system

Abstract

We provide the system. [Solution] Means of collecting personal attribute data, A means of analyzing the attribute data using a machine learning model, A means of proposing the most suitable occupation or educational institution based on the results obtained from the aforementioned analysis, A means for collecting feedback from individuals who have received the aforementioned proposal and updating the machine learning model, 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] In the modern educational environment, many individuals are unable to find a career path that suits their characteristics and aptitudes, and are confused in career selection and further education due to an overabundance of information and inaccurate information. There is also a problem that decisions have to be made without accurate feedback in career selection. Therefore, there is a demand for the realization of an educational support system that presents a highly accurate career plan based on an individual's attributes and supports growth.

Means for Solving the Problems

[0005] This invention provides a system that proposes the most suitable occupation and educational path for each individual by collecting personal attribute data and analyzing it using a machine learning model. Furthermore, it includes means for collecting user feedback and updating the machine learning model to improve the accuracy of the suggestions. In addition, it provides means for generating mock exam and interview practice scenarios to enhance support for individual career development, and a long-term career simulation function to provide guidance on skills and qualifications to acquire at each stage in the future.

[0006] "Personal attribute data" refers to various pieces of information about an individual, such as academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns, which indicate an individual's characteristics and interests.

[0007] A "machine learning model" is an algorithm or mathematical model used to extract patterns from data and perform predictions or classifications.

[0008] "Analysis" is the process of using collected data as a clue to understand and derive specific features and patterns from that information.

[0009] "Suggesting the optimal occupation or educational institution" refers to the act of selecting and presenting occupations and educational institutions that are suitable for an individual and where growth can be expected, based on the individual's characteristics obtained through analysis.

[0010] "Feedback" refers to opinions and evaluations received from users, and is information that shows the reactions and areas for improvement to the suggestions provided by the system.

[0011] A "mock exam or interview practice scenario" is a series of hypothetical exam questions and interview questions designed to help users train for actual exams and interviews.

[0012] A "long-term career simulation" is a process of predicting the career paths an individual can achieve over time and planning how to acquire the skills and qualifications necessary at each stage. [Brief explanation of the drawing]

[0013] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] [[ID=hex]]

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

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

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that provides individualized career plans for high school and university students. This system consists of three components: a server, a terminal, and a user, each playing a specific role.

[0035] First, the user enters their attribute data into the device. This attribute data includes academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns. The device receives this data and sends it to the server.

[0036] The server uses machine learning models to analyze accumulated attribute data. This analysis clarifies each user's individual characteristics, interests, and aptitudes. Based on these analysis results, the server generates highly personalized suggestions for careers and educational paths.

[0037] The generated suggestions are presented to the user via their device. This information is displayed in a visually clear format and designed to be easily understood by the user. The user then refers to the presented information and incorporates it into their own career plan.

[0038] Furthermore, the system has a mechanism in place to collect feedback from users. Users send their opinions and evaluations of suggestions to the server via their terminals, and this is used to further improve the model.

[0039] Through this process, the system provides users with an optimal and continuously evolving career plan, while also offering mock exams and interview practice programs. This enables students to make the most of their strengths when choosing their further education or career.

[0040] For example, if a high school student excels in mathematics and demonstrates leadership in extracurricular activities, the server might suggest career options that emphasize mathematical thinking and management skills, such as pursuing economics or engineering at university. Furthermore, it might offer mock interviews and preparation for specific subjects to support the student's college application process. In this way, the system guides individual users toward building a better future.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users use their devices to input attribute data related to academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns. This information reflects an individual's characteristics and interests.

[0044] Step 2:

[0045] The terminal formats the data entered by the user and sends it to the server. This process verifies the integrity and completeness of the data.

[0046] Step 3:

[0047] The server stores the received attribute data and saves it to a database. During this process, verification is performed to ensure data consistency.

[0048] Step 4:

[0049] The server uses accumulated data to apply machine learning models and perform analysis. This analysis identifies user characteristics, aptitudes, and interests.

[0050] Step 5:

[0051] Based on the analysis results, the server generates a career plan for each individual user, including the most suitable occupation and educational path. This proposal is tailored to the user's characteristics and goals.

[0052] Step 6:

[0053] The device receives the carrier plan sent from the server and presents it through the user interface. The information is displayed in an easy-to-understand visual format.

[0054] Step 7:

[0055] Users review the presented career plan and send necessary feedback to the server via their device. This feedback includes comments on the proposal and any additional information requested.

[0056] Step 8:

[0057] The server receives user feedback and retrains its machine learning model to improve the accuracy of its suggestions. This improves the quality of suggestions for future users.

[0058] Step 9:

[0059] The server generates mock exams and interview practice scenarios based on the user's career plan and sends them to the terminal. Users can then use these to make more specific preparations.

[0060] (Example 1)

[0061] 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."

[0062] When providing students with individualized career and educational plans, traditional systems have difficulty making suggestions that are adequately tailored to individual characteristics. Furthermore, effective feedback collection and improvement of analytical models to enhance the accuracy of these suggestions are not being carried out.

[0063] 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.

[0064] In this invention, the server includes means for receiving personal attribute information from a terminal, means for performing analysis using a generative model based on the attribute information, and means for proposing the most suitable occupation or educational institution based on the analysis results. This makes it possible to provide career plans that take individual characteristics into maximum consideration and to improve the accuracy of the suggestions.

[0065] "Personal attribute information" refers to information that represents an individual's characteristics, such as academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns.

[0066] A "terminal" is a computing device used by a user to input information and receive suggestions.

[0067] A "server" is a computer device that analyzes received attribute information, generates optimal suggestions, and further collects feedback to improve the generative model.

[0068] A "generative model" is a computational algorithm used to analyze an individual's attribute information and, based on the results, suggest appropriate occupations and educational institutions.

[0069] A "proposal" is a specific option regarding occupation or educational institution, provided based on the results of analysis using a generative model.

[0070] "Feedback" refers to the opinions and evaluations that users provide regarding a proposal, and this information is used to improve the generative model.

[0071] "Exam and interview practice scenarios" are training programs designed to simulate situations users might face during the application or employment process and provide the necessary preparations.

[0072] "Simulating a long-term career plan" is a process of predicting an individual's career path over a long period and identifying the skills and qualifications required at each stage.

[0073] This invention provides a system that presents appropriate career plans based on an individual's characteristics and interests. The system consists of three components: the user, the terminal, and the server.

[0074] First, users input personal attribute information such as academic performance, club activities, hobbies, and daily routines into the device. This input is done through an interface designed to be user-friendly.

[0075] The terminal receives attribute information entered by the user and sends it to the server using a secure communication protocol. Encryption technologies such as SSL / TLS are used in this process.

[0076] The server stores the received information and performs data analysis using a generative AI model. This analysis can utilize machine learning libraries such as TENSORFLOW® and PyTorch. Based on the analysis results, the server generates suggestions for the most suitable occupation or educational institution for the user and sends this information to the terminal.

[0077] The user receives visual suggestions through their device. These suggestions utilize graphs and charts designed for ease of understanding. Furthermore, users input feedback on the suggestions, and this information is sent back to the server to improve the generated AI model. This allows the entire system to continuously evolve and provide more accurate suggestions.

[0078] As a concrete example, consider a high school student who is strong in mathematics and demonstrates leadership in extracurricular activities. In this case, the server suggests pursuing a degree in economics or engineering at university, based on their mathematical thinking and management skills. An example of a prompt might be, "This user is good at mathematics and has leadership qualities. What career plan would you suggest?" This allows the user to receive support in choosing a career path that perfectly suits their strengths.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user enters personal attribute information into the terminal's input interface. This information includes academic performance, extracurricular activities, hobbies, and daily behavioral patterns. The terminal organizes the entered data and converts it into a data format that can be transmitted. This input information forms the basis for processing.

[0082] Step 2:

[0083] The terminal sends organized attribute information to the server. This communication is encrypted using a secure protocol (SSL / TLS). The server stores the received data in a database for analysis. Through these steps, the data is safely and reliably delivered to the server.

[0084] Step 3:

[0085] The server retrieves attribute information stored in the database and begins analysis using a generative AI model. Machine learning libraries such as TensorFlow and PyTorch are used during the analysis process. The server identifies individual characteristics and, based on the results, identifies the most suitable occupation or educational institution.

[0086] Step 4:

[0087] The server generates suggestions for the user based on the analysis results. These suggestions include specific options regarding further education and career choices. The generated suggestions are then refined, using the prompt "This user is good at mathematics and has leadership qualities. What career plan would you suggest?" as an example.

[0088] Step 5:

[0089] The server sends the generated proposal to the terminal. The terminal receives this proposal and displays it to the user in a visually easy-to-understand format. The terminal interface is designed to represent the proposal using graphs and charts, making it easy for the user to understand.

[0090] Step 6:

[0091] The user reviews the presented suggestions and provides feedback. This feedback includes the usefulness of the suggestions and areas for improvement. The submitted feedback is sent from the terminal to the server.

[0092] Step 7:

[0093] The server analyzes the feedback it receives and uses it to improve the generated AI model. This cycle is repeated, allowing the entire system to continuously evolve and make better suggestions.

[0094] (Application Example 1)

[0095] 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."

[0096] In recent years, the importance of receiving personalized recommendations for educational institutions and careers in individual career development has increased, but there is a lack of efficient and flexible systems to achieve this. Furthermore, there is a need for mechanisms that allow users to naturally receive career support in their daily lives, but technological solutions to this problem remain insufficient.

[0097] 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.

[0098] In this invention, the server includes means for collecting personal attribute information, means for analyzing the attribute information using an information processing model, means for proposing the most suitable occupation or educational institution according to the results obtained from the analysis, means for providing the proposal through a voice input device, and means for collecting the opinion of the person who received the proposal and updating the information processing model. This enables the user to receive individually optimized career support through a voice interface in their daily life.

[0099] "Personal attribute information" refers to information that shows various characteristics related to an individual, such as academic performance, hobbies, and daily behavioral patterns.

[0100] An "information processing model" is an algorithm or system used to analyze input data and derive a specific result.

[0101] "Occupation" refers to the type of work or job an individual engages in to earn a living.

[0102] An "educational institution" refers to organizations such as schools and universities that are established for the purpose of learning and research.

[0103] A "voice input device" is hardware or software that recognizes a user's voice and processes it as digital data.

[0104] "Opinions" refer to individuals' thoughts and evaluations of a proposal, and are the information that the system uses as feedback to improve its model.

[0105] The system of this invention mainly consists of three elements: a server, a terminal, and a user. The user inputs their attribute information through a smart device, and this information is transmitted from the terminal to the server. The server has an information processing model installed and analyzes the user's attribute information using machine learning libraries such as TensorFlow. Based on this analysis, the server generates individually optimized suggestions for occupations and educational institutions, and further presents them to the user via a voice input device.

[0106] For example, if a user inputs data by voice saying "I'm good at math," the server analyzes this information and suggests math-related occupations and educational institutions. These generated suggestions can be easily received through voice interfaces in the user's daily life. Furthermore, user feedback is fed back to the server, and the information processing model is updated based on this information, improving the accuracy of the suggestions provided.

[0107] As a concrete example, suppose a high school student uses voice input to ask, "My math grades have been improving recently. Should I continue to focus on math?" The server analyzes this and suggests colleges and careers where math skills can be utilized, as well as providing advice on the next steps necessary, such as studying recommended subjects for entrance exams. This allows users to better understand their own strengths and concretize their future options.

[0108] An example of a prompt to input into a generative AI model is, "High school students' math grades are improving. Please generate appropriate career path suggestions based on this data." The server processes the information according to this prompt and can provide accurate advice to the user.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users input their personal information using a smart device. This input can be in voice or text format and includes academic performance, hobbies, and activity history. This information is sent to the device and formatted into data.

