information displayed on the device, the user evaluates the career path options. They also receive a prompt message from a generated AI model, "What kind of encouraging message should be provided to alleviate students' learning stress?", and use the message displayed on the device.

[0882] Step 6:

[0883] Users input feedback on the provided career information and messages using a terminal. The terminal collects the feedback information and sends it to the server. The server records the received feedback in a database and uses it to make future suggestions and adjust the educational plan.

[0884] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0885] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0886] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0887] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0888] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0889] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0890] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0891] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0892] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0893] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0894] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0895] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0896] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0897] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0898] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0899] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0900] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0901] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0902] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0903] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0904] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0905] The following is further disclosed regarding the embodiments described above.

[0906] (Claim 1)

[0907] A means for receiving and integrating learning performance data, aptitude test data, and survey data,

[0908] A means for analyzing students' interests, abilities, and personality traits based on the aforementioned integrated data,

[0909] A means of providing students with optimal career information based on the aforementioned analysis results,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, comprising means for automatically generating a learning plan tailored to a student's desired career path.

[0913] (Claim 3)

[0914] The system according to claim 1, comprising means for sharing the generated career suggestions and study plans with parents.

[0915] "Example 1"

[0916] (Claim 1)

[0917] A means for receiving and integrating learning performance information, aptitude test information, and survey information,

[0918] A means for analyzing students' interests, abilities, and personality traits based on the aforementioned integrated information,

[0919] A means of providing students with optimal career information based on the aforementioned analysis results,

[0920] A means for generating a learning plan related to career path information,

[0921] A means for displaying and sharing generated career information and learning plans through a user terminal,

[0922] A means to automatically generate student-related materials and make them available for download from devices,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, comprising means for automatically generating learning content and skills acquisition plans tailored to students' desired career paths.

[0926] (Claim 3)

[0927] The system according to claim 1, comprising means for sharing generated career information and learning plans with parents.

[0928] "Application Example 1"

[0929] (Claim 1)

[0930] A means for receiving and integrating learning performance information, aptitude test information, and survey data,

[0931] A means for analyzing an individual's interests, abilities, and personality traits based on the aforementioned integrated information,

[0932] A means of providing personalized career information based on the aforementioned analysis results,

[0933] A means of proposing appropriate career paths and learning plans within the region based on citizens' educational data,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, comprising means for automatically generating a learning plan tailored to an individual's desired career path.

[0937] (Claim 3)

[0938] The system according to claim 1, comprising means for sharing the generated career suggestions and study plans with relevant parties.

[0939] "Example 2 of combining an emotion engine"

[0940] (Claim 1)

[0941] A means for receiving and integrating learning performance data, aptitude test data, and survey data,

[0942] A means of analyzing individual characteristics based on integrated data,

[0943] A means of analyzing emotional data and evaluating the user's emotional state,

[0944] A means of providing optimal career path information based on analysis results and emotional state,

[0945] A system that includes this.

[0946] (Claim 2)

[0947] The system according to claim 1, comprising means for automatically generating a learning plan according to the desired career path.

[0948] (Claim 3)

[0949] The system according to claim 1, comprising means for sharing generated career information and learning plans with the user and adjusting the career path based on evaluation.

[0950] "Application example 2 when combining with an emotional engine"

[0951] (Claim 1)

[0952] A means for receiving and integrating learning performance data, aptitude test data, and survey data,

[0953] A means for analyzing students' interests, abilities, and attributes based on the aforementioned integrated data,

[0954] A means for recognizing emotions from a user's voice and visual information and evaluating their emotional state,

[0955] A means of providing students with optimal career information based on the aforementioned analysis results and emotional evaluations,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, comprising means for automatically generating a learning plan according to a student's desired career path and adjusting the plan based on the results of an emotional evaluation.

[0959] (Claim 3)

[0960] The system according to claim 1, which includes means for sharing the generated career suggestions and learning plans with parents and for supporting smooth communication with parents based on the results of emotional evaluations. [Explanation of Symbols]

[0961] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving and integrating learning performance information, aptitude test information, and survey data, A means for analyzing an individual's interests, abilities, and personality traits based on the aforementioned integrated information, A means of providing personalized career information based on the aforementioned analysis results, A means of proposing appropriate career paths and learning plans within the region based on citizens' educational data, A system that includes this.

2. The system according to claim 1, comprising means for automatically generating a learning plan tailored to an individual's desired career path.

3. The system according to claim 1, comprising means for sharing the generated career suggestions and study plans with relevant parties.

Images