system

Abstract

We provide the system. [Solution] A means for receiving and integrating learner evaluation data, aptitude data, and survey data, A means of analyzing integrated data to identify learner characteristics, A means of suggesting career path options based on the characteristics of learners, A means of generating a study plan tailored to the desired career path, A method for automatically generating interview materials and question lists for educators, Means of notifying parents of career guidance, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Career guidance in the educational field requires detailed correspondence for each learner, but the burden on educators is a problem. Also, it is difficult to provide timely and accurate career information suitable for learners, and it is difficult for many learners to make an optimal career choice for themselves.

Means for Solving the Problems

[0005] This invention provides a system that integrates learner evaluation data, aptitude data, and survey data, analyzes them, and identifies learner characteristics. Based on the identified characteristics, this system suggests career path options and automatically generates a learning plan tailored to the desired career path. Furthermore, it includes functions to automatically generate interview materials and question lists for educators and to notify parents of career path information, thereby reducing the burden on educators and providing optimal career support to learners.

[0006] "Evaluation data" refers to information that records learners' learning status in numerical form or reports, such as their academic performance and test results.

[0007] "Aptitude data" refers to information that includes the results of measuring learners' interests, personality traits, and other characteristics, as well as the results of aptitude tests and psychological assessments.

[0008] "Survey data" refers to information collected from responses regarding learners' interests and desires, and is used to understand their career path choices and motivation to learn.

[0009] "Means of integration" refer to the processes and techniques for combining different types of data into a single dataset, and they play a crucial role in data management.

[0010] "Means of analysis" refers to methods and techniques for meticulously analyzing data and identifying learner characteristics and patterns from it.

[0011] "Characteristics" refer to information that indicates a learner's unique interests, personality traits, and ability tendencies, and are important factors in career path selection.

[0012] "Career options" refers to possible educational destinations and career choices for learners, and includes information to suggest them.

[0013] A "learning plan" refers to a document that outlines the learning content and schedule to be included, serving as a guideline for learners to efficiently progress towards a specific goal.

[0014] "Interview materials" refer to a set of information used by educators when conducting interviews with learners, including lists of questions for discussion and instruction, and progress reports.

[0015] "Means of notification" refers to the technologies and methods used to transmit information required by the system to the target audience, and includes email and app notifications. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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), etc.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] To implement this invention, it is necessary to design and develop a system having the following configuration. This system uses a server, terminals, and a user interface to support students' career guidance.

[0038] The server functions as a central data processing unit, collecting and integrating learner evaluation data, aptitude data, and survey data. To achieve this, it establishes database connections and efficiently retrieves data from multiple sources. Specifically, the server converts this data into JSON format and integrates it into a single dataset. Next, the server feeds this integrated dataset into a generating AI model to analyze the learner's characteristics. For example, a learner who excels in science and engineering subjects and demonstrates leadership qualities might be found to be well-suited to a science and engineering-related university.

[0039] The terminal serves as the user interface. Here, career suggestions and study plans sent from the server are displayed in an easy-to-understand visual format. Users can view career option lists and study plans via PCs or mobile devices. The notification function for parents is also utilized here, quickly delivering the latest information regarding the learner's career path via email and push notifications.

[0040] Users, namely educators and learners, utilize this system to receive career guidance. Educators can conduct individual consultations using interview materials and question lists provided by the server via their terminals. For example, if a learner who excels in mathematics wishes to major in engineering, they can view a learning plan on their terminal, including necessary math materials and reference links, based on data provided by the server, and this information is also shared with their parents.

[0041] In this way, this system supports efficient and effective career guidance in educational settings and plays a role in helping learners choose the career path that is best suited to them.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects learner evaluation data, aptitude data, and survey data from various data sources and integrates them. Specifically, it executes SQL queries against multiple databases to retrieve data in CSV or JSON format. After that, it performs data formatting and combines them into a single integrated dataset.

[0045] Step 2:

[0046] The server uses a machine learning model to analyze the integrated data. This model is trained to extract learner characteristics. The analysis clarifies the learner's interests, abilities, and personality traits. For example, the model might determine from the data that "the learner has a strong interest in science."

[0047] Step 3:

[0048] Based on the analysis results, the server selects appropriate career path candidates from the career information database. This involves using filtering and ranking algorithms to find career paths that match the learner's characteristics. This then lists the universities and occupations that the learner can choose from.

[0049] Step 4:

[0050] The server generates a learning plan based on the student's desired career path. Using an algorithm, it identifies the subjects and skills the learner needs and creates a scheduled learning plan. The plan includes daily learning objectives and specific actions to be achieved.

[0051] Step 5:

[0052] The server automatically generates interview materials and question lists for educators. It also generates reports on learner characteristics, streamlining interview preparation. These materials are exported to educators in PDF format.

[0053] Step 6:

[0054] The device displays career path options and study plans generated by the server to the user. Through this, the user can review and deepen their understanding of their career path and study methods. Furthermore, parents receive notifications regarding career guidance via the device, allowing them to share information about their child's progress and plans.

[0055] (Example 1)

[0056] 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."

[0057] This invention relates to a system for efficiently and effectively providing career guidance and creating study plans for learners. Traditional career guidance often involved manual processes, making it difficult to provide personalized advice tailored to each learner, resulting in time-consuming and labor-intensive methods. Furthermore, the lack of smooth information sharing with parents sometimes led to insufficient support for learners.

[0058] 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.

[0059] In this invention, the server includes means for receiving and integrating learner evaluation information, aptitude information, and questionnaire information; means for analyzing the integrated information to identify the learner's characteristics; and means for suggesting career path options based on the learner's characteristics. This enables the generation of individually customized career path suggestions and the rapid provision of information to parents.

[0060] "Learner evaluation information" refers to objective data regarding learners' performance and skills.

[0061] "Aptitude information" refers to information about a learner's interests, areas of expertise, and potential.

[0062] "Survey information" refers to subjective response data provided by learners regarding their self-assessment and career choices.

[0063] "Means of integration" refers to the process or technique for combining data obtained from multiple sources into a consistent format.

[0064] "Methods for analyzing and identifying learner characteristics" refer to methods of processing data to reveal characteristics such as learners' interests, concerns, and ability tendencies.

[0065] A "means of suggesting career path options" refers to a system that presents suitable career paths and educational institutions based on the learner's characteristics.

[0066] A "means for generating a learning plan" refers to a system for creating a specific learning process and schedule based on the learner's career path and characteristics.

[0067] "Methods for automatically generating interview materials and question items" refers to technologies that automatically create materials and questions to enable educators to engage in effective dialogue with learners.

[0068] "Means of notifying parents of career path information" refers to methods for providing parents with the latest information regarding their child's career choices and learning progress.

[0069] "Visual display methods" refer to technologies that present information to users as graphics or diagrams to aid in understanding.

[0070] A "generative AI model" is a technology that uses models generated by machine learning algorithms to analyze data and recognize patterns.

[0071] A "prompt sentence" is a formalized sentence input to a generative AI model, used to induce specific analyses or outputs.

[0072] To implement this invention, it is necessary to design and develop a system including a server, terminals, and a user interface. The server acts as a central control unit, collecting learner evaluation information, aptitude information, and survey information from various data sources and integrating this data. Specifically, the server connects to a database using database software, retrieves data, converts it to JSON format, and integrates it. Next, the server supplies the integrated dataset to a generative AI model, which analyzes the learner's characteristics. For example, for a learner who excels in science and engineering subjects and exhibits leadership qualities, the generative AI model might determine that pursuing a science and engineering degree at university is suitable.

[0073] The device provides an interface for users to visually view career suggestions and study plans provided by the server. Users can access and view this information using a PC or mobile device. The device also has a built-in notification function for parents, providing career information in real time via email and push notifications.

[0074] Users can utilize data retrieved from the server via their devices to receive support in deciding on educational direction. Educators can conduct individual interviews with learners based on interview materials and questions generated by the server. For example, a learner who excels in mathematics and wishes to major in engineering can view a learning plan based on data provided by the server, which includes necessary mathematics materials and reference links. In addition, this information can be shared with parents.

[0075] A concrete example of a prompt would be, "This student excels in science subjects and demonstrates leadership qualities. Please suggest a suitable career path for them."

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server establishes a connection to the database and collects learner evaluation information, aptitude information, and survey information. The input consists of various data obtained from multiple data sources. The server cleanses and formats this data, converting it into a consistent JSON format. This process outputs a single, integrated dataset.

[0079] Step 2:

[0080] The server feeds the integrated dataset into a generative AI model to analyze the learner's characteristics. The input is the integrated data obtained in Step 1. Here, pattern recognition and analysis algorithms are used to identify the learner's strengths and aptitudes. The output of this analysis is a real-time prompt message that indicates the learner's characteristics.

[0081] Step 3:

[0082] The server generates optimal career path suggestions for the learner based on the analysis results. The input is the learner's characteristic prompt sentence obtained in step 2. Utilizing a generation AI model, it selects career path candidates that match the learner's characteristics and outputs them as career path suggestions. For example, a specific suggestion such as, "Since you excel in science subjects and demonstrate leadership, we recommend that you pursue a science and engineering degree at a university," might be output.

[0083] Step 4:

[0084] The terminal receives career suggestions and study plans sent from the server and displays them visually in the user interface. The input is the career suggestions generated in step 3. The terminal displays the list of career options and study plans in a dashboard format for easy understanding by the user. The output is a customized interface for the user.

[0085] Step 5:

[0086] Users receive career guidance by utilizing information on their devices. Educators conduct individual consultations using the provided materials and questionnaires to adjust the learner's educational plan. The final output is feedback on career guidance and materials as needed. This information is also provided to parents through the system, enabling further support.

[0087] (Application Example 1)

[0088] 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."

[0089] In modern education, while career guidance for learners should be individualized, implementing it presents significant challenges due to the considerable effort and time required. In particular, it is difficult for learners and parents to access career information and study plans at the appropriate time, posing a barrier to effective career choices. There is a need to solve these problems and achieve more efficient and effective career guidance.

[0090] 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.

[0091] In this invention, the server includes means for receiving and integrating learner evaluation data, aptitude data, and questionnaire data; means for analyzing the integrated data to identify learner characteristics; and means for learners and guardians to access career suggestions and receive visualized learning plans via mobile devices. This enables learners and guardians to receive career information and learning plans based on their individual characteristics in real time.

[0092] A "learner" refers to someone who is studying at an educational institution and who needs support with their career choices and study plans.

[0093] "Evaluation data" refers to information that quantifies learners' academic performance and extracurricular activities, and serves as basic data for assessing career aptitude and academic ability.

[0094] "Aptitude data" refers to information that quantifies or categorizes learners' abilities, characteristics, and interests, and serves as a basis for making decisions when choosing a career path.

[0095] "Survey data" refers to information that quantifies or documents the opinions and wishes obtained from learners and their guardians, and serves as material for analyzing needs in career guidance.

[0096] "Analysis" refers to all data processing operations performed to clarify learners' characteristics and tendencies based on collected data.

[0097] "Characteristics" refer to the individual abilities, personality traits, and aptitudes that learners possess, and serve as a fundamental indicator in career path selection.

[0098] "Career path" refers to the academic or professional career route that learners choose for their future, and this decision is based on their individual characteristics and aptitudes.

[0099] "Portable information terminals" refer to electronic devices such as smartphones and tablets that are portable and capable of sending and receiving information.

[0100] A "visualized learning plan" refers to a schedule of educational activities and learning objectives created based on the learner's career path, presented in the form of charts, graphs, or lists.

[0101] To implement this invention, it is necessary to build a system that supports career guidance for learners. This system consists of a server, terminals, and users.

[0102] The server is responsible for receiving, integrating, and analyzing learner evaluation data, aptitude data, and survey data. To this end, the server establishes a database connection and integrates the data in JSON format. Using a generative AI model, it analyzes learner characteristics and generates optimal career path suggestions for each individual learner. These generated career path suggestions are automatically notified to parents.

[0103] The device functions as an interface for users to access career guidance and study plans. Users can visually review career information and study plans transmitted from the server using mobile devices such as smartphones and tablets. This information is presented as a visualized study plan in charts and lists, making it easy for learners and their guardians to understand.

[0104] Users, both educators and learners, can utilize this system to receive effective career guidance. For example, a learner who excels in science and mathematics might be suggested to pursue a degree in engineering, and the necessary study materials and learning plans would also be provided. An example of a prompt message that could be input into the generating AI model is, "Please suggest a suitable university major for a student who excels in mathematics."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server receives evaluation data, aptitude data, and survey data about learners. Each dataset is obtained from various data sources and stored in a database. The input data is converted to JSON format and integrated. This allows the server to prepare foundational information for understanding the learners as a whole.