[0112] Step 2:

[0113] The terminal transmits the received user attribute information to the server. The input information is sent to the server as digital data and converted into a specific format. As a concrete example, voice data is converted into text format and recorded in a database.

[0114] Step 3:

[0115] The server uses an information processing model to analyze the received attribute information. This model utilizes libraries such as TensorFlow to extract user characteristics and interests from the input data. The output of this analysis is a selection of the most suitable occupations and educational institutions for the user.

[0116] Step 4:

[0117] The server presents suggestions generated based on the analysis results to the user via a voice input device. The output data is displayed in a way that is easy for the user to understand, either visually or audibly. The suggestions are tailored to the user's current abilities and interests.

[0118] Step 5:

[0119] After receiving a proposal, users provide their opinions and feedback on its content. This user feedback is sent to the server via their device. Specifically, this input includes satisfaction levels with the proposal and any additional requests.

[0120] Step 6:

[0121] The server updates its information processing model based on feedback received from users. This improves the model's accuracy and generates more appropriate suggestions in the future. The updated model is then used in the next attribute information analysis.

[0122] This series of processes allows users to receive individually optimized career support.

[0123] 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.

[0124] This invention is a system for optimizing an individual's career plan, and by combining it with an emotion engine, it provides appropriate suggestions and feedback according to the user's emotional state. The system consists of a server, a terminal, and a user, each playing a specific role.

[0125] The user first inputs their attribute data via a device. This data includes academic performance, club activities, extracurricular activities, hobbies, daily behavior patterns, and information that captures their emotional state. The device then sends this input data to the server.

[0126] Based on the received data, the server uses a machine learning model to analyze the individual's characteristics, interests, and aptitudes, and utilizes an emotion engine to recognize the user's emotional state. Based on this analysis, a career plan is generated that suggests the most suitable occupation or educational path for the user. The emotion engine takes the user's emotional state into consideration and optimizes the content and method of delivery of the suggestions.

[0127] The generated career plan is presented to the user via the device. The information is displayed in a visually effective way to aid user understanding. An emotion engine adjusts the tone and amount of content to match the user's state.

[0128] Furthermore, after the user accepts the career plan, they provide feedback through their device. This feedback is used for further analysis on the server, updating both the machine learning model and the sentiment engine to improve the accuracy of future suggestions.

[0129] For example, if a student is experiencing stress while choosing a school, the emotional engine recognizes this stress level and adjusts the suggestions to make them more easily accepted. Furthermore, mock exams and interview practice scenarios are generated and sent to the student's device, allowing them to prepare at their own pace. In this way, the system provides comprehensive career support that appropriately reflects the user's emotional state.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] Users input data on their academic performance, extracurricular activities, interests, hobbies, and daily behavioral patterns through their device. Simultaneously, they also input information about their emotional state, and the device transmits this data to the server.

[0133] Step 2:

[0134] The server stores the received data in a database. Here, the data's integrity and completeness are verified, and attribute data and sentiment data are prepared.

[0135] Step 3:

[0136] The server applies a machine learning model based on attribute data to analyze user characteristics and interests. In this process, it identifies patterns and generates lists of occupations and educational institutions that match the user's characteristics.

[0137] Step 4:

[0138] The server uses an emotion engine to analyze the user's current emotional state based on their input. In this case, it identifies stress, anxiety, excitement, etc., and adjusts the content and presentation method of the career plan accordingly.

[0139] Step 5:

[0140] Based on the analysis results, the server generates a career plan for the most suitable occupation and educational institution, and adjusts this information according to the user's emotional state. For example, for a user experiencing stress, the information is presented in a relaxed tone.

[0141] Step 6:

[0142] The device receives the carrier plan from the server and presents the information in a visually effective way through the user interface. Here, the tone and volume adjusted by the emotion engine are reflected.

[0143] Step 7:

[0144] Users review the presented career plan and enter feedback on its contents into their device. This feedback includes their thoughts on the proposal and any desired changes.

[0145] Step 8:

[0146] The device sends the collected feedback to the server, which uses this information to update its machine learning model and sentiment engine. This improves the accuracy of future suggestions.

[0147] Step 9:

[0148] The server generates mock exam and interview practice scenarios tailored to the user's career plan and emotional state. These scenarios, sent to the device, allow the user to prepare with confidence.

[0149] (Example 2)

[0150] 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".

[0151] In individual career planning, it is essential to provide flexible suggestions that take into account individual characteristics and interests, as well as reflecting their emotional state at any given time. However, conventional systems often fail to adequately reflect an individual's emotional state, resulting in uniform suggestions. Therefore, the challenge lies in providing a system that enables career suggestions that consider individual emotional states, not just quantitative factors.

[0152] 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.

[0153] In this invention, the server includes means for collecting personal information, means for performing analysis using a computational model based on the information, and means for suggesting appropriate occupations and educational institutions, taking into account the results obtained from the analysis and the individual's emotional state. This makes it possible to suggest a career plan that is best suited to the individual and reflects their individual emotional state.

[0154] "Personal information" refers to attribute data provided by users, such as academic performance, hobbies, behavioral patterns, and emotional states.

[0155] A "computational model" refers to an algorithm and program that uses machine learning to analyze data and identify individual characteristics and aptitudes.

[0156] "Emotional state" refers to information that indicates the user's mental or psychological state or mood, and is taken into consideration when the system adjusts its suggestions.

[0157] "Visual presentation methods" refer to ways of displaying information in a way that is easy for users to understand, using graphics, charts, infographics, and so on.

[0158] "Means of collecting and updating feedback" refers to the data collection and updating process used to receive user feedback and improve the computational model within the system based on that feedback.

[0159] This invention is a system for optimizing an individual's career plan, and it mainly consists of a server, a terminal, and a user. In implementation, the user first inputs their attribute data via the terminal. This data includes academic performance, club activities, extracurricular activities, hobbies, daily behavior patterns, and emotional state. The terminal is responsible for transmitting this information to the server.

[0160] The server analyzes the received data using a computational model. This model incorporates machine learning algorithms to analyze individual characteristics, interests, and aptitudes. Furthermore, it utilizes an emotion engine to recognize the user's emotional state through methods such as text analysis. As a result, a career plan is generated to suggest the most suitable occupation or educational path for the user. The generating AI model can flexibly adjust its responses by inputting prompts based on the user's situation and needs.

[0161] The generated career plan is visually displayed to the user through their device. This uses infographics and charts, designed for easy understanding. Furthermore, an emotion engine adjusts the information to an appropriate tone and amount based on the user's emotional state.

[0162] As a concrete example of a prompt message, you can input a situation such as, "A high school student with excellent academic performance but who doesn't participate much in extracurricular activities, and has recently been feeling stressed about going to college." The server will then provide advice and support that reflects the user's situation.

[0163] Furthermore, users can input feedback on the presented career plan via their device. This feedback is then sent back to the server and used to update the machine learning model and sentiment engine. This is expected to make future suggestions even more accurate.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] Users input academic performance, club activities, extracurricular activities, hobbies, behavioral patterns, and emotional states into the device. The entered data is provided as text and multiple-choice answers in the device's input form. This prepares individual attribute data.

[0167] Step 2:

[0168] The terminal sends the entered user data to the server. A secure protocol is used for transmission, ensuring data encryption and safe transfer. The entered information is delivered to the server in digital format.

[0169] Step 3:

[0170] The server processes the received user data and begins analysis using a computational model. First, the data is preprocessed to impute missing values ​​and correct irregular data. Then, machine learning algorithms are applied to analyze individual characteristics and aptitudes. The output is user-specific analysis results.

[0171] Step 4:

[0172] The server generates career and educational suggestions using a generative AI model, while also referencing the user's emotional state based on the analysis results. The emotion engine recognizes emotions using text analysis techniques and optimizes the suggestions. This generates and prepares specific suggestions.

[0173] Step 5:

[0174] The terminal presents the user with a carrier plan retrieved from the server. Infographics and charts effectively display and organize the information visually. This process provides output that is easily understandable to the user.

[0175] Step 6:

[0176] Users send feedback regarding the presented career plan to the server via their device. This feedback is collected as text data and prepared for further analysis on the server.

[0177] Step 7:

[0178] The server analyzes the received feedback and updates the machine learning model and sentiment engine accordingly. The training data is expanded, and the model is trained to improve the accuracy of the next suggestion. In this way, the improved output is prepared for the next iteration.

[0179] (Application Example 2)

[0180] 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".

[0181] In order to alleviate the stress that individual users experience when choosing a career path or profession, and to provide them with the optimal career plan, conventional systems have the challenge of not adequately providing appropriate feedback tailored to their emotional state. Furthermore, in order to further enhance the effectiveness of the proposed plan, it is necessary to update the system to appropriately reflect users' emotions and feedback.

[0182] 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.

[0183] In this invention, the server includes means for collecting personal attribute information, means for analyzing it using a predictive model, and means for adapting the information to the user's state based on the sentiment analysis results. This makes it possible to make optimal suggestions while taking the user's emotional state into consideration, thereby achieving high performance and user satisfaction through the suggested content.

[0184] "Personal attribute information" refers to individual characteristic data, including a user's educational background, activities, hobbies, and behavioral patterns.

[0185] A "predictive model" is a technology that uses machine learning to analyze data and predict results for new data.

[0186] "Emotion analysis" is the process of evaluating a user's psychological state and identifying their emotions.

[0187] "Emotional analysis results" refer to information indicating the user's emotional state obtained through emotional analysis.

[0188] A "career plan" is a plan of occupation and education proposed based on an individual's characteristics and interests.

[0189] This invention relates to a system for consumer robots designed to optimize an individual's career plan. The system consists of a user, a server, and a terminal, each playing a specific role.

[0190] The user first uses a device to input their personal information. This information includes data such as educational background, activities, hobbies, and daily behavioral patterns. The device sends this personal information, along with the user's voice and text data, to the server. This voice data is converted to text using the Google® Cloud Speech-to-Text API.

[0191] The server analyzes attribute information using a predictive model based on the received data. The analysis utilizes the scikit-learn library to evaluate user characteristics and interests, and proposes the most suitable professions and educational institutions. Furthermore, it uses the Azure® Emotion API to analyze user emotions from speech and text, and adapts the suggestions and communication methods based on the results. The server then uses a generative AI model to generate specific scenarios and career plans. These generated plans are presented via the user's device.

[0192] The user reviews their career plan and enters feedback into their device. The server receives this feedback information and updates its predictive model and sentiment analysis to improve the accuracy of future suggestions.

[0193] As a concrete example, imagine a high school student choosing a college, where a robot presents a simulated exam scenario in a relaxed state. In this process, the following prompt is used for the generative AI model: "Analyze the user's current emotional state and suggest career options that minimize stress. For example, suggest relaxation techniques or explanations that are easy to accept."

[0194] In this way, the invention realizes comprehensive career support that takes into account the user's emotional state.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user uses the device to input personal attribute information. This input includes data such as education, activities, hobbies, and behavioral patterns. The device receives this data via both voice and text input and prepares to send it to the server.

[0198] Step 2:

[0199] The device sends attribute data collected from the user to the server. During this process, it uses the Google Cloud Speech-to-Text API to convert the audio data into text data, and then sends the formatted data to the server.

[0200] Step 3:

[0201] The server analyzes the received attribute and text data. It utilizes a predictive model with scikit-learn for the analysis. Based on the input data, it analyzes individual characteristics and interests and generates suggestions for optimal professions and educational institutions. The analysis results are provided as output.

[0202] Step 4:

[0203] The server uses the Azure Emotion API to analyze the user's emotional state from text data. Using this emotional analysis, the generated career plan is adjusted to reflect the user's emotional state. The adjusted plan is then output.

