System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving and pre-processing information input by a user; means for extracting useful information from the pre-processed information; means for generating a carrier path using a generative AI model based on the extracted information; means for transmitting and displaying the generated carrier path to a user device; and means for receiving feedback from the user and generating information again.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional career diagnosis systems, the information input by users is limited, making it difficult to propose optimal career paths for individual users. Furthermore, the lack of a function to adapt based on user feedback means that it is not possible to provide information that is useful to users. The present invention aims to solve these problems by providing a system that can generate optimal career paths based on user input information and update the information based on user feedback. [Means for solving the problem]

[0005] According to the present invention, a system is provided that includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information, means for transmitting and displaying the generated career path to a user terminal, and means for receiving feedback from the user and regenerating the information. This system makes it possible to propose optimal career paths based on the user's skill set, work experience, and areas of interest, and can flexibly update the information in response to user feedback. By using natural language processing technology in the generative AI model, it is possible to provide proposals that are more intuitive and easy to understand for the user.

[0006] "Users" are individuals or organizations that use the system to receive career path diagnoses and suggestions.

[0007] "Input information" refers to data such as name, age, skill set, work experience, and areas of interest that a user provides to the system.

[0008] "Preprocessing" is the process of formatting the information entered by the user and checking and correcting any omissions or inconsistencies.

[0009] "Useful information" refers to the skill sets, work experience, areas of interest, etc. required to suggest career paths, extracted from pre-processed user data.

[0010] A "generative AI model" is an artificial intelligence model that uses natural language processing and other technologies to generate the optimal career path for a user based on given information.

[0011] A "career path" is a suggestion that shows the direction of a user's future career or job based on their skill set and work experience.

[0012] "Send" refers to sending information such as the career path generated by the server to the user's terminal.

[0013] "Display" refers to the terminal visually presenting the information received from the server to the user.

[0014] "Feedback" refers to opinions or additional information requested by the user regarding the displayed career path.

[0015] "Natural language processing" is a technology that analyzes, understands, and generates human language, enabling generative AI models to interact with users. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that utilizes generative AI to provide effective career assessments based on information entered by users. This system receives the information entered by users, preprocesses it, generates a career path using a generative AI model, and displays it to the user.

[0038] System configuration:

[0039] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[0040] User role:

[0041] Users enter basic information, skills, work experience, and areas of interest required for a career assessment through a terminal. Specifically, users enter their name, age, programming languages ​​they can use, past work history, and areas of interest (e.g., AI or data science).

[0042] Device role:

[0043] The device sends the information entered by the user to the server, displays the career path suggestions received from the server to the user, and receives user feedback and sends it back to the server.

[0044] Server Role:

[0045] The server processes the following series of processes.

[0046] 1. Receiving and preprocessing information:

[0047] The server receives user information sent from the device, preprocesses the received information, and checks for missing or inconsistent data. For example, if age is not entered or invalid skills are included, the server makes appropriate corrections.

[0048] 2. Information Extraction:

[0049] From the preprocessed information, useful information such as the user's skill set, past work experience, areas of interest, etc. is extracted, and the extracted information is used as input data for the AI ​​model.

[0050] 3. Application of generative AI models:

[0051] The server uses a generative AI model based on the extracted information to generate career path candidates. This AI model analyzes the user's information using natural language processing technology and can suggest optimal career paths.

[0052] 4. Career path suggestions and feedback processing:

[0053] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[0054] Examples:

[0055] As a concrete example, consider the case where a user inputs the following information:

[0056] Name: Taro

[0057] Age: 30

[0058] Skills: Programming (Python, Java), Data Analysis

[0059] Experience: 5 years of experience working in an IT company

[0060] Interests: AI, machine learning

[0061] procedure:

[0062] 1. User: Enter the above information using the terminal.

[0063] 2. Terminal: Sends the entered information to the server.

[0064] 3. Server: Receives information, preprocesses it, and extracts formatted data.

[0065] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as suggesting candidates for data scientists, machine learning engineers, and AI researchers.

[0066] 5. Server: Sends the generated career path to the device.

[0067] 6. Terminal: Shows the user a list of suggested career paths.

[0068] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[0069] 8. Device: Sends feedback to the server.

[0070] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[0071] 10. Terminal: Display detailed information to the user.

[0072] In this way, users can receive suggestions for suitable career paths based on their skills and interests, along with specific information on how to get there.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user enters basic information, skill set, work experience, and areas of interest through the terminal. For example, the user enters "name," "age," "programming skills (Python, Java)," "five years of experience working in an IT company," and "areas of interest (AI, machine learning)."

[0076] Step 2:

[0077] The device sends the user's input information to the server, and the data is formatted in JSON format or similar.

[0078] Step 3:

[0079] The server receives the user information sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if the age is not entered, it will fill in the field with a default value or prompt the user to enter it again.

[0080] Step 4:

[0081] The server extracts useful information from the preprocessed data, such as skill sets, work experience, and areas of interest, which is then fed into a generative AI model in the next step.

[0082] Step 5:

[0083] Based on the extracted information, the server uses a generative AI model to generate career paths. This AI model analyzes the user's input data and generates optimal career path candidates. For example, it may suggest careers such as data scientist, machine learning engineer, or AI researcher.

[0084] Step 6:

[0085] The server sends the generated career path candidates to the terminal, formatting the data so that it is easy for the user to understand.

[0086] Step 7:

[0087] The terminal displays the career path candidates received from the server to the user, who can then review them and provide feedback if necessary.

[0088] Step 8:

[0089] The user types their feedback into the terminal, for example, "I'm interested in becoming a data scientist, but I'd like to know more about specific skill sets and learning resources."

[0090] Step 9:

[0091] The terminal transmits the user's feedback to the server, and the transmitted data is formatted in a manner that accurately conveys the feedback content.

[0092] Step 10:

[0093] The server receives the feedback and uses the generative AI model again to generate detailed information based on the feedback, such as the skills required to become a data scientist (database management, understanding machine learning algorithms) and recommended learning resources (online courses, reference books).

[0094] Step 11:

[0095] The server sends the generated detailed information to the terminal.

[0096] Step 12:

[0097] The device displays the detailed information received from the server to the user, who can review it and obtain more specific information about the career path.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] In modern society, individuals have a wide variety of career options, but the sheer number of options makes it difficult to determine the appropriate career path. It is particularly difficult for individuals with specific skill sets and work experience to find the optimal career path. Another issue is the lack of a system that efficiently organizes this information and suggests career paths suited to individuals.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes means for receiving and preprocessing information input by a user, means for extracting useful information from the preprocessed information, and means for generating a career path using a generative AI model based on the extracted information. This makes it possible to generate an optimal career path based on input information such as the user's basic information, skill set, work history, and areas of interest, and to provide detailed feedback as needed.

[0103] "User" refers to an individual who uses the system to input the information necessary for a career diagnosis and receive career path suggestions.

[0104] "Terminal" refers to the device through which a user inputs information and communicates with a server. This includes PCs, smartphones, tablets, etc.

[0105] "Server" refers to the hardware and software that receives and processes information sent by users and generates and proposes career paths using a generative AI model.

[0106] "Preprocessing" refers to a series of processes by which the server detects and corrects deficiencies or inconsistencies in the data received from the user.

[0107] "Generative AI models" refer to artificial intelligence technologies used to generate career paths based on extracted information, including natural language processing technologies.

[0108] A "prompt" is a document containing specific formatting and instructions to be input to a generative AI model.

[0109] "Basic information" refers to personally identifiable information such as the user's name and age.

[0110] A "competence set" refers to the skills and knowledge a user possesses, including programming languages ​​and specialized knowledge.

[0111] "Work history" refers to detailed information about a user's past work experience, including, for example, where they worked and what they did.

[0112] "Areas of Interest" refers to areas or topics that a user is personally interested in, such as AI and data science.

[0113] "Feedback" refers to any additional requests or comments a user provides regarding a career path suggestion.

[0114] This invention is a system that utilizes generative AI based on information entered by the user to provide efficient career diagnosis. This system consists of three entities: a server, a terminal, and a user. The detailed roles of each entity and the overall system flow are explained below.

[0115] Server Roles

[0116] The server receives user information and performs preprocessing, information extraction, and applies generative AI models. The server is equipped with a high-performance processor and sufficient memory to quickly process data and execute AI models. Cloud servers or dedicated data center servers are commonly used.

[0117] 1. Receiving and preprocessing information:

[0118] The server receives user information sent from the device. The received information is passed to the server in JSON or XML format. The server analyzes this data and checks for inconsistencies or missing data. For example, if the age or skill set is abnormal, it is corrected.

[0119] 2. Information Extraction:

[0120] The server extracts the user's basic information, skill set, work history, areas of interest, etc. from the pre-processed information. Natural language processing technology is used for this extraction process, resulting in highly accurate data extraction.

[0121] 3. Application of generative AI models:

[0122] The server uses the extracted information as input to generate a career path using a generative AI model (e.g., GPT-3 or BERT). The generated career path candidates are then presented as multiple options.

[0123] 4. Career path suggestions and feedback processing:

[0124] The generated career path is sent to the device and displayed to the user. If the user provides feedback, the feedback is received again and further detailed information is provided using the generative AI model. This allows the user to find the best career path based on their interests and skills.

[0125] Device Role

[0126] The terminal acts as an intermediary through which the user inputs information and communicates with the server. The terminal can function as a web browser, a smartphone application, or a dedicated device.

[0127] 1. Transmission of Information:

[0128] The terminal sends the user's basic information, skill set, work history, and areas of interest to the server. The information is encrypted using SSL / TLS and transmitted securely.

[0129] 2. View Career Paths:

[0130] The terminal displays the career path suggestions received from the server to the user, providing an intuitive user interface that allows the user to easily understand the suggested career paths.

[0131] 3. Submitting Feedback:

[0132] The feedback information entered by the user is sent to the server, which processes it again and generates new suggestions and detailed information.

[0133] User Roles

[0134] 1. Enter your information:

[0135] Users enter the information required for the career assessment through a terminal, such as their name, age, programming skills (Python, Java, etc.), work history, and areas of interest (AI, data science, etc.).

[0136] 2. Review and feedback on proposals:

[0137] Users can review the career path suggestions displayed on their device and enter feedback as needed. Feedback should be specific, such as "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[0138] Specific operation example

[0139] Assume a case where the user enters the following information:

[0140] Name: Taro

[0141] Age: 30

[0142] Skill set: Programming (Python, Java), Data analysis

[0143] Work history: 5 years of experience working in an IT company

[0144] Interests: AI, machine learning

[0145] When the user enters this information and clicks the send button, the device sends it to the server, which receives the information, performs preprocessing and information extraction, and inputs prompt sentences into the generative AI model.

[0146] Example prompt sentence:

[0147] "Taro is 30 years old, has programming skills in Python and Java, and has experience in data analysis. He currently has five years of work experience in an IT company and is interested in AI and machine learning. Please suggest a career path that would be suitable for Taro."

[0148] The generative AI model analyzes this prompt and suggests career paths such as data scientist, machine learning engineer, and AI researcher. The device displays this to the user, who then provides feedback such as, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set." The server then processes this feedback again, generates more detailed information, and sends it to the device. Ultimately, the user gains a deeper understanding of the skills needed for their career path and the next steps they should take.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] Users enter the information required for the career assessment through their device, including their name, age, programming languages ​​they can use, their past work history, and areas of interest. After filling out the information in the input form and clicking the submit button, the data is converted into JSON or XML format.

[0152] Input: User basic information, skill set, work history, areas of interest

[0153] Output: Input data in JSON or XML format

[0154] Step 2:

[0155] The terminal sends the information entered by the user to the server. When the send button is clicked, the terminal encrypts the data with SSL / TLS and sends it to the server using the HTTP / HTTPS protocol.

[0156] Input: Input data in JSON or XML format

[0157] Output: Data transfer to the server

[0158] Step 3:

[0159] The server receives user information sent from the device. The received information is passed to the server in JSON or XML format. The server analyzes this data and checks for inconsistencies or missing data. For example, if the age or skill set is abnormal, it is corrected.

[0160] Input: Data received from the terminal

[0161] Output: Corrected data

[0162] Specifically, the server parses the received JSON data and checks the integrity of each field before saving the content to the database.

[0163] Step 4:

[0164] The server extracts useful information from the preprocessed information, such as the user's basic information, skill set, work history, and areas of interest, using natural language processing technology.

[0165] Input: Corrected data

[0166] Output: The input dataset for the generative AI model

[0167] Specifically, the server converts the extracted information into a specific format and builds a dataset to be input into the generative AI model.

[0168] Step 5:

[0169] The server uses a generative AI model (e.g., GPT-3 or BERT) to generate career path candidates based on the extracted information. The server calls the generative AI model (e.g., GPT-3 or BERT) via an API, provides the formatted dataset as input, and obtains the model's output.

[0170] Input: The input dataset for the generative AI model

[0171] Output: Possible career paths

[0172] Specifically, the server uses generative AI models such as GPT-3 and BERT to generate multiple career path candidates based on input data.

[0173] Step 6:

[0174] The server sends the generated career path candidates to the terminal, converts the generated career path information into JSON format, and returns it as a response to the terminal using the HTTP / HTTPS protocol.

[0175] Input: Possible career paths

[0176] Output: Data transfer to the device

[0177] As a specific operation, the server transmits the generated carrier path to the terminal.

[0178] Step 7:

[0179] The terminal displays the career path suggestions received from the server to the user in a visually easy-to-understand format (list format or card format) in the user interface.

[0180] Input: Career path suggestions from the server

[0181] Output: Displaying the career path to the user

[0182] Specifically, the device updates its user interface to display a list of suggested career paths.

[0183] Step 8:

[0184] Users review the suggested career paths and provide feedback if they require more specific information or other options, including details about specific career paths or new requirements.

[0185] Input: Career path displayed information

[0186] Output: Feedback information

[0187] As a specific operation, the user enters a comment in the feedback form and clicks the send button.

[0188] Step 9:

[0189] The device sends this feedback to the server. The feedback information is also encrypted using SSL / TLS and sent to the server.

[0190] Input: User feedback information

[0191] Output: Feedback forwarding to the server

[0192] As a specific operation, the terminal transmits the feedback data to the server again.

[0193] Step 10:

[0194] The server receives the feedback and generates new information using the generative AI model again. It analyzes the feedback and generates prompts to generate new career paths and detailed information.

[0195] Input: Feedback information

[0196] Output: New career path or more information

[0197] Specifically, the server generates a prompt sentence, then calls the generative AI model again based on that sentence to generate a new proposal.

[0198] Step 11:

[0199] The terminal again displays the received information to the user, allowing the user to check for new suggestions and detailed information.

[0200] Input: New career path or detailed information from the server

[0201] Output: Redisplay to user

[0202] Specifically, the device updates the user interface to display new suggestions and detailed information, allowing users to receive suggestions for suitable career paths based on their skills and interests, along with specific information on how to get there.

[0203] (Application example 1)

[0204] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0205] Conventional career assessment systems often only have the function of suggesting specific occupations or career paths based on information entered by the user. As a result, they do not suggest training programs or related qualifications based on the user's specific skills and interests in a particular field, resulting in the problem of providing insufficient support for career changes. This limitation is particularly pronounced in highly specialized fields such as the security field.

[0206] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0207] In this invention, the server includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information, and means for proposing optimal job titles, training programs, and related qualifications based on user information. This allows the server to specifically suggest training and qualifications required by the user in the security field, thereby providing more practical and specific support when making a career change.

[0208] "User-entered information" refers to basic information, skill set, work experience, areas of interest, etc. that a user provides to the system.

[0209] The "means for receiving and preprocessing" is a mechanism for receiving information input by a user, formatting the information, and correcting any data deficiencies or inconsistencies.

[0210] A "means for extracting useful information" is a mechanism for picking out and extracting highly useful data (e.g., skills, experience, areas of interest, etc.) from preprocessed information.

[0211] A "generative AI model" is a model that uses machine learning and natural language processing technologies to predict and generate career paths based on input data.

[0212] "Means for generating career paths" refers to the process of using a generative AI model based on extracted data to suggest the most suitable occupational and job position path for the user.

[0213] "User terminal" refers to a device that is directly operated by a user (e.g., smartphone, tablet, PC).

[0214] The "display means" is a function for displaying the generated career path and training program on the screen of the user terminal.

[0215] The "means of receiving feedback and generating new information" is a mechanism that receives ratings and comments from users and generates further optimized career paths and detailed information based on that feedback.

[0216] "Means to suggest job titles, training programs, and related qualifications" refers to a function that uses a generative AI model to automatically recommend specific occupations, job titles, required training programs, and qualifications to be obtained based on user input.

[0217] This invention is a system that proposes optimal career paths and training programs based on information entered by users. This system is mainly composed of three elements: a server, a terminal, and a user, and these elements work together.

[0218] System configuration and operation

[0219] Hardware and Software Use

[0220] Server: The server is used for data processing, running AI models, and data preprocessing. Software used for this purpose includes database management systems (DBMS), data preprocessing tools, and libraries for generative AI models (e.g., TensorFlow and PyTorch).

[0221] Terminal: The terminal is used to provide the user interface (UI), sending information entered by the user to the server and displaying the results received from the server. The software used for this purpose can be a mobile application or a web browser.

[0222] Users: Users use devices such as smartphones, tablets, and computers to enter information and provide feedback.

[0223] Data processing and calculation

[0224] Input and preprocessing information:

[0225] Users enter basic information (name, age), skill set, work experience, and areas of interest through the terminal.

[0226] The terminal sends this information to the server.

[0227] The server pre-processes the received information, checking for data inconsistencies or omissions and making corrections.

[0228] Extracting information and applying AI models:

[0229] From the preprocessed data, useful information such as the user's skill set, work experience, and areas of interest is extracted.

[0230] Based on the extracted data, generative AI models (e.g., natural language processing models) are used to generate career paths, training programs, and related qualifications.

[0231] Result display and feedback:

[0232] The generated career path and training program are sent to the terminal and displayed to the user.

[0233] The user provides feedback.

[0234] The server receives the feedback and uses the AI ​​model again to generate more optimal information and send it to the device.

[0235] Specific examples

[0236] For example, suppose a user enters the following information:

[0237] Name: Yamada Ichiro

[0238] Age: 28

[0239] Skills: Network management, programming (Python)

[0240] Experience: 3 years of system administration experience

[0241] Interests: Cybersecurity, Information and Communication Technology

[0242] In this case, the following processing is performed.

[0243] 1. The user enters information into the smartphone app.

[0244] 2. The app sends the input information to the server.

[0245] 3. The server preprocesses the information, correcting any gaps or inconsistencies and formatting it.

[0246] 4. Extract the user's skill set, experience, and interests from the formatted data.

[0247] 5. Based on the data extracted by the generative AI model, it generates career paths for security analysts and penetration testers, and also suggests related certifications such as CISSP and CEH, how to obtain them, and the necessary training programs.

[0248] 6. The app displays these results to the user.

[0249] 7. Users provide feedback such as "What steps are required to become a security analyst?" and "I would like to know the specific training program to obtain the qualification."

[0250] 8. The server receives the feedback, generates more detailed information, and sends it back to the device.

[0251] Prompt Sentence Examples

[0252] "Based on the information the user enters, suggest the best security career paths and related training programs. For example, if I have experience in network administration, what security roles and certifications would be suitable?"

[0253] In this way, users are given specific support to find the best career path or training program based on their skill set and interests.

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Step 1:

[0256] The user inputs information. The user uses the terminal to input basic information such as name, age, skill set, work experience, and areas of interest. This is the input information.

[0257] Step 2:

[0258] The device sends the input information to the server. The device then sends an HTTP request to send the input information to the server's API endpoint. At this time, the input information is sent in a data format such as JSON.

[0259] Step 3:

[0260] The server receives the information and performs preprocessing. The server analyzes the received JSON data and checks for missing or inconsistent data. For example, if age is not entered, it sets a default value. The preprocessed data is saved as formatted data.

[0261] Step 4:

[0262] The server extracts useful information. It performs data analysis to extract useful information such as skill sets, work experience, and areas of interest from the preprocessed data. The input is the preprocessed data, and the output is the extracted useful data.

[0263] Step 5:

[0264] The server applies a generative AI model (e.g., a natural language processing model) based on the extracted data to generate career paths, training programs, and related qualifications. The input is the extracted useful data, and the output is the generated career path information.