[0108] Step 2:

[0109] The server feeds the integrated data into a generative AI model for analysis. The integrated dataset is used as input. The generative AI model processes this data to identify learner characteristics. For example, academic ability, areas of interest, and aptitudes are output. This prepares the system for career guidance based on these characteristics.

[0110] Step 3:

[0111] The server generates personalized career path options for each learner. It takes the results of an AI model's analysis as input. Based on this information, the server outputs career path options that include fields and occupations the learner might be interested in. This personalizes the learner's future direction.

[0112] Step 4:

[0113] The device displays a visualized learning plan to the user. It takes career path options and learning plans received from the server as input, converts them into a graphical interface, and outputs them. Users can then access information to enhance educational effectiveness using their smartphones or tablets.

[0114] Step 5:

[0115] Users, especially educators, can access materials and question lists for individual consultations via their devices. These are automatically generated based on input data from the server. This allows educators to have the necessary materials readily available to effectively conduct consultations.

[0116] Step 6:

[0117] The server notifies parents of career guidance information. It uses the generated career suggestions and study plans as input and outputs them via email and push notifications. Parents can receive real-time information about their child's career choices from the comfort of their home.

[0118] 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.

[0119] This invention is a system that provides more precise career guidance by integrating learner evaluation data, aptitude data, and survey data, and further recognizing and analyzing user emotions. Through the server, terminals, and user interface, this system deeply understands the individual needs of learners and enables emotion-based career guidance and customization.

[0120] As the core data processing unit, the server not only collects diverse data from learners but also receives the user's emotional state through the emotion engine. Specifically, the server uses natural language processing and facial recognition technologies to analyze emotions from text-based communication. This data is combined with conventional training data to construct a single integrated profile.

[0121] The emotion engine recognizes emotions in real time when interacting with learners and educators, and provides feedback based on these emotions. For example, if a learner is feeling anxious about career choices, the engine detects this and adjusts the career option list and study plan to alleviate the anxiety by incorporating encouraging elements.

[0122] The device displays career options and learning plans sent from the server through its user interface, and also provides customized information that reflects the results of emotion recognition. Users can receive visual feedback to help them understand how their emotional state influences their career plans. For example, the career list suggested by emotion analysis also displays the reasons for the recommendations based on those emotions.

[0123] Users can make career choices with greater confidence by leveraging emotion-based feedback. Educators can use generated interview materials and question lists to provide instruction that is sensitive to the learner's emotional state.

[0124] This system not only provides each learner with the most suitable career path and study plan, but also enables meticulous career guidance that addresses the learner's emotional needs. As a result, learners can make career choices in a better mental state.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server collects learner evaluation data, aptitude data, and survey data from various data sources and integrates them. Specifically, it retrieves data from databases, converts them into JSON format, and creates a single integrated dataset.

[0128] Step 2:

[0129] The server uses an emotion engine to analyze emotional data from text and audio during interactions with learners. This employs natural language processing and speech analysis technologies to quantify emotional states in real time.

[0130] Step 3:

[0131] The server combines integrated datasets and sentiment data, feeds them into a machine learning model, and comprehensively analyzes learners' characteristics and emotional states. This analysis provides insights that can help learners make informed career choices.

[0132] Step 4:

[0133] Based on the analysis results, the server selects career path candidates from the career path information database that match the learner's characteristics and emotional state. This involves filtering and prioritizing algorithms, with adjustments made based on emotions.

[0134] Step 5:

[0135] The server generates a customized learning plan that reflects the learner's emotional state. This plan consists of incorporating learning steps that align with future goals and emotional needs.

[0136] Step 6:

[0137] The device visually displays career options and study plans provided by the server to the user. Furthermore, based on the results of the emotion engine, career recommendations include emotional support and advice.

[0138] Step 7:

[0139] Users can review career suggestions and study plans through their devices and make career choices while receiving emotion-based feedback. Educators can use the provided interview materials to provide guidance that is appropriate to the student's emotions.

[0140] (Example 2)

[0141] 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".

[0142] Traditional career guidance systems based career choices on student evaluation and aptitude information, but failed to address emotional needs, making students prone to anxiety and indecision regarding their career paths. Furthermore, information gathering from external databases was limited, and the provision of customized visual information was insufficient. A system is needed to solve these problems and support students' career choices from an emotional perspective as well.

[0143] 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.

[0144] In this invention, the server includes means for receiving and integrating learner evaluation information, aptitude information, and questionnaire information; means for analyzing emotional data in real time and identifying emotional factors that influence career choices; and means for visually providing emotion-based feedback. This makes it possible to support learners' career choices more precisely and with greater emotional consideration.

[0145] A "learner" refers to an individual who acquires knowledge through an educational institution or through self-study.

[0146] "Evaluation information" refers to data that shows learners' academic performance and overall performance.

[0147] "Aptitude information" refers to data related to learners' interests and abilities, and is useful information for career choices and academic planning.

[0148] "Survey information" refers to data that compiles learners' self-assessments and opinions.

[0149] "Means of integration" refers to technologies for aggregating information collected from multiple data sources and combining it into a single profile.

[0150] "Emotional data" refers to information that indicates a learner's emotional state, and includes emotional data that can be extracted from text, audio, and video data.

[0151] "Means of analysis" refers to the process of extracting useful information from collected data and deriving meaningful results.

[0152] "Career path selection" refers to the process by which learners decide on their future occupation or direction of study.

[0153] "Feedback" refers to the responses and suggestions provided by the system to the learner, with the aim of improving or supporting the learner's behavior.

[0154] "Visual means of presentation" refers to techniques for presenting information to learners in a visual format, including methods of displaying information in graphic, dashboard, or infographic format.

[0155] This invention is a system that provides more precise career guidance to learners, while also supporting their emotional well-being. The system primarily involves interaction and data processing between a server, a terminal, and the user.

[0156] The server plays a central role in the system, collecting and integrating learner evaluation information, aptitude information, and survey information from various sources. The server works in conjunction with a high-performance database to manage the integrated information. It also features an emotion analysis engine utilizing natural language processing and facial recognition technologies, regularly collecting emotional data from learners' text communications and facial expressions. This helps identify learners' emotional needs and alleviate anxiety regarding career choices.

[0157] The terminal's role is to present the integrated profile sent from the server to the learner. Specifically, the terminal uses visual display functions to provide the user with career options and study plans, and visually displays feedback based on sentiment analysis. The system's user interface is intuitive and designed to allow learners to smoothly understand the career selection process.

[0158] Users utilize feedback provided through their devices to make career choices while taking their emotional state into consideration. For example, if a learner feels anxious about a particular career path, the server displays encouragement and additional information on the device that reflects the results of an emotional analysis. Educators have the ability to provide individualized support to learners using interview materials and question lists generated by the server.

[0159] For example, if a learner feels "I'm not confident about pursuing this profession," the device will display specific ways to address that anxiety and reasons for its recommendation.

[0160] Example prompt to input into a generative AI model: "Integrate recent learner sentiment data and generate recommended career paths."

[0161] This makes it possible to provide two-way career guidance that also takes emotional aspects into consideration.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server collects learner assessment information, aptitude information, and survey information from educational institution databases and online platforms. It accesses external data sources using API keys and authentication credentials as input. The data is stored in the database in an integrated format. Specifically, it performs periodic queries to retrieve the latest information and remove duplicate data.

[0165] Step 2:

[0166] The server collects and analyzes learner emotional data via the terminal using natural language processing and facial recognition technologies. Input includes learner text messages and image data from the camera. This data is sent to an emotion analysis engine, where it is classified into one of three emotional states: positive, negative, or neutral. Specifically, it utilizes a Sentiment Analysis algorithm for text and facial recognition software for images.

[0167] Step 3:

[0168] The server processes the collected evaluation information, aptitude information, and sentiment data into an integrated profile. This profile is then processed by a generative AI model. The generative AI model analyzes the input data to generate career path candidates optimized for the learner. The output is a list of customized career path candidates, each accompanied by sentiment feedback.

[0169] Step 4:

[0170] The terminal visually presents the integrated profile received from the server to the learner. The input is a data stream from the server. Through the system's user interface, the terminal also provides a graphical display of career options and learning plans, as well as feedback based on sentiment analysis. Specifically, it displays emotional feedback using colors and icons in the career list.

[0171] Step 5:

[0172] Users select a path by utilizing information on their device. Input includes path options and emotional feedback from the device. Based on this information, users choose a path and provide feedback to the system. Specifically, users confirm their selection through taps and clicks, and enter additional opinions or emotions in text fields.

[0173] This processing flow allows the system to provide learners with real-time emotional support while assisting them in making the optimal career choices.

[0174] (Application Example 2)

[0175] 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".

[0176] In modern career guidance, there is a need for precise advice that takes into account not only the learner's learning data and aptitude, but also their emotional state at any given time. However, conventional systems have difficulty recognizing learners' emotions in real time and reflecting them in career paths and learning plans. As a result, there is a risk that learners will make inappropriate choices. Therefore, a system is needed that can analyze learners' emotional states and enable detailed career guidance based on that analysis.

[0177] 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.

[0178] In this invention, the server includes means for receiving and integrating learner evaluation data, aptitude data, and questionnaire data; means for analyzing the integrated data to identify learner characteristics and identify the user's emotional state; and means for suggesting career path options based on the identified emotional state. This enables learners to make career choices that take their emotional state into consideration and to accept a more appropriate learning plan.

[0179] A "learner" is an individual who participates in the learning process and receives career guidance.

[0180] "Evaluation data" refers to information such as tests, grades, and feedback regarding learners' knowledge and skills.

[0181] "Aptitude data" refers to information related to an individual's characteristics and abilities, such as a learner's interests, personality, and skills.

[0182] "Survey data" refers to the results of surveys answered by learners, and is information that captures trends in opinions and emotions.

[0183] "Emotional state" refers to the emotional state that learners experience in real time, and is analyzed through facial expressions and text data.

[0184] A "server" is a central device in information processing that receives data, processes it, and provides the results.

[0185] "Integrated data" refers to information that combines evaluation data, aptitude data, and survey data obtained from learners.

[0186] "Career options" refer to future career paths and learning field choices that are suggested based on the learner's characteristics and emotional state.

[0187] A "learning plan" is a plan that specifically outlines the educational approach and activities that are appropriate for the learner's career path and goals.

[0188] The term "educator" refers to teachers and instructors who are in a position to provide career guidance and learning support.

[0189] "Parents" refer to the learner's parents or supervisors, and are the people with whom information about their studies and future career paths should be shared.

[0190] This invention is a system that integrates learner evaluation data, aptitude data, and questionnaire data, uses these to perform emotional analysis, and provides learners with career path suggestions based on their emotional state. Specifically, the system is configured as follows.

[0191] The server functions as the core information processing unit. First, it receives evaluation data, aptitude data, and survey data from learners. This data is integrated with stored historical information to build a user profile. In this process, the server uses natural language processing to analyze emotions from text data and utilizes facial recognition technology to identify the user's real-time emotional state.

[0192] Specifically, the software involves an emotion analysis module running on a server that utilizes the Emotion API, and programming languages ​​such as Python are used for natural language processing. Furthermore, cloud services such as AWS Lambda enable real-time data processing.

[0193] The device functions as the learner's interface, visually displaying suggested paths and learning plans. This allows learners to intuitively understand feedback based on their emotional state. The user interface is built using React Native and is cross-platform compatible.

[0194] Users can check their career paths and study plans through their devices and receive feedback. This feedback is customized to take into account the learner's emotional state, allowing them to consider more appropriate options. For example, if a learner is feeling anxious about career choices, encouraging messages are automatically provided to promote a positive outlook on their future.

[0195] A specific example of a prompt might be: "Design an application that integrates learner evaluation data, aptitude data, and survey data, analyzes user emotions, and suggests the optimal path. Use the Emotion API for emotion analysis, process the data with AWS Lambda, and develop the user interface with React Native." Following this prompt, the system will provide an integrated learning plan.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The server receives evaluation data, aptitude data, and survey data from learners. As input, this data consists of information from various forms and test results provided by the learners. Each dataset is stored in a database to integrate the data and build an integrated profile. This generates a comprehensive profile of the learner.

[0199] Step 2:

[0200] The server uses the Emotion API to analyze the learner's emotions from text data. The input is text data written by the learner. Natural language processing is used to analyze the context and identify emotional states (e.g., anxiety, excitement). The analysis results are added to the learner's integrated profile.

[0201] Step 3:

[0202] The server generates appropriate career path candidates based on the learner's integrated profile. Inputs include evaluation data, aptitude data, survey data, and sentiment analysis results. Using machine learning algorithms, it predicts the optimal career path and generates a candidate list that takes emotional states into account. The output is a personalized career path candidate list for the learner.