[0204] Step 5:

[0205] The server uses a generative AI model to generate prompt messages and create a career plan that includes specific suggestions. The generated plan is then sent to the terminal.

[0206] Step 6:

[0207] The device displays the adjusted carrier plan received from the server. This display uses a method that is visually easy for the user to understand.

[0208] Step 7:

[0209] The user enters feedback on the presented career plan into their device. This feedback data is then sent back to the server.

[0210] Step 8:

[0211] The server analyzes user feedback and updates its predictive models and sentiment analysis. This improves the accuracy of future suggestions. Evaluation results based on the updates are then output.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is a system that provides individualized career plans for high school and university students. This system consists of three components: a server, a terminal, and a user, each playing a specific role.

[0229] First, the user enters their attribute data into the device. This attribute data includes academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns. The device receives this data and sends it to the server.

[0230] The server uses machine learning models to analyze accumulated attribute data. This analysis clarifies each user's individual characteristics, interests, and aptitudes. Based on these analysis results, the server generates highly personalized suggestions for careers and educational paths.

[0231] The generated suggestions are presented to the user via their device. This information is displayed in a visually clear format and designed to be easily understood by the user. The user then refers to the presented information and incorporates it into their own career plan.

[0232] Furthermore, the system has a mechanism in place to collect feedback from users. Users send their opinions and evaluations of suggestions to the server via their terminals, and this is used to further improve the model.

[0233] Through this process, the system provides users with an optimal and continuously evolving career plan, while also offering mock exams and interview practice programs. This enables students to make the most of their strengths when choosing their further education or career.

[0234] For example, if a high school student excels in mathematics and demonstrates leadership in extracurricular activities, the server might suggest career options that emphasize mathematical thinking and management skills, such as pursuing economics or engineering at university. Furthermore, it might offer mock interviews and preparation for specific subjects to support the student's college application process. In this way, the system guides individual users toward building a better future.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] Users use their devices to input attribute data related to academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns. This information reflects an individual's characteristics and interests.

[0238] Step 2:

[0239] The terminal formats the data entered by the user and sends it to the server. This process verifies the integrity and completeness of the data.

[0240] Step 3:

[0241] The server stores the received attribute data and saves it to a database. During this process, verification is performed to ensure data consistency.

[0242] Step 4:

[0243] The server uses accumulated data to apply machine learning models and perform analysis. This analysis identifies user characteristics, aptitudes, and interests.

[0244] Step 5:

[0245] Based on the analysis results, the server generates a career plan for each individual user, including the most suitable occupation and educational destination. This proposal is tailored to the user's characteristics and goals.

[0246] Step 6:

[0247] The device receives the carrier plan sent from the server and presents it through the user interface. The information is displayed in an easy-to-understand visual format.

[0248] Step 7:

[0249] Users review the presented career plan and send necessary feedback to the server via their device. This feedback includes comments on the proposal and any additional information requested.

[0250] Step 8:

[0251] The server receives user feedback and retrains its machine learning model to improve the accuracy of its suggestions. This improves the quality of suggestions for future users.

[0252] Step 9:

[0253] The server generates mock exams and interview practice scenarios based on the user's career plan and sends them to the terminal. Users can then use these to make more specific preparations.

[0254] (Example 1)

[0255] 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."

[0256] When providing students with individualized career and educational plans, traditional systems have difficulty making suggestions that are adequately tailored to individual characteristics. Furthermore, effective feedback collection and improvement of analytical models to enhance the accuracy of these suggestions are not being carried out.

[0257] 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.

[0258] In this invention, the server includes means for receiving personal attribute information from a terminal, means for performing analysis using a generative model based on the attribute information, and means for proposing the most suitable occupation or educational institution based on the analysis results. This makes it possible to provide career plans that take individual characteristics into maximum consideration and to improve the accuracy of the suggestions.

[0259] "Personal attribute information" refers to information that represents an individual's characteristics, such as academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns.

[0260] A "terminal" is a computing device used by a user to input information and receive suggestions.

[0261] A "server" is a computer device that analyzes received attribute information, generates optimal suggestions, and further collects feedback to improve the generative model.

[0262] A "generative model" is a computational algorithm used to analyze an individual's attribute information and, based on the results, suggest appropriate occupations and educational institutions.

[0263] A "proposal" is a specific option regarding occupation or educational institution, provided based on the results of analysis using a generative model.

[0264] "Feedback" refers to the opinions and evaluations that users provide regarding a proposal, and this information is used to improve the generative model.

[0265] "Exam and interview practice scenarios" are training programs designed to simulate situations users might face during the application or employment process and provide the necessary preparations.

[0266] "Simulating a long-term career plan" is the process of predicting an individual's career path over a long period and identifying the skills and qualifications required at each stage.

[0267] This invention provides a system that presents appropriate career plans based on an individual's characteristics and interests. The system consists of three components: the user, the terminal, and the server.

[0268] First, users input personal attribute information such as academic performance, club activities, hobbies, and daily routines into the device. This input is done through an interface designed to be user-friendly.

[0269] The terminal receives attribute information entered by the user and sends it to the server using a secure communication protocol. Encryption technologies such as SSL / TLS are used in this process.

[0270] The server stores the received information and performs data analysis using a generative AI model. This analysis can utilize machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, the server generates suggestions for the most suitable occupation or educational institution for the user and sends this information to the terminal.

[0271] The user receives visual suggestions through their device. These suggestions utilize graphs and charts designed for ease of understanding. Furthermore, users input feedback on the suggestions, and this information is sent back to the server to improve the generated AI model. This allows the entire system to continuously evolve and provide more accurate suggestions.

[0272] As a concrete example, consider a high school student who is strong in mathematics and demonstrates leadership in extracurricular activities. In this case, the server suggests pursuing a degree in economics or engineering at university, based on their mathematical thinking and management skills. An example of a prompt might be, "This user is good at mathematics and has leadership qualities. What career plan would you suggest?" This allows the user to receive support in choosing a career path that perfectly suits their strengths.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user enters personal attribute information into the terminal's input interface. This information includes academic performance, extracurricular activities, hobbies, and daily behavioral patterns. The terminal organizes the entered data and converts it into a data format that can be transmitted. This input information forms the basis for processing.

[0276] Step 2:

[0277] The terminal sends the organized attribute information to the server. This communication is encrypted using a secure protocol (SSL / TLS). The server stores the received data in an analysis database. In these steps, the data is safely and surely transferred to the server.

[0278] Step 3:

[0279] The server retrieves the attribute information stored in the database and starts the analysis using a generated AI model. In the process of analysis, machine learning libraries such as TensorFlow and PyTorch are utilized. The server reveals the characteristics of an individual and identifies the optimal occupation or educational institution based on the results.

[0280] Step 4:

[0281] Based on the analysis results, the server generates proposals for the user. These proposals include specific options regarding further education or career choices. The generated proposals are adjusted taking the prompt sentence "This user is good at mathematics and has leadership skills. What career plans would you propose?" as an example.

[0282] Step 5:

[0283] The server sends the generated proposals to the terminal. The terminal receives this proposal and displays it to the user in a visually understandable form. The interface of the terminal is designed to represent the proposal in graphs or charts so that the user can easily understand it.

[0284] Step 6:

[0285] The user checks the presented proposal and inputs feedback on it. The feedback includes the usefulness of the proposal and points for improvement. The input feedback is sent from the terminal to the server.

[0286] Step 7:

[0287] The server analyzes the feedback it receives and uses it to improve the generated AI model. This cycle is repeated, allowing the entire system to continuously evolve and make better suggestions.

[0288] (Application Example 1)

[0289] 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."

[0290] In recent years, the importance of receiving personalized recommendations for educational institutions and careers in individual career development has increased, but there is a lack of efficient and flexible systems to achieve this. Furthermore, there is a need for mechanisms that allow users to naturally receive career support in their daily lives, but technological solutions to this problem remain insufficient.

[0291] 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.

[0292] In this invention, the server includes means for collecting personal attribute information, means for analyzing the attribute information using an information processing model, means for proposing the most suitable occupation or educational institution according to the results obtained from the analysis, means for providing the proposal through a voice input device, and means for collecting the opinion of the person who received the proposal and updating the information processing model. This enables the user to receive individually optimized career support through a voice interface in their daily life.

[0293] "Personal attribute information" refers to information that shows various characteristics related to an individual, such as academic performance, hobbies, and daily behavioral patterns.

[0294] An "information processing model" is an algorithm or system used to analyze input data and derive a specific result.

[0295] "Occupation" refers to the type of work or job an individual engages in to earn a living.

[0296] An "educational institution" refers to organizations such as schools and universities that are established for the purpose of learning and research.

[0297] A "voice input device" is hardware or software that recognizes a user's voice and processes it as digital data.

[0298] "Opinions" refer to individuals' thoughts and evaluations of a proposal, and are the information that the system uses as feedback to improve its model.

[0299] The system of this invention mainly consists of three elements: a server, a terminal, and a user. The user inputs their attribute information through a smart device, and this information is transmitted from the terminal to the server. The server has an information processing model installed and analyzes the user's attribute information using machine learning libraries such as TensorFlow. Based on this analysis, the server generates individually optimized suggestions for occupations and educational institutions, and further presents them to the user via a voice input device.

[0300] For example, if a user inputs data by voice saying "I'm good at math," the server analyzes this information and suggests math-related occupations and educational institutions. These generated suggestions can be easily received through voice interfaces in the user's daily life. Furthermore, user feedback is fed back to the server, and the information processing model is updated based on this information, improving the accuracy of the suggestions provided.

[0301] As a specific example, suppose a high school student makes a voice input saying, "Recently, my math grades have been improving. Should I continue to focus on math?" The server analyzes this and proposes college majors or careers that can make use of math, and also advises on the next steps necessary for that, such as learning recommended subjects for exams. As a result, the user can better understand their own characteristics and materialize future options.

[0302] As an example of a prompt sentence to be input into the generative AI model, "A high school student's math grades are improving. Please generate a proposal for an appropriate career path based on this data." can be cited. Along with this prompt, the server can perform information processing and provide accurate advice to the user.

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The user inputs their own attribute information using a smart device. The input here is in voice or text format and includes academic performance, hobbies, and activity history. This information is transmitted to the terminal and arranged in a data format.

[0306] Step 2:

[0307] The terminal transmits the received user attribute information to the server. The input information is transmitted to the server as digital data and converted into a specific format. As a specific example, voice data is converted into text format and recorded in a database.

[0308] Step 3:

[0309] The server uses an information processing model to analyze the received attribute information. This model utilizes libraries such as TensorFlow to extract user characteristics and interests from the input data. The output of this analysis is a selection of the most suitable occupations and educational institutions for the user.

[0310] Step 4:

[0311] The server presents suggestions generated based on the analysis results to the user via a voice input device. The output data is displayed in a way that is easy for the user to understand, either visually or audibly. The suggestions are tailored to the user's current abilities and interests.

[0312] Step 5:

[0313] After receiving a proposal, users provide their opinions and feedback on its content. This user feedback is sent to the server via their device. Specifically, this input includes satisfaction levels with the proposal and any additional requests.

[0314] Step 6:

[0315] The server updates its information processing model based on feedback received from users. This improves the model's accuracy and generates more appropriate suggestions in the future. The updated model is then used in the next attribute information analysis.

[0316] This series of processes allows users to receive individually optimized career support.

[0317] 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.

[0318] This invention is a system for optimizing an individual's career plan, and by combining it with an emotion engine, it provides appropriate suggestions and feedback according to the user's emotional state. The system consists of a server, a terminal, and a user, each playing a specific role.