[0265] Step 6:

[0266] The server sends the generated career path to the terminal. The server then sends the generated career path and related training program information to the terminal as an API response. The output is the sent career path information.

[0267] Step 7:

[0268] The terminal displays the career path to the user. The terminal displays the received career path information in a user-friendly graphical format, including detailed data on related qualifications and training programs.

[0269] Step 8:

[0270] The user provides feedback. The user checks the displayed career path information and inputs any further information or additional requests as feedback. This becomes new input information.

[0271] Step 9:

[0272] The terminal sends feedback information to the server. The terminal resends the feedback information to the server and sends an HTTP request to reflect it in the next generation process. The input is the feedback information, and the output is the data sent to the server.

[0273] Step 10:

[0274] The server receives the feedback information and again uses the AI ​​model to generate optimal information. The server then analyzes the data again and generates new career paths, detailed training programs, and qualification information. The input is the feedback information, and the output is updated career path information.

[0275] Through these steps, users can find the best career path based on their skills and interests, as well as information on the training programs and certifications they need.

[0276] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0277] This invention combines a system that utilizes generative AI to provide effective career diagnosis based on information entered by the user with an emotion engine that recognizes the user's emotions. This system receives and preprocesses the information entered by the user, generates a career path using a generative AI model, and performs a series of processes from displaying the result to the user, as well as emotion recognition using the emotion engine.

[0278] System configuration:

[0279] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[0280] User role:

[0281] Users input the basic information required for career assessment, including their skill set, work experience, and areas of interest, via their device. Furthermore, an input environment is created that can recognize emotions from the user's facial expressions and voice. Specifically, users can input their name, age, programming skills (Python, Java), five years of experience working at an IT company, and areas of interest (AI, machine learning), and can also provide video and audio data.

[0282] Device role:

[0283] The device sends the information and emotion data entered by the user to the server, displays the career path suggestions received from the server to the user, and receives user feedback and sends it back to the server.

[0284] Server Role:

[0285] The server processes the following series of processes.

[0286] 1. Receiving and preprocessing information:

[0287] The server receives the user's basic information, skillset, work experience, interests, and emotional data sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if age is not entered, it will either fill in the field with a default value or prompt the user to enter it again.

[0288] 2. Information Extraction:

[0289] From the preprocessed basic information, useful information such as the user's skill set, work experience, and areas of interest is extracted. Furthermore, an emotion engine is used to recognize emotions from the user's emotion data.

[0290] 3. Application of generative AI models:

[0291] The server generates a career path using a generative AI model based on the extracted information and recognized emotions. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates. For example, if the user is feeling anxious, it will suggest a career path that includes support to alleviate that anxiety.

[0292] 4. Career path suggestions and feedback processing:

[0293] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[0294] Examples:

[0295] As a concrete example, consider the case where a user inputs the following information:

[0296] Name: Taro

[0297] Age: 30

[0298] Skills: Programming (Python, Java), Data Analysis

[0299] Experience: 5 years of experience working in an IT company

[0300] Interests: AI, machine learning

[0301] Emotion: Video data (facial expressions) and audio data

[0302] procedure:

[0303] 1. User: Enter the above information and emotion data using the terminal.

[0304] 2. Terminal: Sends the input information and emotion data to the server.

[0305] 3. Server: Receives information, pre-processes it, extracts formatted data, and uses an emotion engine to recognize emotions from facial expressions and voice.

[0306] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as data scientist, machine learning engineer, or AI researcher. It also includes specific support measures to alleviate any concerns the user may have.

[0307] 5. Server: Sends the generated career path to the device.

[0308] 6. Terminal: Shows the user a list of suggested career paths.

[0309] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[0310] 8. Device: Sends feedback to the server.

[0311] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[0312] 10. Terminal: Display detailed information to the user.

[0313] In this way, users can receive suggestions for suitable career paths based on their skills, interests, and feelings, along with specific information on how to get there.

[0314] The processing flow will be explained below.

[0315] Step 1:

[0316] The user uses a device to input basic information (name, age, skill set, work experience, areas of interest) and emotional data (facial expressions and voice). For example, the user might input "Name: Taro," "Age: 30," "Skills: Programming (Python, Java), data analysis," "Experience: 5 years of work experience at an IT company," and "Interests: AI, machine learning," and then capture their facial expressions with a camera and record emotional comments via voice.

[0317] Step 2:

[0318] The device sends the basic information and emotion data entered to the server, which formats the data in JSON or other formats.

[0319] Step 3:

[0320] The server receives the data sent from the device. It analyzes the received data and performs preprocessing. For example, if the age is not entered, it will be filled in with a default value or a message will be generated to prompt the user to enter it again.

[0321] Step 4:

[0322] The server extracts useful information from the pre-processed data, such as skill sets, work experience, and areas of interest, and also uses an emotion engine to recognize the user's emotions from the captured facial and voice data.

[0323] Step 5:

[0324] The server uses a generative AI model to generate career paths based on the extracted data and the recognized emotions. For example, the server can suggest career paths such as "Data Scientist," "Machine Learning Engineer," and "AI Researcher" based on the user's data, and can also suggest support measures for each career path based on the user's emotions (e.g., anxiety).

[0325] Step 6:

[0326] The server then sends the generated career path candidates and accompanying emotion-based support information to the terminal, formatting the data in a user-friendly format.

[0327] Step 7:

[0328] The terminal displays the career path candidates and emotion-based support information sent from the server to the user, who then checks the information and inputs feedback based on their emotions and preferences.

[0329] Step 8:

[0330] The user enters the necessary details about the career path provided as feedback into the device, for example, "I'm interested in becoming a data scientist, but I'd like to know the specific skill set and recommended learning resources."

[0331] Step 9:

[0332] The device sends the user's feedback to the server, and the data is formatted to accurately convey the feedback content.

[0333] Step 10:

[0334] The server receives the feedback, analyzes the content, and reapplies the generative AI model to generate detailed information based on the feedback (e.g., required skills, learning resources). It also reassess the user's emotions and provides appropriate support measures.

[0335] Step 11:

[0336] The server transmits the generated detailed information and additional support information based on the emotion to the terminal.

[0337] Step 12:

[0338] The terminal displays the detailed information and additional support information received from the server to the user, who can then check it and obtain useful information for creating a specific action plan or study plan.

[0339] This process provides users with specific recommendations for the best career path and the necessary action plan based on their skill set, work experience, areas of interest, and emotions.

[0340] Example 2

[0341] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0342] Conventional career diagnosis systems suggest career paths by collecting information such as a user's basic information, skill set, and work experience, but because they do not take the user's emotional state into consideration, they have the problem of being unable to make appropriate career suggestions based on the user's psychological state.In addition, the process of regenerating information based on feedback is cumbersome, which causes a poor user experience.

[0343] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and preprocessing information and emotional data input by the user, means for extracting useful information from the preprocessed information and emotional data and recognizing emotions, means for generating a career path using a generative AI model based on the extracted information and recognized emotional data, means for transmitting and displaying the generated career path to the user terminal, and means for receiving feedback from the user and regenerating information. This makes it possible to propose career paths according to the emotional state, thereby improving the user experience.

[0344] "Information and emotional data entered by the user" refers to the user's basic information, skill set, work experience, areas of interest, and emotional data such as facial expressions and voice.

[0345] "Preprocessing" refers to the process of preparing the received information into a data format that is easy to analyze by performing processes such as filling in missing values, standardizing the format, and normalizing it.

[0346] "Emotion Engine" refers to the software and algorithms used to recognize a user's emotional state from video and audio data.

[0347] A "generative AI model" refers to an artificial intelligence model that includes an algorithm for generating career paths based on user input and recognized emotional data.

[0348] A "career path" refers to the optimal career direction, specific job title, required skill set, etc. generated based on the user's information and emotional state.

[0349] "Feedback" refers to opinions, additional questions, supplementary information, etc. that users enter regarding the proposed career path.

[0350] This invention combines a system that utilizes generative AI to provide effective career diagnosis based on information entered by the user with an emotion engine that recognizes the user's emotions. This system receives and preprocesses the information entered by the user, generates a career path using a generative AI model, and performs a series of processes from displaying the result to the user, as well as emotion recognition using the emotion engine.

[0351] (System configuration)

[0352] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[0353] User role:

[0354] Users input the basic information required for career assessment, including their skill set, work experience, and areas of interest, via their device. Furthermore, an input environment is created that can recognize emotions from the user's facial expressions and voice. Specifically, users can input their name, age, programming skills (Python, Java), five years of experience working at an IT company, and areas of interest (AI, machine learning), and can also provide video and audio data.

[0355] Device role:

[0356] The device sends the information and emotional data entered by the user to the server. It also displays career path suggestions received from the server to the user. It also accepts user feedback and sends it back to the server. Specifically, the device sends the user's input data to the server as an HTTP request, receives a response from the server, and displays it on the screen.

[0357] Server Role:

[0358] The server processes the following series of processes.

[0359] 1. Receiving and preprocessing information:

[0360] The server receives the user's basic information, skillset, work experience, interests, and emotional data sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if age is not entered, it will either fill in the field with a default value or prompt the user to enter it again.

[0361] 2. Information extraction and emotion recognition:

[0362] The server extracts useful information from the preprocessed basic information, such as the user's skill set, work experience, and areas of interest. It then uses an emotion engine to recognize emotions from the user's emotional data. For example, it analyzes facial expressions of smiles and sadness from video data and evaluates the tone and speed of voice from audio data.

[0363] 3. Application of generative AI models:

[0364] The server generates a career path using a generative AI model (such as GPT-3) based on the extracted information and recognized emotions. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates. For example, if the user is feeling anxious, it will suggest a career path that includes support to alleviate that anxiety.

[0365] 4. Career path suggestions and feedback processing:

[0366] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[0367] (Example)

[0368] As a concrete example, consider the case where a user inputs the following information:

[0369] Name: Taro

[0370] Age: 30

[0371] Skills: Programming (Python, Java), Data Analysis

[0372] Experience: 5 years of experience working in an IT company

[0373] Interests: AI, machine learning

[0374] Emotion: Video data (facial expressions) and audio data

[0375] procedure:

[0376] 1. User: Enter the above information and emotion data using the terminal.

[0377] Example prompt: "Please enter your name" → "Taro"

[0378] Example prompt: "Please enter your age" → "30"

[0379] Example prompt: "Please tell us your programming skills" → "Python, Java"

[0380] Example prompt: "Please enter your work experience" → "5 years of work experience in an IT company"

[0381] Example prompt: "Please tell us your area of ​​interest" → "AI, machine learning"

[0382] Example prompt: "Please enter emotion data" → "Video data, audio data"

[0383] 2. Terminal: Sends the input information and emotion data to the server.

[0384] 3. Server: Receives information, pre-processes it, extracts formatted data, and uses an emotion engine to recognize emotions from facial expressions and voice.

[0385] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as data scientist, machine learning engineer, or AI researcher. It also includes specific support measures to alleviate any concerns the user may have.

[0386] 5. Server: Sends the generated career path to the device.

[0387] 6. Terminal: Shows the user a list of suggested career paths.

[0388] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[0389] 8. Device: Sends feedback to the server.

[0390] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[0391] 10. Terminal: Display detailed information to the user.

[0392] In this way, users can receive suggestions for suitable career paths based on their skills, interests, and feelings, along with specific information on how to get there.

[0393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0394] Step 1:

[0395] User input of information and emotional data:

[0396] The user uses the device to enter basic information (e.g., name, age), skill set (e.g., Python, Java), work experience (e.g., five years of experience working in an IT company), and areas of interest (e.g., AI, machine learning). In addition, the device's camera and microphone are used to record video and audio data, and emotional data is provided. Specifically, the user enters the required information according to each prompt, and then presses the send button after completing the input.

[0397] Input: User basic information, skill set, work experience, areas of interest, video data, audio data

[0398] Output: A package of user input data that is saved to the device.

[0399] Step 2:

[0400] Device transmission of information and emotional data:

[0401] The device sends the basic information and emotion data entered by the user to the server as a single data package. Specifically, it packages the data as an HTTP request and sends it to the server's specified API endpoint. Once the transmission is complete, the device displays a message to the user indicating that the transmission was successful.

[0402] Input: User input data package

[0403] Output: User information package sent to server, user receives success message

[0404] Step 3:

[0405] Receiving and preprocessing information by the server:

[0406] The server analyzes the user's basic information and emotion data received from the device, fills in missing values, and standardizes the data format. For example, if age is not entered, it fills in the default value and prompts the user to re-enter it. It also converts video and audio data into an analyzable format.

[0407] Input: User information package sent from the terminal

[0408] Output: Preprocessed user information and emotion data

[0409] Step 4:

[0410] Information extraction and emotion recognition by the server:

[0411] The server extracts useful information such as skill sets, work experience, and areas of interest from the preprocessed basic information. It also activates an emotion engine to recognize the user's emotions from video and audio data. For example, facial expression analysis can detect smiles and sadness, and voice analysis can evaluate the tone and speed of the voice.

[0412] Input: Preprocessed user information and emotion data

[0413] Output: Extracted useful information and recognized emotion data

[0414] Step 5:

[0415] Server-based application of generative AI models:

[0416] The server inputs the extracted information and recognized emotion data into a generative AI model (e.g., GPT-3) to generate an optimal career path. This model analyzes the user's skill set and emotional state and suggests specific career path candidates. For example, if the user is feeling anxious, it will suggest a career path to alleviate that anxiety.

[0417] Input: extracted information, recognized emotion data

[0418] Output: Generated career path candidates

[0419] Step 6:

[0420] Career path suggestions by the server:

[0421] The server then sends the generated list of career path candidates to the device, which includes specific job titles, required skill sets, and supporting information related to the career path.

[0422] Input: Generated career path candidates

[0423] Output: A list of possible career paths sent to the terminal

[0424] Step 7:

[0425] View career paths by device:

[0426] The terminal displays the list of candidate career paths received from the server to the user, who can then view the details of each career path and select the one that interests them.

[0427] Input: List of potential career paths

[0428] Output: Career path details displayed on the terminal screen

[0429] Step 8:

[0430] User feedback:

[0431] Users can enter feedback on the displayed career paths, such as, "I'm interested in becoming a data scientist, but I'd like to know more about the required skill set."

[0432] Input: Feedback on career paths

[0433] Output: Feedback data stored on the device

[0434] Step 9:

[0435] Send feedback via device:

[0436] The device packages the user's feedback and sends it back to the server, specifically by sending the feedback data as an HTTP request.

[0437] Input: User feedback data

[0438] Output: Feedback data sent to the server

[0439] Step 10:

[0440] Server feedback processing:

[0441] The server receives the feedback and uses a generative AI model to generate detailed information based on the feedback, such as "The skill sets required for a data scientist are Python, statistics, machine learning techniques, and data visualization."

[0442] Input: User feedback data

[0443] Output: Detailed information generated

[0444] Step 11:

[0445] Displaying detailed information via terminal:

[0446] The device displays the details sent from the server to the user, who can then decide what to do next, such as enrolling in a related online course or training program.

[0447] Input: Generated details

[0448] Output: Detailed information displayed on the terminal screen

[0449] Through this series of steps, users receive recommendations for the best career path based on their skills, interests, and feelings, along with specific information on how to get there.

[0450] (Application example 2)

[0451] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0452] In today's world, many users seek optimal advice regarding their careers. Furthermore, career path suggestions that take into account the user's emotional state are crucial for improving user satisfaction. However, conventional systems do not adequately suggest individual career paths based on the user's emotions. As a result, the suggested career paths may not fully meet the user's needs.

[0453] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0454] In this invention, the server includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information and the user's emotional data, means for transmitting and displaying the generated career path to a user terminal, means for acquiring the user's facial expression and voice data and extracting emotional data using an emotion recognition engine, and means for receiving feedback from the user and regenerating information. This makes it possible to reflect the user's emotional state and propose optimal career paths tailored to individual needs.

[0455] The "means for receiving and preprocessing information entered by the user" refers to the system's ability to receive information provided by the user, such as skill set, work experience, and areas of interest, and to format it in a way that is easy to analyze.

[0456] The "means for extracting useful information from preprocessed information" is a function that selects important data that is useful for generating career paths from preprocessed information.

[0457] "Means for generating career paths using a generative AI model based on extracted information and user emotional data" refers to a function in which the generative AI model automatically creates optimal career paths by utilizing the user's input information and emotional data.

[0458] The "means for transmitting the generated career path to the user terminal and displaying it" has the function of transmitting information about the generated career path to the user terminal and displaying it.

[0459] "Means for acquiring a user's facial expression and voice data and extracting emotion data using an emotion recognition engine" refers to a function that analyzes a user's facial expression and voice to identify emotions and extract that data.

[0460] The "means for receiving feedback from users and regenerating information" has the function of receiving feedback provided by users and regenerating new career paths and recommendations based on that information.

[0461] The present invention provides an effective career assessment system that utilizes a generative AI model based on user input information and emotional data. The specific configuration for realizing this system is described below.

[0462] Overall system configuration:

[0463] This system consists of three main components: a user terminal, a server, and a user.

[0464] User role:

[0465] Users use a smartphone, smart glasses, or head-mounted display to input basic information, skills, work experience, and areas of interest required for career assessment, and then provide video and audio data to the device to obtain emotional data from the user's facial expressions and voice.

[0466] As a concrete example, the user inputs the following information:

[0467] Name: Taro

[0468] Age: 30

[0469] Skills: Programming (Python, Java), Data Analysis

[0470] Work experience: 5 years of experience working in an IT company

[0471] Areas of interest: AI, machine learning

[0472] Emotion: Video data (facial expressions) and audio data

[0473] Device role:

[0474] The device receives the information and emotion data entered by the user and transmits them to the server, displays the career path suggestions received from the server to the user, and transmits the user's feedback back to the server.

[0475] Server Role:

[0476] The server performs a series of processes using the following means.

[0477] User information preprocessing means: The server receives the user's basic information, skill set, work experience, areas of interest, and emotional data sent from the terminal and preprocesses it into a format that is easy to analyze.

[0478] Means for extracting useful information: From the preprocessed information, select data that is useful for generating career paths.

[0479] Career path generation method: Based on the extracted information and the user's emotional data, a generative AI model (e.g., OpenAI GPT-4) is used to generate the optimal career path. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates.

[0480] Emotion data extraction method: Recognize the user's facial expressions and voice data and extract emotion data using an emotion engine (e.g., Azure Cognitive Services).

[0481] Career path suggestion means: The generated career path candidates are sent to the terminal and displayed to the user.

[0482] Feedback processing means: Accepts user feedback and generates new information based on it using the AI ​​model again.

[0483] For example, if a user provides feedback such as "I'm interested in becoming a data scientist, but I'd like to know more about the required skill set," the server will take this information and generate detailed skill set suggestions.

[0484] Example prompt sentence:

[0485] User Information:

[0486] Name: Taro

[0487] Age: 30

[0488] Skills: Python, Java, Data Analysis

[0489] Work experience: 5 years of experience working in an IT company

[0490] Areas of interest: AI, machine learning

[0491] User sentiment data:

[0492] Facial expression data (video)

[0493] Audio data

[0494] This system makes it possible to suggest optimal career paths tailored to individual needs, reflecting the user's emotional state.

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1:

[0497] The user uses a smartphone, smart glasses, or head-mounted display to input basic information such as name, age, skill set, work experience, and areas of interest. In addition, the user provides video and audio data to capture facial expressions and voice data. The information and emotional data collected in this step are sent from the device to the server.

[0498] Input: Name, age, skill set, work experience, areas of interest, facial expression data (video), audio data

[0499] Output: Basic information and emotion data are sent to the server.

[0500] Step 2:

[0501] The server preprocesses the received user basic information and emotion data. This preprocessing includes filling in missing information and normalizing the data. Emotion data is extracted by analyzing video and audio data and using an emotion recognition engine (e.g., Azure Cognitive Services).

[0502] Input: Basic information, emotion data (video, audio)

[0503] Output: Preprocessed basic information, extracted emotion data

[0504] Step 3:

[0505] The server selects the data necessary for career path generation by extracting useful information from the preprocessed information. Specifically, the server extracts the user's skill set, work experience, and areas of interest. Based on the extracted information, the server also incorporates the user's emotional data.

[0506] Input: Preprocessed basic information, extracted emotion data

[0507] Output: Data required to generate a career path

[0508] Step 4:

[0509] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an optimal career path based on the extracted information and emotional data. This generation also takes into account the user's current emotional state. For example, if the user is feeling anxious, a career path will be generated that includes support to alleviate those feelings.