[0203] Step 4:

[0204] The terminal receives a list of career options from the server and presents it visually to the learner. The input is the career option data provided by the server. Using React Native, the list of options is displayed intuitively through a user interface. The output is a visual representation that allows learners to easily consider career options.

[0205] Step 5:

[0206] Users provide feedback and select career path options through their devices. Input is through user interface operations. Selected options and feedback are sent to a server for further analysis and recording. This allows learners to leverage the product's features to make optimal choices.

[0207] Through these steps, the system provides customized career guidance based on the learner's emotions and characteristics.

[0208] 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.

[0209] Data generation model 58 is a 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] To implement this invention, it is necessary to design and develop a system having the following configuration. This system uses a server, terminals, and a user interface to support students' career guidance.

[0225] The server functions as a central data processing unit, collecting and integrating learner evaluation data, aptitude data, and survey data. To achieve this, it establishes database connections and efficiently retrieves data from multiple sources. Specifically, the server converts this data into JSON format and integrates it into a single dataset. Next, the server feeds this integrated dataset into a generating AI model to analyze the learner's characteristics. For example, a learner who excels in science and engineering subjects and demonstrates leadership qualities might be found to be well-suited to a science and engineering-related university.

[0226] The terminal serves as the user interface. Here, career suggestions and study plans sent from the server are displayed in an easy-to-understand visual format. Users can view career option lists and study plans via PCs or mobile devices. The notification function for parents is also utilized here, quickly delivering the latest information regarding the learner's career path via email and push notifications.

[0227] Users, namely educators and learners, utilize this system to receive career guidance. Educators can conduct individual consultations using interview materials and question lists provided by the server via their terminals. For example, if a learner who excels in mathematics wishes to major in engineering, they can view a learning plan on their terminal, including necessary math materials and reference links, based on data provided by the server, and this information is also shared with their parents.

[0228] In this way, this system supports efficient and effective career guidance in educational settings and plays a role in helping learners choose the career path that is best suited to them.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server collects learner evaluation data, aptitude data, and survey data from various data sources and integrates them. Specifically, it executes SQL queries against multiple databases to retrieve data in CSV or JSON format. After that, it performs data formatting and combines them into a single integrated dataset.

[0232] Step 2:

[0233] The server uses a machine learning model to analyze the integrated data. This model is trained to extract learner characteristics. The analysis clarifies the learner's interests, abilities, and personality traits. For example, the model might determine from the data that "the learner has a strong interest in science."

[0234] Step 3:

[0235] Based on the analysis results, the server selects appropriate career path candidates from the career information database. This involves using filtering and ranking algorithms to find career paths that match the learner's characteristics. This then lists the universities and occupations that the learner can choose from.

[0236] Step 4:

[0237] The server generates a learning plan based on the student's desired career path. Using an algorithm, it identifies the subjects and skills the learner needs and creates a scheduled learning plan. The plan includes daily learning objectives and specific actions to be achieved.

[0238] Step 5:

[0239] The server automatically generates interview materials and question lists for educators. It also generates reports on learner characteristics, streamlining interview preparation. These materials are exported to educators in PDF format.

[0240] Step 6:

[0241] The device displays career path options and study plans generated by the server to the user. Through this, the user can review and deepen their understanding of their career path and study methods. Furthermore, parents receive notifications regarding career guidance via the device, allowing them to share information about their child's progress and plans.

[0242] (Example 1)

[0243] 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."

[0244] This invention relates to a system for efficiently and effectively providing career guidance and creating study plans for learners. Traditional career guidance often involved manual processes, making it difficult to provide personalized advice tailored to each learner, resulting in time-consuming and labor-intensive methods. Furthermore, the lack of smooth information sharing with parents sometimes led to insufficient support for learners.

[0245] 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.

[0246] In this invention, the server includes means for receiving and integrating learner evaluation information, aptitude information, and questionnaire information; means for analyzing the integrated information to identify the learner's characteristics; and means for suggesting career path options based on the learner's characteristics. This enables the generation of individually customized career path suggestions and the rapid provision of information to parents.

[0247] "Learner evaluation information" refers to objective data regarding learners' performance and skills.

[0248] "Aptitude information" refers to information about a learner's interests, areas of expertise, and potential.

[0249] "Survey information" refers to subjective response data provided by learners regarding their self-assessment and career choices.

[0250] "Means of integration" refers to the process or technique for combining data obtained from multiple sources into a consistent format.

[0251] "Methods for analyzing and identifying learner characteristics" refer to methods of processing data to reveal characteristics such as learners' interests, concerns, and ability tendencies.

[0252] A "means of suggesting career path options" refers to a system that presents suitable career paths and educational institutions based on the learner's characteristics.

[0253] A "means for generating a learning plan" refers to a system for creating a specific learning process and schedule based on the learner's career path and characteristics.

[0254] "Methods for automatically generating interview materials and question items" refers to technologies that automatically create materials and questions to enable educators to engage in effective dialogue with learners.

[0255] "Means of notifying parents of career path information" refers to methods for providing parents with the latest information regarding their child's career choices and learning progress.

[0256] "Visual display methods" refer to technologies that present information to users as graphics or diagrams to aid in understanding.

[0257] A "generative AI model" is a technology that uses models generated by machine learning algorithms to analyze data and recognize patterns.

[0258] A "prompt sentence" is a formalized sentence input to a generative AI model, used to induce specific analyses or outputs.

[0259] To implement this invention, it is necessary to design and develop a system including a server, terminals, and a user interface. The server acts as a central control unit, collecting learner evaluation information, aptitude information, and survey information from various data sources and integrating this data. Specifically, the server connects to a database using database software, retrieves data, converts it to JSON format, and integrates it. Next, the server supplies the integrated dataset to a generative AI model, which analyzes the learner's characteristics. For example, for a learner who excels in science and engineering subjects and exhibits leadership qualities, the generative AI model might determine that pursuing a science and engineering degree at university is suitable.

[0260] The device provides an interface for users to visually view career suggestions and study plans provided by the server. Users can access and view this information using a PC or mobile device. The device also has a built-in notification function for parents, providing career information in real time via email and push notifications.

[0261] Users can utilize data retrieved from the server via their devices to receive support in deciding on educational direction. Educators can conduct individual interviews with learners based on interview materials and questions generated by the server. For example, a learner who excels in mathematics and wishes to major in engineering can view a learning plan based on data provided by the server, which includes necessary mathematics materials and reference links. In addition, this information can be shared with parents.

[0262] A concrete example of a prompt would be, "This student excels in science subjects and demonstrates leadership qualities. Please suggest a suitable career path for them."

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The server establishes a connection to the database and collects learner evaluation information, aptitude information, and survey information. The input consists of various data obtained from multiple data sources. The server cleanses and formats this data, converting it into a consistent JSON format. This process outputs a single, integrated dataset.

[0266] Step 2:

[0267] The server feeds the integrated dataset into a generative AI model to analyze the learner's characteristics. The input is the integrated data obtained in Step 1. Here, pattern recognition and analysis algorithms are used to identify the learner's strengths and aptitudes. The output of this analysis is a real-time prompt message that indicates the learner's characteristics.

[0268] Step 3:

[0269] The server generates optimal career path suggestions for the learner based on the analysis results. The input is the learner's characteristic prompt sentence obtained in step 2. Utilizing a generation AI model, it selects career path candidates that match the learner's characteristics and outputs them as career path suggestions. For example, a specific suggestion such as, "Since you excel in science subjects and demonstrate leadership, we recommend that you pursue a science and engineering degree at a university," might be output.

[0270] Step 4:

[0271] The terminal receives career suggestions and study plans sent from the server and displays them visually in the user interface. The input is the career suggestions generated in step 3. The terminal displays the list of career options and study plans in a dashboard format for easy understanding by the user. The output is a customized interface for the user.

[0272] Step 5:

[0273] Users receive career guidance by utilizing information on their devices. Educators conduct individual consultations using the provided materials and questionnaires to adjust the learner's educational plan. The final output is feedback on career guidance and materials as needed. This information is also provided to parents through the system, enabling further support.

[0274] (Application Example 1)

[0275] 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."

[0276] In modern education, while career guidance for learners should be individualized, implementing it presents significant challenges due to the considerable effort and time required. In particular, it is difficult for learners and parents to access career information and study plans at the appropriate time, posing a barrier to effective career choices. There is a need to solve these problems and achieve more efficient and effective career guidance.

[0277] 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.

[0278] In this invention, the server includes means for receiving and integrating learner evaluation data, aptitude data, and questionnaire data; means for analyzing the integrated data to identify learner characteristics; and means for learners and guardians to access career suggestions and receive visualized learning plans via mobile devices. This enables learners and guardians to receive career information and learning plans based on their individual characteristics in real time.

[0279] A "learner" refers to someone who is studying at an educational institution and who needs support with their career choices and study plans.

[0280] "Evaluation data" refers to information that quantifies the academic achievements and activity performance of learners, and serves as the basic material for career suitability and academic ability evaluation.

[0281] "Suitability data" refers to information that quantifies or categorizes the abilities, characteristics, interests, etc. of learners, and serves as the basis for decision-making in career selection.

[0282] "Questionnaire data" refers to information that quantifies or documents the opinions and wishes obtained from learners and guardians, and serves as the material for analyzing needs in career guidance.

[0283] "Analysis" refers to all data processing operations carried out to clarify the characteristics and tendencies of learners based on the collected data.

[0284] "Characteristics" refers to the individual abilities, personalities, suitability, etc. of learners, and serves as the basic indicator in career selection.

[0285] "Career path" refers to the academic and vocational paths that learners choose for the future, and the decision is based on the individual's characteristics and suitability.

[0286] "Mobile information terminal" refers to electronic devices such as smartphones and tablets that are portable and capable of receiving and transmitting information.

[0287] "Visualized learning plan" refers to the schedule of educational activities and learning goals created based on the learner's career path, presented in the form of charts or lists.

[0288] To implement this invention, it is required to construct a system that supports the career guidance of learners. This system is composed of terminals and users centered around a server.

[0289] The server is responsible for receiving, integrating, and analyzing learner evaluation data, aptitude data, and survey data. To this end, the server establishes a database connection and integrates the data in JSON format. Using a generative AI model, it analyzes learner characteristics and generates optimal career path suggestions for each individual learner. These generated career path suggestions are automatically notified to parents.

[0290] The device functions as an interface for users to access career guidance and study plans. Users can visually review career information and study plans transmitted from the server using mobile devices such as smartphones and tablets. This information is presented as a visualized study plan in charts and lists, making it easy for learners and their guardians to understand.

[0291] Users, both educators and learners, can utilize this system to receive effective career guidance. For example, a learner who excels in science and mathematics might be suggested to pursue a degree in engineering, and the necessary study materials and learning plans would also be provided. An example of a prompt message that could be input into the generating AI model is, "Please suggest a suitable university major for a student who excels in mathematics."

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The server receives evaluation data, aptitude data, and survey data about learners. Each dataset is obtained from various data sources and stored in a database. The input data is converted to JSON format and integrated. This allows the server to prepare foundational information for understanding the learners as a whole.

[0295] Step 2:

[0296] The server feeds the integrated data into a generative AI model for analysis. The integrated dataset is used as input. The generative AI model processes this data to identify learner characteristics. For example, academic ability, areas of interest, and aptitudes are output. This prepares the system for career guidance based on these characteristics.

[0297] Step 3:

[0298] The server generates personalized career path options for each learner. It takes the results of an AI model's analysis as input. Based on this information, the server outputs career path options that include fields and occupations the learner might be interested in. This personalizes the learner's future direction.

[0299] Step 4:

[0300] The device displays a visualized learning plan to the user. It takes career path options and learning plans received from the server as input, converts them into a graphical interface, and outputs them. Users can then access information to enhance educational effectiveness using their smartphones or tablets.

[0301] Step 5:

[0302] Users, especially educators, can access materials and question lists for individual consultations via their devices. These are automatically generated based on input data from the server. This allows educators to have the necessary materials readily available to effectively conduct consultations.

[0303] Step 6:

[0304] The server notifies parents of career guidance information. It uses the generated career suggestions and study plans as input and outputs them via email and push notifications. Parents can receive real-time information about their child's career choices from the comfort of their home.

[0305] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0306] The present invention is a system that more precisely conducts course guidance by integrating learners' evaluation data, suitability data, and questionnaire data, and further recognizing and analyzing the emotions of users. Through a server, a terminal, and a user interface, this system deeply understands the individual needs of learners and enables the proposal and customization of courses based on emotions.

[0307] As a core data processing unit, the server not only collects various data of learners but also receives the user's emotional state through an emotion engine. Specifically, the server uses natural language processing technology and facial expression recognition technology to analyze emotions from text-based communication. This data is combined with conventional learning data to construct an integrated profile.