[0319] The user first inputs their attribute data via a device. This data includes academic performance, club activities, extracurricular activities, hobbies, daily behavior patterns, and information that captures their emotional state. The device then sends this input data to the server.

[0320] Based on the received data, the server uses a machine learning model to analyze the individual's characteristics, interests, and aptitudes, and utilizes an emotion engine to recognize the user's emotional state. Based on this analysis, a career plan is generated that suggests the most suitable occupation or educational path for the user. The emotion engine takes the user's emotional state into consideration and optimizes the content and method of delivery of the suggestions.

[0321] The generated career plan is presented to the user via the device. The information is displayed in a visually effective way to aid user understanding. An emotion engine adjusts the tone and amount of content to match the user's state.

[0322] Furthermore, after the user accepts the career plan, they provide feedback through their device. This feedback is used for further analysis on the server, updating both the machine learning model and the sentiment engine to improve the accuracy of future suggestions.

[0323] For example, if a student is experiencing stress while choosing a school, the emotional engine recognizes this stress level and adjusts the suggestions to make them more easily accepted. Furthermore, mock exams and interview practice scenarios are generated and sent to the student's device, allowing them to prepare at their own pace. In this way, the system provides comprehensive career support that appropriately reflects the user's emotional state.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] Users input data on their academic performance, extracurricular activities, interests, hobbies, and daily behavioral patterns through their device. Simultaneously, they also input information about their emotional state, and the device transmits this data to the server.

[0327] Step 2:

[0328] The server stores the received data in a database. Here, the data's integrity and completeness are verified, and attribute data and sentiment data are prepared.

[0329] Step 3:

[0330] The server applies a machine learning model based on attribute data to analyze user characteristics and interests. In this process, it identifies patterns and generates lists of occupations and educational institutions that match the user's characteristics.

[0331] Step 4:

[0332] The server uses an emotion engine to analyze the user's current emotional state based on their input. In this case, it identifies stress, anxiety, excitement, etc., and adjusts the content and presentation method of the career plan accordingly.

[0333] Step 5:

[0334] Based on the analysis results, the server generates a career plan for the most suitable occupation and educational institution, and adjusts this information according to the user's emotional state. For example, for a user experiencing stress, the information is presented in a relaxed tone.

[0335] Step 6:

[0336] The device receives the carrier plan from the server and presents the information in a visually effective way through the user interface. Here, the tone and volume adjusted by the emotion engine are reflected.

[0337] Step 7:

[0338] Users review the presented career plan and enter feedback on its contents into their device. This feedback includes their thoughts on the proposal and any desired changes.

[0339] Step 8:

[0340] The device sends the collected feedback to the server, which uses this information to update its machine learning model and sentiment engine. This improves the accuracy of future suggestions.

[0341] Step 9:

[0342] The server generates mock exam and interview practice scenarios tailored to the user's career plan and emotional state. These scenarios, sent to the device, allow the user to prepare with confidence.

[0343] (Example 2)

[0344] 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".

[0345] In individual career planning, it is essential to provide flexible suggestions that take into account individual characteristics and interests, as well as reflecting their emotional state at any given time. However, conventional systems often fail to adequately reflect an individual's emotional state, resulting in uniform suggestions. Therefore, the challenge lies in providing a system that enables career suggestions that consider individual emotional states, not just quantitative factors.

[0346] 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.

[0347] In this invention, the server includes means for collecting personal information, means for performing analysis using a computational model based on the information, and means for suggesting appropriate occupations and educational institutions, taking into account the results obtained from the analysis and the individual's emotional state. This makes it possible to suggest a career plan that is best suited to the individual and reflects their individual emotional state.

[0348] "Personal information" refers to attribute data provided by users, such as academic performance, hobbies, behavioral patterns, and emotional states.

[0349] A "computational model" refers to an algorithm and program that uses machine learning to analyze data and identify individual characteristics and aptitudes.

[0350] "Emotional state" refers to information that indicates the user's mental or psychological state or mood, and is taken into consideration when the system adjusts its suggestions.

[0351] "Visual presentation methods" refer to ways of displaying information in a way that is easy for users to understand, using graphics, charts, infographics, and so on.

[0352] "Means of collecting and updating feedback" refers to the data collection and updating process used to receive user feedback and improve the computational model within the system based on that feedback.

[0353] This invention is a system for optimizing an individual's career plan, and it mainly consists of a server, a terminal, and a user. In implementation, the user first inputs their attribute data via the terminal. This data includes academic performance, club activities, extracurricular activities, hobbies, daily behavior patterns, and emotional state. The terminal is responsible for transmitting this information to the server.

[0354] The server analyzes the received data using a computational model. This model incorporates machine learning algorithms to analyze individual characteristics, interests, and aptitudes. Furthermore, it utilizes an emotion engine to recognize the user's emotional state through methods such as text analysis. As a result, a career plan is generated to suggest the most suitable occupation or educational path for the user. The generating AI model can flexibly adjust its responses by inputting prompts based on the user's situation and needs.

[0355] The generated career plan is visually displayed to the user through their device. This uses infographics and charts, designed for easy understanding. Furthermore, an emotion engine adjusts the information to an appropriate tone and amount based on the user's emotional state.

[0356] As a concrete example of a prompt message, you can input a situation such as, "A high school student with excellent academic performance but who doesn't participate much in extracurricular activities, and has recently been feeling stressed about going to college." The server will then provide advice and support that reflects the user's situation.

[0357] Furthermore, users can input feedback on the presented career plan via their device. This feedback is then sent back to the server and used to update the machine learning model and sentiment engine. This is expected to make future suggestions even more accurate.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] Users input academic performance, club activities, extracurricular activities, hobbies, behavioral patterns, and emotional states into the device. The entered data is provided as text and multiple-choice answers in the device's input form. This prepares individual attribute data.

[0361] Step 2:

[0362] The terminal sends the entered user data to the server. A secure protocol is used for transmission, ensuring data encryption and safe transfer. The entered information is delivered to the server in digital format.

[0363] Step 3:

[0364] The server processes the received user data and begins analysis using a computational model. First, the data is preprocessed to impute missing values ​​and correct irregular data. Then, machine learning algorithms are applied to analyze individual characteristics and aptitudes. The output is user-specific analysis results.

[0365] Step 4:

[0366] The server generates career and educational suggestions using a generative AI model, while also referencing the user's emotional state based on the analysis results. The emotion engine recognizes emotions using text analysis techniques and optimizes the suggestions. This generates and prepares specific suggestions.

[0367] Step 5:

[0368] The terminal presents the user with a carrier plan retrieved from the server. Infographics and charts effectively display and organize the information visually. This process provides output that is easily understandable to the user.

[0369] Step 6:

[0370] Users send feedback regarding the presented career plan to the server via their device. This feedback is collected as text data and prepared for further analysis on the server.

[0371] Step 7:

[0372] The server analyzes the received feedback and updates the machine learning model and sentiment engine accordingly. The training data is expanded, and the model is trained to improve the accuracy of the next suggestion. In this way, the improved output is prepared for the next iteration.

[0373] (Application Example 2)

[0374] 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."

[0375] In order to alleviate the stress that individual users experience when choosing a career path or profession, and to provide them with the optimal career plan, conventional systems have the challenge of not adequately providing appropriate feedback tailored to their emotional state. Furthermore, in order to further enhance the effectiveness of the proposed plan, it is necessary to update the system to appropriately reflect users' emotions and feedback.

[0376] 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.

[0377] In this invention, the server includes means for collecting personal attribute information, means for analyzing it using a predictive model, and means for adapting the information to the user's state based on the sentiment analysis results. This makes it possible to make optimal suggestions while taking the user's emotional state into consideration, thereby achieving high performance and user satisfaction through the suggested content.

[0378] "Personal attribute information" refers to individual characteristic data, including a user's educational background, activities, hobbies, and behavioral patterns.

[0379] A "predictive model" is a technology that uses machine learning to analyze data and predict results for new data.

[0380] "Emotion analysis" is the process of evaluating a user's psychological state and identifying their emotions.

[0381] "Emotional analysis results" refer to information indicating the user's emotional state obtained through emotional analysis.

[0382] A "career plan" is a plan of occupation and education proposed based on an individual's characteristics and interests.

[0383] This invention relates to a system for consumer robots designed to optimize an individual's career plan. The system consists of a user, a server, and a terminal, each playing a specific role.

[0384] The user first uses a device to input their personal information. This information includes data such as education, activities, hobbies, and daily behavioral patterns. The device sends this personal information, along with the user's voice and text data, to the server. This voice data is converted to text using the Google Cloud Speech-to-Text API.

[0385] The server analyzes attribute information using a predictive model based on the received data. The analysis utilizes the scikit-learn library to evaluate user characteristics and interests, and proposes the most suitable professions and educational institutions. Furthermore, it uses the Azure Emotion API to analyze user emotions from speech and text, and adapts the suggestions and communication methods based on the results. The server then uses a generative AI model to generate specific scenarios and career plans. These generated plans are presented via the user's device.

[0386] The user reviews their career plan and enters feedback into their device. The server receives this feedback information and updates its predictive model and sentiment analysis to improve the accuracy of future suggestions.

[0387] As a concrete example, imagine a high school student choosing a college, where a robot presents a simulated exam scenario in a relaxed state. In this process, the following prompt is used for the generative AI model: "Analyze the user's current emotional state and suggest career options that minimize stress. For example, suggest relaxation techniques or explanations that are easy to accept."

[0388] In this way, the invention realizes comprehensive career support that takes into account the user's emotional state.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The user uses the device to input personal attribute information. This input includes data such as education, activities, hobbies, and behavioral patterns. The device receives this data via both voice and text input and prepares to send it to the server.

[0392] Step 2:

[0393] The device sends attribute data collected from the user to the server. During this process, it uses the Google Cloud Speech-to-Text API to convert the audio data into text data, and then sends the formatted data to the server.

[0394] Step 3:

[0395] The server analyzes the received attribute and text data. It utilizes a predictive model with scikit-learn for the analysis. Based on the input data, it analyzes individual characteristics and interests and generates suggestions for optimal professions and educational institutions. The analysis results are provided as output.

[0396] Step 4:

[0397] The server uses the Azure Emotion API to analyze the user's emotional state from text data. Using this emotional analysis, the generated career plan is adjusted to reflect the user's emotional state. The adjusted plan is then output.

[0398] Step 5:

[0399] The server uses a generative AI model to generate prompt messages and create a career plan that includes specific suggestions. The generated plan is then sent to the terminal.

[0400] Step 6:

[0401] The device displays the adjusted carrier plan received from the server. This display uses a method that is visually easy for the user to understand.

[0402] Step 7:

[0403] The user enters feedback on the presented career plan into their device. This feedback data is then sent back to the server.

[0404] Step 8:

[0405] The server analyzes user feedback and updates its predictive models and sentiment analysis. This improves the accuracy of future suggestions. Evaluation results based on the updates are then output.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] This invention is a system that provides individualized career plans for high school and university students. This system consists of three components: a server, a terminal, and a user, each playing a specific role.

[0423] First, the user enters their attribute data into the device. This attribute data includes academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns. The device receives this data and sends it to the server.

[0424] The server uses machine learning models to analyze accumulated attribute data. This analysis clarifies each user's individual characteristics, interests, and aptitudes. Based on these analysis results, the server generates highly personalized suggestions for careers and educational paths.

[0425] The generated suggestions are presented to the user via their device. This information is displayed in a visually clear format and designed to be easily understood by the user. The user then refers to the presented information and incorporates it into their own career plan.

[0426] Furthermore, the system has a mechanism in place to collect feedback from users. Users send their opinions and evaluations of suggestions to the server via their terminals, and this is used to further improve the model.