[0510] Input: Data required for career path generation, emotional data

[0511] Output: Generated career paths

[0512] Step 5:

[0513] The server sends the generated career path to the terminal, which displays it to the user. The user can check the displayed career path and provide feedback if necessary.

[0514] Input: Generated career path

[0515] Output: The career path displayed to the user

[0516] Step 6:

[0517] Users enter their feedback on career paths into the device, which then sends it to the server, which receives the feedback and uses the generative AI model to generate new career paths and detailed information based on the feedback.

[0518] Input: User feedback

[0519] Output: Newly generated career paths and detailed information

[0520] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0521] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0522] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0523] [Second embodiment]

[0524] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0525] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0526] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0527] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0528] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0529] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0530] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0531] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0532] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0533] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0534] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0535] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0536] This invention is a system that utilizes generative AI to provide effective career assessments based on information entered by users. This system receives the information entered by users, preprocesses it, generates a career path using a generative AI model, and displays it to the user.

[0537] System configuration:

[0538] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[0539] User role:

[0540] Users enter basic information, skills, work experience, and areas of interest required for a career assessment through a terminal. Specifically, users enter their name, age, programming languages ​​they can use, past work history, and areas of interest (e.g., AI or data science).

[0541] Device role:

[0542] The device sends the information entered by the user to the server, displays the career path suggestions received from the server to the user, and receives user feedback and sends it back to the server.

[0543] Server Role:

[0544] The server processes the following series of processes.

[0545] 1. Receiving and preprocessing information:

[0546] The server receives user information sent from the device, preprocesses the received information, and checks for missing or inconsistent data. For example, if age is not entered or invalid skills are included, the server makes appropriate corrections.

[0547] 2. Information Extraction:

[0548] From the preprocessed information, useful information such as the user's skill set, past work experience, areas of interest, etc. is extracted, and the extracted information is used as input data for the AI ​​model.

[0549] 3. Application of generative AI models:

[0550] The server uses a generative AI model based on the extracted information to generate career path candidates. This AI model analyzes the user's information using natural language processing technology and can suggest optimal career paths.

[0551] 4. Career path suggestions and feedback processing:

[0552] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[0553] Examples:

[0554] As a concrete example, consider the case where a user inputs the following information:

[0555] Name: Taro

[0556] Age: 30

[0557] Skills: Programming (Python, Java), Data Analysis

[0558] Experience: 5 years of experience working in an IT company

[0559] Interests: AI, machine learning

[0560] procedure:

[0561] 1. User: Enter the above information using the terminal.

[0562] 2. Terminal: Sends the entered information to the server.

[0563] 3. Server: Receives information, preprocesses it, and extracts formatted data.

[0564] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as suggesting candidates for data scientists, machine learning engineers, and AI researchers.

[0565] 5. Server: Sends the generated career path to the device.

[0566] 6. Terminal: Shows the user a list of suggested career paths.

[0567] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[0568] 8. Device: Sends feedback to the server.

[0569] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[0570] 10. Terminal: Display detailed information to the user.

[0571] In this way, users can receive suggestions for suitable career paths based on their skills and interests, along with specific information on how to get there.

[0572] The processing flow will be explained below.

[0573] Step 1:

[0574] The user enters basic information, skill set, work experience, and areas of interest through the terminal. For example, the user enters "name," "age," "programming skills (Python, Java)," "five years of experience working in an IT company," and "areas of interest (AI, machine learning)."

[0575] Step 2:

[0576] The device sends the user's input information to the server, and the data is formatted in JSON format or similar.

[0577] Step 3:

[0578] The server receives the user information sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if the age is not entered, it will fill in the field with a default value or prompt the user to enter it again.

[0579] Step 4:

[0580] The server extracts useful information from the preprocessed data, such as skill sets, work experience, and areas of interest, which is then fed into a generative AI model in the next step.

[0581] Step 5:

[0582] Based on the extracted information, the server uses a generative AI model to generate career paths. This AI model analyzes the user's input data and generates optimal career path candidates. For example, it may suggest careers such as data scientist, machine learning engineer, or AI researcher.

[0583] Step 6:

[0584] The server sends the generated career path candidates to the terminal, formatting the data so that it is easy for the user to understand.

[0585] Step 7:

[0586] The terminal displays the career path candidates received from the server to the user, who can then review them and provide feedback if necessary.

[0587] Step 8:

[0588] The user types their feedback into the terminal, for example, "I'm interested in becoming a data scientist, but I'd like to know more about specific skill sets and learning resources."

[0589] Step 9:

[0590] The terminal transmits the user's feedback to the server, and the transmitted data is formatted in a manner that accurately conveys the feedback content.

[0591] Step 10:

[0592] The server receives the feedback and uses the generative AI model again to generate detailed information based on the feedback, such as the skills required to become a data scientist (database management, understanding machine learning algorithms) and recommended learning resources (online courses, reference books).

[0593] Step 11:

[0594] The server sends the generated detailed information to the terminal.

[0595] Step 12:

[0596] The device displays the detailed information received from the server to the user, who can review it and obtain more specific information about the career path.

[0597] Example 1

[0598] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0599] In modern society, individuals have a wide variety of career options, but the sheer number of options makes it difficult to determine the appropriate career path. It is particularly difficult for individuals with specific skill sets and work experience to find the optimal career path. Another issue is the lack of a system that efficiently organizes this information and suggests career paths suited to individuals.

[0600] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0601] In this invention, the server includes means for receiving and preprocessing information input by a user, means for extracting useful information from the preprocessed information, and means for generating a career path using a generative AI model based on the extracted information. This makes it possible to generate an optimal career path based on input information such as the user's basic information, skill set, work history, and areas of interest, and to provide detailed feedback as needed.

[0602] "User" refers to an individual who uses the system to input the information necessary for a career diagnosis and receive career path suggestions.

[0603] "Terminal" refers to the device through which a user inputs information and communicates with a server. This includes PCs, smartphones, tablets, etc.

[0604] "Server" refers to the hardware and software that receives and processes information sent by users and generates and proposes career paths using a generative AI model.

[0605] "Preprocessing" refers to a series of processes by which the server detects and corrects deficiencies or inconsistencies in the data received from the user.

[0606] "Generative AI models" refer to artificial intelligence technologies used to generate career paths based on extracted information, including natural language processing technologies.

[0607] A "prompt" is a document containing specific formatting and instructions to be input to a generative AI model.

[0608] "Basic information" refers to personally identifiable information such as the user's name and age.

[0609] A "competence set" refers to the skills and knowledge a user possesses, including programming languages ​​and specialized knowledge.

[0610] "Work history" refers to detailed information about a user's past work experience, including, for example, where they worked and what they did.

[0611] "Areas of Interest" refers to areas or topics that a user is personally interested in, such as AI and data science.

[0612] "Feedback" refers to any additional requests or comments a user provides regarding a career path suggestion.

[0613] This invention is a system that utilizes generative AI based on information entered by the user to provide efficient career diagnosis. This system consists of three entities: a server, a terminal, and a user. The detailed roles of each entity and the overall system flow are explained below.

[0614] Server Roles

[0615] The server receives user information and performs preprocessing, information extraction, and applies generative AI models. The server is equipped with a high-performance processor and sufficient memory to quickly process data and execute AI models. Cloud servers or dedicated data center servers are commonly used.

[0616] 1. Receiving and preprocessing information:

[0617] The server receives user information sent from the device. The received information is passed to the server in JSON or XML format. The server analyzes this data and checks for inconsistencies or missing data. For example, if the age or skill set is abnormal, it is corrected.

[0618] 2. Information Extraction:

[0619] The server extracts the user's basic information, skill set, work history, areas of interest, etc. from the pre-processed information. Natural language processing technology is used for this extraction process, resulting in highly accurate data extraction.

[0620] 3. Application of generative AI models:

[0621] The server uses the extracted information as input to generate a career path using a generative AI model (e.g., GPT-3 or BERT). The generated career path candidates are then presented as multiple options.

[0622] 4. Career path suggestions and feedback processing:

[0623] The generated career path is sent to the device and displayed to the user. If the user provides feedback, the feedback is received again and further detailed information is provided using the generative AI model. This allows the user to find the best career path based on their interests and skills.

[0624] Device Role

[0625] The terminal acts as an intermediary through which the user inputs information and communicates with the server. The terminal can function as a web browser, a smartphone application, or a dedicated device.

[0626] 1. Transmission of Information:

[0627] The terminal sends the user's basic information, skill set, work history, and areas of interest to the server. The information is encrypted using SSL / TLS and transmitted securely.

[0628] 2. View Career Paths:

[0629] The terminal displays the career path suggestions received from the server to the user, providing an intuitive user interface that allows the user to easily understand the suggested career paths.

[0630] 3. Submitting Feedback:

[0631] The feedback information entered by the user is sent to the server, which processes it again and generates new suggestions and detailed information.

[0632] User Roles

[0633] 1. Enter your information:

[0634] Users enter the information required for the career assessment through a terminal, such as their name, age, programming skills (Python, Java, etc.), work history, and areas of interest (AI, data science, etc.).

[0635] 2. Review and feedback on proposals:

[0636] Users can review the career path suggestions displayed on their device and enter feedback as needed. Feedback should be specific, such as "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[0637] Specific operation example

[0638] Assume a case where the user enters the following information:

[0639] Name: Taro

[0640] Age: 30

[0641] Skill set: Programming (Python, Java), Data analysis

[0642] Work history: 5 years of experience working in an IT company

[0643] Interests: AI, machine learning

[0644] When the user enters this information and clicks the send button, the device sends it to the server, which receives the information, performs preprocessing and information extraction, and inputs prompt sentences into the generative AI model.

[0645] Example prompt sentence:

[0646] "Taro is 30 years old, has programming skills in Python and Java, and has experience in data analysis. He currently has five years of work experience in an IT company and is interested in AI and machine learning. Please suggest a career path that would be suitable for Taro."

[0647] The generative AI model analyzes this prompt and suggests career paths such as data scientist, machine learning engineer, and AI researcher. The device displays this to the user, who then provides feedback such as, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set." The server then processes this feedback again, generates more detailed information, and sends it to the device. Ultimately, the user gains a deeper understanding of the skills needed for their career path and the next steps they should take.

[0648] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0649] Step 1:

[0650] Users enter the information required for the career assessment through their device, including their name, age, programming languages ​​they can use, their past work history, and areas of interest. After filling out the information in the input form and clicking the submit button, the data is converted into JSON or XML format.

[0651] Input: User basic information, skill set, work history, areas of interest

[0652] Output: Input data in JSON or XML format

[0653] Step 2:

[0654] The terminal sends the information entered by the user to the server. When the send button is clicked, the terminal encrypts the data with SSL / TLS and sends it to the server using the HTTP / HTTPS protocol.

[0655] Input: Input data in JSON or XML format

[0656] Output: Data transfer to the server

[0657] Step 3:

[0658] The server receives user information sent from the device. The received information is passed to the server in JSON or XML format. The server analyzes this data and checks for inconsistencies or missing data. For example, if the age or skill set is abnormal, it is corrected.

[0659] Input: Data received from the terminal

[0660] Output: Corrected data

[0661] Specifically, the server parses the received JSON data and checks the integrity of each field before saving the content to the database.

[0662] Step 4:

[0663] The server extracts useful information from the preprocessed information, such as the user's basic information, skill set, work history, and areas of interest, using natural language processing technology.

[0664] Input: Corrected data

[0665] Output: The input dataset for the generative AI model

[0666] Specifically, the server converts the extracted information into a specific format and builds a dataset to be input into the generative AI model.

[0667] Step 5:

[0668] The server uses a generative AI model (e.g., GPT-3 or BERT) to generate career path candidates based on the extracted information. The server calls the generative AI model (e.g., GPT-3 or BERT) via an API, provides the formatted dataset as input, and obtains the model's output.

[0669] Input: The input dataset for the generative AI model

[0670] Output: Possible career paths

[0671] Specifically, the server uses generative AI models such as GPT-3 and BERT to generate multiple career path candidates based on input data.

[0672] Step 6:

[0673] The server sends the generated career path candidates to the terminal, converts the generated career path information into JSON format, and returns it as a response to the terminal using the HTTP / HTTPS protocol.

[0674] Input: Possible career paths

[0675] Output: Data transfer to the device

[0676] As a specific operation, the server transmits the generated carrier path to the terminal.

[0677] Step 7:

[0678] The terminal displays the career path suggestions received from the server to the user in a visually easy-to-understand format (list format or card format) in the user interface.

[0679] Input: Career path suggestions from the server

[0680] Output: Displaying the career path to the user

[0681] Specifically, the device updates its user interface to display a list of suggested career paths.

[0682] Step 8:

[0683] Users review the suggested career paths and provide feedback if they require more specific information or other options, including details about specific career paths or new requirements.

[0684] Input: Career path displayed information

[0685] Output: Feedback information

[0686] As a specific operation, the user enters a comment in the feedback form and clicks the send button.

[0687] Step 9:

[0688] The device sends this feedback to the server. The feedback information is also encrypted using SSL / TLS and sent to the server.

[0689] Input: User feedback information

[0690] Output: Feedback forwarding to the server

[0691] As a specific operation, the terminal transmits the feedback data to the server again.

[0692] Step 10:

[0693] The server receives the feedback and generates new information using the generative AI model again. It analyzes the feedback and generates prompts to generate new career paths and detailed information.

[0694] Input: Feedback information

[0695] Output: New career path or more information

[0696] Specifically, the server generates a prompt sentence, then calls the generative AI model again based on that sentence to generate a new proposal.

[0697] Step 11:

[0698] The terminal again displays the received information to the user, allowing the user to check for new suggestions and detailed information.

[0699] Input: New career path or detailed information from the server

[0700] Output: Redisplay to user

[0701] Specifically, the device updates the user interface to display new suggestions and detailed information, allowing users to receive suggestions for suitable career paths based on their skills and interests, along with specific information on how to get there.

[0702] (Application example 1)

[0703] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0704] Conventional career assessment systems often only have the function of suggesting specific occupations or career paths based on information entered by the user. As a result, they do not suggest training programs or related qualifications based on the user's specific skills and interests in a particular field, resulting in the problem of providing insufficient support for career changes. This limitation is particularly pronounced in highly specialized fields such as the security field.

[0705] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0706] In this invention, the server includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information, and means for proposing optimal job titles, training programs, and related qualifications based on user information. This allows the server to specifically suggest training and qualifications required by the user in the security field, thereby providing more practical and specific support when making a career change.

[0707] "User-entered information" refers to basic information, skill set, work experience, areas of interest, etc. that a user provides to the system.

[0708] The "means for receiving and preprocessing" is a mechanism for receiving information input by a user, formatting the information, and correcting any data deficiencies or inconsistencies.

[0709] A "means for extracting useful information" is a mechanism for picking out and extracting highly useful data (e.g., skills, experience, areas of interest, etc.) from preprocessed information.

[0710] A "generative AI model" is a model that uses machine learning and natural language processing technologies to predict and generate career paths based on input data.

[0711] "Means for generating career paths" refers to the process of using a generative AI model based on extracted data to suggest the most suitable occupational and job position path for the user.

[0712] "User terminal" refers to a device that is directly operated by a user (e.g., smartphone, tablet, PC).

[0713] The "display means" is a function for displaying the generated career path and training program on the screen of the user terminal.

[0714] The "means of receiving feedback and generating new information" is a mechanism that receives ratings and comments from users and generates further optimized career paths and detailed information based on that feedback.

[0715] "Means to suggest job titles, training programs, and related qualifications" refers to a function that uses a generative AI model to automatically recommend specific occupations, job titles, required training programs, and qualifications to be obtained based on user input.

[0716] This invention is a system that proposes optimal career paths and training programs based on information entered by users. This system is mainly composed of three elements: a server, a terminal, and a user, and these elements work together.

[0717] System configuration and operation

[0718] Hardware and Software Use

[0719] Server: The server is used for data processing, running AI models, and data preprocessing. Software used for this purpose includes database management systems (DBMS), data preprocessing tools, and libraries for generative AI models (e.g., TensorFlow and PyTorch).

[0720] Terminal: The terminal is used to provide the user interface (UI), sending information entered by the user to the server and displaying the results received from the server. The software used for this purpose can be a mobile application or a web browser.

[0721] Users: Users use devices such as smartphones, tablets, and computers to enter information and provide feedback.

[0722] Data processing and calculation

[0723] Input and preprocessing information:

[0724] Users enter basic information (name, age), skill set, work experience, and areas of interest through the terminal.

[0725] The terminal sends this information to the server.

[0726] The server pre-processes the received information, checking for data inconsistencies or omissions and making corrections.

[0727] Extracting information and applying AI models:

[0728] From the preprocessed data, useful information such as the user's skill set, work experience, and areas of interest is extracted.

[0729] Based on the extracted data, generative AI models (e.g., natural language processing models) are used to generate career paths, training programs, and related qualifications.

[0730] Result display and feedback:

[0731] The generated career path and training program are sent to the terminal and displayed to the user.

[0732] The user provides feedback.

[0733] The server receives the feedback and uses the AI ​​model again to generate more optimal information and send it to the device.

[0734] Specific examples

[0735] For example, suppose a user enters the following information:

[0736] Name: Yamada Ichiro

[0737] Age: 28

[0738] Skills: Network management, programming (Python)

[0739] Experience: 3 years of system administration experience

[0740] Interests: Cybersecurity, Information and Communication Technology

[0741] In this case, the following processing is performed.

[0742] 1. The user enters information into the smartphone app.

[0743] 2. The app sends the input information to the server.

[0744] 3. The server preprocesses the information, correcting any gaps or inconsistencies and formatting it.

[0745] 4. Extract the user's skill set, experience, and interests from the formatted data.

[0746] 5. Based on the data extracted by the generative AI model, it generates career paths for security analysts and penetration testers, and also suggests related certifications such as CISSP and CEH, how to obtain them, and the necessary training programs.

[0747] 6. The app displays these results to the user.

[0748] 7. Users provide feedback such as "What steps are required to become a security analyst?" and "I would like to know the specific training program to obtain the qualification."

[0749] 8. The server receives the feedback, generates more detailed information, and sends it back to the device.

[0750] Prompt Sentence Examples

[0751] "Based on the information the user enters, suggest the best security career paths and related training programs. For example, if I have experience in network administration, what security roles and certifications would be suitable?"

[0752] In this way, users are given specific support to find the best career path or training program based on their skill set and interests.

[0753] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0754] Step 1:

[0755] The user inputs information. The user uses the terminal to input basic information such as name, age, skill set, work experience, and areas of interest. This is the input information.

[0756] Step 2:

[0757] The device sends the input information to the server. The device then sends an HTTP request to send the input information to the server's API endpoint. At this time, the input information is sent in a data format such as JSON.

[0758] Step 3:

[0759] The server receives the information and performs preprocessing. The server analyzes the received JSON data and checks for missing or inconsistent data. For example, if age is not entered, it sets a default value. The preprocessed data is saved as formatted data.

[0760] Step 4:

[0761] The server extracts useful information. It performs data analysis to extract useful information such as skill sets, work experience, and areas of interest from the preprocessed data. The input is the preprocessed data, and the output is the extracted useful data.

[0762] Step 5:

[0763] The server applies a generative AI model (e.g., a natural language processing model) based on the extracted data to generate career paths, training programs, and related qualifications. The input is the extracted useful data, and the output is the generated career path information.

[0764] Step 6:

[0765] The server sends the generated career path to the terminal. The server then sends the generated career path and related training program information to the terminal as an API response. The output is the sent career path information.

[0766] Step 7:

[0767] The terminal displays the career path to the user. The terminal displays the received career path information in a user-friendly graphical format, including detailed data on related qualifications and training programs.

[0768] Step 8:

[0769] The user provides feedback. The user checks the displayed career path information and inputs any further information or additional requests as feedback. This becomes new input information.

[0770] Step 9:

[0771] The terminal sends feedback information to the server. The terminal resends the feedback information to the server and sends an HTTP request to reflect it in the next generation process. The input is the feedback information, and the output is the data sent to the server.

[0772] Step 10:

[0773] The server receives the feedback information and again uses the AI ​​model to generate optimal information. The server then analyzes the data again and generates new career paths, detailed training programs, and qualification information. The input is the feedback information, and the output is updated career path information.