[0308] When interacting with learners or educators, the emotion engine recognizes emotions in real time and provides feedback based on them. For example, when a learner feels anxious about course selection, the engine detects it and adjusts in a direction to reduce anxiety by incorporating encouraging elements into the course candidate list and learning plan.

[0309] The terminal displays the course candidates and learning plans sent from the server through the user interface and further provides customization information reflecting the emotion recognition results. The user can receive visual feedback to easily understand how the emotional state affects the course plan. For example, the recommended reasons based on emotions are also displayed in the course list proposed by emotion analysis.

[0310] Users can make career choices with greater confidence by leveraging emotion-based feedback. Educators can use generated interview materials and question lists to provide instruction that is sensitive to the learner's emotional state.

[0311] This system not only provides each learner with the most suitable career path and study plan, but also enables meticulous career guidance that addresses the learner's emotional needs. As a result, learners can make career choices in a better mental state.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The server collects learner evaluation data, aptitude data, and survey data from various data sources and integrates them. Specifically, it retrieves data from databases, converts them into JSON format, and creates a single integrated dataset.

[0315] Step 2:

[0316] The server uses an emotion engine to analyze emotional data from text and audio during interactions with learners. This employs natural language processing and speech analysis technologies to quantify emotional states in real time.

[0317] Step 3:

[0318] The server combines integrated datasets and sentiment data, feeds them into a machine learning model, and comprehensively analyzes learners' characteristics and emotional states. This analysis provides insights that can help learners make informed career choices.

[0319] Step 4:

[0320] Based on the analysis results, the server selects career path candidates from the career path information database that match the learner's characteristics and emotional state. This involves filtering and prioritizing algorithms, with adjustments made based on emotions.

[0321] Step 5:

[0322] The server generates a customized learning plan that reflects the learner's emotional state. This plan consists of incorporating learning steps that align with future goals and emotional needs.

[0323] Step 6:

[0324] The device visually displays career options and study plans provided by the server to the user. Furthermore, based on the results of the emotion engine, career recommendations include emotional support and advice.

[0325] Step 7:

[0326] Users can review career suggestions and study plans through their devices and make career choices while receiving emotion-based feedback. Educators can use the provided interview materials to provide guidance that is appropriate to the student's emotions.

[0327] (Example 2)

[0328] 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".

[0329] Traditional career guidance systems based career choices on student evaluation and aptitude information, but failed to address emotional needs, making students prone to anxiety and indecision regarding their career paths. Furthermore, information gathering from external databases was limited, and the provision of customized visual information was insufficient. A system is needed to solve these problems and support students' career choices from an emotional perspective as well.

[0330] 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.

[0331] In this invention, the server includes means for receiving and integrating learner evaluation information, aptitude information, and questionnaire information; means for analyzing emotional data in real time and identifying emotional factors that influence career choices; and means for visually providing emotion-based feedback. This makes it possible to support learners' career choices more precisely and with greater emotional consideration.

[0332] A "learner" refers to an individual who acquires knowledge through an educational institution or through self-study.

[0333] "Evaluation information" refers to data that shows learners' academic performance and overall performance.

[0334] "Aptitude information" refers to data related to learners' interests and abilities, and is useful information for career choices and academic planning.

[0335] "Survey information" refers to data that compiles learners' self-assessments and opinions.

[0336] "Means of integration" refers to technologies for aggregating information collected from multiple data sources and combining it into a single profile.

[0337] "Emotional data" refers to information that indicates a learner's emotional state, and includes emotional data that can be extracted from text, audio, and video data.

[0338] "Means of analysis" refers to the process of extracting useful information from collected data and deriving meaningful results.

[0339] "Career path selection" refers to the process by which learners decide on their future occupation or direction of study.

[0340] "Feedback" refers to the responses and suggestions provided by the system to the learner, with the aim of improving or supporting the learner's behavior.

[0341] "Visual means of presentation" refers to techniques for presenting information to learners in a visual format, including methods of displaying information in graphic, dashboard, or infographic format.

[0342] This invention is a system that provides more precise career guidance to learners, while also supporting their emotional well-being. The system primarily involves interaction and data processing between a server, a terminal, and the user.

[0343] The server plays a central role in the system, collecting and integrating learner evaluation information, aptitude information, and survey information from various sources. The server works in conjunction with a high-performance database to manage the integrated information. It also features an emotion analysis engine utilizing natural language processing and facial recognition technologies, regularly collecting emotional data from learners' text communications and facial expressions. This helps identify learners' emotional needs and alleviate anxiety regarding career choices.

[0344] The terminal's role is to present the integrated profile sent from the server to the learner. Specifically, the terminal uses visual display functions to provide the user with career options and study plans, and visually displays feedback based on sentiment analysis. The system's user interface is intuitive and designed to allow learners to smoothly understand the career selection process.

[0345] Users utilize feedback provided through their devices to make career choices while taking their emotional state into consideration. For example, if a learner feels anxious about a particular career path, the server displays encouragement and additional information on the device that reflects the results of an emotional analysis. Educators have the ability to provide individualized support to learners using interview materials and question lists generated by the server.

[0346] For example, if a learner feels "I'm not confident about pursuing this profession," the device will display specific ways to address that anxiety and reasons for its recommendation.

[0347] Example prompt to input into a generative AI model: "Integrate recent learner sentiment data and generate recommended career paths."

[0348] This makes it possible to provide two-way career guidance that also takes emotional aspects into consideration.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The server collects learner assessment information, aptitude information, and survey information from educational institution databases and online platforms. It accesses external data sources using API keys and authentication credentials as input. The data is stored in the database in an integrated format. Specifically, it performs periodic queries to retrieve the latest information and remove duplicate data.

[0352] Step 2:

[0353] The server collects and analyzes learner emotional data via the terminal using natural language processing and facial recognition technologies. Input includes learner text messages and image data from the camera. This data is sent to an emotion analysis engine, where it is classified into one of three emotional states: positive, negative, or neutral. Specifically, it utilizes a Sentiment Analysis algorithm for text and facial recognition software for images.

[0354] Step 3:

[0355] The server processes the collected evaluation information, aptitude information, and sentiment data into an integrated profile. This profile is then processed by a generative AI model. The generative AI model analyzes the input data to generate career path candidates optimized for the learner. The output is a list of customized career path candidates, each accompanied by sentiment feedback.

[0356] Step 4:

[0357] The terminal visually presents the integrated profile received from the server to the learner. The input is a data stream from the server. Through the system's user interface, the terminal also provides a graphical display of career options and learning plans, as well as feedback based on sentiment analysis. Specifically, it displays emotional feedback using colors and icons in the career list.

[0358] Step 5:

[0359] Users select a path by utilizing information on their device. Input includes path options and emotional feedback from the device. Based on this information, users choose a path and provide feedback to the system. Specifically, users confirm their selection through taps and clicks, and enter additional opinions or emotions in text fields.

[0360] This processing flow allows the system to provide learners with real-time emotional support while assisting them in making the optimal career choices.

[0361] (Application Example 2)

[0362] 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."

[0363] In modern career guidance, there is a need for precise advice that takes into account not only the learner's learning data and aptitude, but also their emotional state at any given time. However, conventional systems have difficulty recognizing learners' emotions in real time and reflecting them in career paths and learning plans. As a result, there is a risk that learners will make inappropriate choices. Therefore, a system is needed that can analyze learners' emotional states and enable detailed career guidance based on that analysis.

[0364] 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.

[0365] In this invention, the server includes means for receiving and integrating learner evaluation data, aptitude data, and questionnaire data; means for analyzing the integrated data to identify learner characteristics and identify the user's emotional state; and means for suggesting career path options based on the identified emotional state. This enables learners to make career choices that take their emotional state into consideration and to accept a more appropriate learning plan.

[0366] A "learner" is an individual who participates in the learning process and receives career guidance.

[0367] "Evaluation data" refers to information such as tests, grades, and feedback regarding learners' knowledge and skills.

[0368] "Aptitude data" refers to information related to an individual's characteristics and abilities, such as a learner's interests, personality, and skills.

[0369] "Survey data" refers to the results of surveys answered by learners, and is information that captures trends in opinions and emotions.

[0370] "Emotional state" refers to the emotional state that learners experience in real time, and is analyzed through facial expressions and text data.

[0371] A "server" is a central device in information processing that receives data, processes it, and provides the results.

[0372] "Integrated data" refers to information that combines evaluation data, aptitude data, and survey data obtained from learners.

[0373] "Career options" refer to future career paths and learning field choices that are suggested based on the learner's characteristics and emotional state.

[0374] A "learning plan" is a plan that specifically outlines the educational approach and activities that are appropriate for the learner's career path and goals.

[0375] The term "educator" refers to teachers and instructors who are in a position to provide career guidance and learning support.

[0376] "Parents" refer to the learner's parents or supervisors, and are the people with whom information about their studies and future career paths should be shared.

[0377] This invention is a system that integrates learner evaluation data, aptitude data, and questionnaire data, uses these to perform emotional analysis, and provides learners with career path suggestions based on their emotional state. Specifically, the system is configured as follows.

[0378] The server functions as the core information processing unit. First, it receives evaluation data, aptitude data, and survey data from learners. This data is integrated with stored historical information to build a user profile. In this process, the server uses natural language processing to analyze emotions from text data and utilizes facial recognition technology to identify the user's real-time emotional state.

[0379] Specifically, the software involves an emotion analysis module running on a server that utilizes the Emotion API, and programming languages ​​such as Python are used for natural language processing. Furthermore, cloud services such as AWS Lambda enable real-time data processing.

[0380] The device functions as the learner's interface, visually displaying suggested paths and learning plans. This allows learners to intuitively understand feedback based on their emotional state. The user interface is built using React Native and is cross-platform compatible.

[0381] Users can check their career paths and study plans through their devices and receive feedback. This feedback is customized to take into account the learner's emotional state, allowing them to consider more appropriate options. For example, if a learner is feeling anxious about career choices, encouraging messages are automatically provided to promote a positive outlook on their future.

[0382] A specific example of a prompt might be: "Design an application that integrates learner evaluation data, aptitude data, and survey data, analyzes user emotions, and suggests the optimal path. Use the Emotion API for emotion analysis, process the data with AWS Lambda, and develop the user interface with React Native." Following this prompt, the system will provide an integrated learning plan.

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The server receives evaluation data, aptitude data, and survey data from learners. As input, this data consists of information from various forms and test results provided by the learners. Each dataset is stored in a database to integrate the data and build an integrated profile. This generates a comprehensive profile of the learner.

[0386] Step 2:

[0387] The server uses the Emotion API to analyze the learner's emotions from text data. The input is text data written by the learner. Natural language processing is used to analyze the context and identify emotional states (e.g., anxiety, excitement). The analysis results are added to the learner's integrated profile.

[0388] Step 3:

[0389] The server generates appropriate career path candidates based on the learner's integrated profile. Inputs include evaluation data, aptitude data, survey data, and sentiment analysis results. Using machine learning algorithms, it predicts the optimal career path and generates a candidate list that takes emotional states into account. The output is a personalized career path candidate list for the learner.

[0390] Step 4:

[0391] The terminal receives a list of career options from the server and presents it visually to the learner. The input is the career option data provided by the server. Using React Native, the list of options is displayed intuitively through a user interface. The output is a visual representation that allows learners to easily consider career options.

[0392] Step 5:

[0393] Users provide feedback and select career path options through their devices. Input is through user interface operations. Selected options and feedback are sent to a server for further analysis and recording. This allows learners to leverage the product's features to make optimal choices.

[0394] Through these steps, the system provides customized career guidance based on the learner's emotions and characteristics.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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".

[0411] To implement this invention, it is necessary to design and develop a system having the following configuration. This system uses a server, terminals, and a user interface to support students' career guidance.

[0412] The server functions as a central data processing unit, collecting and integrating learner evaluation data, aptitude data, and survey data. To achieve this, it establishes database connections and efficiently retrieves data from multiple sources. Specifically, the server converts this data into JSON format and integrates it into a single dataset. Next, the server feeds this integrated dataset into a generating AI model to analyze the learner's characteristics. For example, a learner who excels in science and engineering subjects and demonstrates leadership qualities might be found to be well-suited to a science and engineering-related university.

[0413] The terminal serves as the user interface. Here, career suggestions and study plans sent from the server are displayed in an easy-to-understand visual format. Users can view career option lists and study plans via PCs or mobile devices. The notification function for parents is also utilized here, quickly delivering the latest information regarding the learner's career path via email and push notifications.

[0414] Users, namely educators and learners, utilize this system to receive career guidance. Educators can conduct individual consultations using interview materials and question lists provided by the server via their terminals. For example, if a learner who excels in mathematics wishes to major in engineering, they can view a learning plan on their terminal, including necessary math materials and reference links, based on data provided by the server, and this information is also shared with their parents.