[0427] Through this process, the system provides users with an optimal and continuously evolving career plan, while also offering mock exams and interview practice programs. This enables students to make the most of their strengths when choosing their further education or career.

[0428] For example, if a high school student excels in mathematics and demonstrates leadership in extracurricular activities, the server might suggest career options that emphasize mathematical thinking and management skills, such as pursuing economics or engineering at university. Furthermore, it might offer mock interviews and preparation for specific subjects to support the student's college application process. In this way, the system guides individual users toward building a better future.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] Users use their devices to input attribute data related to academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns. This information reflects an individual's characteristics and interests.

[0432] Step 2:

[0433] The terminal formats the data entered by the user and sends it to the server. This process verifies the integrity and completeness of the data.

[0434] Step 3:

[0435] The server stores the received attribute data and saves it to a database. During this process, verification is performed to ensure data consistency.

[0436] Step 4:

[0437] The server uses accumulated data to apply machine learning models and perform analysis. This analysis identifies user characteristics, aptitudes, and interests.

[0438] Step 5:

[0439] Based on the analysis results, the server generates a career plan for each individual user, including the most suitable occupation and educational destination. This proposal is tailored to the user's characteristics and goals.

[0440] Step 6:

[0441] The device receives the carrier plan sent from the server and presents it through the user interface. The information is displayed in an easy-to-understand visual format.

[0442] Step 7:

[0443] Users review the presented career plan and send necessary feedback to the server via their device. This feedback includes comments on the proposal and any additional information requested.

[0444] Step 8:

[0445] The server receives user feedback and retrains its machine learning model to improve the accuracy of its suggestions. This improves the quality of suggestions for future users.

[0446] Step 9:

[0447] The server generates mock exams and interview practice scenarios based on the user's career plan and sends them to the terminal. Users can then use these to make more specific preparations.

[0448] (Example 1)

[0449] 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."

[0450] When providing students with individualized career and educational plans, traditional systems have difficulty making suggestions that are adequately tailored to individual characteristics. Furthermore, effective feedback collection and improvement of analytical models to enhance the accuracy of these suggestions are not being carried out.

[0451] 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.

[0452] In this invention, the server includes means for receiving personal attribute information from a terminal, means for performing analysis using a generative model based on the attribute information, and means for proposing the most suitable occupation or educational institution based on the analysis results. This makes it possible to provide career plans that take individual characteristics into maximum consideration and to improve the accuracy of the suggestions.

[0453] "Personal attribute information" refers to information that represents an individual's characteristics, such as academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns.

[0454] A "terminal" is a computing device used by a user to input information and receive suggestions.

[0455] A "server" is a computer device that analyzes received attribute information, generates optimal suggestions, and further collects feedback to improve the generative model.

[0456] A "generative model" is a computational algorithm used to analyze an individual's attribute information and, based on the results, suggest appropriate occupations and educational institutions.

[0457] A "proposal" is a specific option regarding occupation or educational institution, provided based on the results of analysis using a generative model.

[0458] "Feedback" refers to the opinions and evaluations that users provide regarding a proposal, and this information is used to improve the generative model.

[0459] "Exam and interview practice scenarios" are training programs designed to simulate situations users might face during the application or employment process and provide the necessary preparations.

[0460] "Simulating a long-term career plan" is the process of predicting an individual's career path over a long period and identifying the skills and qualifications required at each stage.

[0461] This invention provides a system that presents appropriate career plans based on an individual's characteristics and interests. The system consists of three components: the user, the terminal, and the server.

[0462] First, users input personal attribute information such as academic performance, club activities, hobbies, and daily routines into the device. This input is done through an interface designed to be user-friendly.

[0463] The terminal receives attribute information entered by the user and sends it to the server using a secure communication protocol. Encryption technologies such as SSL / TLS are used in this process.

[0464] The server stores the received information and performs data analysis using a generative AI model. This analysis can utilize machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, the server generates suggestions for the most suitable occupation or educational institution for the user and sends this information to the terminal.

[0465] The user receives visual suggestions through their device. These suggestions utilize graphs and charts designed for ease of understanding. Furthermore, users input feedback on the suggestions, and this information is sent back to the server to improve the generated AI model. This allows the entire system to continuously evolve and provide more accurate suggestions.

[0466] As a concrete example, consider a high school student who is strong in mathematics and demonstrates leadership in extracurricular activities. In this case, the server suggests pursuing a degree in economics or engineering at university, based on their mathematical thinking and management skills. An example of a prompt might be, "This user is good at mathematics and has leadership qualities. What career plan would you suggest?" This allows the user to receive support in choosing a career path that perfectly suits their strengths.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The user enters personal attribute information into the terminal's input interface. This information includes academic performance, extracurricular activities, hobbies, and daily behavioral patterns. The terminal organizes the entered data and converts it into a data format that can be transmitted. This input information forms the basis for processing.

[0470] Step 2:

[0471] The terminal sends organized attribute information to the server. This communication is encrypted using a secure protocol (SSL / TLS). The server stores the received data in a database for analysis. Through these steps, the data is safely and securely delivered to the server.

[0472] Step 3:

[0473] The server retrieves attribute information stored in the database and begins analysis using a generative AI model. Machine learning libraries such as TensorFlow and PyTorch are used during the analysis process. The server identifies individual characteristics and, based on the results, identifies the most suitable occupation or educational institution.

[0474] Step 4:

[0475] The server generates suggestions for the user based on the analysis results. These suggestions include specific options regarding further education and career choices. The generated suggestions are then refined, using the prompt "This user is good at mathematics and has leadership qualities. What career plan would you suggest?" as an example.

[0476] Step 5:

[0477] The server sends the generated proposal to the terminal. The terminal receives this proposal and displays it to the user in a visually easy-to-understand format. The terminal interface is designed to represent the proposal using graphs and charts, making it easy for the user to understand.

[0478] Step 6:

[0479] The user reviews the presented suggestions and provides feedback. This feedback includes the usefulness of the suggestions and areas for improvement. The submitted feedback is sent from the terminal to the server.

[0480] Step 7:

[0481] The server analyzes the feedback it receives and uses it to improve the generated AI model. This cycle is repeated, allowing the entire system to continuously evolve and make better suggestions.

[0482] (Application Example 1)

[0483] 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."

[0484] In recent years, the importance of receiving personalized recommendations for educational institutions and careers in individual career development has increased, but there is a lack of efficient and flexible systems to achieve this. Furthermore, there is a need for mechanisms that allow users to naturally receive career support in their daily lives, but technological solutions to this problem remain insufficient.

[0485] 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.

[0486] In this invention, the server includes means for collecting personal attribute information, means for analyzing the attribute information using an information processing model, means for proposing the most suitable occupation or educational institution according to the results obtained from the analysis, means for providing the proposal through a voice input device, and means for collecting the opinion of the person who received the proposal and updating the information processing model. This enables the user to receive individually optimized career support through a voice interface in their daily life.

[0487] "Personal attribute information" refers to information that shows various characteristics related to an individual, such as academic performance, hobbies, and daily behavioral patterns.

[0488] An "information processing model" is an algorithm or system used to analyze input data and derive a specific result.

[0489] "Occupation" refers to the type of work or job an individual engages in to earn a living.

[0490] An "educational institution" refers to organizations such as schools and universities that are established for the purpose of learning and research.

[0491] A "voice input device" is hardware or software that recognizes a user's voice and processes it as digital data.

[0492] "Opinions" refer to individuals' thoughts and evaluations of a proposal, and are the information that the system uses as feedback to improve its model.

[0493] The system of this invention mainly consists of three elements: a server, a terminal, and a user. The user inputs their attribute information through a smart device, and this information is transmitted from the terminal to the server. The server has an information processing model installed and analyzes the user's attribute information using machine learning libraries such as TensorFlow. Based on this analysis, the server generates individually optimized suggestions for occupations and educational institutions, and further presents them to the user via a voice input device.

[0494] For example, if a user inputs data by voice saying "I'm good at math," the server analyzes this information and suggests math-related occupations and educational institutions. These generated suggestions can be easily received through voice interfaces in the user's daily life. Furthermore, user feedback is fed back to the server, and the information processing model is updated based on this information, improving the accuracy of the suggestions provided.

[0495] As a concrete example, suppose a high school student uses voice input to ask, "My math grades have been improving recently. Should I continue to focus on math?" The server analyzes this and suggests colleges and careers where math skills can be utilized, as well as providing advice on the next steps necessary, such as studying recommended subjects for entrance exams. This allows users to better understand their own strengths and concretize their future options.

[0496] An example of a prompt to input into a generative AI model is, "High school students' math grades are improving. Please generate appropriate career path suggestions based on this data." The server processes the information according to this prompt and can provide accurate advice to the user.

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] Users input their personal information using a smart device. This input can be in voice or text format and includes academic performance, hobbies, and activity history. This information is sent to the device and formatted into a data format.

[0500] Step 2:

[0501] The terminal transmits the received user attribute information to the server. The input information is sent to the server as digital data and converted into a specific format. As a concrete example, voice data is converted into text format and recorded in a database.

[0502] Step 3:

[0503] The server uses an information processing model to analyze the received attribute information. This model utilizes libraries such as TensorFlow to extract user characteristics and interests from the input data. The output of this analysis is a selection of the most suitable occupations and educational institutions for the user.

[0504] Step 4:

[0505] The server presents suggestions generated based on the analysis results to the user via a voice input device. The output data is displayed in a way that is easy for the user to understand, either visually or audibly. The suggestions are tailored to the user's current abilities and interests.

[0506] Step 5:

[0507] After receiving a proposal, users provide their opinions and feedback on its content. This user feedback is sent to the server via their device. Specifically, this input includes satisfaction levels with the proposal and any additional requests.

[0508] Step 6:

[0509] The server updates its information processing model based on feedback received from users. This improves the model's accuracy and generates more appropriate suggestions in the future. The updated model is then used in the next attribute information analysis.

[0510] This series of processes allows users to receive individually optimized career support.

[0511] 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.

[0512] This invention is a system for optimizing an individual's career plan, and by combining it with an emotion engine, it provides appropriate suggestions and feedback according to the user's emotional state. The system consists of a server, a terminal, and a user, each playing a specific role.

[0513] The user first inputs their attribute data via a device. This data includes academic performance, club activities, extracurricular activities, hobbies, daily behavior patterns, and information that captures their emotional state. The device then sends this input data to the server.

[0514] Based on the received data, the server uses a machine learning model to analyze the individual's characteristics, interests, and aptitudes, and utilizes an emotion engine to recognize the user's emotional state. Based on this analysis, a career plan is generated that suggests the most suitable occupation or educational path for the user. The emotion engine takes the user's emotional state into consideration and optimizes the content and method of delivery of the suggestions.

[0515] The generated career plan is presented to the user via the device. The information is displayed in a visually effective way to aid user understanding. An emotion engine adjusts the tone and amount of content to match the user's state.

[0516] Furthermore, after the user accepts the career plan, they provide feedback through their device. This feedback is used for further analysis on the server, updating both the machine learning model and the sentiment engine to improve the accuracy of future suggestions.

[0517] For example, if a student is experiencing stress while choosing a school, the emotional engine recognizes this stress level and adjusts the suggestions to make them more easily accepted. Furthermore, mock exams and interview practice scenarios are generated and sent to the student's device, allowing them to prepare at their own pace. In this way, the system provides comprehensive career support that appropriately reflects the user's emotional state.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] Users input data on their academic performance, extracurricular activities, interests, hobbies, and daily behavioral patterns through their device. Simultaneously, they also input information about their emotional state, and the device transmits this data to the server.

[0521] Step 2:

[0522] The server stores the received data in a database. Here, the data's integrity and completeness are verified, and attribute data and sentiment data are prepared.