[0774] Through these steps, users can find the best career path based on their skills and interests, as well as information on the training programs and certifications they need.

[0775] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0776] This invention combines a system that utilizes generative AI to provide effective career diagnosis based on information entered by the user with an emotion engine that recognizes the user's emotions. This system receives and preprocesses the information entered by the user, generates a career path using a generative AI model, and performs a series of processes from displaying the result to the user, as well as emotion recognition using the emotion engine.

[0777] System configuration:

[0778] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[0779] User role:

[0780] Users input the basic information required for career assessment, including their skill set, work experience, and areas of interest, via their device. Furthermore, an input environment is created that can recognize emotions from the user's facial expressions and voice. Specifically, users can input their name, age, programming skills (Python, Java), five years of experience working at an IT company, and areas of interest (AI, machine learning), and can also provide video and audio data.

[0781] Device role:

[0782] The device sends the information and emotion data entered by the user to the server, displays the career path suggestions received from the server to the user, and receives user feedback and sends it back to the server.

[0783] Server Role:

[0784] The server processes the following series of processes.

[0785] 1. Receiving and preprocessing information:

[0786] The server receives the user's basic information, skillset, work experience, interests, and emotional data sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if age is not entered, it will either fill in the field with a default value or prompt the user to enter it again.

[0787] 2. Information Extraction:

[0788] From the preprocessed basic information, useful information such as the user's skill set, work experience, and areas of interest is extracted. Furthermore, an emotion engine is used to recognize emotions from the user's emotion data.

[0789] 3. Application of generative AI models:

[0790] The server generates a career path using a generative AI model based on the extracted information and recognized emotions. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates. For example, if the user is feeling anxious, it will suggest a career path that includes support to alleviate that anxiety.

[0791] 4. Career path suggestions and feedback processing:

[0792] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[0793] Examples:

[0794] As a concrete example, consider the case where a user inputs the following information:

[0795] Name: Taro

[0796] Age: 30

[0797] Skills: Programming (Python, Java), Data Analysis

[0798] Experience: 5 years of experience working in an IT company

[0799] Interests: AI, machine learning

[0800] Emotion: Video data (facial expressions) and audio data

[0801] procedure:

[0802] 1. User: Enter the above information and emotion data using the terminal.

[0803] 2. Terminal: Sends the input information and emotion data to the server.

[0804] 3. Server: Receives information, pre-processes it, extracts formatted data, and uses an emotion engine to recognize emotions from facial expressions and voice.

[0805] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as data scientist, machine learning engineer, or AI researcher. It also includes specific support measures to alleviate any concerns the user may have.

[0806] 5. Server: Sends the generated career path to the device.

[0807] 6. Terminal: Shows the user a list of suggested career paths.

[0808] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[0809] 8. Device: Sends feedback to the server.

[0810] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[0811] 10. Terminal: Display detailed information to the user.

[0812] In this way, users can receive suggestions for suitable career paths based on their skills, interests, and feelings, along with specific information on how to get there.

[0813] The processing flow will be explained below.

[0814] Step 1:

[0815] The user uses a device to input basic information (name, age, skill set, work experience, areas of interest) and emotional data (facial expressions and voice). For example, the user might input "Name: Taro," "Age: 30," "Skills: Programming (Python, Java), data analysis," "Experience: 5 years of work experience at an IT company," and "Interests: AI, machine learning," and then capture their facial expressions with a camera and record emotional comments via voice.

[0816] Step 2:

[0817] The device sends the basic information and emotion data entered to the server, which formats the data in JSON or other formats.

[0818] Step 3:

[0819] The server receives the data sent from the device. It analyzes the received data and performs preprocessing. For example, if the age is not entered, it will be filled in with a default value or a message will be generated to prompt the user to enter it again.

[0820] Step 4:

[0821] The server extracts useful information from the pre-processed data, such as skill sets, work experience, and areas of interest, and also uses an emotion engine to recognize the user's emotions from the captured facial and voice data.

[0822] Step 5:

[0823] The server uses a generative AI model to generate career paths based on the extracted data and the recognized emotions. For example, the server can suggest career paths such as "Data Scientist," "Machine Learning Engineer," and "AI Researcher" based on the user's data, and can also suggest support measures for each career path based on the user's emotions (e.g., anxiety).

[0824] Step 6:

[0825] The server then sends the generated career path candidates and accompanying emotion-based support information to the terminal, formatting the data in a user-friendly format.

[0826] Step 7:

[0827] The terminal displays the career path candidates and emotion-based support information sent from the server to the user, who then checks the information and inputs feedback based on their emotions and preferences.

[0828] Step 8:

[0829] The user enters the necessary details about the career path provided as feedback into the device, for example, "I'm interested in becoming a data scientist, but I'd like to know the specific skill set and recommended learning resources."

[0830] Step 9:

[0831] The device sends the user's feedback to the server, and the data is formatted to accurately convey the feedback content.

[0832] Step 10:

[0833] The server receives the feedback, analyzes the content, and reapplies the generative AI model to generate detailed information based on the feedback (e.g., required skills, learning resources). It also reassess the user's emotions and provides appropriate support measures.

[0834] Step 11:

[0835] The server transmits the generated detailed information and additional support information based on the emotion to the terminal.

[0836] Step 12:

[0837] The terminal displays the detailed information and additional support information received from the server to the user, who can then check it and obtain useful information for creating a specific action plan or study plan.

[0838] This process provides users with specific recommendations for the best career path and the necessary action plan based on their skill set, work experience, areas of interest, and emotions.

[0839] Example 2

[0840] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0841] Conventional career diagnosis systems suggest career paths by collecting information such as a user's basic information, skill set, and work experience, but because they do not take the user's emotional state into consideration, they have the problem of being unable to make appropriate career suggestions based on the user's psychological state.In addition, the process of regenerating information based on feedback is cumbersome, which causes a poor user experience.

[0842] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and preprocessing information and emotional data input by the user, means for extracting useful information from the preprocessed information and emotional data and recognizing emotions, means for generating a career path using a generative AI model based on the extracted information and recognized emotional data, means for transmitting and displaying the generated career path to the user terminal, and means for receiving feedback from the user and regenerating information. This makes it possible to propose career paths according to the emotional state, thereby improving the user experience.

[0843] "Information and emotional data entered by the user" refers to the user's basic information, skill set, work experience, areas of interest, and emotional data such as facial expressions and voice.

[0844] "Preprocessing" refers to the process of preparing the received information into a data format that is easy to analyze by performing processes such as filling in missing values, standardizing the format, and normalizing it.

[0845] "Emotion Engine" refers to the software and algorithms used to recognize a user's emotional state from video and audio data.

[0846] A "generative AI model" refers to an artificial intelligence model that includes an algorithm for generating career paths based on user input and recognized emotional data.

[0847] A "career path" refers to the optimal career direction, specific job title, required skill set, etc. generated based on the user's information and emotional state.

[0848] "Feedback" refers to opinions, additional questions, supplementary information, etc. that users enter regarding the proposed career path.

[0849] This invention combines a system that utilizes generative AI to provide effective career diagnosis based on information entered by the user with an emotion engine that recognizes the user's emotions. This system receives and preprocesses the information entered by the user, generates a career path using a generative AI model, and performs a series of processes from displaying the result to the user, as well as emotion recognition using the emotion engine.

[0850] (System configuration)

[0851] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[0852] User role:

[0853] Users input the basic information required for career assessment, including their skill set, work experience, and areas of interest, via their device. Furthermore, an input environment is created that can recognize emotions from the user's facial expressions and voice. Specifically, users can input their name, age, programming skills (Python, Java), five years of experience working at an IT company, and areas of interest (AI, machine learning), and can also provide video and audio data.

[0854] Device role:

[0855] The device sends the information and emotional data entered by the user to the server. It also displays career path suggestions received from the server to the user. It also accepts user feedback and sends it back to the server. Specifically, the device sends the user's input data to the server as an HTTP request, receives a response from the server, and displays it on the screen.

[0856] Server Role:

[0857] The server processes the following series of processes.

[0858] 1. Receiving and preprocessing information:

[0859] The server receives the user's basic information, skillset, work experience, interests, and emotional data sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if age is not entered, it will either fill in the field with a default value or prompt the user to enter it again.

[0860] 2. Information extraction and emotion recognition:

[0861] The server extracts useful information from the preprocessed basic information, such as the user's skill set, work experience, and areas of interest. It then uses an emotion engine to recognize emotions from the user's emotional data. For example, it analyzes facial expressions of smiles and sadness from video data and evaluates the tone and speed of voice from audio data.

[0862] 3. Application of generative AI models:

[0863] The server generates a career path using a generative AI model (such as GPT-3) based on the extracted information and recognized emotions. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates. For example, if the user is feeling anxious, it will suggest a career path that includes support to alleviate that anxiety.

[0864] 4. Career path suggestions and feedback processing:

[0865] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[0866] (Example)

[0867] As a concrete example, consider the case where a user inputs the following information:

[0868] Name: Taro

[0869] Age: 30

[0870] Skills: Programming (Python, Java), Data Analysis

[0871] Experience: 5 years of experience working in an IT company

[0872] Interests: AI, machine learning

[0873] Emotion: Video data (facial expressions) and audio data

[0874] procedure:

[0875] 1. User: Enter the above information and emotion data using the terminal.

[0876] Example prompt: "Please enter your name" → "Taro"

[0877] Example prompt: "Please enter your age" → "30"

[0878] Example prompt: "Please tell us your programming skills" → "Python, Java"

[0879] Example prompt: "Please enter your work experience" → "5 years of work experience in an IT company"

[0880] Example prompt: "Please tell us your area of ​​interest" → "AI, machine learning"

[0881] Example prompt: "Please enter emotion data" → "Video data, audio data"

[0882] 2. Terminal: Sends the input information and emotion data to the server.

[0883] 3. Server: Receives information, pre-processes it, extracts formatted data, and uses an emotion engine to recognize emotions from facial expressions and voice.

[0884] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as data scientist, machine learning engineer, or AI researcher. It also includes specific support measures to alleviate any concerns the user may have.

[0885] 5. Server: Sends the generated career path to the device.

[0886] 6. Terminal: Shows the user a list of suggested career paths.

[0887] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[0888] 8. Device: Sends feedback to the server.

[0889] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[0890] 10. Terminal: Display detailed information to the user.

[0891] In this way, users can receive suggestions for suitable career paths based on their skills, interests, and feelings, along with specific information on how to get there.

[0892] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0893] Step 1:

[0894] User input of information and emotional data:

[0895] The user uses the device to enter basic information (e.g., name, age), skill set (e.g., Python, Java), work experience (e.g., five years of experience working in an IT company), and areas of interest (e.g., AI, machine learning). In addition, the device's camera and microphone are used to record video and audio data, and emotional data is provided. Specifically, the user enters the required information according to each prompt, and then presses the send button after completing the input.

[0896] Input: User basic information, skill set, work experience, areas of interest, video data, audio data

[0897] Output: A package of user input data that is saved to the device.

[0898] Step 2:

[0899] Device transmission of information and emotional data:

[0900] The device sends the basic information and emotion data entered by the user to the server as a single data package. Specifically, it packages the data as an HTTP request and sends it to the server's specified API endpoint. Once the transmission is complete, the device displays a message to the user indicating that the transmission was successful.

[0901] Input: User input data package

[0902] Output: User information package sent to server, user receives success message

[0903] Step 3:

[0904] Receiving and preprocessing information by the server:

[0905] The server analyzes the user's basic information and emotion data received from the device, fills in missing values, and standardizes the data format. For example, if age is not entered, it fills in the default value and prompts the user to re-enter it. It also converts video and audio data into an analyzable format.

[0906] Input: User information package sent from the terminal

[0907] Output: Preprocessed user information and emotion data

[0908] Step 4:

[0909] Information extraction and emotion recognition by the server:

[0910] The server extracts useful information such as skill sets, work experience, and areas of interest from the preprocessed basic information. It also activates an emotion engine to recognize the user's emotions from video and audio data. For example, facial expression analysis can detect smiles and sadness, and voice analysis can evaluate the tone and speed of the voice.

[0911] Input: Preprocessed user information and emotion data

[0912] Output: Extracted useful information and recognized emotion data

[0913] Step 5:

[0914] Server-based application of generative AI models:

[0915] The server inputs the extracted information and recognized emotion data into a generative AI model (e.g., GPT-3) to generate an optimal career path. This model analyzes the user's skill set and emotional state and suggests specific career path candidates. For example, if the user is feeling anxious, it will suggest a career path to alleviate that anxiety.

[0916] Input: extracted information, recognized emotion data

[0917] Output: Generated career path candidates

[0918] Step 6:

[0919] Career path suggestions by the server:

[0920] The server then sends the generated list of career path candidates to the device, which includes specific job titles, required skill sets, and supporting information related to the career path.

[0921] Input: Generated career path candidates

[0922] Output: A list of possible career paths sent to the terminal

[0923] Step 7:

[0924] View career paths by device:

[0925] The terminal displays the list of candidate career paths received from the server to the user, who can then view the details of each career path and select the one that interests them.

[0926] Input: List of potential career paths

[0927] Output: Career path details displayed on the terminal screen

[0928] Step 8:

[0929] User feedback:

[0930] Users can enter feedback on the displayed career paths, such as, "I'm interested in becoming a data scientist, but I'd like to know more about the required skill set."

[0931] Input: Feedback on career paths

[0932] Output: Feedback data stored on the device

[0933] Step 9:

[0934] Send feedback via device:

[0935] The device packages the user's feedback and sends it back to the server, specifically by sending the feedback data as an HTTP request.

[0936] Input: User feedback data

[0937] Output: Feedback data sent to the server

[0938] Step 10:

[0939] Server feedback processing:

[0940] The server receives the feedback and uses a generative AI model to generate detailed information based on the feedback, such as "The skill sets required for a data scientist are Python, statistics, machine learning techniques, and data visualization."

[0941] Input: User feedback data

[0942] Output: Detailed information generated

[0943] Step 11:

[0944] Displaying detailed information via terminal:

[0945] The device displays the details sent from the server to the user, who can then decide what to do next, such as enrolling in a related online course or training program.

[0946] Input: Generated details

[0947] Output: Detailed information displayed on the terminal screen

[0948] Through this series of steps, users receive recommendations for the best career path based on their skills, interests, and feelings, along with specific information on how to get there.

[0949] (Application example 2)

[0950] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0951] In today's world, many users seek optimal advice regarding their careers. Furthermore, career path suggestions that take into account the user's emotional state are crucial for improving user satisfaction. However, conventional systems do not adequately suggest individual career paths based on the user's emotions. As a result, the suggested career paths may not fully meet the user's needs.

[0952] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0953] In this invention, the server includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information and the user's emotional data, means for transmitting and displaying the generated career path to a user terminal, means for acquiring the user's facial expression and voice data and extracting emotional data using an emotion recognition engine, and means for receiving feedback from the user and regenerating information. This makes it possible to reflect the user's emotional state and propose optimal career paths tailored to individual needs.

[0954] The "means for receiving and preprocessing information entered by the user" refers to the system's ability to receive information provided by the user, such as skill set, work experience, and areas of interest, and to format it in a way that is easy to analyze.

[0955] The "means for extracting useful information from preprocessed information" is a function that selects important data that is useful for generating career paths from preprocessed information.

[0956] "Means for generating career paths using a generative AI model based on extracted information and user emotional data" refers to a function in which the generative AI model automatically creates optimal career paths by utilizing the user's input information and emotional data.

[0957] The "means for transmitting the generated career path to the user terminal and displaying it" has the function of transmitting information about the generated career path to the user terminal and displaying it.

[0958] "Means for acquiring a user's facial expression and voice data and extracting emotion data using an emotion recognition engine" refers to a function that analyzes a user's facial expression and voice to identify emotions and extract that data.

[0959] The "means for receiving feedback from users and regenerating information" has the function of receiving feedback provided by users and regenerating new career paths and recommendations based on that information.

[0960] The present invention provides an effective career assessment system that utilizes a generative AI model based on user input information and emotional data. The specific configuration for realizing this system is described below.

[0961] Overall system configuration:

[0962] This system consists of three main components: a user terminal, a server, and a user.

[0963] User role:

[0964] Users use a smartphone, smart glasses, or head-mounted display to input basic information, skills, work experience, and areas of interest required for career assessment, and then provide video and audio data to the device to obtain emotional data from the user's facial expressions and voice.

[0965] As a concrete example, the user inputs the following information:

[0966] Name: Taro

[0967] Age: 30

[0968] Skills: Programming (Python, Java), Data Analysis

[0969] Work experience: 5 years of experience working in an IT company

[0970] Areas of interest: AI, machine learning

[0971] Emotion: Video data (facial expressions) and audio data

[0972] Device role:

[0973] The device receives the information and emotion data entered by the user and transmits them to the server, displays the career path suggestions received from the server to the user, and transmits the user's feedback back to the server.

[0974] Server Role:

[0975] The server performs a series of processes using the following means.

[0976] User information preprocessing means: The server receives the user's basic information, skill set, work experience, areas of interest, and emotional data sent from the terminal and preprocesses it into a format that is easy to analyze.

[0977] Means for extracting useful information: From the preprocessed information, select data that is useful for generating career paths.

[0978] Career path generation method: Based on the extracted information and the user's emotional data, a generative AI model (e.g., OpenAI GPT-4) is used to generate the optimal career path. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates.

[0979] Emotion data extraction method: Recognize the user's facial expressions and voice data and extract emotion data using an emotion engine (e.g., Azure Cognitive Services).

[0980] Career path suggestion means: The generated career path candidates are sent to the terminal and displayed to the user.

[0981] Feedback processing means: Accepts user feedback and generates new information based on it using the AI ​​model again.

[0982] For example, if a user provides feedback such as "I'm interested in becoming a data scientist, but I'd like to know more about the required skill set," the server will take this information and generate detailed skill set suggestions.

[0983] Example prompt sentence:

[0984] User Information:

[0985] Name: Taro

[0986] Age: 30

[0987] Skills: Python, Java, Data Analysis

[0988] Work experience: 5 years of experience working in an IT company

[0989] Areas of interest: AI, machine learning

[0990] User sentiment data:

[0991] Facial expression data (video)

[0992] Audio data

[0993] This system makes it possible to suggest optimal career paths tailored to individual needs, reflecting the user's emotional state.

[0994] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0995] Step 1:

[0996] The user uses a smartphone, smart glasses, or head-mounted display to input basic information such as name, age, skill set, work experience, and areas of interest. In addition, the user provides video and audio data to capture facial expressions and voice data. The information and emotional data collected in this step are sent from the device to the server.

[0997] Input: Name, age, skill set, work experience, areas of interest, facial expression data (video), audio data

[0998] Output: Basic information and emotion data are sent to the server.

[0999] Step 2:

[1000] The server preprocesses the received user basic information and emotion data. This preprocessing includes filling in missing information and normalizing the data. Emotion data is extracted by analyzing video and audio data and using an emotion recognition engine (e.g., Azure Cognitive Services).

[1001] Input: Basic information, emotion data (video, audio)

[1002] Output: Preprocessed basic information, extracted emotion data

[1003] Step 3:

[1004] The server selects the data necessary for career path generation by extracting useful information from the preprocessed information. Specifically, the server extracts the user's skill set, work experience, and areas of interest. Based on the extracted information, the server also incorporates the user's emotional data.

[1005] Input: Preprocessed basic information, extracted emotion data

[1006] Output: Data required to generate a career path

[1007] Step 4:

[1008] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an optimal career path based on the extracted information and emotional data. This generation also takes into account the user's current emotional state. For example, if the user is feeling anxious, a career path will be generated that includes support to alleviate those feelings.

[1009] Input: Data required for career path generation, emotional data

[1010] Output: Generated career paths

[1011] Step 5:

[1012] The server sends the generated career path to the terminal, which displays it to the user. The user can check the displayed career path and provide feedback if necessary.

[1013] Input: Generated career path

[1014] Output: The career path displayed to the user

[1015] Step 6:

[1016] Users enter their feedback on career paths into the device, which then sends it to the server, which receives the feedback and uses the generative AI model to generate new career paths and detailed information based on the feedback.