[0415] In this way, this system supports efficient and effective career guidance in educational settings and plays a role in helping learners choose the career path that is best suited to them.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The server collects learner evaluation data, aptitude data, and survey data from various data sources and integrates them. Specifically, it executes SQL queries against multiple databases to retrieve data in CSV or JSON format. After that, it performs data formatting and combines them into a single integrated dataset.

[0419] Step 2:

[0420] The server uses a machine learning model to analyze the integrated data. This model is trained to extract learner characteristics. The analysis clarifies the learner's interests, abilities, and personality traits. For example, the model might determine from the data that "the learner has a strong interest in science."

[0421] Step 3:

[0422] Based on the analysis results, the server selects appropriate career path candidates from the career information database. This involves using filtering and ranking algorithms to find career paths that match the learner's characteristics. This then lists the universities and occupations that the learner can choose from.

[0423] Step 4:

[0424] The server generates a learning plan based on the student's desired career path. Using an algorithm, it identifies the subjects and skills the learner needs and creates a scheduled learning plan. The plan includes daily learning objectives and specific actions to be achieved.

[0425] Step 5:

[0426] The server automatically generates interview materials and question lists for educators. It also generates reports on learner characteristics, streamlining interview preparation. These materials are exported to educators in PDF format.

[0427] Step 6:

[0428] The device displays career path options and study plans generated by the server to the user. Through this, the user can review and deepen their understanding of their career path and study methods. Furthermore, parents receive notifications regarding career guidance via the device, allowing them to share information about their child's progress and plans.

[0429] (Example 1)

[0430] 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."

[0431] This invention relates to a system for efficiently and effectively providing career guidance and creating study plans for learners. Traditional career guidance often involved manual processes, making it difficult to provide personalized advice tailored to each learner, resulting in time-consuming and labor-intensive methods. Furthermore, the lack of smooth information sharing with parents sometimes led to insufficient support for learners.

[0432] 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.

[0433] In this invention, the server includes means for receiving and integrating learner evaluation information, aptitude information, and questionnaire information; means for analyzing the integrated information to identify the learner's characteristics; and means for suggesting career path options based on the learner's characteristics. This enables the generation of individually customized career path suggestions and the rapid provision of information to parents.

[0434] "Learner evaluation information" refers to objective data regarding learners' performance and skills.

[0435] "Aptitude information" refers to information about a learner's interests, areas of expertise, and potential.

[0436] "Survey information" refers to subjective response data provided by learners regarding their self-assessment and career choices.

[0437] "Means of integration" refers to the process or technique for combining data obtained from multiple sources into a consistent format.

[0438] "Methods for analyzing and identifying learner characteristics" refer to methods of processing data to reveal characteristics such as learners' interests, concerns, and ability tendencies.

[0439] A "means of suggesting career path options" refers to a system that presents suitable career paths and educational institutions based on the learner's characteristics.

[0440] A "means for generating a learning plan" refers to a system for creating a specific learning process and schedule based on the learner's career path and characteristics.

[0441] "Methods for automatically generating interview materials and question items" refers to technologies that automatically create materials and questions to enable educators to engage in effective dialogue with learners.

[0442] "Means of notifying parents of career path information" refers to methods for providing parents with the latest information regarding their child's career choices and learning progress.

[0443] "Visual display methods" refer to technologies that present information to users as graphics or diagrams to aid in understanding.

[0444] A "generative AI model" is a technology that uses models generated by machine learning algorithms to analyze data and recognize patterns.

[0445] A "prompt sentence" is a formalized sentence input to a generative AI model, used to induce specific analyses or outputs.

[0446] To implement this invention, it is necessary to design and develop a system including a server, terminals, and a user interface. The server acts as a central control unit, collecting learner evaluation information, aptitude information, and survey information from various data sources and integrating this data. Specifically, the server connects to a database using database software, retrieves data, converts it to JSON format, and integrates it. Next, the server supplies the integrated dataset to a generative AI model, which analyzes the learner's characteristics. For example, for a learner who excels in science and engineering subjects and exhibits leadership qualities, the generative AI model might determine that pursuing a science and engineering degree at university is suitable.

[0447] The device provides an interface for users to visually view career suggestions and study plans provided by the server. Users can access and view this information using a PC or mobile device. The device also has a built-in notification function for parents, providing career information in real time via email and push notifications.

[0448] Users can utilize data retrieved from the server via their devices to receive support in deciding on educational direction. Educators can conduct individual interviews with learners based on interview materials and questions generated by the server. For example, a learner who excels in mathematics and wishes to major in engineering can view a learning plan based on data provided by the server, which includes necessary mathematics materials and reference links. In addition, this information can be shared with parents.

[0449] A concrete example of a prompt would be, "This student excels in science subjects and demonstrates leadership qualities. Please suggest a suitable career path for them."

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] The server establishes a connection to the database and collects learner evaluation information, aptitude information, and survey information. The input consists of various data obtained from multiple data sources. The server cleanses and formats this data, converting it into a consistent JSON format. This process outputs a single, integrated dataset.

[0453] Step 2:

[0454] The server feeds the integrated dataset into a generative AI model to analyze the learner's characteristics. The input is the integrated data obtained in Step 1. Here, pattern recognition and analysis algorithms are used to identify the learner's strengths and aptitudes. The output of this analysis is a real-time prompt message that indicates the learner's characteristics.

[0455] Step 3:

[0456] The server generates optimal career path suggestions for the learner based on the analysis results. The input is the learner's characteristic prompt sentence obtained in step 2. Utilizing a generation AI model, it selects career path candidates that match the learner's characteristics and outputs them as career path suggestions. For example, a specific suggestion such as, "Since you excel in science subjects and demonstrate leadership, we recommend that you pursue a science and engineering degree at a university," might be output.

[0457] Step 4:

[0458] The terminal receives career suggestions and study plans sent from the server and displays them visually in the user interface. The input is the career suggestions generated in step 3. The terminal displays the list of career options and study plans in a dashboard format for easy understanding by the user. The output is a customized interface for the user.

[0459] Step 5:

[0460] Users receive career guidance by utilizing information on their devices. Educators conduct individual consultations using the provided materials and questionnaires to adjust the learner's educational plan. The final output is feedback on career guidance and materials as needed. This information is also provided to parents through the system, enabling further support.

[0461] (Application Example 1)

[0462] 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."

[0463] In modern education, while career guidance for learners should be individualized, implementing it presents significant challenges due to the considerable effort and time required. In particular, it is difficult for learners and parents to access career information and study plans at the appropriate time, posing a barrier to effective career choices. There is a need to solve these problems and achieve more efficient and effective career guidance.

[0464] 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.

[0465] In this invention, the server includes means for receiving and integrating learner evaluation data, aptitude data, and questionnaire data; means for analyzing the integrated data to identify learner characteristics; and means for learners and guardians to access career suggestions and receive visualized learning plans via mobile devices. This enables learners and guardians to receive career information and learning plans based on their individual characteristics in real time.

[0466] A "learner" refers to someone who is studying at an educational institution and who needs support with their career choices and study plans.

[0467] "Evaluation data" refers to information that quantifies learners' academic performance and extracurricular activities, and serves as basic data for assessing career aptitude and academic ability.

[0468] "Aptitude data" refers to information that quantifies or categorizes learners' abilities, characteristics, and interests, and serves as a basis for making decisions when choosing a career path.

[0469] "Survey data" refers to information that quantifies or documents the opinions and wishes obtained from learners and their guardians, and serves as material for analyzing needs in career guidance.

[0470] "Analysis" refers to all data processing operations performed to clarify learners' characteristics and tendencies based on collected data.

[0471] "Characteristics" refer to the individual abilities, personality traits, and aptitudes that learners possess, and serve as a fundamental indicator in career path selection.

[0472] "Career path" refers to the academic or professional career route that learners choose for their future, and this decision is based on their individual characteristics and aptitudes.

[0473] "Portable information terminals" refer to electronic devices such as smartphones and tablets that are portable and capable of sending and receiving information.

[0474] A "visualized learning plan" refers to a schedule of educational activities and learning objectives created based on the learner's career path, presented in the form of charts, graphs, or lists.

[0475] To implement this invention, it is necessary to build a system that supports career guidance for learners. This system consists of a server, terminals, and users.

[0476] The server is responsible for receiving, integrating, and analyzing learner evaluation data, aptitude data, and survey data. To this end, the server establishes a database connection and integrates the data in JSON format. Using a generative AI model, it analyzes learner characteristics and generates optimal career path suggestions for each individual learner. These generated career path suggestions are automatically notified to parents.

[0477] The device functions as an interface for users to access career guidance and study plans. Users can visually review career information and study plans transmitted from the server using mobile devices such as smartphones and tablets. This information is presented as a visualized study plan in charts and lists, making it easy for learners and their guardians to understand.

[0478] Users, both educators and learners, can utilize this system to receive effective career guidance. For example, a learner who excels in science and mathematics might be suggested to pursue a degree in engineering, and the necessary study materials and learning plans would also be provided. An example of a prompt message that could be input into the generating AI model is, "Please suggest a suitable university major for a student who excels in mathematics."

[0479] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0480] Step 1:

[0481] The server receives evaluation data, aptitude data, and survey data about learners. Each dataset is obtained from various data sources and stored in a database. The input data is converted to JSON format and integrated. This allows the server to prepare foundational information for understanding the learners as a whole.

[0482] Step 2:

[0483] The server feeds the integrated data into a generative AI model for analysis. The integrated dataset is used as input. The generative AI model processes this data to identify learner characteristics. For example, academic ability, areas of interest, and aptitudes are output. This prepares the system for career guidance based on these characteristics.

[0484] Step 3:

[0485] The server generates personalized career path options for each learner. It takes the results of an AI model's analysis as input. Based on this information, the server outputs career path options that include fields and occupations the learner might be interested in. This personalizes the learner's future direction.

[0486] Step 4:

[0487] The device displays a visualized learning plan to the user. It takes career path options and learning plans received from the server as input, converts them into a graphical interface, and outputs them. Users can then access information to enhance educational effectiveness using their smartphones or tablets.

[0488] Step 5:

[0489] Users, especially educators, can access materials and question lists for individual consultations via their devices. These are automatically generated based on input data from the server. This allows educators to have the necessary materials readily available to effectively conduct consultations.

[0490] Step 6:

[0491] The server notifies parents of career guidance information. It uses the generated career suggestions and study plans as input and outputs them via email and push notifications. Parents can receive real-time information about their child's career choices from the comfort of their home.

[0492] 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.

[0493] This invention is a system that provides more precise career guidance by integrating learner evaluation data, aptitude data, and survey data, and further recognizing and analyzing user emotions. Through the server, terminals, and user interface, this system deeply understands the individual needs of learners and enables emotion-based career guidance and customization.

[0494] As the core data processing unit, the server not only collects diverse data from learners but also receives the user's emotional state through the emotion engine. Specifically, the server uses natural language processing and facial recognition technologies to analyze emotions from text-based communication. This data is combined with conventional training data to construct a single integrated profile.

[0495] The emotion engine recognizes emotions in real time when interacting with learners and educators, and provides feedback based on these emotions. For example, if a learner is feeling anxious about career choices, the engine detects this and adjusts the career option list and study plan to alleviate the anxiety by incorporating encouraging elements.

[0496] The device displays career options and learning plans sent from the server through its user interface, and also provides customized information that reflects the results of emotion recognition. Users can receive visual feedback to help them understand how their emotional state influences their career plans. For example, the career list suggested by emotion analysis also displays the reasons for the recommendations based on those emotions.

[0497] Users can make career choices with greater confidence by leveraging emotion-based feedback. Educators can use generated interview materials and question lists to provide instruction that is sensitive to the learner's emotional state.

[0498] This system not only provides each learner with the most suitable career path and study plan, but also enables meticulous career guidance that addresses the learner's emotional needs. As a result, learners can make career choices in a better mental state.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] The server collects learner evaluation data, aptitude data, and survey data from various data sources and integrates them. Specifically, it retrieves data from databases, converts them into JSON format, and creates a single integrated dataset.

[0502] Step 2:

[0503] The server uses an emotion engine to analyze emotional data from text and audio during interactions with learners. This employs natural language processing and speech analysis technologies to quantify emotional states in real time.

[0504] Step 3:

[0505] The server combines integrated datasets and sentiment data, feeds them into a machine learning model, and comprehensively analyzes learners' characteristics and emotional states. This analysis provides insights that can help learners make informed career choices.

[0506] Step 4:

[0507] Based on the analysis results, the server selects career path candidates from the career path information database that match the learner's characteristics and emotional state. This involves filtering and prioritizing algorithms, with adjustments made based on emotions.

[0508] Step 5:

[0509] The server generates a customized learning plan that reflects the learner's emotional state. This plan consists of incorporating learning steps that align with future goals and emotional needs.