[0523] Step 3:

[0524] The server applies a machine learning model based on attribute data to analyze user characteristics and interests. In this process, it identifies patterns and generates lists of occupations and educational institutions that match the user's characteristics.

[0525] Step 4:

[0526] The server uses an emotion engine to analyze the user's current emotional state based on their input. In this case, it identifies stress, anxiety, excitement, etc., and adjusts the content and presentation method of the career plan accordingly.

[0527] Step 5:

[0528] Based on the analysis results, the server generates a career plan for the most suitable occupation and educational institution, and adjusts this information according to the user's emotional state. For example, for a user experiencing stress, the information is presented in a relaxed tone.

[0529] Step 6:

[0530] The device receives the carrier plan from the server and presents the information in a visually effective way through the user interface. Here, the tone and volume adjusted by the emotion engine are reflected.

[0531] Step 7:

[0532] Users review the presented career plan and enter feedback on its contents into their device. This feedback includes their thoughts on the proposal and any desired changes.

[0533] Step 8:

[0534] The device sends the collected feedback to the server, which uses this information to update its machine learning model and sentiment engine. This improves the accuracy of future suggestions.

[0535] Step 9:

[0536] The server generates mock exam and interview practice scenarios tailored to the user's career plan and emotional state. These scenarios, sent to the device, allow the user to prepare with confidence.

[0537] (Example 2)

[0538] 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."

[0539] In individual career planning, it is essential to provide flexible suggestions that take into account individual characteristics and interests, as well as reflecting their emotional state at any given time. However, conventional systems often fail to adequately reflect an individual's emotional state, resulting in uniform suggestions. Therefore, the challenge lies in providing a system that enables career suggestions that consider individual emotional states, not just quantitative factors.

[0540] 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.

[0541] In this invention, the server includes means for collecting personal information, means for performing analysis using a computational model based on the information, and means for suggesting appropriate occupations and educational institutions, taking into account the results obtained from the analysis and the individual's emotional state. This makes it possible to suggest a career plan that is best suited to the individual and reflects their individual emotional state.

[0542] "Personal information" refers to attribute data provided by users, such as academic performance, hobbies, behavioral patterns, and emotional states.

[0543] A "computational model" refers to an algorithm and program that uses machine learning to analyze data and identify individual characteristics and aptitudes.

[0544] "Emotional state" refers to information that indicates the user's mental or psychological state or mood, and is taken into consideration when the system adjusts its suggestions.

[0545] "Visual presentation methods" refer to ways of displaying information in a way that is easy for users to understand, using graphics, charts, infographics, and so on.

[0546] "Means of collecting and updating feedback" refers to the data collection and updating process used to receive user feedback and improve the computational model within the system based on that feedback.

[0547] This invention is a system for optimizing an individual's career plan, and it mainly consists of a server, a terminal, and a user. In implementation, the user first inputs their attribute data via the terminal. This data includes academic performance, club activities, extracurricular activities, hobbies, daily behavior patterns, and emotional state. The terminal is responsible for transmitting this information to the server.

[0548] The server analyzes the received data using a computational model. This model incorporates machine learning algorithms to analyze individual characteristics, interests, and aptitudes. Furthermore, it utilizes an emotion engine to recognize the user's emotional state through methods such as text analysis. As a result, a career plan is generated to suggest the most suitable occupation or educational path for the user. The generating AI model can flexibly adjust its responses by inputting prompts based on the user's situation and needs.

[0549] The generated career plan is visually displayed to the user through their device. This uses infographics and charts, designed for easy understanding. Furthermore, an emotion engine adjusts the information to an appropriate tone and amount based on the user's emotional state.

[0550] As a concrete example of a prompt message, you can input a situation such as, "A high school student with excellent academic performance but who doesn't participate much in extracurricular activities, and has recently been feeling stressed about going to college." The server will then provide advice and support that reflects the user's situation.

[0551] Furthermore, users can input feedback on the presented career plan via their device. This feedback is then sent back to the server and used to update the machine learning model and sentiment engine. This is expected to further improve the accuracy of future suggestions.

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] Users input academic performance, club activities, extracurricular activities, hobbies, behavioral patterns, and emotional states into the device. The entered data is provided as text and multiple-choice answers in the device's input form. This prepares individual attribute data.

[0555] Step 2:

[0556] The terminal sends the entered user data to the server. A secure protocol is used for transmission, ensuring data encryption and safe transfer. The entered information is delivered to the server in digital format.

[0557] Step 3:

[0558] The server processes the received user data and begins analysis using a computational model. First, the data is preprocessed to impute missing values ​​and correct irregular data. Then, machine learning algorithms are applied to analyze individual characteristics and aptitudes. The output is user-specific analysis results.

[0559] Step 4:

[0560] The server uses a generative AI model to generate suggestions for careers and educational institutions, taking into account the user's emotional state based on the analysis results. The emotion engine recognizes emotions using text analysis techniques and optimizes the suggestions. This generates and prepares specific suggestions.

[0561] Step 5:

[0562] The terminal presents the user with a carrier plan retrieved from the server. Infographics and charts effectively display and organize the information visually. This process provides output that is easily understandable to the user.

[0563] Step 6:

[0564] Users send feedback regarding the presented career plan to the server via their device. This feedback is collected as text data and prepared for further analysis on the server.

[0565] Step 7:

[0566] The server analyzes the received feedback and updates the machine learning model and sentiment engine accordingly. The training data is expanded, and the model is trained to improve the accuracy of the next suggestion. In this way, the improved output is prepared for the next iteration.

[0567] (Application Example 2)

[0568] 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."

[0569] In order to alleviate the stress that individual users experience when choosing a career path or profession, and to provide them with the optimal career plan, conventional systems have the challenge of not adequately providing appropriate feedback tailored to their emotional state. Furthermore, in order to further enhance the effectiveness of the proposed plan, it is necessary to update the system to appropriately reflect users' emotions and feedback.

[0570] 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.

[0571] In this invention, the server includes means for collecting personal attribute information, means for analyzing it using a predictive model, and means for adapting the information to the user's state based on the sentiment analysis results. This makes it possible to make optimal suggestions while taking the user's emotional state into consideration, thereby achieving high performance and user satisfaction through the suggested content.

[0572] "Personal attribute information" refers to individual characteristic data, including a user's educational background, activities, hobbies, and behavioral patterns.

[0573] A "predictive model" is a technology that uses machine learning to analyze data and predict results for new data.

[0574] "Emotion analysis" is the process of evaluating a user's psychological state and identifying their emotions.

[0575] "Emotional analysis results" refer to information indicating the user's emotional state obtained through emotional analysis.

[0576] A "career plan" is a plan of occupation and education proposed based on an individual's characteristics and interests.

[0577] This invention relates to a system for consumer robots designed to optimize an individual's career plan. The system consists of a user, a server, and a terminal, each playing a specific role.

[0578] The user first uses a device to input their personal information. This information includes data such as education, activities, hobbies, and daily behavioral patterns. The device sends this personal information, along with the user's voice and text data, to the server. This voice data is converted to text using the Google Cloud Speech-to-Text API.

[0579] The server analyzes attribute information using a predictive model based on the received data. The analysis utilizes the scikit-learn library to evaluate user characteristics and interests, and proposes the most suitable professions and educational institutions. Furthermore, it uses the Azure Emotion API to analyze user emotions from speech and text, and adapts the suggestions and communication methods based on the results. The server then uses a generative AI model to generate specific scenarios and career plans. These generated plans are presented via the user's device.

[0580] The user reviews their career plan and enters feedback into their device. The server receives this feedback information and updates its predictive model and sentiment analysis to improve the accuracy of future suggestions.

[0581] As a concrete example, imagine a high school student choosing a college, where a robot presents a simulated exam scenario in a relaxed state. In this process, the following prompt is used for the generative AI model: "Analyze the user's current emotional state and suggest career options that minimize stress. For example, suggest relaxation techniques or explanations that are easy to accept."

[0582] In this way, the invention realizes comprehensive career support that takes into account the user's emotional state.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The user uses the device to input personal attribute information. This input includes data such as education, activities, hobbies, and behavioral patterns. The device receives this data via both voice and text input and prepares to send it to the server.

[0586] Step 2:

[0587] The device sends attribute data collected from the user to the server. During this process, it uses the Google Cloud Speech-to-Text API to convert the audio data into text data and then sends the formatted data to the server.

[0588] Step 3:

[0589] The server analyzes the received attribute and text data. It utilizes a predictive model with scikit-learn for the analysis. Based on the input data, it analyzes individual characteristics and interests and generates suggestions for optimal professions and educational institutions. The analysis results are provided as output.

[0590] Step 4:

[0591] The server uses the Azure Emotion API to analyze the user's emotional state from text data. Using this emotional analysis, the generated career plan is adjusted to reflect the user's emotional state. The adjusted plan is then output.

[0592] Step 5:

[0593] The server uses a generative AI model to generate prompt messages and create a career plan that includes specific suggestions. The generated plan is then sent to the terminal.

[0594] Step 6:

[0595] The device displays the adjusted carrier plan received from the server. This display uses a method that is visually easy for the user to understand.

[0596] Step 7:

[0597] The user enters feedback on the presented career plan into their device. This feedback data is then sent back to the server.

[0598] Step 8:

[0599] The server analyzes user feedback and updates its predictive models and sentiment analysis. This improves the accuracy of future suggestions. Evaluation results based on the updates are then output.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] This invention is a system that provides individualized career plans for high school and university students. This system consists of three components: a server, a terminal, and a user, each playing a specific role.

[0618] First, the user enters their attribute data into the device. This attribute data includes academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns. The device receives this data and sends it to the server.

[0619] The server uses machine learning models to analyze accumulated attribute data. This analysis clarifies each user's individual characteristics, interests, and aptitudes. Based on these analysis results, the server generates highly personalized suggestions for careers and educational paths.

[0620] The generated suggestions are presented to the user via their device. This information is displayed in a visually clear format and designed to be easily understood by the user. The user then refers to the presented information and incorporates it into their own career plan.

[0621] Furthermore, the system has a mechanism in place to collect feedback from users. Users send their opinions and evaluations of suggestions to the server via their terminals, and this is used to further improve the model.

[0622] Through this process, the system provides users with an optimal and continuously evolving career plan, while also offering mock exams and interview practice programs. This enables students to make the most of their strengths when choosing their further education or career.

[0623] For example, if a high school student excels in mathematics and demonstrates leadership in extracurricular activities, the server might suggest career options that emphasize mathematical thinking and management skills, such as pursuing economics or engineering at university. Furthermore, it might offer mock interviews and preparation for specific subjects to support the student's college application process. In this way, the system guides individual users toward building a better future.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] Users use their devices to input attribute data related to academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns. This information reflects an individual's characteristics and interests.

[0627] Step 2:

[0628] The terminal formats the data entered by the user and sends it to the server. This process verifies the integrity and completeness of the data.

[0629] Step 3:

[0630] The server stores the received attribute data and saves it to a database. During this process, verification is performed to ensure data consistency.

[0631] Step 4:

[0632] The server uses accumulated data to apply machine learning models and perform analysis. This analysis identifies user characteristics, aptitudes, and interests.

[0633] Step 5:

[0634] Based on the analysis results, the server generates a career plan for each individual user, including the most suitable occupation and educational destination. This proposal is tailored to the user's characteristics and goals.

[0635] Step 6:

[0636] The device receives the carrier plan sent from the server and presents it through the user interface. The information is displayed in an easy-to-understand visual format.

[0637] Step 7:

[0638] Users review the presented career plan and send necessary feedback to the server via their device. This feedback includes comments on the proposal and any additional information requested.