[1017] Input: User feedback

[1018] Output: Newly generated career paths and detailed information

[1019] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1020] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1021] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1022] [Third embodiment]

[1023] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1024] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1026] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1027] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1028] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1030] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1031] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1033] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1034] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1035] This invention is a system that utilizes generative AI to provide effective career assessments based on information entered by users. This system receives the information entered by users, preprocesses it, generates a career path using a generative AI model, and displays it to the user.

[1036] System configuration:

[1037] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[1038] User role:

[1039] Users enter basic information, skills, work experience, and areas of interest required for a career assessment through a terminal. Specifically, users enter their name, age, programming languages ​​they can use, past work history, and areas of interest (e.g., AI or data science).

[1040] Device role:

[1041] The device sends the information entered by the user to the server, displays the career path suggestions received from the server to the user, and receives user feedback and sends it back to the server.

[1042] Server Role:

[1043] The server processes the following series of processes.

[1044] 1. Receiving and preprocessing information:

[1045] The server receives user information sent from the device, preprocesses the received information, and checks for missing or inconsistent data. For example, if age is not entered or invalid skills are included, the server makes appropriate corrections.

[1046] 2. Information Extraction:

[1047] From the preprocessed information, useful information such as the user's skill set, past work experience, areas of interest, etc. is extracted, and the extracted information is used as input data for the AI ​​model.

[1048] 3. Application of generative AI models:

[1049] The server uses a generative AI model based on the extracted information to generate career path candidates. This AI model analyzes the user's information using natural language processing technology and can suggest optimal career paths.

[1050] 4. Career path suggestions and feedback processing:

[1051] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[1052] Examples:

[1053] As a concrete example, consider the case where a user inputs the following information:

[1054] Name: Taro

[1055] Age: 30

[1056] Skills: Programming (Python, Java), Data Analysis

[1057] Experience: 5 years of experience working in an IT company

[1058] Interests: AI, machine learning

[1059] procedure:

[1060] 1. User: Enter the above information using the terminal.

[1061] 2. Terminal: Sends the entered information to the server.

[1062] 3. Server: Receives information, preprocesses it, and extracts formatted data.

[1063] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as suggesting candidates for data scientists, machine learning engineers, and AI researchers.

[1064] 5. Server: Sends the generated career path to the device.

[1065] 6. Terminal: Shows the user a list of suggested career paths.

[1066] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[1067] 8. Device: Sends feedback to the server.

[1068] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[1069] 10. Terminal: Display detailed information to the user.

[1070] In this way, users can receive suggestions for suitable career paths based on their skills and interests, along with specific information on how to get there.

[1071] The processing flow will be explained below.

[1072] Step 1:

[1073] The user enters basic information, skill set, work experience, and areas of interest through the terminal. For example, the user enters "name," "age," "programming skills (Python, Java)," "five years of experience working in an IT company," and "areas of interest (AI, machine learning)."

[1074] Step 2:

[1075] The device sends the user's input information to the server, and the data is formatted in JSON format or similar.

[1076] Step 3:

[1077] The server receives the user information sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if the age is not entered, it will fill in the field with a default value or prompt the user to enter it again.

[1078] Step 4:

[1079] The server extracts useful information from the preprocessed data, such as skill sets, work experience, and areas of interest, which is then fed into a generative AI model in the next step.

[1080] Step 5:

[1081] Based on the extracted information, the server uses a generative AI model to generate career paths. This AI model analyzes the user's input data and generates optimal career path candidates. For example, it may suggest careers such as data scientist, machine learning engineer, or AI researcher.

[1082] Step 6:

[1083] The server sends the generated career path candidates to the terminal, formatting the data so that it is easy for the user to understand.

[1084] Step 7:

[1085] The terminal displays the career path candidates received from the server to the user, who can then review them and provide feedback if necessary.

[1086] Step 8:

[1087] The user types their feedback into the terminal, for example, "I'm interested in becoming a data scientist, but I'd like to know more about specific skill sets and learning resources."

[1088] Step 9:

[1089] The terminal transmits the user's feedback to the server, and the transmitted data is formatted in a manner that accurately conveys the feedback content.

[1090] Step 10:

[1091] The server receives the feedback and uses the generative AI model again to generate detailed information based on the feedback, such as the skills required to become a data scientist (database management, understanding machine learning algorithms) and recommended learning resources (online courses, reference books).

[1092] Step 11:

[1093] The server sends the generated detailed information to the terminal.

[1094] Step 12:

[1095] The device displays the detailed information received from the server to the user, who can review it and obtain more specific information about the career path.

[1096] Example 1

[1097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1098] In modern society, individuals have a wide variety of career options, but the sheer number of options makes it difficult to determine the appropriate career path. It is particularly difficult for individuals with specific skill sets and work experience to find the optimal career path. Another issue is the lack of a system that efficiently organizes this information and suggests career paths suited to individuals.

[1099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1100] In this invention, the server includes means for receiving and preprocessing information input by a user, means for extracting useful information from the preprocessed information, and means for generating a career path using a generative AI model based on the extracted information. This makes it possible to generate an optimal career path based on input information such as the user's basic information, skill set, work history, and areas of interest, and to provide detailed feedback as needed.

[1101] "User" refers to an individual who uses the system to input the information necessary for a career diagnosis and receive career path suggestions.

[1102] "Terminal" refers to the device through which a user inputs information and communicates with a server. This includes PCs, smartphones, tablets, etc.

[1103] "Server" refers to the hardware and software that receives and processes information sent by users and generates and proposes career paths using a generative AI model.

[1104] "Preprocessing" refers to a series of processes by which the server detects and corrects deficiencies or inconsistencies in the data received from the user.

[1105] "Generative AI models" refer to artificial intelligence technologies used to generate career paths based on extracted information, including natural language processing technologies.

[1106] A "prompt" is a document containing specific formatting and instructions to be input to a generative AI model.

[1107] "Basic information" refers to personally identifiable information such as the user's name and age.

[1108] A "competence set" refers to the skills and knowledge a user possesses, including programming languages ​​and specialized knowledge.

[1109] "Work history" refers to detailed information about a user's past work experience, including, for example, where they worked and what they did.

[1110] "Areas of Interest" refers to areas or topics that a user is personally interested in, such as AI and data science.

[1111] "Feedback" refers to any additional requests or comments a user provides regarding a career path suggestion.

[1112] This invention is a system that utilizes generative AI based on information entered by the user to provide efficient career diagnosis. This system consists of three entities: a server, a terminal, and a user. The detailed roles of each entity and the overall system flow are explained below.

[1113] Server Roles

[1114] The server receives user information and performs preprocessing, information extraction, and applies generative AI models. The server is equipped with a high-performance processor and sufficient memory to quickly process data and execute AI models. Cloud servers or dedicated data center servers are commonly used.

[1115] 1. Receiving and preprocessing information:

[1116] The server receives user information sent from the device. The received information is passed to the server in JSON or XML format. The server analyzes this data and checks for inconsistencies or missing data. For example, if the age or skill set is abnormal, it is corrected.

[1117] 2. Information Extraction:

[1118] The server extracts the user's basic information, skill set, work history, areas of interest, etc. from the pre-processed information. Natural language processing technology is used for this extraction process, resulting in highly accurate data extraction.

[1119] 3. Application of generative AI models:

[1120] The server uses the extracted information as input to generate a career path using a generative AI model (e.g., GPT-3 or BERT). The generated career path candidates are then presented as multiple options.

[1121] 4. Career path suggestions and feedback processing:

[1122] The generated career path is sent to the device and displayed to the user. If the user provides feedback, the feedback is received again and further detailed information is provided using the generative AI model. This allows the user to find the best career path based on their interests and skills.

[1123] Device Role

[1124] The terminal acts as an intermediary through which the user inputs information and communicates with the server. The terminal can function as a web browser, a smartphone application, or a dedicated device.

[1125] 1. Transmission of Information:

[1126] The terminal sends the user's basic information, skill set, work history, and areas of interest to the server. The information is encrypted using SSL / TLS and transmitted securely.

[1127] 2. View Career Paths:

[1128] The terminal displays the career path suggestions received from the server to the user, providing an intuitive user interface that allows the user to easily understand the suggested career paths.

[1129] 3. Submitting Feedback:

[1130] The feedback information entered by the user is sent to the server, which processes it again and generates new suggestions and detailed information.

[1131] User Roles

[1132] 1. Enter your information:

[1133] Users enter the information required for the career assessment through a terminal, such as their name, age, programming skills (Python, Java, etc.), work history, and areas of interest (AI, data science, etc.).

[1134] 2. Review and feedback on proposals:

[1135] Users can review the career path suggestions displayed on their device and enter feedback as needed. Feedback should be specific, such as "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[1136] Specific operation example

[1137] Assume a case where the user enters the following information:

[1138] Name: Taro

[1139] Age: 30

[1140] Skill set: Programming (Python, Java), Data analysis

[1141] Work history: 5 years of experience working in an IT company

[1142] Interests: AI, machine learning

[1143] When the user enters this information and clicks the send button, the device sends it to the server, which receives the information, performs preprocessing and information extraction, and inputs prompt sentences into the generative AI model.

[1144] Example prompt sentence:

[1145] "Taro is 30 years old, has programming skills in Python and Java, and has experience in data analysis. He currently has five years of work experience in an IT company and is interested in AI and machine learning. Please suggest a career path that would be suitable for Taro."

[1146] The generative AI model analyzes this prompt and suggests career paths such as data scientist, machine learning engineer, and AI researcher. The device displays this to the user, who then provides feedback such as, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set." The server then processes this feedback again, generates more detailed information, and sends it to the device. Ultimately, the user gains a deeper understanding of the skills needed for their career path and the next steps they should take.

[1147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1148] Step 1:

[1149] Users enter the information required for the career assessment through their device, including their name, age, programming languages ​​they can use, their past work history, and areas of interest. After filling out the information in the input form and clicking the submit button, the data is converted into JSON or XML format.

[1150] Input: User basic information, skill set, work history, areas of interest

[1151] Output: Input data in JSON or XML format

[1152] Step 2:

[1153] The terminal sends the information entered by the user to the server. When the send button is clicked, the terminal encrypts the data with SSL / TLS and sends it to the server using the HTTP / HTTPS protocol.

[1154] Input: Input data in JSON or XML format

[1155] Output: Data transfer to the server

[1156] Step 3:

[1157] The server receives user information sent from the device. The received information is passed to the server in JSON or XML format. The server analyzes this data and checks for inconsistencies or missing data. For example, if the age or skill set is abnormal, it is corrected.

[1158] Input: Data received from the terminal

[1159] Output: Corrected data

[1160] Specifically, the server parses the received JSON data and checks the integrity of each field before saving the content to the database.

[1161] Step 4:

[1162] The server extracts useful information from the preprocessed information, such as the user's basic information, skill set, work history, and areas of interest, using natural language processing technology.

[1163] Input: Corrected data

[1164] Output: The input dataset for the generative AI model

[1165] Specifically, the server converts the extracted information into a specific format and builds a dataset to be input into the generative AI model.

[1166] Step 5:

[1167] The server uses a generative AI model (e.g., GPT-3 or BERT) to generate career path candidates based on the extracted information. The server calls the generative AI model (e.g., GPT-3 or BERT) via an API, provides the formatted dataset as input, and obtains the model's output.

[1168] Input: The input dataset for the generative AI model

[1169] Output: Possible career paths

[1170] Specifically, the server uses generative AI models such as GPT-3 and BERT to generate multiple career path candidates based on input data.

[1171] Step 6:

[1172] The server sends the generated career path candidates to the terminal, converts the generated career path information into JSON format, and returns it as a response to the terminal using the HTTP / HTTPS protocol.

[1173] Input: Possible career paths

[1174] Output: Data transfer to the device

[1175] As a specific operation, the server transmits the generated carrier path to the terminal.

[1176] Step 7:

[1177] The terminal displays the career path suggestions received from the server to the user in a visually easy-to-understand format (list format or card format) in the user interface.

[1178] Input: Career path suggestions from the server

[1179] Output: Displaying the career path to the user

[1180] Specifically, the device updates its user interface to display a list of suggested career paths.

[1181] Step 8:

[1182] Users review the suggested career paths and provide feedback if they require more specific information or other options, including details about specific career paths or new requirements.

[1183] Input: Career path displayed information

[1184] Output: Feedback information

[1185] As a specific operation, the user enters a comment in the feedback form and clicks the send button.

[1186] Step 9:

[1187] The device sends this feedback to the server. The feedback information is also encrypted using SSL / TLS and sent to the server.

[1188] Input: User feedback information

[1189] Output: Feedback forwarding to the server

[1190] As a specific operation, the terminal transmits the feedback data to the server again.

[1191] Step 10:

[1192] The server receives the feedback and generates new information using the generative AI model again. It analyzes the feedback and generates prompts to generate new career paths and detailed information.

[1193] Input: Feedback information

[1194] Output: New career path or more information

[1195] Specifically, the server generates a prompt sentence, then calls the generative AI model again based on that sentence to generate a new proposal.

[1196] Step 11:

[1197] The terminal again displays the received information to the user, allowing the user to check for new suggestions and detailed information.

[1198] Input: New career path or detailed information from the server

[1199] Output: Redisplay to user

[1200] Specifically, the device updates the user interface to display new suggestions and detailed information, allowing users to receive suggestions for suitable career paths based on their skills and interests, along with specific information on how to get there.

[1201] (Application example 1)

[1202] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1203] Conventional career assessment systems often only have the function of suggesting specific occupations or career paths based on information entered by the user. As a result, they do not suggest training programs or related qualifications based on the user's specific skills and interests in a particular field, resulting in the problem of providing insufficient support for career changes. This limitation is particularly pronounced in highly specialized fields such as the security field.

[1204] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1205] In this invention, the server includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information, and means for proposing optimal job titles, training programs, and related qualifications based on user information. This allows the server to specifically suggest training and qualifications required by the user in the security field, thereby providing more practical and specific support when making a career change.

[1206] "User-entered information" refers to basic information, skill set, work experience, areas of interest, etc. that a user provides to the system.

[1207] The "means for receiving and preprocessing" is a mechanism for receiving information input by a user, formatting the information, and correcting any data deficiencies or inconsistencies.

[1208] A "means for extracting useful information" is a mechanism for picking out and extracting highly useful data (e.g., skills, experience, areas of interest, etc.) from preprocessed information.

[1209] A "generative AI model" is a model that uses machine learning and natural language processing technologies to predict and generate career paths based on input data.

[1210] "Means for generating career paths" refers to the process of using a generative AI model based on extracted data to suggest the most suitable occupational and job position path for the user.

[1211] "User terminal" refers to a device that is directly operated by a user (e.g., smartphone, tablet, PC).

[1212] The "display means" is a function for displaying the generated career path and training program on the screen of the user terminal.

[1213] The "means of receiving feedback and generating new information" is a mechanism that receives ratings and comments from users and generates further optimized career paths and detailed information based on that feedback.

[1214] "Means to suggest job titles, training programs, and related qualifications" refers to a function that uses a generative AI model to automatically recommend specific occupations, job titles, required training programs, and qualifications to be obtained based on user input.

[1215] This invention is a system that proposes optimal career paths and training programs based on information entered by users. This system is mainly composed of three elements: a server, a terminal, and a user, and these elements work together.

[1216] System configuration and operation

[1217] Hardware and Software Use

[1218] Server: The server is used for data processing, running AI models, and data preprocessing. Software used for this purpose includes database management systems (DBMS), data preprocessing tools, and libraries for generative AI models (e.g., TensorFlow and PyTorch).

[1219] Terminal: The terminal is used to provide the user interface (UI), sending information entered by the user to the server and displaying the results received from the server. The software used for this purpose can be a mobile application or a web browser.

[1220] Users: Users use devices such as smartphones, tablets, and computers to enter information and provide feedback.

[1221] Data processing and calculation

[1222] Input and preprocessing information:

[1223] Users enter basic information (name, age), skill set, work experience, and areas of interest through the terminal.

[1224] The terminal sends this information to the server.

[1225] The server pre-processes the received information, checking for data inconsistencies or omissions and making corrections.

[1226] Extracting information and applying AI models:

[1227] From the preprocessed data, useful information such as the user's skill set, work experience, and areas of interest is extracted.

[1228] Based on the extracted data, generative AI models (e.g., natural language processing models) are used to generate career paths, training programs, and related qualifications.

[1229] Result display and feedback:

[1230] The generated career path and training program are sent to the terminal and displayed to the user.

[1231] The user provides feedback.

[1232] The server receives the feedback and uses the AI ​​model again to generate more optimal information and send it to the device.

[1233] Specific examples

[1234] For example, suppose a user enters the following information:

[1235] Name: Yamada Ichiro

[1236] Age: 28

[1237] Skills: Network management, programming (Python)

[1238] Experience: 3 years of system administration experience

[1239] Interests: Cybersecurity, Information and Communication Technology

[1240] In this case, the following processing is performed.

[1241] 1. The user enters information into the smartphone app.

[1242] 2. The app sends the input information to the server.

[1243] 3. The server preprocesses the information, correcting any gaps or inconsistencies and formatting it.

[1244] 4. Extract the user's skill set, experience, and interests from the formatted data.

[1245] 5. Based on the data extracted by the generative AI model, it generates career paths for security analysts and penetration testers, and also suggests related certifications such as CISSP and CEH, how to obtain them, and the necessary training programs.

[1246] 6. The app displays these results to the user.

[1247] 7. Users provide feedback such as "What steps are required to become a security analyst?" and "I would like to know the specific training program to obtain the qualification."

[1248] 8. The server receives the feedback, generates more detailed information, and sends it back to the device.

[1249] Prompt Sentence Examples

[1250] "Based on the information the user enters, suggest the best security career paths and related training programs. For example, if I have experience in network administration, what security roles and certifications would be suitable?"

[1251] In this way, users are given specific support to find the best career path or training program based on their skill set and interests.

[1252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1253] Step 1:

[1254] The user inputs information. The user uses the terminal to input basic information such as name, age, skill set, work experience, and areas of interest. This is the input information.

[1255] Step 2:

[1256] The device sends the input information to the server. The device then sends an HTTP request to send the input information to the server's API endpoint. At this time, the input information is sent in a data format such as JSON.

[1257] Step 3:

[1258] The server receives the information and performs preprocessing. The server analyzes the received JSON data and checks for missing or inconsistent data. For example, if age is not entered, it sets a default value. The preprocessed data is saved as formatted data.

[1259] Step 4:

[1260] The server extracts useful information. It performs data analysis to extract useful information such as skill sets, work experience, and areas of interest from the preprocessed data. The input is the preprocessed data, and the output is the extracted useful data.

[1261] Step 5:

[1262] The server applies a generative AI model (e.g., a natural language processing model) based on the extracted data to generate career paths, training programs, and related qualifications. The input is the extracted useful data, and the output is the generated career path information.

[1263] Step 6:

[1264] The server sends the generated career path to the terminal. The server then sends the generated career path and related training program information to the terminal as an API response. The output is the sent career path information.

[1265] Step 7:

[1266] The terminal displays the career path to the user. The terminal displays the received career path information in a user-friendly graphical format, including detailed data on related qualifications and training programs.

[1267] Step 8:

[1268] The user provides feedback. The user checks the displayed career path information and inputs any further information or additional requests as feedback. This becomes new input information.

[1269] Step 9:

[1270] The terminal sends feedback information to the server. The terminal resends the feedback information to the server and sends an HTTP request to reflect it in the next generation process. The input is the feedback information, and the output is the data sent to the server.

[1271] Step 10:

[1272] The server receives the feedback information and again uses the AI ​​model to generate optimal information. The server then analyzes the data again and generates new career paths, detailed training programs, and qualification information. The input is the feedback information, and the output is updated career path information.

[1273] Through these steps, users can find the best career path based on their skills and interests, as well as information on the training programs and certifications they need.

[1274] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1275] This invention combines a system that utilizes generative AI to provide effective career diagnosis based on information entered by the user with an emotion engine that recognizes the user's emotions. This system receives and preprocesses the information entered by the user, generates a career path using a generative AI model, and performs a series of processes from displaying the result to the user, as well as emotion recognition using the emotion engine.

[1276] System configuration:

[1277] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[1278] User role:

[1279] Users input the basic information required for career assessment, including their skill set, work experience, and areas of interest, via their device. Furthermore, an input environment is created that can recognize emotions from the user's facial expressions and voice. Specifically, users can input their name, age, programming skills (Python, Java), five years of experience working at an IT company, and areas of interest (AI, machine learning), and can also provide video and audio data.