[0510] Step 6:

[0511] The device visually displays career options and study plans provided by the server to the user. Furthermore, based on the results of the emotion engine, career recommendations include emotional support and advice.

[0512] Step 7:

[0513] Users can review career suggestions and study plans through their devices and make career choices while receiving emotion-based feedback. Educators can use the provided interview materials to provide guidance that is appropriate to the student's emotions.

[0514] (Example 2)

[0515] 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."

[0516] Traditional career guidance systems based career choices on student evaluation and aptitude information, but failed to address emotional needs, making students prone to anxiety and indecision regarding their career paths. Furthermore, information gathering from external databases was limited, and the provision of customized visual information was insufficient. A system is needed to solve these problems and support students' career choices from an emotional perspective as well.

[0517] 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.

[0518] In this invention, the server includes means for receiving and integrating learner evaluation information, aptitude information, and questionnaire information; means for analyzing emotional data in real time and identifying emotional factors that influence career choices; and means for visually providing emotion-based feedback. This makes it possible to support learners' career choices more precisely and with greater emotional consideration.

[0519] A "learner" refers to an individual who acquires knowledge through an educational institution or through self-study.

[0520] "Evaluation information" refers to data that shows learners' academic performance and overall performance.

[0521] "Aptitude information" refers to data related to learners' interests and abilities, and is useful information for career choices and academic planning.

[0522] "Survey information" refers to data that compiles learners' self-assessments and opinions.

[0523] "Means of integration" refers to technologies for aggregating information collected from multiple data sources and combining it into a single profile.

[0524] "Emotional data" refers to information that indicates a learner's emotional state, and includes emotional data that can be extracted from text, audio, and video data.

[0525] "Means of analysis" refers to the process of extracting useful information from collected data and deriving meaningful results.

[0526] "Career path selection" refers to the process by which learners decide on their future occupation or direction of study.

[0527] "Feedback" refers to the responses and suggestions provided by the system to the learner, with the aim of improving or supporting the learner's behavior.

[0528] "Visual means of presentation" refers to techniques for presenting information to learners in a visual format, including methods of displaying information in graphic, dashboard, or infographic format.

[0529] This invention is a system that provides more precise career guidance to learners, while also supporting their emotional well-being. The system primarily involves interaction and data processing between a server, a terminal, and the user.

[0530] The server plays a central role in the system, collecting and integrating learner evaluation information, aptitude information, and survey information from various sources. The server works in conjunction with a high-performance database to manage the integrated information. It also features an emotion analysis engine utilizing natural language processing and facial recognition technologies, regularly collecting emotional data from learners' text communications and facial expressions. This helps identify learners' emotional needs and alleviate anxiety regarding career choices.

[0531] The terminal's role is to present the integrated profile sent from the server to the learner. Specifically, the terminal uses visual display functions to provide the user with career options and study plans, and visually displays feedback based on sentiment analysis. The system's user interface is intuitive and designed to allow learners to smoothly understand the career selection process.

[0532] Users utilize feedback provided through their devices to make career choices while taking their emotional state into consideration. For example, if a learner feels anxious about a particular career path, the server displays encouragement and additional information on the device that reflects the results of an emotional analysis. Educators have the ability to provide individualized support to learners using interview materials and question lists generated by the server.

[0533] For example, if a learner feels "I'm not confident about pursuing this profession," the device will display specific ways to address that anxiety and reasons for its recommendation.

[0534] Example prompt to input into a generative AI model: "Integrate recent learner sentiment data and generate recommended career paths."

[0535] This makes it possible to provide two-way career guidance that also takes emotional aspects into consideration.

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] The server collects learner assessment information, aptitude information, and survey information from educational institution databases and online platforms. It accesses external data sources using API keys and authentication credentials as input. The data is stored in the database in an integrated format. Specifically, it performs periodic queries to retrieve the latest information and remove duplicate data.

[0539] Step 2:

[0540] The server collects and analyzes learner emotional data via the terminal using natural language processing and facial recognition technologies. Input includes learner text messages and image data from the camera. This data is sent to an emotion analysis engine, where it is classified into one of three emotional states: positive, negative, or neutral. Specifically, it utilizes a Sentiment Analysis algorithm for text and facial recognition software for images.

[0541] Step 3:

[0542] The server processes the collected evaluation information, aptitude information, and sentiment data into an integrated profile. This profile is then processed by a generative AI model. The generative AI model analyzes the input data to generate career path candidates optimized for the learner. The output is a list of customized career path candidates, each accompanied by sentiment feedback.

[0543] Step 4:

[0544] The terminal visually presents the integrated profile received from the server to the learner. The input is a data stream from the server. Through the system's user interface, the terminal also provides a graphical display of career options and learning plans, as well as feedback based on sentiment analysis. Specifically, it displays emotional feedback using colors and icons in the career list.

[0545] Step 5:

[0546] Users select a path by utilizing information on their device. Input includes path options and emotional feedback from the device. Based on this information, users choose a path and provide feedback to the system. Specifically, users confirm their selection through taps and clicks, and enter additional opinions or emotions in text fields.

[0547] This processing flow allows the system to provide learners with real-time emotional support while assisting them in making the optimal career choices.

[0548] (Application Example 2)

[0549] 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."

[0550] In modern career guidance, there is a need for precise advice that takes into account not only the learner's learning data and aptitude, but also their emotional state at any given time. However, conventional systems have difficulty recognizing learners' emotions in real time and reflecting them in career paths and learning plans. As a result, there is a risk that learners will make inappropriate choices. Therefore, a system is needed that can analyze learners' emotional states and enable detailed career guidance based on that analysis.

[0551] 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.

[0552] In this invention, the server includes means for receiving and integrating learner evaluation data, aptitude data, and questionnaire data; means for analyzing the integrated data to identify learner characteristics and identify the user's emotional state; and means for suggesting career path options based on the identified emotional state. This enables learners to make career choices that take their emotional state into consideration and to accept a more appropriate learning plan.

[0553] A "learner" is an individual who participates in the learning process and receives career guidance.

[0554] "Evaluation data" refers to information such as tests, grades, and feedback regarding learners' knowledge and skills.

[0555] "Aptitude data" refers to information related to an individual's characteristics and abilities, such as a learner's interests, personality, and skills.

[0556] "Survey data" refers to the results of surveys answered by learners, and is information that captures trends in opinions and emotions.

[0557] "Emotional state" refers to the emotional state that learners experience in real time, and is analyzed through facial expressions and text data.

[0558] A "server" is a central device in information processing that receives data, processes it, and provides the results.

[0559] "Integrated data" refers to information that combines evaluation data, aptitude data, and survey data obtained from learners.

[0560] "Career options" refer to future career paths and learning field choices that are suggested based on the learner's characteristics and emotional state.

[0561] A "learning plan" is a plan that specifically outlines the educational approach and activities that are appropriate for the learner's career path and goals.

[0562] The term "educator" refers to teachers and instructors who are in a position to provide career guidance and learning support.

[0563] "Parents" refer to the learner's parents or supervisors, and are the people with whom information about their studies and future career paths should be shared.

[0564] This invention is a system that integrates learner evaluation data, aptitude data, and questionnaire data, uses these to perform emotional analysis, and provides learners with career path suggestions based on their emotional state. Specifically, the system is configured as follows.

[0565] The server functions as the core information processing unit. First, it receives evaluation data, aptitude data, and survey data from learners. This data is integrated with stored historical information to build a user profile. In this process, the server uses natural language processing to analyze emotions from text data and utilizes facial recognition technology to identify the user's real-time emotional state.

[0566] Specifically, the software involves an emotion analysis module running on a server that utilizes the Emotion API, and programming languages ​​such as Python are used for natural language processing. Furthermore, cloud services such as AWS Lambda enable real-time data processing.

[0567] The device functions as the learner's interface, visually displaying suggested paths and learning plans. This allows learners to intuitively understand feedback based on their emotional state. The user interface is built using React Native and is cross-platform compatible.

[0568] Users can check their career paths and study plans through their devices and receive feedback. This feedback is customized to take into account the learner's emotional state, allowing them to consider more appropriate options. For example, if a learner is feeling anxious about career choices, encouraging messages are automatically provided to promote a positive outlook on their future.

[0569] A specific example of a prompt might be: "Design an application that integrates learner evaluation data, aptitude data, and survey data, analyzes user emotions, and suggests the optimal path. Use the Emotion API for emotion analysis, process the data with AWS Lambda, and develop the user interface with React Native." Following this prompt, the system will provide an integrated learning plan.

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The server receives evaluation data, aptitude data, and survey data from learners. As input, this data consists of information from various forms and test results provided by the learners. Each dataset is stored in a database to integrate the data and build an integrated profile. This generates a comprehensive profile of the learner.

[0573] Step 2:

[0574] The server uses the Emotion API to analyze the learner's emotions from text data. The input is text data written by the learner. Natural language processing is used to analyze the context and identify emotional states (e.g., anxiety, excitement). The analysis results are added to the learner's integrated profile.

[0575] Step 3:

[0576] The server generates appropriate career path candidates based on the learner's integrated profile. Inputs include evaluation data, aptitude data, survey data, and sentiment analysis results. Using machine learning algorithms, it predicts the optimal career path and generates a candidate list that takes emotional states into account. The output is a personalized career path candidate list for the learner.

[0577] Step 4:

[0578] The terminal receives a list of career options from the server and presents it visually to the learner. The input is the career option data provided by the server. Using React Native, the list of options is displayed intuitively through a user interface. The output is a visual representation that allows learners to easily consider career options.

[0579] Step 5:

[0580] Users provide feedback and select career path options through their devices. Input is through user interface operations. Selected options and feedback are sent to a server for further analysis and recording. This allows learners to leverage the product's features to make optimal choices.

[0581] Through these steps, the system provides customized career guidance based on the learner's emotions and characteristics.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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".

[0599] To implement this invention, it is necessary to design and develop a system having the following configuration. This system uses a server, terminals, and a user interface to support students' career guidance.

[0600] The server functions as a central data processing unit, collecting and integrating learner evaluation data, aptitude data, and survey data. To achieve this, it establishes database connections and efficiently retrieves data from multiple sources. Specifically, the server converts this data into JSON format and integrates it into a single dataset. Next, the server feeds this integrated dataset into a generating AI model to analyze the learner's characteristics. For example, a learner who excels in science and engineering subjects and demonstrates leadership qualities might be found to be well-suited to a science and engineering-related university.

[0601] The terminal serves as the user interface. Here, career suggestions and study plans sent from the server are displayed in an easy-to-understand visual format. Users can view career option lists and study plans via PCs or mobile devices. The notification function for parents is also utilized here, quickly delivering the latest information regarding the learner's career path via email and push notifications.

[0602] Users, namely educators and learners, utilize this system to receive career guidance. Educators can conduct individual consultations using interview materials and question lists provided by the server via their terminals. For example, if a learner who excels in mathematics wishes to major in engineering, they can view a learning plan on their terminal, including necessary math materials and reference links, based on data provided by the server, and this information is also shared with their parents.

[0603] In this way, this system supports efficient and effective career guidance in educational settings and plays a role in helping learners choose the career path that is best suited to them.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The server collects learner evaluation data, aptitude data, and survey data from various data sources and integrates them. Specifically, it executes SQL queries against multiple databases to retrieve data in CSV or JSON format. After that, it performs data formatting and combines them into a single integrated dataset.

[0607] Step 2:

[0608] The server uses a machine learning model to analyze the integrated data. This model is trained to extract learner characteristics. The analysis clarifies the learner's interests, abilities, and personality traits. For example, the model might determine from the data that "the learner has a strong interest in science."

[0609] Step 3:

[0610] Based on the analysis results, the server selects appropriate career path candidates from the career information database. This involves using filtering and ranking algorithms to find career paths that match the learner's characteristics. This then lists the universities and occupations that the learner can choose from.

[0611] Step 4:

[0612] The server generates a learning plan based on the student's desired career path. Using an algorithm, it identifies the subjects and skills the learner needs and creates a scheduled learning plan. The plan includes daily learning objectives and specific actions to be achieved.

[0613] Step 5:

[0614] The server automatically generates interview materials and question lists for educators. It also generates reports on learner characteristics, streamlining interview preparation. These materials are exported to educators in PDF format.

[0615] Step 6:

[0616] The device displays career path options and study plans generated by the server to the user. Through this, the user can review and deepen their understanding of their career path and study methods. Furthermore, parents receive notifications regarding career guidance via the device, allowing them to share information about their child's progress and plans.

[0617] (Example 1)

[0618] 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".