[0639] Step 8:

[0640] The server receives user feedback and retrains its machine learning model to improve the accuracy of its suggestions. This improves the quality of suggestions for future users.

[0641] Step 9:

[0642] The server generates mock exams and interview practice scenarios based on the user's career plan and sends them to the terminal. Users can then use these to make more specific preparations.

[0643] (Example 1)

[0644] 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".

[0645] When providing students with individualized career and educational plans, traditional systems have difficulty making suggestions that are adequately tailored to individual characteristics. Furthermore, effective feedback collection and improvement of analytical models to enhance the accuracy of these suggestions are not being carried out.

[0646] 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.

[0647] In this invention, the server includes means for receiving personal attribute information from a terminal, means for performing analysis using a generative model based on the attribute information, and means for proposing the most suitable occupation or educational institution based on the analysis results. This makes it possible to provide career plans that take individual characteristics into maximum consideration and to improve the accuracy of the suggestions.

[0648] "Personal attribute information" refers to information that represents an individual's characteristics, such as academic performance, club activities, extracurricular activities, hobbies, and daily behavioral patterns.

[0649] A "terminal" is a computing device used by a user to input information and receive suggestions.

[0650] A "server" is a computer device that analyzes received attribute information, generates optimal suggestions, and further collects feedback to improve the generative model.

[0651] A "generative model" is a computational algorithm used to analyze an individual's attribute information and, based on the results, suggest appropriate occupations and educational institutions.

[0652] A "proposal" is a specific option regarding occupation or educational institution, provided based on the results of analysis using a generative model.

[0653] "Feedback" refers to the opinions and evaluations that users provide regarding a proposal, and this information is used to improve the generative model.

[0654] "Exam and interview practice scenarios" are training programs designed to simulate situations users might face during the application or employment process and provide the necessary preparations.

[0655] "Simulating a long-term career plan" is the process of predicting an individual's career path over a long period and identifying the skills and qualifications required at each stage.

[0656] This invention provides a system that presents appropriate career plans based on an individual's characteristics and interests. The system consists of three components: the user, the terminal, and the server.

[0657] First, users input personal attribute information such as academic performance, club activities, hobbies, and daily routines into the device. This input is done through an interface designed to be user-friendly.

[0658] The terminal receives attribute information entered by the user and sends it to the server using a secure communication protocol. Encryption technologies such as SSL / TLS are used in this process.

[0659] The server stores the received information and performs data analysis using a generative AI model. This analysis can utilize machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, the server generates suggestions for the most suitable occupation or educational institution for the user and sends this information to the terminal.

[0660] The user receives visual suggestions through their device. These suggestions utilize graphs and charts designed for ease of understanding. Furthermore, users input feedback on the suggestions, and this information is sent back to the server to improve the generated AI model. This allows the entire system to continuously evolve and provide more accurate suggestions.

[0661] As a concrete example, consider a high school student who is strong in mathematics and demonstrates leadership in extracurricular activities. In this case, the server suggests pursuing a degree in economics or engineering at university, based on their mathematical thinking and management skills. An example of a prompt might be, "This user is good at mathematics and has leadership qualities. What career plan would you suggest?" This allows the user to receive support in choosing a career path that perfectly suits their strengths.

[0662] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0663] Step 1:

[0664] The user enters personal attribute information into the terminal's input interface. This information includes academic performance, extracurricular activities, hobbies, and daily behavioral patterns. The terminal organizes the entered data and converts it into a data format that can be transmitted. This input information forms the basis for processing.

[0665] Step 2:

[0666] The terminal sends organized attribute information to the server. This communication is encrypted using a secure protocol (SSL / TLS). The server stores the received data in a database for analysis. Through these steps, the data is safely and securely delivered to the server.

[0667] Step 3:

[0668] The server retrieves attribute information stored in the database and begins analysis using a generative AI model. Machine learning libraries such as TensorFlow and PyTorch are used during the analysis process. The server identifies individual characteristics and, based on the results, identifies the most suitable occupation or educational institution.

[0669] Step 4:

[0670] The server generates suggestions for the user based on the analysis results. These suggestions include specific options regarding further education and career choices. The generated suggestions are then refined, using the prompt "This user is good at mathematics and has leadership qualities. What career plan would you suggest?" as an example.

[0671] Step 5:

[0672] The server sends the generated proposal to the terminal. The terminal receives this proposal and displays it to the user in a visually easy-to-understand format. The terminal interface is designed to represent the proposal using graphs and charts, making it easy for the user to understand.

[0673] Step 6:

[0674] The user reviews the presented suggestions and provides feedback. This feedback includes the usefulness of the suggestions and areas for improvement. The submitted feedback is sent from the terminal to the server.

[0675] Step 7:

[0676] The server analyzes the feedback it receives and uses it to improve the generated AI model. This cycle is repeated, allowing the entire system to continuously evolve and make better suggestions.

[0677] (Application Example 1)

[0678] 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".

[0679] In recent years, the importance of receiving personalized recommendations for educational institutions and careers in individual career development has increased, but there is a lack of efficient and flexible systems to achieve this. Furthermore, there is a need for mechanisms that allow users to naturally receive career support in their daily lives, but technological solutions to this problem remain insufficient.

[0680] 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.

[0681] In this invention, the server includes means for collecting personal attribute information, means for analyzing the attribute information using an information processing model, means for proposing the most suitable occupation or educational institution according to the results obtained from the analysis, means for providing the proposal through a voice input device, and means for collecting the opinion of the person who received the proposal and updating the information processing model. This enables the user to receive individually optimized career support through a voice interface in their daily life.

[0682] "Personal attribute information" refers to information that shows various characteristics related to an individual, such as academic performance, hobbies, and daily behavioral patterns.

[0683] An "information processing model" is an algorithm or system used to analyze input data and derive a specific result.

[0684] "Occupation" refers to the type of work or job an individual engages in to earn a living.

[0685] An "educational institution" refers to organizations such as schools and universities that are established for the purpose of learning and research.

[0686] A "voice input device" is hardware or software that recognizes a user's voice and processes it as digital data.

[0687] "Opinions" refer to individuals' thoughts and evaluations of a proposal, and are the information that the system uses as feedback to improve its model.

[0688] The system of this invention mainly consists of three elements: a server, a terminal, and a user. The user inputs their attribute information through a smart device, and this information is transmitted from the terminal to the server. The server has an information processing model installed and analyzes the user's attribute information using machine learning libraries such as TensorFlow. Based on this analysis, the server generates individually optimized suggestions for occupations and educational institutions, and further presents them to the user via a voice input device.

[0689] For example, if a user inputs data by voice saying "I'm good at math," the server analyzes this information and suggests math-related occupations and educational institutions. These generated suggestions can be easily received through voice interfaces in the user's daily life. Furthermore, user feedback is fed back to the server, and the information processing model is updated based on this information, improving the accuracy of the suggestions provided.

[0690] As a concrete example, suppose a high school student uses voice input to ask, "My math grades have been improving recently. Should I continue to focus on math?" The server analyzes this and suggests colleges and careers where math skills can be utilized, as well as providing advice on the next steps necessary, such as studying recommended subjects for entrance exams. This allows users to better understand their own strengths and concretize their future options.

[0691] An example of a prompt to input into a generative AI model is, "High school students' math grades are improving. Please generate appropriate career path suggestions based on this data." The server processes the information according to this prompt and can provide accurate advice to the user.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] Users input their personal information using a smart device. This input can be in voice or text format and includes academic performance, hobbies, and activity history. This information is sent to the device and formatted into a data format.

[0695] Step 2:

[0696] The terminal transmits the received user attribute information to the server. The input information is sent to the server as digital data and converted into a specific format. As a concrete example, voice data is converted into text format and recorded in a database.

[0697] Step 3:

[0698] The server uses an information processing model to analyze the received attribute information. This model utilizes libraries such as TensorFlow to extract user characteristics and interests from the input data. The output of this analysis is a selection of the most suitable occupations and educational institutions for the user.

[0699] Step 4:

[0700] The server presents suggestions generated based on the analysis results to the user via a voice input device. The output data is displayed in a way that is easy for the user to understand, either visually or audibly. The suggestions are tailored to the user's current abilities and interests.

[0701] Step 5:

[0702] After receiving a proposal, users provide their opinions and feedback on its content. This user feedback is sent to the server via their device. Specifically, this input includes satisfaction levels with the proposal and any additional requests.

[0703] Step 6:

[0704] The server updates its information processing model based on feedback received from users. This improves the model's accuracy and generates more appropriate suggestions in the future. The updated model is then used in the next attribute information analysis.

[0705] This series of processes allows users to receive individually optimized career support.

[0706] 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.

[0707] This invention is a system for optimizing an individual's career plan, and by combining it with an emotion engine, it provides appropriate suggestions and feedback according to the user's emotional state. The system consists of a server, a terminal, and a user, each playing a specific role.

[0708] The user first inputs their attribute data via a device. This data includes academic performance, club activities, extracurricular activities, hobbies, daily behavior patterns, and information that captures their emotional state. The device then sends this input data to the server.

[0709] Based on the received data, the server uses a machine learning model to analyze the individual's characteristics, interests, and aptitudes, and utilizes an emotion engine to recognize the user's emotional state. Based on this analysis, a career plan is generated that suggests the most suitable occupation or educational path for the user. The emotion engine takes the user's emotional state into consideration and optimizes the content and method of delivery of the suggestions.

[0710] The generated career plan is presented to the user via the device. The information is displayed in a visually effective way to aid user understanding. An emotion engine adjusts the tone and amount of content to match the user's state.

[0711] Furthermore, after the user accepts the career plan, they provide feedback through their device. This feedback is used for further analysis on the server, updating both the machine learning model and the sentiment engine to improve the accuracy of future suggestions.

[0712] For example, if a student is experiencing stress while choosing a school, the emotional engine recognizes this stress level and adjusts the suggestions to make them more easily accepted. Furthermore, mock exams and interview practice scenarios are generated and sent to the student's device, allowing them to prepare at their own pace. In this way, the system provides comprehensive career support that appropriately reflects the user's emotional state.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] Users input data on their academic performance, extracurricular activities, interests, hobbies, and daily behavioral patterns through their device. Simultaneously, they also input information about their emotional state, and the device transmits this data to the server.

[0716] Step 2:

[0717] The server stores the received data in a database. Here, the data's integrity and completeness are verified, and attribute data and sentiment data are prepared.

[0718] Step 3:

[0719] The server applies a machine learning model based on attribute data to analyze user characteristics and interests. In this process, it identifies patterns and generates lists of occupations and educational institutions that match the user's characteristics.

[0720] Step 4:

[0721] The server uses an emotion engine to analyze the user's current emotional state based on their input. In this case, it identifies stress, anxiety, excitement, etc., and adjusts the content and presentation method of the career plan accordingly.

[0722] Step 5:

[0723] Based on the analysis results, the server generates a career plan for the most suitable occupation and educational institution, and adjusts this information according to the user's emotional state. For example, for a user experiencing stress, the information is presented in a relaxed tone.

[0724] Step 6:

[0725] The device receives the carrier plan from the server and presents the information in a visually effective way through the user interface. Here, the tone and volume adjusted by the emotion engine are reflected.

[0726] Step 7:

[0727] Users review the presented career plan and enter feedback on its contents into their device. This feedback includes their thoughts on the proposal and any desired changes.

[0728] Step 8:

[0729] The device sends the collected feedback to the server, which uses this information to update its machine learning model and sentiment engine. This improves the accuracy of future suggestions.

[0730] Step 9:

[0731] The server generates mock exam and interview practice scenarios tailored to the user's career plan and emotional state. These scenarios, sent to the device, allow the user to prepare with confidence.

[0732] (Example 2)

[0733] 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".