[1280] Device role:

[1281] The device sends the information and emotion data entered by the user to the server, displays the career path suggestions received from the server to the user, and receives user feedback and sends it back to the server.

[1282] Server Role:

[1283] The server processes the following series of processes.

[1284] 1. Receiving and preprocessing information:

[1285] The server receives the user's basic information, skillset, work experience, interests, and emotional data sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if age is not entered, it will either fill in the field with a default value or prompt the user to enter it again.

[1286] 2. Information Extraction:

[1287] From the preprocessed basic information, useful information such as the user's skill set, work experience, and areas of interest is extracted. Furthermore, an emotion engine is used to recognize emotions from the user's emotion data.

[1288] 3. Application of generative AI models:

[1289] The server generates a career path using a generative AI model based on the extracted information and recognized emotions. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates. For example, if the user is feeling anxious, it will suggest a career path that includes support to alleviate that anxiety.

[1290] 4. Career path suggestions and feedback processing:

[1291] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[1292] Examples:

[1293] As a concrete example, consider the case where a user inputs the following information:

[1294] Name: Taro

[1295] Age: 30

[1296] Skills: Programming (Python, Java), Data Analysis

[1297] Experience: 5 years of experience working in an IT company

[1298] Interests: AI, machine learning

[1299] Emotion: Video data (facial expressions) and audio data

[1300] procedure:

[1301] 1. User: Enter the above information and emotion data using the terminal.

[1302] 2. Terminal: Sends the input information and emotion data to the server.

[1303] 3. Server: Receives information, pre-processes it, extracts formatted data, and uses an emotion engine to recognize emotions from facial expressions and voice.

[1304] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as data scientist, machine learning engineer, or AI researcher. It also includes specific support measures to alleviate any concerns the user may have.

[1305] 5. Server: Sends the generated career path to the device.

[1306] 6. Terminal: Shows the user a list of suggested career paths.

[1307] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[1308] 8. Device: Sends feedback to the server.

[1309] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[1310] 10. Terminal: Display detailed information to the user.

[1311] In this way, users can receive suggestions for suitable career paths based on their skills, interests, and feelings, along with specific information on how to get there.

[1312] The processing flow will be explained below.

[1313] Step 1:

[1314] The user uses a device to input basic information (name, age, skill set, work experience, areas of interest) and emotional data (facial expressions and voice). For example, the user might input "Name: Taro," "Age: 30," "Skills: Programming (Python, Java), data analysis," "Experience: 5 years of work experience at an IT company," and "Interests: AI, machine learning," and then capture their facial expressions with a camera and record emotional comments via voice.

[1315] Step 2:

[1316] The device sends the basic information and emotion data entered to the server, which formats the data in JSON or other formats.

[1317] Step 3:

[1318] The server receives the data sent from the device. It analyzes the received data and performs preprocessing. For example, if the age is not entered, it will be filled in with a default value or a message will be generated to prompt the user to enter it again.

[1319] Step 4:

[1320] The server extracts useful information from the pre-processed data, such as skill sets, work experience, and areas of interest, and also uses an emotion engine to recognize the user's emotions from the captured facial and voice data.

[1321] Step 5:

[1322] The server uses a generative AI model to generate career paths based on the extracted data and the recognized emotions. For example, the server can suggest career paths such as "Data Scientist," "Machine Learning Engineer," and "AI Researcher" based on the user's data, and can also suggest support measures for each career path based on the user's emotions (e.g., anxiety).

[1323] Step 6:

[1324] The server then sends the generated career path candidates and accompanying emotion-based support information to the terminal, formatting the data in a user-friendly format.

[1325] Step 7:

[1326] The terminal displays the career path candidates and emotion-based support information sent from the server to the user, who then checks the information and inputs feedback based on their emotions and preferences.

[1327] Step 8:

[1328] The user enters the necessary details about the career path provided as feedback into the device, for example, "I'm interested in becoming a data scientist, but I'd like to know the specific skill set and recommended learning resources."

[1329] Step 9:

[1330] The device sends the user's feedback to the server, and the data is formatted to accurately convey the feedback content.

[1331] Step 10:

[1332] The server receives the feedback, analyzes the content, and reapplies the generative AI model to generate detailed information based on the feedback (e.g., required skills, learning resources). It also reassess the user's emotions and provides appropriate support measures.

[1333] Step 11:

[1334] The server transmits the generated detailed information and additional support information based on the emotion to the terminal.

[1335] Step 12:

[1336] The terminal displays the detailed information and additional support information received from the server to the user, who can then check it and obtain useful information for creating a specific action plan or study plan.

[1337] This process provides users with specific recommendations for the best career path and the necessary action plan based on their skill set, work experience, areas of interest, and emotions.

[1338] Example 2

[1339] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1340] Conventional career diagnosis systems suggest career paths by collecting information such as a user's basic information, skill set, and work experience, but because they do not take the user's emotional state into consideration, they have the problem of being unable to make appropriate career suggestions based on the user's psychological state.In addition, the process of regenerating information based on feedback is cumbersome, which causes a poor user experience.

[1341] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and preprocessing information and emotional data input by the user, means for extracting useful information from the preprocessed information and emotional data and recognizing emotions, means for generating a career path using a generative AI model based on the extracted information and recognized emotional data, means for transmitting and displaying the generated career path to the user terminal, and means for receiving feedback from the user and regenerating information. This makes it possible to propose career paths according to the emotional state, thereby improving the user experience.

[1342] "Information and emotional data entered by the user" refers to the user's basic information, skill set, work experience, areas of interest, and emotional data such as facial expressions and voice.

[1343] "Preprocessing" refers to the process of preparing the received information into a data format that is easy to analyze by performing processes such as filling in missing values, standardizing the format, and normalizing it.

[1344] "Emotion Engine" refers to the software and algorithms used to recognize a user's emotional state from video and audio data.

[1345] A "generative AI model" refers to an artificial intelligence model that includes an algorithm for generating career paths based on user input and recognized emotional data.

[1346] A "career path" refers to the optimal career direction, specific job title, required skill set, etc. generated based on the user's information and emotional state.

[1347] "Feedback" refers to opinions, additional questions, supplementary information, etc. that users enter regarding the proposed career path.

[1348] This invention combines a system that utilizes generative AI to provide effective career diagnosis based on information entered by the user with an emotion engine that recognizes the user's emotions. This system receives and preprocesses the information entered by the user, generates a career path using a generative AI model, and performs a series of processes from displaying the result to the user, as well as emotion recognition using the emotion engine.

[1349] (System configuration)

[1350] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[1351] User role:

[1352] Users input the basic information required for career assessment, including their skill set, work experience, and areas of interest, via their device. Furthermore, an input environment is created that can recognize emotions from the user's facial expressions and voice. Specifically, users can input their name, age, programming skills (Python, Java), five years of experience working at an IT company, and areas of interest (AI, machine learning), and can also provide video and audio data.

[1353] Device role:

[1354] The device sends the information and emotional data entered by the user to the server. It also displays career path suggestions received from the server to the user. It also accepts user feedback and sends it back to the server. Specifically, the device sends the user's input data to the server as an HTTP request, receives a response from the server, and displays it on the screen.

[1355] Server Role:

[1356] The server processes the following series of processes.

[1357] 1. Receiving and preprocessing information:

[1358] The server receives the user's basic information, skillset, work experience, interests, and emotional data sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if age is not entered, it will either fill in the field with a default value or prompt the user to enter it again.

[1359] 2. Information extraction and emotion recognition:

[1360] The server extracts useful information from the preprocessed basic information, such as the user's skill set, work experience, and areas of interest. It then uses an emotion engine to recognize emotions from the user's emotional data. For example, it analyzes facial expressions of smiles and sadness from video data and evaluates the tone and speed of voice from audio data.

[1361] 3. Application of generative AI models:

[1362] The server generates a career path using a generative AI model (such as GPT-3) based on the extracted information and recognized emotions. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates. For example, if the user is feeling anxious, it will suggest a career path that includes support to alleviate that anxiety.

[1363] 4. Career path suggestions and feedback processing:

[1364] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[1365] (Example)

[1366] As a concrete example, consider the case where a user inputs the following information:

[1367] Name: Taro

[1368] Age: 30

[1369] Skills: Programming (Python, Java), Data Analysis

[1370] Experience: 5 years of experience working in an IT company

[1371] Interests: AI, machine learning

[1372] Emotion: Video data (facial expressions) and audio data

[1373] procedure:

[1374] 1. User: Enter the above information and emotion data using the terminal.

[1375] Example prompt: "Please enter your name" → "Taro"

[1376] Example prompt: "Please enter your age" → "30"

[1377] Example prompt: "Please tell us your programming skills" → "Python, Java"

[1378] Example prompt: "Please enter your work experience" → "5 years of work experience in an IT company"

[1379] Example prompt: "Please tell us your area of ​​interest" → "AI, machine learning"

[1380] Example prompt: "Please enter emotion data" → "Video data, audio data"

[1381] 2. Terminal: Sends the input information and emotion data to the server.

[1382] 3. Server: Receives information, pre-processes it, extracts formatted data, and uses an emotion engine to recognize emotions from facial expressions and voice.

[1383] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as data scientist, machine learning engineer, or AI researcher. It also includes specific support measures to alleviate any concerns the user may have.

[1384] 5. Server: Sends the generated career path to the device.

[1385] 6. Terminal: Shows the user a list of suggested career paths.

[1386] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[1387] 8. Device: Sends feedback to the server.

[1388] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[1389] 10. Terminal: Display detailed information to the user.

[1390] In this way, users can receive suggestions for suitable career paths based on their skills, interests, and feelings, along with specific information on how to get there.

[1391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1392] Step 1:

[1393] User input of information and emotional data:

[1394] The user uses the device to enter basic information (e.g., name, age), skill set (e.g., Python, Java), work experience (e.g., five years of experience working in an IT company), and areas of interest (e.g., AI, machine learning). In addition, the device's camera and microphone are used to record video and audio data, and emotional data is provided. Specifically, the user enters the required information according to each prompt, and then presses the send button after completing the input.

[1395] Input: User basic information, skill set, work experience, areas of interest, video data, audio data

[1396] Output: A package of user input data that is saved to the device.

[1397] Step 2:

[1398] Device transmission of information and emotional data:

[1399] The device sends the basic information and emotion data entered by the user to the server as a single data package. Specifically, it packages the data as an HTTP request and sends it to the server's specified API endpoint. Once the transmission is complete, the device displays a message to the user indicating that the transmission was successful.

[1400] Input: User input data package

[1401] Output: User information package sent to server, user receives success message

[1402] Step 3:

[1403] Receiving and preprocessing information by the server:

[1404] The server analyzes the user's basic information and emotion data received from the device, fills in missing values, and standardizes the data format. For example, if age is not entered, it fills in the default value and prompts the user to re-enter it. It also converts video and audio data into an analyzable format.

[1405] Input: User information package sent from the terminal

[1406] Output: Preprocessed user information and emotion data

[1407] Step 4:

[1408] Information extraction and emotion recognition by the server:

[1409] The server extracts useful information such as skill sets, work experience, and areas of interest from the preprocessed basic information. It also activates an emotion engine to recognize the user's emotions from video and audio data. For example, facial expression analysis can detect smiles and sadness, and voice analysis can evaluate the tone and speed of the voice.

[1410] Input: Preprocessed user information and emotion data

[1411] Output: Extracted useful information and recognized emotion data

[1412] Step 5:

[1413] Server-based application of generative AI models:

[1414] The server inputs the extracted information and recognized emotion data into a generative AI model (e.g., GPT-3) to generate an optimal career path. This model analyzes the user's skill set and emotional state and suggests specific career path candidates. For example, if the user is feeling anxious, it will suggest a career path to alleviate that anxiety.

[1415] Input: extracted information, recognized emotion data

[1416] Output: Generated career path candidates

[1417] Step 6:

[1418] Career path suggestions by the server:

[1419] The server then sends the generated list of career path candidates to the device, which includes specific job titles, required skill sets, and supporting information related to the career path.

[1420] Input: Generated career path candidates

[1421] Output: A list of possible career paths sent to the terminal

[1422] Step 7:

[1423] View career paths by device:

[1424] The terminal displays the list of candidate career paths received from the server to the user, who can then view the details of each career path and select the one that interests them.

[1425] Input: List of potential career paths

[1426] Output: Career path details displayed on the terminal screen

[1427] Step 8:

[1428] User feedback:

[1429] Users can enter feedback on the displayed career paths, such as, "I'm interested in becoming a data scientist, but I'd like to know more about the required skill set."

[1430] Input: Feedback on career paths

[1431] Output: Feedback data stored on the device

[1432] Step 9:

[1433] Send feedback via device:

[1434] The device packages the user's feedback and sends it back to the server, specifically by sending the feedback data as an HTTP request.

[1435] Input: User feedback data

[1436] Output: Feedback data sent to the server

[1437] Step 10:

[1438] Server feedback processing:

[1439] The server receives the feedback and uses a generative AI model to generate detailed information based on the feedback, such as "The skill sets required for a data scientist are Python, statistics, machine learning techniques, and data visualization."

[1440] Input: User feedback data

[1441] Output: Detailed information generated

[1442] Step 11:

[1443] Displaying detailed information via terminal:

[1444] The device displays the details sent from the server to the user, who can then decide what to do next, such as enrolling in a related online course or training program.

[1445] Input: Generated details

[1446] Output: Detailed information displayed on the terminal screen

[1447] Through this series of steps, users receive recommendations for the best career path based on their skills, interests, and feelings, along with specific information on how to get there.

[1448] (Application example 2)

[1449] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1450] In today's world, many users seek optimal advice regarding their careers. Furthermore, career path suggestions that take into account the user's emotional state are crucial for improving user satisfaction. However, conventional systems do not adequately suggest individual career paths based on the user's emotions. As a result, the suggested career paths may not fully meet the user's needs.

[1451] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1452] In this invention, the server includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information and the user's emotional data, means for transmitting and displaying the generated career path to a user terminal, means for acquiring the user's facial expression and voice data and extracting emotional data using an emotion recognition engine, and means for receiving feedback from the user and regenerating information. This makes it possible to reflect the user's emotional state and propose optimal career paths tailored to individual needs.

[1453] The "means for receiving and preprocessing information entered by the user" refers to the system's ability to receive information provided by the user, such as skill set, work experience, and areas of interest, and to format it in a way that is easy to analyze.

[1454] The "means for extracting useful information from preprocessed information" is a function that selects important data that is useful for generating career paths from preprocessed information.

[1455] "Means for generating career paths using a generative AI model based on extracted information and user emotional data" refers to a function in which the generative AI model automatically creates optimal career paths by utilizing the user's input information and emotional data.

[1456] The "means for transmitting the generated career path to the user terminal and displaying it" has the function of transmitting information about the generated career path to the user terminal and displaying it.

[1457] "Means for acquiring a user's facial expression and voice data and extracting emotion data using an emotion recognition engine" refers to a function that analyzes a user's facial expression and voice to identify emotions and extract that data.

[1458] The "means for receiving feedback from users and regenerating information" has the function of receiving feedback provided by users and regenerating new career paths and recommendations based on that information.

[1459] The present invention provides an effective career assessment system that utilizes a generative AI model based on user input information and emotional data. The specific configuration for realizing this system is described below.

[1460] Overall system configuration:

[1461] This system consists of three main components: a user terminal, a server, and a user.

[1462] User role:

[1463] Users use a smartphone, smart glasses, or head-mounted display to input basic information, skills, work experience, and areas of interest required for career assessment, and then provide video and audio data to the device to obtain emotional data from the user's facial expressions and voice.

[1464] As a concrete example, the user inputs the following information:

[1465] Name: Taro

[1466] Age: 30

[1467] Skills: Programming (Python, Java), Data Analysis

[1468] Work experience: 5 years of experience working in an IT company

[1469] Areas of interest: AI, machine learning

[1470] Emotion: Video data (facial expressions) and audio data

[1471] Device role:

[1472] The device receives the information and emotion data entered by the user and transmits them to the server, displays the career path suggestions received from the server to the user, and transmits the user's feedback back to the server.

[1473] Server Role:

[1474] The server performs a series of processes using the following means.

[1475] User information preprocessing means: The server receives the user's basic information, skill set, work experience, areas of interest, and emotional data sent from the terminal and preprocesses it into a format that is easy to analyze.

[1476] Means for extracting useful information: From the preprocessed information, select data that is useful for generating career paths.

[1477] Career path generation method: Based on the extracted information and the user's emotional data, a generative AI model (e.g., OpenAI GPT-4) is used to generate the optimal career path. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates.

[1478] Emotion data extraction method: Recognize the user's facial expressions and voice data and extract emotion data using an emotion engine (e.g., Azure Cognitive Services).

[1479] Career path suggestion means: The generated career path candidates are sent to the terminal and displayed to the user.

[1480] Feedback processing means: Accepts user feedback and generates new information based on it using the AI ​​model again.

[1481] For example, if a user provides feedback such as "I'm interested in becoming a data scientist, but I'd like to know more about the required skill set," the server will take this information and generate detailed skill set suggestions.

[1482] Example prompt sentence:

[1483] User Information:

[1484] Name: Taro

[1485] Age: 30

[1486] Skills: Python, Java, Data Analysis

[1487] Work experience: 5 years of experience working in an IT company

[1488] Areas of interest: AI, machine learning

[1489] User sentiment data:

[1490] Facial expression data (video)

[1491] Audio data

[1492] This system makes it possible to suggest optimal career paths tailored to individual needs, reflecting the user's emotional state.

[1493] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1494] Step 1:

[1495] The user uses a smartphone, smart glasses, or head-mounted display to input basic information such as name, age, skill set, work experience, and areas of interest. In addition, the user provides video and audio data to capture facial expressions and voice data. The information and emotional data collected in this step are sent from the device to the server.

[1496] Input: Name, age, skill set, work experience, areas of interest, facial expression data (video), audio data

[1497] Output: Basic information and emotion data are sent to the server.

[1498] Step 2:

[1499] The server preprocesses the received user basic information and emotion data. This preprocessing includes filling in missing information and normalizing the data. Emotion data is extracted by analyzing video and audio data and using an emotion recognition engine (e.g., Azure Cognitive Services).

[1500] Input: Basic information, emotion data (video, audio)

[1501] Output: Preprocessed basic information, extracted emotion data

[1502] Step 3:

[1503] The server selects the data necessary for career path generation by extracting useful information from the preprocessed information. Specifically, the server extracts the user's skill set, work experience, and areas of interest. Based on the extracted information, the server also incorporates the user's emotional data.

[1504] Input: Preprocessed basic information, extracted emotion data

[1505] Output: Data required to generate a career path

[1506] Step 4:

[1507] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an optimal career path based on the extracted information and emotional data. This generation also takes into account the user's current emotional state. For example, if the user is feeling anxious, a career path will be generated that includes support to alleviate those feelings.

[1508] Input: Data required for career path generation, emotional data

[1509] Output: Generated career paths

[1510] Step 5:

[1511] The server sends the generated career path to the terminal, which displays it to the user. The user can check the displayed career path and provide feedback if necessary.

[1512] Input: Generated career path

[1513] Output: The career path displayed to the user

[1514] Step 6:

[1515] Users enter their feedback on career paths into the device, which then sends it to the server, which receives the feedback and uses the generative AI model to generate new career paths and detailed information based on the feedback.

[1516] Input: User feedback

[1517] Output: Newly generated career paths and detailed information

[1518] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1519] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1520] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1521] [Fourth embodiment]

[1522] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1523] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1524] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1525] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1526] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1527] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1528] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1529] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1530] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1531] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1532] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1533] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1534] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1535] This invention is a system that utilizes generative AI to provide effective career assessments based on information entered by users. This system receives the information entered by users, preprocesses it, generates a career path using a generative AI model, and displays it to the user.

[1536] System configuration:

[1537] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[1538] User role:

[1539] Users enter basic information, skills, work experience, and areas of interest required for a career assessment through a terminal. Specifically, users enter their name, age, programming languages ​​they can use, past work history, and areas of interest (e.g., AI or data science).

[1540] Device role:

[1541] The device sends the information entered by the user to the server, displays the career path suggestions received from the server to the user, and receives user feedback and sends it back to the server.

[1542] Server Role:

[1543] The server processes the following series of processes.

[1544] 1. Receiving and preprocessing information:

[1545] The server receives user information sent from the device, preprocesses the received information, and checks for missing or inconsistent data. For example, if age is not entered or invalid skills are included, the server makes appropriate corrections.