[0619] This invention relates to a system for efficiently and effectively providing career guidance and creating study plans for learners. Traditional career guidance often involved manual processes, making it difficult to provide personalized advice tailored to each learner, resulting in time-consuming and labor-intensive methods. Furthermore, the lack of smooth information sharing with parents sometimes led to insufficient support for learners.

[0620] 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.

[0621] In this invention, the server includes means for receiving and integrating learner evaluation information, aptitude information, and questionnaire information; means for analyzing the integrated information to identify the learner's characteristics; and means for suggesting career path options based on the learner's characteristics. This enables the generation of individually customized career path suggestions and the rapid provision of information to parents.

[0622] "Learner evaluation information" refers to objective data regarding learners' performance and skills.

[0623] "Aptitude information" refers to information about a learner's interests, areas of expertise, and potential.

[0624] "Survey information" refers to subjective response data provided by learners regarding their self-assessment and career choices.

[0625] "Means of integration" refers to the process or technique for combining data obtained from multiple sources into a consistent format.

[0626] "Methods for analyzing and identifying learner characteristics" refer to methods of processing data to reveal characteristics such as learners' interests, concerns, and ability tendencies.

[0627] A "means of suggesting career path options" refers to a system that presents suitable career paths and educational institutions based on the learner's characteristics.

[0628] A "means for generating a learning plan" refers to a system for creating a specific learning process and schedule based on the learner's career path and characteristics.

[0629] "Methods for automatically generating interview materials and question items" refers to technologies that automatically create materials and questions to enable educators to engage in effective dialogue with learners.

[0630] "Means of notifying parents of career path information" refers to methods for providing parents with the latest information regarding their child's career choices and learning progress.

[0631] "Visual display methods" refer to technologies that present information to users as graphics or diagrams to aid in understanding.

[0632] A "generative AI model" is a technology that uses models generated by machine learning algorithms to analyze data and recognize patterns.

[0633] A "prompt sentence" is a formalized sentence input to a generative AI model, used to induce specific analyses or outputs.

[0634] To implement this invention, it is necessary to design and develop a system including a server, terminals, and a user interface. The server acts as a central control unit, collecting learner evaluation information, aptitude information, and survey information from various data sources and integrating this data. Specifically, the server connects to a database using database software, retrieves data, converts it to JSON format, and integrates it. Next, the server supplies the integrated dataset to a generative AI model, which analyzes the learner's characteristics. For example, for a learner who excels in science and engineering subjects and exhibits leadership qualities, the generative AI model might determine that pursuing a science and engineering degree at university is suitable.

[0635] The device provides an interface for users to visually view career suggestions and study plans provided by the server. Users can access and view this information using a PC or mobile device. The device also has a built-in notification function for parents, providing career information in real time via email and push notifications.

[0636] Users can utilize data retrieved from the server via their devices to receive support in deciding on educational direction. Educators can conduct individual interviews with learners based on interview materials and questions generated by the server. For example, a learner who excels in mathematics and wishes to major in engineering can view a learning plan based on data provided by the server, which includes necessary mathematics materials and reference links. In addition, this information can be shared with parents.

[0637] A concrete example of a prompt would be, "This student excels in science subjects and demonstrates leadership qualities. Please suggest a suitable career path for them."

[0638] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0639] Step 1:

[0640] The server establishes a connection to the database and collects learner evaluation information, aptitude information, and survey information. The input consists of various data obtained from multiple data sources. The server cleanses and formats this data, converting it into a consistent JSON format. This process outputs a single, integrated dataset.

[0641] Step 2:

[0642] The server feeds the integrated dataset into a generative AI model to analyze the learner's characteristics. The input is the integrated data obtained in Step 1. Here, pattern recognition and analysis algorithms are used to identify the learner's strengths and aptitudes. The output of this analysis is a real-time prompt message that indicates the learner's characteristics.

[0643] Step 3:

[0644] The server generates optimal career path suggestions for the learner based on the analysis results. The input is the learner's characteristic prompt sentence obtained in step 2. Utilizing a generation AI model, it selects career path candidates that match the learner's characteristics and outputs them as career path suggestions. For example, a specific suggestion such as, "Since you excel in science subjects and demonstrate leadership, we recommend that you pursue a science and engineering degree at a university," might be output.

[0645] Step 4:

[0646] The terminal receives career suggestions and study plans sent from the server and displays them visually in the user interface. The input is the career suggestions generated in step 3. The terminal displays the list of career options and study plans in a dashboard format for easy understanding by the user. The output is a customized interface for the user.

[0647] Step 5:

[0648] Users receive career guidance by utilizing information on their devices. Educators conduct individual consultations using the provided materials and questionnaires to adjust the learner's educational plan. The final output is feedback on career guidance and materials as needed. This information is also provided to parents through the system, enabling further support.

[0649] (Application Example 1)

[0650] 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".

[0651] In modern education, while career guidance for learners should be individualized, implementing it presents significant challenges due to the considerable effort and time required. In particular, it is difficult for learners and parents to access career information and study plans at the appropriate time, posing a barrier to effective career choices. There is a need to solve these problems and achieve more efficient and effective career guidance.

[0652] 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.

[0653] In this invention, the server includes means for receiving and integrating learner evaluation data, aptitude data, and questionnaire data; means for analyzing the integrated data to identify learner characteristics; and means for learners and guardians to access career suggestions and receive visualized learning plans via mobile devices. This enables learners and guardians to receive career information and learning plans based on their individual characteristics in real time.

[0654] A "learner" refers to someone who is studying at an educational institution and who needs support with their career choices and study plans.

[0655] "Evaluation data" refers to information that quantifies learners' academic performance and extracurricular activities, and serves as basic data for assessing career aptitude and academic ability.

[0656] "Aptitude data" refers to information that quantifies or categorizes learners' abilities, characteristics, and interests, and serves as a basis for making decisions when choosing a career path.

[0657] "Survey data" refers to information that quantifies or documents the opinions and wishes obtained from learners and their guardians, and serves as material for analyzing needs in career guidance.

[0658] "Analysis" refers to all data processing operations performed to clarify learners' characteristics and tendencies based on collected data.

[0659] "Characteristics" refer to the individual abilities, personality traits, and aptitudes that learners possess, and serve as a fundamental indicator in career path selection.

[0660] "Career path" refers to the academic or professional career route that learners choose for their future, and this decision is based on their individual characteristics and aptitudes.

[0661] "Portable information terminals" refer to electronic devices such as smartphones and tablets that are portable and capable of sending and receiving information.

[0662] A "visualized learning plan" refers to a schedule of educational activities and learning objectives created based on the learner's career path, presented in the form of charts, graphs, or lists.

[0663] To implement this invention, it is necessary to build a system that supports career guidance for learners. This system consists of a server, terminals, and users.

[0664] The server is responsible for receiving, integrating, and analyzing learner evaluation data, aptitude data, and survey data. To this end, the server establishes a database connection and integrates the data in JSON format. Using a generative AI model, it analyzes learner characteristics and generates optimal career path suggestions for each individual learner. These generated career path suggestions are automatically notified to parents.

[0665] The device functions as an interface for users to access career guidance and study plans. Users can visually review career information and study plans transmitted from the server using mobile devices such as smartphones and tablets. This information is presented as a visualized study plan in charts and lists, making it easy for learners and their guardians to understand.

[0666] Users, both educators and learners, can utilize this system to receive effective career guidance. For example, a learner who excels in science and mathematics might be suggested to pursue a degree in engineering, and the necessary study materials and learning plans would also be provided. An example of a prompt message that could be input into the generating AI model is, "Please suggest a suitable university major for a student who excels in mathematics."

[0667] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0668] Step 1:

[0669] The server receives evaluation data, aptitude data, and survey data about learners. Each dataset is obtained from various data sources and stored in a database. The input data is converted to JSON format and integrated. This allows the server to prepare foundational information for understanding the learners as a whole.

[0670] Step 2:

[0671] The server feeds the integrated data into a generative AI model for analysis. The integrated dataset is used as input. The generative AI model processes this data to identify learner characteristics. For example, academic ability, areas of interest, and aptitudes are output. This prepares the system for career guidance based on these characteristics.

[0672] Step 3:

[0673] The server generates personalized career path options for each learner. It takes the results of an AI model's analysis as input. Based on this information, the server outputs career path options that include fields and occupations the learner might be interested in. This personalizes the learner's future direction.

[0674] Step 4:

[0675] The device displays a visualized learning plan to the user. It takes career path options and learning plans received from the server as input, converts them into a graphical interface, and outputs them. Users can then access information to enhance educational effectiveness using their smartphones or tablets.

[0676] Step 5:

[0677] Users, especially educators, can access materials and question lists for individual consultations via their devices. These are automatically generated based on input data from the server. This allows educators to have the necessary materials readily available to effectively conduct consultations.

[0678] Step 6:

[0679] The server notifies parents of career guidance information. It uses the generated career suggestions and study plans as input and outputs them via email and push notifications. Parents can receive real-time information about their child's career choices from the comfort of their home.

[0680] 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.

[0681] This invention is a system that provides more precise career guidance by integrating learner evaluation data, aptitude data, and survey data, and further recognizing and analyzing user emotions. Through the server, terminals, and user interface, this system deeply understands the individual needs of learners and enables emotion-based career guidance and customization.

[0682] As the core data processing unit, the server not only collects diverse data from learners but also receives the user's emotional state through the emotion engine. Specifically, the server uses natural language processing and facial recognition technologies to analyze emotions from text-based communication. This data is combined with conventional training data to construct a single integrated profile.

[0683] The emotion engine recognizes emotions in real time when interacting with learners and educators, and provides feedback based on these emotions. For example, if a learner is feeling anxious about career choices, the engine detects this and adjusts the career option list and study plan to alleviate the anxiety by incorporating encouraging elements.

[0684] The device displays career options and learning plans sent from the server through its user interface, and also provides customized information that reflects the results of emotion recognition. Users can receive visual feedback to help them understand how their emotional state influences their career plans. For example, the career list suggested by emotion analysis also displays the reasons for the recommendations based on those emotions.

[0685] Users can make career choices with greater confidence by leveraging emotion-based feedback. Educators can use generated interview materials and question lists to provide instruction that is sensitive to the learner's emotional state.

[0686] This system not only provides each learner with the most suitable career path and study plan, but also enables meticulous career guidance that addresses the learner's emotional needs. As a result, learners can make career choices in a better mental state.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The server collects learner evaluation data, aptitude data, and survey data from various data sources and integrates them. Specifically, it retrieves data from databases, converts them into JSON format, and creates a single integrated dataset.

[0690] Step 2:

[0691] The server uses an emotion engine to analyze emotional data from text and audio during interactions with learners. This employs natural language processing and speech analysis technologies to quantify emotional states in real time.

[0692] Step 3:

[0693] The server combines integrated datasets and sentiment data, feeds them into a machine learning model, and comprehensively analyzes learners' characteristics and emotional states. This analysis provides insights that can help learners make informed career choices.

[0694] Step 4:

[0695] Based on the analysis results, the server selects career path candidates from the career path information database that match the learner's characteristics and emotional state. This involves filtering and prioritizing algorithms, with adjustments made based on emotions.

[0696] Step 5:

[0697] The server generates a customized learning plan that reflects the learner's emotional state. This plan consists of incorporating learning steps that align with future goals and emotional needs.

[0698] Step 6:

[0699] The device visually displays career options and study plans provided by the server to the user. Furthermore, based on the results of the emotion engine, career recommendations include emotional support and advice.

[0700] Step 7:

[0701] Users can review career suggestions and study plans through their devices and make career choices while receiving emotion-based feedback. Educators can use the provided interview materials to provide guidance that is appropriate to the student's emotions.

[0702] (Example 2)

[0703] 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".

[0704] Traditional career guidance systems based career choices on student evaluation and aptitude information, but failed to address emotional needs, making students prone to anxiety and indecision regarding their career paths. Furthermore, information gathering from external databases was limited, and the provision of customized visual information was insufficient. A system is needed to solve these problems and support students' career choices from an emotional perspective as well.

[0705] 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.

[0706] In this invention, the server includes means for receiving and integrating learner evaluation information, aptitude information, and questionnaire information; means for analyzing emotional data in real time and identifying emotional factors that influence career choices; and means for visually providing emotion-based feedback. This makes it possible to support learners' career choices more precisely and with greater emotional consideration.

[0707] A "learner" refers to an individual who acquires knowledge through an educational institution or through self-study.

[0708] "Evaluation information" refers to data that shows learners' academic performance and overall performance.

[0709] "Aptitude information" refers to data related to learners' interests and abilities, and is useful information for career choices and academic planning.

[0710] "Survey information" refers to data that compiles learners' self-assessments and opinions.

[0711] "Means of integration" refers to technologies for aggregating information collected from multiple data sources and combining it into a single profile.

[0712] "Emotional data" refers to information that indicates a learner's emotional state, and includes emotional data that can be extracted from text, audio, and video data.