[0734] In individual career planning, it is essential to provide flexible suggestions that take into account individual characteristics and interests, as well as reflecting their emotional state at any given time. However, conventional systems often fail to adequately reflect an individual's emotional state, resulting in uniform suggestions. Therefore, the challenge lies in providing a system that enables career suggestions that consider individual emotional states, not just quantitative factors.

[0735] 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.

[0736] In this invention, the server includes means for collecting personal information, means for performing analysis using a computational model based on the information, and means for suggesting appropriate occupations and educational institutions, taking into account the results obtained from the analysis and the individual's emotional state. This makes it possible to suggest a career plan that is best suited to the individual and reflects their individual emotional state.

[0737] "Personal information" refers to attribute data provided by users, such as academic performance, hobbies, behavioral patterns, and emotional states.

[0738] A "computational model" refers to an algorithm and program that uses machine learning to analyze data and identify individual characteristics and aptitudes.

[0739] "Emotional state" refers to information that indicates the user's mental or psychological state or mood, and is taken into consideration when the system adjusts its suggestions.

[0740] "Visual presentation methods" refer to ways of displaying information in a way that is easy for users to understand, using graphics, charts, infographics, and so on.

[0741] "Means of collecting and updating feedback" refers to the data collection and updating process used to receive user feedback and improve the computational model within the system based on that feedback.

[0742] This invention is a system for optimizing an individual's career plan, and it mainly consists of a server, a terminal, and a user. In implementation, the user first inputs their attribute data via the terminal. This data includes academic performance, club activities, extracurricular activities, hobbies, daily behavior patterns, and emotional state. The terminal is responsible for transmitting this information to the server.

[0743] The server analyzes the received data using a computational model. This model incorporates machine learning algorithms to analyze individual characteristics, interests, and aptitudes. Furthermore, it utilizes an emotion engine to recognize the user's emotional state through methods such as text analysis. As a result, a career plan is generated to suggest the most suitable occupation or educational path for the user. The generating AI model can flexibly adjust its responses by inputting prompts based on the user's situation and needs.

[0744] The generated career plan is visually displayed to the user through their device. This uses infographics and charts, designed for easy understanding. Furthermore, an emotion engine adjusts the information to an appropriate tone and amount based on the user's emotional state.

[0745] As a concrete example of a prompt message, you can input a situation such as, "A high school student with excellent academic performance but who doesn't participate much in extracurricular activities, and has recently been feeling stressed about going to college." The server will then provide advice and support that reflects the user's situation.

[0746] Furthermore, users can input feedback on the presented career plan via their device. This feedback is then sent back to the server and used to update the machine learning model and sentiment engine. This is expected to further improve the accuracy of future suggestions.

[0747] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0748] Step 1:

[0749] Users input academic performance, club activities, extracurricular activities, hobbies, behavioral patterns, and emotional states into the device. The entered data is provided as text and multiple-choice answers in the device's input form. This prepares individual attribute data.

[0750] Step 2:

[0751] The terminal sends the entered user data to the server. A secure protocol is used for transmission, ensuring data encryption and safe transfer. The entered information is delivered to the server in digital format.

[0752] Step 3:

[0753] The server processes the received user data and begins analysis using a computational model. First, the data is preprocessed to impute missing values ​​and correct irregular data. Then, machine learning algorithms are applied to analyze individual characteristics and aptitudes. The output is user-specific analysis results.

[0754] Step 4:

[0755] The server uses a generative AI model to generate suggestions for careers and educational institutions, taking into account the user's emotional state based on the analysis results. The emotion engine recognizes emotions using text analysis techniques and optimizes the suggestions. This generates and prepares specific suggestions.

[0756] Step 5:

[0757] The terminal presents the user with a carrier plan retrieved from the server. Infographics and charts effectively display and organize the information visually. This process provides output that is easily understandable to the user.

[0758] Step 6:

[0759] Users send feedback regarding the presented career plan to the server via their device. This feedback is collected as text data and prepared for further analysis on the server.

[0760] Step 7:

[0761] The server analyzes the received feedback and updates the machine learning model and sentiment engine accordingly. The training data is expanded, and the model is trained to improve the accuracy of the next suggestion. In this way, the improved output is prepared for the next iteration.

[0762] (Application Example 2)

[0763] 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".

[0764] In order to alleviate the stress that individual users experience when choosing a career path or profession, and to provide them with the optimal career plan, conventional systems have the challenge of not adequately providing appropriate feedback tailored to their emotional state. Furthermore, in order to further enhance the effectiveness of the proposed plan, it is necessary to update the system to appropriately reflect users' emotions and feedback.

[0765] 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.

[0766] In this invention, the server includes means for collecting personal attribute information, means for analyzing it using a predictive model, and means for adapting the information to the user's state based on the sentiment analysis results. This makes it possible to make optimal suggestions while taking the user's emotional state into consideration, thereby achieving high performance and user satisfaction through the suggested content.

[0767] "Personal attribute information" refers to individual characteristic data, including a user's educational background, activities, hobbies, and behavioral patterns.

[0768] A "predictive model" is a technology that uses machine learning to analyze data and predict results for new data.

[0769] "Emotion analysis" is the process of evaluating a user's psychological state and identifying their emotions.

[0770] "Emotional analysis results" refer to information indicating the user's emotional state obtained through emotional analysis.

[0771] A "career plan" is a plan of occupation and education proposed based on an individual's characteristics and interests.

[0772] This invention relates to a system for consumer robots designed to optimize an individual's career plan. The system consists of a user, a server, and a terminal, each playing a specific role.

[0773] The user first uses a device to input their personal information. This information includes data such as education, activities, hobbies, and daily behavioral patterns. The device sends this personal information, along with the user's voice and text data, to the server. This voice data is converted to text using the Google Cloud Speech-to-Text API.

[0774] The server analyzes attribute information using a predictive model based on the received data. The analysis utilizes the scikit-learn library to evaluate user characteristics and interests, and proposes the most suitable professions and educational institutions. Furthermore, it uses the Azure Emotion API to analyze user emotions from speech and text, and adapts the suggestions and communication methods based on the results. The server then uses a generative AI model to generate specific scenarios and career plans. These generated plans are presented via the user's device.

[0775] The user reviews their career plan and enters feedback into their device. The server receives this feedback information and updates its predictive model and sentiment analysis to improve the accuracy of future suggestions.

[0776] As a concrete example, imagine a high school student choosing a college, where a robot presents a simulated exam scenario in a relaxed state. In this process, the following prompt is used for the generative AI model: "Analyze the user's current emotional state and suggest career options that minimize stress. For example, suggest relaxation techniques or explanations that are easy to accept."

[0777] In this way, the invention realizes comprehensive career support that takes into account the user's emotional state.

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The user uses the device to input personal attribute information. This input includes data such as education, activities, hobbies, and behavioral patterns. The device receives this data via both voice and text input and prepares to send it to the server.

[0781] Step 2:

[0782] The device sends attribute data collected from the user to the server. During this process, it uses the Google Cloud Speech-to-Text API to convert the audio data into text data and then sends the formatted data to the server.

[0783] Step 3:

[0784] The server analyzes the received attribute and text data. It utilizes a predictive model with scikit-learn for the analysis. Based on the input data, it analyzes individual characteristics and interests and generates suggestions for optimal professions and educational institutions. The analysis results are provided as output.

[0785] Step 4:

[0786] The server uses the Azure Emotion API to analyze the user's emotional state from text data. Using this emotional analysis, the generated career plan is adjusted to reflect the user's emotional state. The adjusted plan is then output.

[0787] Step 5:

[0788] The server uses a generative AI model to generate prompt messages and create a career plan that includes specific suggestions. The generated plan is then sent to the terminal.

[0789] Step 6:

[0790] The device displays the adjusted carrier plan received from the server. This display uses a method that is visually easy for the user to understand.

[0791] Step 7:

[0792] The user enters feedback on the presented career plan into their device. This feedback data is then sent back to the server.

[0793] Step 8:

[0794] The server analyzes user feedback and updates its predictive models and sentiment analysis. This improves the accuracy of future suggestions. Evaluation results based on the updates are then output.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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."

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] Means of collecting personal attribute data,

[0819] A means of analyzing the attribute data using a machine learning model,

[0820] A means of proposing the most suitable occupation or educational institution based on the results obtained from the aforementioned analysis,

[0821] A means for collecting feedback from individuals who have received the aforementioned proposal and updating the machine learning model,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising means for generating mock exam and interview practice scenarios based on the above proposal.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising means for conducting a long-term career simulation for an individual and indicating the necessary skills and qualifications at each stage.

[0827] "Example 1"

[0828] (Claim 1)

[0829] Means of collecting personal attribute information,

[0830] Means for transmitting the aforementioned attribute information from the terminal to the server,

[0831] A means for performing analysis using a generative model based on the aforementioned attribute information,

[0832] A means of proposing the most suitable occupation or educational institution based on the results obtained from the above analysis,

[0833] A means for collecting the opinions of individuals who have received the aforementioned proposal and for improving the generative model,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, further comprising means for generating practice scenarios for tests and interviews based on the above proposal.

[0837] (Claim 3)

[0838] The system according to claim 1, further comprising means for simulating an individual's long-term career plan and indicating the skills or certifications required at each stage.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] Means of collecting personal attribute information,

[0842] A means for analyzing the attribute information using an information processing model,

[0843] A means of proposing the most suitable occupation or educational institution based on the results obtained from the aforementioned analysis,

[0844] Means for providing the above proposal through an audio input device,

[0845] A means for collecting the opinions of individuals who have received the aforementioned proposal and updating the information processing model,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising means for generating mock exam and interview practice scenarios based on the above proposal.

[0849] (Claim 3)

[0850] The system according to claim 1, further comprising means for creating an individual's long-term career plan and indicating the necessary skills and qualifications at each stage.

[0851] "Example 2 of combining an emotion engine"

[0852] (Claim 1)

[0853] Means of collecting personal information,

[0854] A means for performing analysis using a computational model based on the aforementioned information,

[0855] A means of suggesting appropriate occupations and educational institutions, taking into account the results obtained from the analysis and the emotional state of individuals.

[0856] A means of visually presenting the proposed plan,

[0857] A means for collecting the opinions of individuals who have received the aforementioned proposal and updating the calculation model,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising means for generating practice tests and evaluation scenarios in relation to the above proposal.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising means for conducting a long-term career simulation for an individual and indicating the necessary skills and qualifications at each stage.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] Means of collecting personal attribute information,

[0866] A means for performing analysis using a predictive model based on the aforementioned attribute information,

[0867] A means of proposing the most suitable professional or educational institution based on the results obtained from the aforementioned analysis,

[0868] A means of analyzing the emotional state of a user,

[0869] Based on the aforementioned emotion analysis results, a means for adapting the proposed content to the user's state,

[0870] A means for collecting the evaluations of individuals who have received the aforementioned proposal and updating the predictive model,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, further comprising means for producing mock exams and interview practice products based on the above proposal.

[0874] (Claim 3)

[0875] The system according to claim 1, further comprising means for conducting a long-term career simulation for an individual, presenting the necessary skills and qualifications at each stage, and means for adjusting the proposed content based on the emotion analysis. [Explanation of symbols]

[0876] 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. Means of collecting personal attribute data, A means of analyzing the attribute data using a machine learning model, A means of proposing the most suitable occupation or educational institution based on the results obtained from the aforementioned analysis, A means for collecting feedback from individuals who have received the aforementioned proposal and updating the machine learning model, A system that includes this.

2. The system according to claim 1, further comprising means for generating mock exam and interview practice scenarios based on the above proposal.

3. The system according to claim 1, further comprising means for conducting a long-term career simulation for an individual and indicating the necessary skills and qualifications at each stage.

Images