[1546] 2. Information Extraction:

[1547] From the preprocessed information, useful information such as the user's skill set, past work experience, areas of interest, etc. is extracted, and the extracted information is used as input data for the AI ​​model.

[1548] 3. Application of generative AI models:

[1549] The server uses a generative AI model based on the extracted information to generate career path candidates. This AI model analyzes the user's information using natural language processing technology and can suggest optimal career paths.

[1550] 4. Career path suggestions and feedback processing:

[1551] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[1552] Examples:

[1553] As a concrete example, consider the case where a user inputs the following information:

[1554] Name: Taro

[1555] Age: 30

[1556] Skills: Programming (Python, Java), Data Analysis

[1557] Experience: 5 years of experience working in an IT company

[1558] Interests: AI, machine learning

[1559] procedure:

[1560] 1. User: Enter the above information using the terminal.

[1561] 2. Terminal: Sends the entered information to the server.

[1562] 3. Server: Receives information, preprocesses it, and extracts formatted data.

[1563] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as suggesting candidates for data scientists, machine learning engineers, and AI researchers.

[1564] 5. Server: Sends the generated career path to the device.

[1565] 6. Terminal: Shows the user a list of suggested career paths.

[1566] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[1567] 8. Device: Sends feedback to the server.

[1568] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[1569] 10. Terminal: Display detailed information to the user.

[1570] In this way, users can receive suggestions for suitable career paths based on their skills and interests, along with specific information on how to get there.

[1571] The processing flow will be explained below.

[1572] Step 1:

[1573] The user enters basic information, skill set, work experience, and areas of interest through the terminal. For example, the user enters "name," "age," "programming skills (Python, Java)," "five years of experience working in an IT company," and "areas of interest (AI, machine learning)."

[1574] Step 2:

[1575] The device sends the user's input information to the server, and the data is formatted in JSON format or similar.

[1576] Step 3:

[1577] The server receives the user information sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if the age is not entered, it will fill in the field with a default value or prompt the user to enter it again.

[1578] Step 4:

[1579] The server extracts useful information from the preprocessed data, such as skill sets, work experience, and areas of interest, which is then fed into a generative AI model in the next step.

[1580] Step 5:

[1581] Based on the extracted information, the server uses a generative AI model to generate career paths. This AI model analyzes the user's input data and generates optimal career path candidates. For example, it may suggest careers such as data scientist, machine learning engineer, or AI researcher.

[1582] Step 6:

[1583] The server sends the generated career path candidates to the terminal, formatting the data so that it is easy for the user to understand.

[1584] Step 7:

[1585] The terminal displays the career path candidates received from the server to the user, who can then review them and provide feedback if necessary.

[1586] Step 8:

[1587] The user types their feedback into the terminal, for example, "I'm interested in becoming a data scientist, but I'd like to know more about specific skill sets and learning resources."

[1588] Step 9:

[1589] The terminal transmits the user's feedback to the server, and the transmitted data is formatted in a manner that accurately conveys the feedback content.

[1590] Step 10:

[1591] The server receives the feedback and uses the generative AI model again to generate detailed information based on the feedback, such as the skills required to become a data scientist (database management, understanding machine learning algorithms) and recommended learning resources (online courses, reference books).

[1592] Step 11:

[1593] The server sends the generated detailed information to the terminal.

[1594] Step 12:

[1595] The device displays the detailed information received from the server to the user, who can review it and obtain more specific information about the career path.

[1596] Example 1

[1597] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1598] In modern society, individuals have a wide variety of career options, but the sheer number of options makes it difficult to determine the appropriate career path. It is particularly difficult for individuals with specific skill sets and work experience to find the optimal career path. Another issue is the lack of a system that efficiently organizes this information and suggests career paths suited to individuals.

[1599] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1600] In this invention, the server includes means for receiving and preprocessing information input by a user, means for extracting useful information from the preprocessed information, and means for generating a career path using a generative AI model based on the extracted information. This makes it possible to generate an optimal career path based on input information such as the user's basic information, skill set, work history, and areas of interest, and to provide detailed feedback as needed.

[1601] "User" refers to an individual who uses the system to input the information necessary for a career diagnosis and receive career path suggestions.

[1602] "Terminal" refers to the device through which a user inputs information and communicates with a server. This includes PCs, smartphones, tablets, etc.

[1603] "Server" refers to the hardware and software that receives and processes information sent by users and generates and proposes career paths using a generative AI model.

[1604] "Preprocessing" refers to a series of processes by which the server detects and corrects deficiencies or inconsistencies in the data received from the user.

[1605] "Generative AI models" refer to artificial intelligence technologies used to generate career paths based on extracted information, including natural language processing technologies.

[1606] A "prompt" is a document containing specific formatting and instructions to be input to a generative AI model.

[1607] "Basic information" refers to personally identifiable information such as the user's name and age.

[1608] A "competence set" refers to the skills and knowledge a user possesses, including programming languages ​​and specialized knowledge.

[1609] "Work history" refers to detailed information about a user's past work experience, including, for example, where they worked and what they did.

[1610] "Areas of Interest" refers to areas or topics that a user is personally interested in, such as AI and data science.

[1611] "Feedback" refers to any additional requests or comments a user provides regarding a career path suggestion.

[1612] This invention is a system that utilizes generative AI based on information entered by the user to provide efficient career diagnosis. This system consists of three entities: a server, a terminal, and a user. The detailed roles of each entity and the overall system flow are explained below.

[1613] Server Roles

[1614] The server receives user information and performs preprocessing, information extraction, and applies generative AI models. The server is equipped with a high-performance processor and sufficient memory to quickly process data and execute AI models. Cloud servers or dedicated data center servers are commonly used.

[1615] 1. Receiving and preprocessing information:

[1616] The server receives user information sent from the device. The received information is passed to the server in JSON or XML format. The server analyzes this data and checks for inconsistencies or missing data. For example, if the age or skill set is abnormal, it is corrected.

[1617] 2. Information Extraction:

[1618] The server extracts the user's basic information, skill set, work history, areas of interest, etc. from the pre-processed information. Natural language processing technology is used for this extraction process, resulting in highly accurate data extraction.

[1619] 3. Application of generative AI models:

[1620] The server uses the extracted information as input to generate a career path using a generative AI model (e.g., GPT-3 or BERT). The generated career path candidates are then presented as multiple options.

[1621] 4. Career path suggestions and feedback processing:

[1622] The generated career path is sent to the device and displayed to the user. If the user provides feedback, the feedback is received again and further detailed information is provided using the generative AI model. This allows the user to find the best career path based on their interests and skills.

[1623] Device Role

[1624] The terminal acts as an intermediary through which the user inputs information and communicates with the server. The terminal can function as a web browser, a smartphone application, or a dedicated device.

[1625] 1. Transmission of Information:

[1626] The terminal sends the user's basic information, skill set, work history, and areas of interest to the server. The information is encrypted using SSL / TLS and transmitted securely.

[1627] 2. View Career Paths:

[1628] The terminal displays the career path suggestions received from the server to the user, providing an intuitive user interface that allows the user to easily understand the suggested career paths.

[1629] 3. Submitting Feedback:

[1630] The feedback information entered by the user is sent to the server, which processes it again and generates new suggestions and detailed information.

[1631] User Roles

[1632] 1. Enter your information:

[1633] Users enter the information required for the career assessment through a terminal, such as their name, age, programming skills (Python, Java, etc.), work history, and areas of interest (AI, data science, etc.).

[1634] 2. Review and feedback on proposals:

[1635] Users can review the career path suggestions displayed on their device and enter feedback as needed. Feedback should be specific, such as "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[1636] Specific operation example

[1637] Assume a case where the user enters the following information:

[1638] Name: Taro

[1639] Age: 30

[1640] Skill set: Programming (Python, Java), Data analysis

[1641] Work history: 5 years of experience working in an IT company

[1642] Interests: AI, machine learning

[1643] When the user enters this information and clicks the send button, the device sends it to the server, which receives the information, performs preprocessing and information extraction, and inputs prompt sentences into the generative AI model.

[1644] Example prompt sentence:

[1645] "Taro is 30 years old, has programming skills in Python and Java, and has experience in data analysis. He currently has five years of work experience in an IT company and is interested in AI and machine learning. Please suggest a career path that would be suitable for Taro."

[1646] The generative AI model analyzes this prompt and suggests career paths such as data scientist, machine learning engineer, and AI researcher. The device displays this to the user, who then provides feedback such as, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set." The server then processes this feedback again, generates more detailed information, and sends it to the device. Ultimately, the user gains a deeper understanding of the skills needed for their career path and the next steps they should take.

[1647] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1648] Step 1:

[1649] Users enter the information required for the career assessment through their device, including their name, age, programming languages ​​they can use, their past work history, and areas of interest. After filling out the information in the input form and clicking the submit button, the data is converted into JSON or XML format.

[1650] Input: User basic information, skill set, work history, areas of interest

[1651] Output: Input data in JSON or XML format

[1652] Step 2:

[1653] The terminal sends the information entered by the user to the server. When the send button is clicked, the terminal encrypts the data with SSL / TLS and sends it to the server using the HTTP / HTTPS protocol.

[1654] Input: Input data in JSON or XML format

[1655] Output: Data transfer to the server

[1656] Step 3:

[1657] The server receives user information sent from the device. The received information is passed to the server in JSON or XML format. The server analyzes this data and checks for inconsistencies or missing data. For example, if the age or skill set is abnormal, it is corrected.

[1658] Input: Data received from the terminal

[1659] Output: Corrected data

[1660] Specifically, the server parses the received JSON data and checks the integrity of each field before saving the content to the database.

[1661] Step 4:

[1662] The server extracts useful information from the preprocessed information, such as the user's basic information, skill set, work history, and areas of interest, using natural language processing technology.

[1663] Input: Corrected data

[1664] Output: The input dataset for the generative AI model

[1665] Specifically, the server converts the extracted information into a specific format and builds a dataset to be input into the generative AI model.

[1666] Step 5:

[1667] The server uses a generative AI model (e.g., GPT-3 or BERT) to generate career path candidates based on the extracted information. The server calls the generative AI model (e.g., GPT-3 or BERT) via an API, provides the formatted dataset as input, and obtains the model's output.

[1668] Input: The input dataset for the generative AI model

[1669] Output: Possible career paths

[1670] Specifically, the server uses generative AI models such as GPT-3 and BERT to generate multiple career path candidates based on input data.

[1671] Step 6:

[1672] The server sends the generated career path candidates to the terminal, converts the generated career path information into JSON format, and returns it as a response to the terminal using the HTTP / HTTPS protocol.

[1673] Input: Possible career paths

[1674] Output: Data transfer to the device

[1675] As a specific operation, the server transmits the generated carrier path to the terminal.

[1676] Step 7:

[1677] The terminal displays the career path suggestions received from the server to the user in a visually easy-to-understand format (list format or card format) in the user interface.

[1678] Input: Career path suggestions from the server

[1679] Output: Displaying the career path to the user

[1680] Specifically, the device updates its user interface to display a list of suggested career paths.

[1681] Step 8:

[1682] Users review the suggested career paths and provide feedback if they require more specific information or other options, including details about specific career paths or new requirements.

[1683] Input: Career path displayed information

[1684] Output: Feedback information

[1685] As a specific operation, the user enters a comment in the feedback form and clicks the send button.

[1686] Step 9:

[1687] The device sends this feedback to the server. The feedback information is also encrypted using SSL / TLS and sent to the server.

[1688] Input: User feedback information

[1689] Output: Feedback forwarding to the server

[1690] As a specific operation, the terminal transmits the feedback data to the server again.

[1691] Step 10:

[1692] The server receives the feedback and generates new information using the generative AI model again. It analyzes the feedback and generates prompts to generate new career paths and detailed information.

[1693] Input: Feedback information

[1694] Output: New career path or more information

[1695] Specifically, the server generates a prompt sentence, then calls the generative AI model again based on that sentence to generate a new proposal.

[1696] Step 11:

[1697] The terminal again displays the received information to the user, allowing the user to check for new suggestions and detailed information.

[1698] Input: New career path or detailed information from the server

[1699] Output: Redisplay to user

[1700] Specifically, the device updates the user interface to display new suggestions and detailed information, allowing users to receive suggestions for suitable career paths based on their skills and interests, along with specific information on how to get there.

[1701] (Application example 1)

[1702] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1703] Conventional career assessment systems often only have the function of suggesting specific occupations or career paths based on information entered by the user. As a result, they do not suggest training programs or related qualifications based on the user's specific skills and interests in a particular field, resulting in the problem of providing insufficient support for career changes. This limitation is particularly pronounced in highly specialized fields such as the security field.

[1704] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1705] In this invention, the server includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information, and means for proposing optimal job titles, training programs, and related qualifications based on user information. This allows the server to specifically suggest training and qualifications required by the user in the security field, thereby providing more practical and specific support when making a career change.

[1706] "User-entered information" refers to basic information, skill set, work experience, areas of interest, etc. that a user provides to the system.

[1707] The "means for receiving and preprocessing" is a mechanism for receiving information input by a user, formatting the information, and correcting any data deficiencies or inconsistencies.

[1708] A "means for extracting useful information" is a mechanism for picking out and extracting highly useful data (e.g., skills, experience, areas of interest, etc.) from preprocessed information.

[1709] A "generative AI model" is a model that uses machine learning and natural language processing technologies to predict and generate career paths based on input data.

[1710] "Means for generating career paths" refers to the process of using a generative AI model based on extracted data to suggest the most suitable occupational and job position path for the user.

[1711] "User terminal" refers to a device that is directly operated by a user (e.g., smartphone, tablet, PC).

[1712] The "display means" is a function for displaying the generated career path and training program on the screen of the user terminal.

[1713] The "means of receiving feedback and generating new information" is a mechanism that receives ratings and comments from users and generates further optimized career paths and detailed information based on that feedback.

[1714] "Means to suggest job titles, training programs, and related qualifications" refers to a function that uses a generative AI model to automatically recommend specific occupations, job titles, required training programs, and qualifications to be obtained based on user input.

[1715] This invention is a system that proposes optimal career paths and training programs based on information entered by users. This system is mainly composed of three elements: a server, a terminal, and a user, and these elements work together.

[1716] System configuration and operation

[1717] Hardware and Software Use

[1718] Server: The server is used for data processing, running AI models, and data preprocessing. Software used for this purpose includes database management systems (DBMS), data preprocessing tools, and libraries for generative AI models (e.g., TensorFlow and PyTorch).

[1719] Terminal: The terminal is used to provide the user interface (UI), sending information entered by the user to the server and displaying the results received from the server. The software used for this purpose can be a mobile application or a web browser.

[1720] Users: Users use devices such as smartphones, tablets, and computers to enter information and provide feedback.

[1721] Data processing and calculation

[1722] Input and preprocessing information:

[1723] Users enter basic information (name, age), skill set, work experience, and areas of interest through the terminal.

[1724] The terminal sends this information to the server.

[1725] The server pre-processes the received information, checking for data inconsistencies or omissions and making corrections.

[1726] Extracting information and applying AI models:

[1727] From the preprocessed data, useful information such as the user's skill set, work experience, and areas of interest is extracted.

[1728] Based on the extracted data, generative AI models (e.g., natural language processing models) are used to generate career paths, training programs, and related qualifications.

[1729] Result display and feedback:

[1730] The generated career path and training program are sent to the terminal and displayed to the user.

[1731] The user provides feedback.

[1732] The server receives the feedback and uses the AI ​​model again to generate more optimal information and send it to the device.

[1733] Specific examples

[1734] For example, suppose a user enters the following information:

[1735] Name: Yamada Ichiro

[1736] Age: 28

[1737] Skills: Network management, programming (Python)

[1738] Experience: 3 years of system administration experience

[1739] Interests: Cybersecurity, Information and Communication Technology

[1740] In this case, the following processing is performed.

[1741] 1. The user enters information into the smartphone app.

[1742] 2. The app sends the input information to the server.

[1743] 3. The server preprocesses the information, correcting any gaps or inconsistencies and formatting it.

[1744] 4. Extract the user's skill set, experience, and interests from the formatted data.

[1745] 5. Based on the data extracted by the generative AI model, it generates career paths for security analysts and penetration testers, and also suggests related certifications such as CISSP and CEH, how to obtain them, and the necessary training programs.

[1746] 6. The app displays these results to the user.

[1747] 7. Users provide feedback such as "What steps are required to become a security analyst?" and "I would like to know the specific training program to obtain the qualification."

[1748] 8. The server receives the feedback, generates more detailed information, and sends it back to the device.

[1749] Prompt Sentence Examples

[1750] "Based on the information the user enters, suggest the best security career paths and related training programs. For example, if I have experience in network administration, what security roles and certifications would be suitable?"

[1751] In this way, users are given specific support to find the best career path or training program based on their skill set and interests.

[1752] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1753] Step 1:

[1754] The user inputs information. The user uses the terminal to input basic information such as name, age, skill set, work experience, and areas of interest. This is the input information.

[1755] Step 2:

[1756] The device sends the input information to the server. The device then sends an HTTP request to send the input information to the server's API endpoint. At this time, the input information is sent in a data format such as JSON.

[1757] Step 3:

[1758] The server receives the information and performs preprocessing. The server analyzes the received JSON data and checks for missing or inconsistent data. For example, if age is not entered, it sets a default value. The preprocessed data is saved as formatted data.

[1759] Step 4:

[1760] The server extracts useful information. It performs data analysis to extract useful information such as skill sets, work experience, and areas of interest from the preprocessed data. The input is the preprocessed data, and the output is the extracted useful data.

[1761] Step 5:

[1762] The server applies a generative AI model (e.g., a natural language processing model) based on the extracted data to generate career paths, training programs, and related qualifications. The input is the extracted useful data, and the output is the generated career path information.

[1763] Step 6:

[1764] The server sends the generated career path to the terminal. The server then sends the generated career path and related training program information to the terminal as an API response. The output is the sent career path information.

[1765] Step 7:

[1766] The terminal displays the career path to the user. The terminal displays the received career path information in a user-friendly graphical format, including detailed data on related qualifications and training programs.

[1767] Step 8:

[1768] The user provides feedback. The user checks the displayed career path information and inputs any further information or additional requests as feedback. This becomes new input information.

[1769] Step 9:

[1770] The terminal sends feedback information to the server. The terminal resends the feedback information to the server and sends an HTTP request to reflect it in the next generation process. The input is the feedback information, and the output is the data sent to the server.

[1771] Step 10:

[1772] The server receives the feedback information and again uses the AI ​​model to generate optimal information. The server then analyzes the data again and generates new career paths, detailed training programs, and qualification information. The input is the feedback information, and the output is updated career path information.

[1773] Through these steps, users can find the best career path based on their skills and interests, as well as information on the training programs and certifications they need.

[1774] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1775] This invention combines a system that utilizes generative AI to provide effective career diagnosis based on information entered by the user with an emotion engine that recognizes the user's emotions. This system receives and preprocesses the information entered by the user, generates a career path using a generative AI model, and performs a series of processes from displaying the result to the user, as well as emotion recognition using the emotion engine.

[1776] System configuration:

[1777] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[1778] User role:

[1779] Users input the basic information required for career assessment, including their skill set, work experience, and areas of interest, via their device. Furthermore, an input environment is created that can recognize emotions from the user's facial expressions and voice. Specifically, users can input their name, age, programming skills (Python, Java), five years of experience working at an IT company, and areas of interest (AI, machine learning), and can also provide video and audio data.

[1780] Device role:

[1781] The device sends the information and emotion data entered by the user to the server, displays the career path suggestions received from the server to the user, and receives user feedback and sends it back to the server.

[1782] Server Role:

[1783] The server processes the following series of processes.

[1784] 1. Receiving and preprocessing information:

[1785] The server receives the user's basic information, skillset, work experience, interests, and emotional data sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if age is not entered, it will either fill in the field with a default value or prompt the user to enter it again.

[1786] 2. Information Extraction:

[1787] From the preprocessed basic information, useful information such as the user's skill set, work experience, and areas of interest is extracted. Furthermore, an emotion engine is used to recognize emotions from the user's emotion data.

[1788] 3. Application of generative AI models:

[1789] The server generates a career path using a generative AI model based on the extracted information and recognized emotions. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates. For example, if the user is feeling anxious, it will suggest a career path that includes support to alleviate that anxiety.