[0713] "Means of analysis" refers to the process of extracting useful information from collected data and deriving meaningful results.

[0714] "Career path selection" refers to the process by which learners decide on their future occupation or direction of study.

[0715] "Feedback" refers to the responses and suggestions provided by the system to the learner, with the aim of improving or supporting the learner's behavior.

[0716] "Visual means of presentation" refers to techniques for presenting information to learners in a visual format, including methods of displaying information in graphic, dashboard, or infographic format.

[0717] This invention is a system that provides more precise career guidance to learners, while also supporting their emotional well-being. The system primarily involves interaction and data processing between a server, a terminal, and the user.

[0718] The server plays a central role in the system, collecting and integrating learner evaluation information, aptitude information, and survey information from various sources. The server works in conjunction with a high-performance database to manage the integrated information. It also features an emotion analysis engine utilizing natural language processing and facial recognition technologies, regularly collecting emotional data from learners' text communications and facial expressions. This helps identify learners' emotional needs and alleviate anxiety regarding career choices.

[0719] The terminal's role is to present the integrated profile sent from the server to the learner. Specifically, the terminal uses visual display functions to provide the user with career options and study plans, and visually displays feedback based on sentiment analysis. The system's user interface is intuitive and designed to allow learners to smoothly understand the career selection process.

[0720] Users utilize feedback provided through their devices to make career choices while taking their emotional state into consideration. For example, if a learner feels anxious about a particular career path, the server displays encouragement and additional information on the device that reflects the results of an emotional analysis. Educators have the ability to provide individualized support to learners using interview materials and question lists generated by the server.

[0721] For example, if a learner feels "I'm not confident about pursuing this profession," the device will display specific ways to address that anxiety and reasons for its recommendation.

[0722] Example prompt to input into a generative AI model: "Integrate recent learner sentiment data and generate recommended career paths."

[0723] This makes it possible to provide two-way career guidance that also takes emotional aspects into consideration.

[0724] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0725] Step 1:

[0726] The server collects learner assessment information, aptitude information, and survey information from educational institution databases and online platforms. It accesses external data sources using API keys and authentication credentials as input. The data is stored in the database in an integrated format. Specifically, it performs periodic queries to retrieve the latest information and remove duplicate data.

[0727] Step 2:

[0728] The server collects and analyzes learner emotional data via the terminal using natural language processing and facial recognition technologies. Input includes learner text messages and image data from the camera. This data is sent to an emotion analysis engine, where it is classified into one of three emotional states: positive, negative, or neutral. Specifically, it utilizes a Sentiment Analysis algorithm for text and facial recognition software for images.

[0729] Step 3:

[0730] The server processes the collected evaluation information, aptitude information, and sentiment data into an integrated profile. This profile is then processed by a generative AI model. The generative AI model analyzes the input data to generate career path candidates optimized for the learner. The output is a list of customized career path candidates, each accompanied by sentiment feedback.

[0731] Step 4:

[0732] The terminal visually presents the integrated profile received from the server to the learner. The input is a data stream from the server. Through the system's user interface, the terminal also provides a graphical display of career options and learning plans, as well as feedback based on sentiment analysis. Specifically, it displays emotional feedback using colors and icons in the career list.

[0733] Step 5:

[0734] Users select a path by utilizing information on their device. Input includes path options and emotional feedback from the device. Based on this information, users choose a path and provide feedback to the system. Specifically, users confirm their selection through taps and clicks, and enter additional opinions or emotions in text fields.

[0735] This processing flow allows the system to provide learners with real-time emotional support while assisting them in making the optimal career choices.

[0736] (Application Example 2)

[0737] 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".

[0738] In modern career guidance, there is a need for precise advice that takes into account not only the learner's learning data and aptitude, but also their emotional state at any given time. However, conventional systems have difficulty recognizing learners' emotions in real time and reflecting them in career paths and learning plans. As a result, there is a risk that learners will make inappropriate choices. Therefore, a system is needed that can analyze learners' emotional states and enable detailed career guidance based on that analysis.

[0739] 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.

[0740] In this invention, the server includes means for receiving and integrating learner evaluation data, aptitude data, and questionnaire data; means for analyzing the integrated data to identify learner characteristics and identify the user's emotional state; and means for suggesting career path options based on the identified emotional state. This enables learners to make career choices that take their emotional state into consideration and to accept a more appropriate learning plan.

[0741] A "learner" is an individual who participates in the learning process and receives career guidance.

[0742] "Evaluation data" refers to information such as tests, grades, and feedback regarding learners' knowledge and skills.

[0743] "Aptitude data" refers to information related to an individual's characteristics and abilities, such as a learner's interests, personality, and skills.

[0744] "Survey data" refers to the results of surveys answered by learners, and is information that captures trends in opinions and emotions.

[0745] "Emotional state" refers to the emotional state that learners experience in real time, and is analyzed through facial expressions and text data.

[0746] A "server" is a central device in information processing that receives data, processes it, and provides the results.

[0747] "Integrated data" refers to information that combines evaluation data, aptitude data, and survey data obtained from learners.

[0748] "Career options" refer to future career paths and learning field choices that are suggested based on the learner's characteristics and emotional state.

[0749] A "learning plan" is a plan that specifically outlines the educational approach and activities that are appropriate for the learner's career path and goals.

[0750] The term "educator" refers to teachers and instructors who are in a position to provide career guidance and learning support.

[0751] "Parents" refer to the learner's parents or supervisors, and are the people with whom information about their studies and future career paths should be shared.

[0752] This invention is a system that integrates learner evaluation data, aptitude data, and questionnaire data, uses these to perform emotional analysis, and provides learners with career path suggestions based on their emotional state. Specifically, the system is configured as follows.

[0753] The server functions as the core information processing unit. First, it receives evaluation data, aptitude data, and survey data from learners. This data is integrated with stored historical information to build a user profile. In this process, the server uses natural language processing to analyze emotions from text data and utilizes facial recognition technology to identify the user's real-time emotional state.

[0754] Specifically, the software involves an emotion analysis module running on a server that utilizes the Emotion API, and programming languages ​​such as Python are used for natural language processing. Furthermore, cloud services such as AWS Lambda enable real-time data processing.

[0755] The device functions as the learner's interface, visually displaying suggested paths and learning plans. This allows learners to intuitively understand feedback based on their emotional state. The user interface is built using React Native and is cross-platform compatible.

[0756] Users can check their career paths and study plans through their devices and receive feedback. This feedback is customized to take into account the learner's emotional state, allowing them to consider more appropriate options. For example, if a learner is feeling anxious about career choices, encouraging messages are automatically provided to promote a positive outlook on their future.

[0757] A specific example of a prompt might be: "Design an application that integrates learner evaluation data, aptitude data, and survey data, analyzes user emotions, and suggests the optimal path. Use the Emotion API for emotion analysis, process the data with AWS Lambda, and develop the user interface with React Native." Following this prompt, the system will provide an integrated learning plan.

[0758] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0759] Step 1:

[0760] The server receives evaluation data, aptitude data, and survey data from learners. As input, this data consists of information from various forms and test results provided by the learners. Each dataset is stored in a database to integrate the data and build an integrated profile. This generates a comprehensive profile of the learner.

[0761] Step 2:

[0762] The server uses the Emotion API to analyze the learner's emotions from text data. The input is text data written by the learner. Natural language processing is used to analyze the context and identify emotional states (e.g., anxiety, excitement). The analysis results are added to the learner's integrated profile.

[0763] Step 3:

[0764] The server generates appropriate career path candidates based on the learner's integrated profile. Inputs include evaluation data, aptitude data, survey data, and sentiment analysis results. Using machine learning algorithms, it predicts the optimal career path and generates a candidate list that takes emotional states into account. The output is a personalized career path candidate list for the learner.

[0765] Step 4:

[0766] The terminal receives a list of career options from the server and presents it visually to the learner. The input is the career option data provided by the server. Using React Native, the list of options is displayed intuitively through a user interface. The output is a visual representation that allows learners to easily consider career options.

[0767] Step 5:

[0768] Users provide feedback and select career path options through their devices. Input is through user interface operations. Selected options and feedback are sent to a server for further analysis and recording. This allows learners to leverage the product's features to make optimal choices.

[0769] Through these steps, the system provides customized career guidance based on the learner's emotions and characteristics.

[0770] 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.

[0771] 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.

[0772] In the above embodiment, an example was given in which the 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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."

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] The following is further disclosed regarding the embodiments described above.

[0792] (Claim 1)

[0793] A means for receiving and integrating learner evaluation data, aptitude data, and survey data,

[0794] A means of analyzing integrated data to identify learner characteristics,

[0795] A means of suggesting career path options based on the characteristics of learners,

[0796] A means of generating a study plan tailored to the desired career path,

[0797] A method for automatically generating interview materials and question lists for educators,

[0798] Means of notifying parents of career guidance,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the means for identifying the characteristics of the learner uses a machine learning model to analyze data and identify the optimal career path for the learner.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the means for suggesting career path candidates is to collect career path information from an external database and present it to the learner in a customized visual list format.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A means for receiving and integrating learner evaluation information, aptitude information, and survey information,

[0807] A means of analyzing integrated information to identify learner characteristics,

[0808] A means of suggesting career path options based on the characteristics of learners,

[0809] A means of generating a study plan tailored to the desired career path,

[0810] A method for automatically generating interview materials and questions for educators,

[0811] Means of notifying parents of career guidance,

[0812] A means of visually displaying career guidance and educational plans,

[0813] A means of sharing information with parents based on the generated information,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, wherein the means for identifying the characteristics of the learner analyzes information using a generative AI model and identifies the optimal career path for the learner.

[0817] (Claim 3)

[0818] The system according to claim 1, wherein the means for suggesting career path candidates is to collect career path information from an external information source and present it to the learner in a customized visual list format.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A means for receiving and integrating learner evaluation data, aptitude data, and survey data,

[0822] A means of analyzing integrated data to identify learner characteristics,

[0823] A means of suggesting career path options based on the characteristics of learners,

[0824] A means of generating a study plan tailored to the desired career path,

[0825] A method for automatically generating interview materials and question lists for educators,

[0826] Means of notifying parents of career guidance,

[0827] A means for learners and guardians to access career guidance suggestions and receive visualized learning plans via mobile devices,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein the means for identifying the characteristics of the learner uses a machine learning model to analyze data and identify the optimal career path for the learner.

[0831] (Claim 3)

[0832] The system according to claim 1, wherein the means for suggesting career path candidates is to collect career path information from external information resources and present it to the learner in a visual format customized for them.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] A means for receiving and integrating learner evaluation information, aptitude information, and survey information,

[0836] A means of analyzing integrated information to identify learner characteristics,

[0837] A means of suggesting career options based on the characteristics of learners,

[0838] A means of creating a study plan tailored to your desired career path,

[0839] A method for automatically generating interview materials and question lists for educators,

[0840] Means of notifying parents of career guidance,

[0841] A means of analyzing emotional data in real time to identify emotional factors that influence career choices,

[0842] Means of providing emotion-based feedback visually,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the means for identifying the characteristics of the learner uses a machine learning model to analyze information and identify the optimal career path for the learner.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the means for proposing career options is to collect career information from an external information database and present it to the learner in a customized visual format.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A means for receiving and integrating learner evaluation data, aptitude data, and survey data,

[0851] A means of analyzing integrated data to identify learner characteristics and identify the emotional state of users,

[0852] A means of suggesting career path options based on identified emotional states,

[0853] A means of generating a learning plan tailored to the desired career path and providing feedback adapted to the emotional state,

[0854] A means for automatically generating interview materials and question lists for educators, and adding question items according to the emotional state,

[0855] A means of notifying parents of career guidance information, as well as reporting an overview of the learner's emotional state,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein the means for identifying the characteristics of the learner involves analyzing data using machine learning technology to identify the optimal career path for the learner and taking into account the associated sentiment analysis results.

[0859] (Claim 3)

[0860] The system according to claim 1, wherein the means for suggesting career path candidates includes collecting career path information from an external data management system, presenting it to the learner in a customized visual list format, and including reasons for the suggestion based on emotional state. [Explanation of symbols]

[0861] 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 learner evaluation data, aptitude data, and survey data, A means of analyzing integrated data to identify learner characteristics, A means of suggesting career path options based on the characteristics of learners, A means of generating a study plan tailored to the desired career path, A method for automatically generating interview materials and question lists for educators, Means of notifying parents of career guidance, A system that includes this.

2. The system according to claim 1, wherein the means for identifying the characteristics of the learner uses a machine learning model to analyze data and identify the optimal career path for the learner.

3. The system according to claim 1, wherein the means for suggesting career path candidates is to collect career path information from an external database and present it to the learner in a customized visual list format.

Images