[1790] 4. Career path suggestions and feedback processing:

[1791] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[1792] Examples:

[1793] As a concrete example, consider the case where a user inputs the following information:

[1794] Name: Taro

[1795] Age: 30

[1796] Skills: Programming (Python, Java), Data Analysis

[1797] Experience: 5 years of experience working in an IT company

[1798] Interests: AI, machine learning

[1799] Emotion: Video data (facial expressions) and audio data

[1800] procedure:

[1801] 1. User: Enter the above information and emotion data using the terminal.

[1802] 2. Terminal: Sends the input information and emotion data to the server.

[1803] 3. Server: Receives information, pre-processes it, extracts formatted data, and uses an emotion engine to recognize emotions from facial expressions and voice.

[1804] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as data scientist, machine learning engineer, or AI researcher. It also includes specific support measures to alleviate any concerns the user may have.

[1805] 5. Server: Sends the generated career path to the device.

[1806] 6. Terminal: Shows the user a list of suggested career paths.

[1807] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[1808] 8. Device: Sends feedback to the server.

[1809] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[1810] 10. Terminal: Display detailed information to the user.

[1811] In this way, users can receive suggestions for suitable career paths based on their skills, interests, and feelings, along with specific information on how to get there.

[1812] The processing flow will be explained below.

[1813] Step 1:

[1814] The user uses a device to input basic information (name, age, skill set, work experience, areas of interest) and emotional data (facial expressions and voice). For example, the user might input "Name: Taro," "Age: 30," "Skills: Programming (Python, Java), data analysis," "Experience: 5 years of work experience at an IT company," and "Interests: AI, machine learning," and then capture their facial expressions with a camera and record emotional comments via voice.

[1815] Step 2:

[1816] The device sends the basic information and emotion data entered to the server, which formats the data in JSON or other formats.

[1817] Step 3:

[1818] The server receives the data sent from the device. It analyzes the received data and performs preprocessing. For example, if the age is not entered, it will be filled in with a default value or a message will be generated to prompt the user to enter it again.

[1819] Step 4:

[1820] The server extracts useful information from the pre-processed data, such as skill sets, work experience, and areas of interest, and also uses an emotion engine to recognize the user's emotions from the captured facial and voice data.

[1821] Step 5:

[1822] The server uses a generative AI model to generate career paths based on the extracted data and the recognized emotions. For example, the server can suggest career paths such as "Data Scientist," "Machine Learning Engineer," and "AI Researcher" based on the user's data, and can also suggest support measures for each career path based on the user's emotions (e.g., anxiety).

[1823] Step 6:

[1824] The server then sends the generated career path candidates and accompanying emotion-based support information to the terminal, formatting the data in a user-friendly format.

[1825] Step 7:

[1826] The terminal displays the career path candidates and emotion-based support information sent from the server to the user, who then checks the information and inputs feedback based on their emotions and preferences.

[1827] Step 8:

[1828] The user enters the necessary details about the career path provided as feedback into the device, for example, "I'm interested in becoming a data scientist, but I'd like to know the specific skill set and recommended learning resources."

[1829] Step 9:

[1830] The device sends the user's feedback to the server, and the data is formatted to accurately convey the feedback content.

[1831] Step 10:

[1832] The server receives the feedback, analyzes the content, and reapplies the generative AI model to generate detailed information based on the feedback (e.g., required skills, learning resources). It also reassess the user's emotions and provides appropriate support measures.

[1833] Step 11:

[1834] The server transmits the generated detailed information and additional support information based on the emotion to the terminal.

[1835] Step 12:

[1836] The terminal displays the detailed information and additional support information received from the server to the user, who can then check it and obtain useful information for creating a specific action plan or study plan.

[1837] This process provides users with specific recommendations for the best career path and the necessary action plan based on their skill set, work experience, areas of interest, and emotions.

[1838] Example 2

[1839] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1840] Conventional career diagnosis systems suggest career paths by collecting information such as a user's basic information, skill set, and work experience, but because they do not take the user's emotional state into consideration, they have the problem of being unable to make appropriate career suggestions based on the user's psychological state.In addition, the process of regenerating information based on feedback is cumbersome, which causes a poor user experience.

[1841] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and preprocessing information and emotional data input by the user, means for extracting useful information from the preprocessed information and emotional data and recognizing emotions, means for generating a career path using a generative AI model based on the extracted information and recognized emotional data, means for transmitting and displaying the generated career path to the user terminal, and means for receiving feedback from the user and regenerating information. This makes it possible to propose career paths according to the emotional state, thereby improving the user experience.

[1842] "Information and emotional data entered by the user" refers to the user's basic information, skill set, work experience, areas of interest, and emotional data such as facial expressions and voice.

[1843] "Preprocessing" refers to the process of preparing the received information into a data format that is easy to analyze by performing processes such as filling in missing values, standardizing the format, and normalizing it.

[1844] "Emotion Engine" refers to the software and algorithms used to recognize a user's emotional state from video and audio data.

[1845] A "generative AI model" refers to an artificial intelligence model that includes an algorithm for generating career paths based on user input and recognized emotional data.

[1846] A "career path" refers to the optimal career direction, specific job title, required skill set, etc. generated based on the user's information and emotional state.

[1847] "Feedback" refers to opinions, additional questions, supplementary information, etc. that users enter regarding the proposed career path.

[1848] This invention combines a system that utilizes generative AI to provide effective career diagnosis based on information entered by the user with an emotion engine that recognizes the user's emotions. This system receives and preprocesses the information entered by the user, generates a career path using a generative AI model, and performs a series of processes from displaying the result to the user, as well as emotion recognition using the emotion engine.

[1849] (System configuration)

[1850] This system consists of three entities: a server, a terminal, and a user. Each entity has the following roles:

[1851] User role:

[1852] Users input the basic information required for career assessment, including their skill set, work experience, and areas of interest, via their device. Furthermore, an input environment is created that can recognize emotions from the user's facial expressions and voice. Specifically, users can input their name, age, programming skills (Python, Java), five years of experience working at an IT company, and areas of interest (AI, machine learning), and can also provide video and audio data.

[1853] Device role:

[1854] The device sends the information and emotional data entered by the user to the server. It also displays career path suggestions received from the server to the user. It also accepts user feedback and sends it back to the server. Specifically, the device sends the user's input data to the server as an HTTP request, receives a response from the server, and displays it on the screen.

[1855] Server Role:

[1856] The server processes the following series of processes.

[1857] 1. Receiving and preprocessing information:

[1858] The server receives the user's basic information, skillset, work experience, interests, and emotional data sent from the device. It analyzes the received data and performs necessary preprocessing. For example, if age is not entered, it will either fill in the field with a default value or prompt the user to enter it again.

[1859] 2. Information extraction and emotion recognition:

[1860] The server extracts useful information from the preprocessed basic information, such as the user's skill set, work experience, and areas of interest. It then uses an emotion engine to recognize emotions from the user's emotional data. For example, it analyzes facial expressions of smiles and sadness from video data and evaluates the tone and speed of voice from audio data.

[1861] 3. Application of generative AI models:

[1862] The server generates a career path using a generative AI model (such as GPT-3) based on the extracted information and recognized emotions. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates. For example, if the user is feeling anxious, it will suggest a career path that includes support to alleviate that anxiety.

[1863] 4. Career path suggestions and feedback processing:

[1864] The server sends the generated career path candidates to the device, which displays them to the user. When the user provides feedback, the device sends it back to the server, which uses a generative AI model to provide new information based on the feedback.

[1865] (Example)

[1866] As a concrete example, consider the case where a user inputs the following information:

[1867] Name: Taro

[1868] Age: 30

[1869] Skills: Programming (Python, Java), Data Analysis

[1870] Experience: 5 years of experience working in an IT company

[1871] Interests: AI, machine learning

[1872] Emotion: Video data (facial expressions) and audio data

[1873] procedure:

[1874] 1. User: Enter the above information and emotion data using the terminal.

[1875] Example prompt: "Please enter your name" → "Taro"

[1876] Example prompt: "Please enter your age" → "30"

[1877] Example prompt: "Please tell us your programming skills" → "Python, Java"

[1878] Example prompt: "Please enter your work experience" → "5 years of work experience in an IT company"

[1879] Example prompt: "Please tell us your area of ​​interest" → "AI, machine learning"

[1880] Example prompt: "Please enter emotion data" → "Video data, audio data"

[1881] 2. Terminal: Sends the input information and emotion data to the server.

[1882] 3. Server: Receives information, pre-processes it, extracts formatted data, and uses an emotion engine to recognize emotions from facial expressions and voice.

[1883] 4. Server: This data is fed into a generative AI model to generate appropriate career paths, such as data scientist, machine learning engineer, or AI researcher. It also includes specific support measures to alleviate any concerns the user may have.

[1884] 5. Server: Sends the generated career path to the device.

[1885] 6. Terminal: Shows the user a list of suggested career paths.

[1886] 7. User: Enters feedback saying, "I'm interested in becoming a data scientist, but I'd like to learn more about the required skill set."

[1887] 8. Device: Sends feedback to the server.

[1888] 9. Server: Receives the feedback, uses the AI ​​model again to generate detailed information, and sends it to the device.

[1889] 10. Terminal: Display detailed information to the user.

[1890] In this way, users can receive suggestions for suitable career paths based on their skills, interests, and feelings, along with specific information on how to get there.

[1891] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1892] Step 1:

[1893] User input of information and emotional data:

[1894] The user uses the device to enter basic information (e.g., name, age), skill set (e.g., Python, Java), work experience (e.g., five years of experience working in an IT company), and areas of interest (e.g., AI, machine learning). In addition, the device's camera and microphone are used to record video and audio data, and emotional data is provided. Specifically, the user enters the required information according to each prompt, and then presses the send button after completing the input.

[1895] Input: User basic information, skill set, work experience, areas of interest, video data, audio data

[1896] Output: A package of user input data that is saved to the device.

[1897] Step 2:

[1898] Device transmission of information and emotional data:

[1899] The device sends the basic information and emotion data entered by the user to the server as a single data package. Specifically, it packages the data as an HTTP request and sends it to the server's specified API endpoint. Once the transmission is complete, the device displays a message to the user indicating that the transmission was successful.

[1900] Input: User input data package

[1901] Output: User information package sent to server, user receives success message

[1902] Step 3:

[1903] Receiving and preprocessing information by the server:

[1904] The server analyzes the user's basic information and emotion data received from the device, fills in missing values, and standardizes the data format. For example, if age is not entered, it fills in the default value and prompts the user to re-enter it. It also converts video and audio data into an analyzable format.

[1905] Input: User information package sent from the terminal

[1906] Output: Preprocessed user information and emotion data

[1907] Step 4:

[1908] Information extraction and emotion recognition by the server:

[1909] The server extracts useful information such as skill sets, work experience, and areas of interest from the preprocessed basic information. It also activates an emotion engine to recognize the user's emotions from video and audio data. For example, facial expression analysis can detect smiles and sadness, and voice analysis can evaluate the tone and speed of the voice.

[1910] Input: Preprocessed user information and emotion data

[1911] Output: Extracted useful information and recognized emotion data

[1912] Step 5:

[1913] Server-based application of generative AI models:

[1914] The server inputs the extracted information and recognized emotion data into a generative AI model (e.g., GPT-3) to generate an optimal career path. This model analyzes the user's skill set and emotional state and suggests specific career path candidates. For example, if the user is feeling anxious, it will suggest a career path to alleviate that anxiety.

[1915] Input: extracted information, recognized emotion data

[1916] Output: Generated career path candidates

[1917] Step 6:

[1918] Career path suggestions by the server:

[1919] The server then sends the generated list of career path candidates to the device, which includes specific job titles, required skill sets, and supporting information related to the career path.

[1920] Input: Generated career path candidates

[1921] Output: A list of possible career paths sent to the terminal

[1922] Step 7:

[1923] View career paths by device:

[1924] The terminal displays the list of candidate career paths received from the server to the user, who can then view the details of each career path and select the one that interests them.

[1925] Input: List of potential career paths

[1926] Output: Career path details displayed on the terminal screen

[1927] Step 8:

[1928] User feedback:

[1929] Users can enter feedback on the displayed career paths, such as, "I'm interested in becoming a data scientist, but I'd like to know more about the required skill set."

[1930] Input: Feedback on career paths

[1931] Output: Feedback data stored on the device

[1932] Step 9:

[1933] Send feedback via device:

[1934] The device packages the user's feedback and sends it back to the server, specifically by sending the feedback data as an HTTP request.

[1935] Input: User feedback data

[1936] Output: Feedback data sent to the server

[1937] Step 10:

[1938] Server feedback processing:

[1939] The server receives the feedback and uses a generative AI model to generate detailed information based on the feedback, such as "The skill sets required for a data scientist are Python, statistics, machine learning techniques, and data visualization."

[1940] Input: User feedback data

[1941] Output: Detailed information generated

[1942] Step 11:

[1943] Displaying detailed information via terminal:

[1944] The device displays the details sent from the server to the user, who can then decide what to do next, such as enrolling in a related online course or training program.

[1945] Input: Generated details

[1946] Output: Detailed information displayed on the terminal screen

[1947] Through this series of steps, users receive recommendations for the best career path based on their skills, interests, and feelings, along with specific information on how to get there.

[1948] (Application example 2)

[1949] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1950] In today's world, many users seek optimal advice regarding their careers. Furthermore, career path suggestions that take into account the user's emotional state are crucial for improving user satisfaction. However, conventional systems do not adequately suggest individual career paths based on the user's emotions. As a result, the suggested career paths may not fully meet the user's needs.

[1951] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1952] In this invention, the server includes means for receiving and preprocessing information entered by a user, means for extracting useful information from the preprocessed information, means for generating a career path using a generative AI model based on the extracted information and the user's emotional data, means for transmitting and displaying the generated career path to a user terminal, means for acquiring the user's facial expression and voice data and extracting emotional data using an emotion recognition engine, and means for receiving feedback from the user and regenerating information. This makes it possible to reflect the user's emotional state and propose optimal career paths tailored to individual needs.

[1953] The "means for receiving and preprocessing information entered by the user" refers to the system's ability to receive information provided by the user, such as skill set, work experience, and areas of interest, and to format it in a way that is easy to analyze.

[1954] The "means for extracting useful information from preprocessed information" is a function that selects important data that is useful for generating career paths from preprocessed information.

[1955] "Means for generating career paths using a generative AI model based on extracted information and user emotional data" refers to a function in which the generative AI model automatically creates optimal career paths by utilizing the user's input information and emotional data.

[1956] The "means for transmitting the generated career path to the user terminal and displaying it" has the function of transmitting information about the generated career path to the user terminal and displaying it.

[1957] "Means for acquiring a user's facial expression and voice data and extracting emotion data using an emotion recognition engine" refers to a function that analyzes a user's facial expression and voice to identify emotions and extract that data.

[1958] The "means for receiving feedback from users and regenerating information" has the function of receiving feedback provided by users and regenerating new career paths and recommendations based on that information.

[1959] The present invention provides an effective career assessment system that utilizes a generative AI model based on user input information and emotional data. The specific configuration for realizing this system is described below.

[1960] Overall system configuration:

[1961] This system consists of three main components: a user terminal, a server, and a user.

[1962] User role:

[1963] Users use a smartphone, smart glasses, or head-mounted display to input basic information, skills, work experience, and areas of interest required for career assessment, and then provide video and audio data to the device to obtain emotional data from the user's facial expressions and voice.

[1964] As a concrete example, the user inputs the following information:

[1965] Name: Taro

[1966] Age: 30

[1967] Skills: Programming (Python, Java), Data Analysis

[1968] Work experience: 5 years of experience working in an IT company

[1969] Areas of interest: AI, machine learning

[1970] Emotion: Video data (facial expressions) and audio data

[1971] Device role:

[1972] The device receives the information and emotion data entered by the user and transmits them to the server, displays the career path suggestions received from the server to the user, and transmits the user's feedback back to the server.

[1973] Server Role:

[1974] The server performs a series of processes using the following means.

[1975] User information preprocessing means: The server receives the user's basic information, skill set, work experience, areas of interest, and emotional data sent from the terminal and preprocesses it into a format that is easy to analyze.

[1976] Means for extracting useful information: From the preprocessed information, select data that is useful for generating career paths.

[1977] Career path generation method: Based on the extracted information and the user's emotional data, a generative AI model (e.g., OpenAI GPT-4) is used to generate the optimal career path. This AI model analyzes the user's input data and emotional state to generate optimal career path candidates.

[1978] Emotion data extraction method: Recognize the user's facial expressions and voice data and extract emotion data using an emotion engine (e.g., Azure Cognitive Services).

[1979] Career path suggestion means: The generated career path candidates are sent to the terminal and displayed to the user.

[1980] Feedback processing means: Accepts user feedback and generates new information based on it using the AI ​​model again.

[1981] For example, if a user provides feedback such as "I'm interested in becoming a data scientist, but I'd like to know more about the required skill set," the server will take this information and generate detailed skill set suggestions.

[1982] Example prompt sentence:

[1983] User Information:

[1984] Name: Taro

[1985] Age: 30

[1986] Skills: Python, Java, Data Analysis

[1987] Work experience: 5 years of experience working in an IT company

[1988] Areas of interest: AI, machine learning

[1989] User sentiment data:

[1990] Facial expression data (video)

[1991] Audio data

[1992] This system makes it possible to suggest optimal career paths tailored to individual needs, reflecting the user's emotional state.

[1993] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1994] Step 1:

[1995] The user uses a smartphone, smart glasses, or head-mounted display to input basic information such as name, age, skill set, work experience, and areas of interest. In addition, the user provides video and audio data to capture facial expressions and voice data. The information and emotional data collected in this step are sent from the device to the server.

[1996] Input: Name, age, skill set, work experience, areas of interest, facial expression data (video), audio data

[1997] Output: Basic information and emotion data are sent to the server.

[1998] Step 2:

[1999] The server preprocesses the received user basic information and emotion data. This preprocessing includes filling in missing information and normalizing the data. Emotion data is extracted by analyzing video and audio data and using an emotion recognition engine (e.g., Azure Cognitive Services).

[2000] Input: Basic information, emotion data (video, audio)

[2001] Output: Preprocessed basic information, extracted emotion data

[2002] Step 3:

[2003] The server selects the data necessary for career path generation by extracting useful information from the preprocessed information. Specifically, the server extracts the user's skill set, work experience, and areas of interest. Based on the extracted information, the server also incorporates the user's emotional data.

[2004] Input: Preprocessed basic information, extracted emotion data

[2005] Output: Data required to generate a career path

[2006] Step 4:

[2007] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an optimal career path based on the extracted information and emotional data. This generation also takes into account the user's current emotional state. For example, if the user is feeling anxious, a career path will be generated that includes support to alleviate those feelings.

[2008] Input: Data required for career path generation, emotional data

[2009] Output: Generated career paths

[2010] Step 5:

[2011] The server sends the generated career path to the terminal, which displays it to the user. The user can check the displayed career path and provide feedback if necessary.

[2012] Input: Generated career path

[2013] Output: The career path displayed to the user

[2014] Step 6:

[2015] Users enter their feedback on career paths into the device, which then sends it to the server, which receives the feedback and uses the generative AI model to generate new career paths and detailed information based on the feedback.

[2016] Input: User feedback

[2017] Output: Newly generated career paths and detailed information

[2018] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2019] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2020] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2021] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2022] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2023] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2024] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2025] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2026] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2027] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2028] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2029] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2030] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2031] 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.

[2032] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2033] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2034] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2035] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2036] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2037] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2038] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2039] The following is further disclosed regarding the above embodiment.

[2040] (Claim 1)

[2041] means for receiving and preprocessing user input information;

[2042] means for extracting useful information from the preprocessed information;

[2043] A means for generating a career path using a generative AI model based on the extracted information; and

[2044] means for transmitting the generated career path to a user terminal and displaying the same;

[2045] A means for receiving feedback from users and generating information again;

[2046] A system including:

[2047] (Claim 2)

[2048] The system of claim 1 , wherein the information input by the user includes at least a skill set, work experience, and areas of interest.

[2049] (Claim 3)

[2050] The system of claim 1 , wherein the generative AI model uses natural language processing.

[2051] "Example 1"

[2052] (Claim 1)

[2053] means for receiving and preprocessing user input information;

[2054] means for extracting useful information from the preprocessed information;

[2055] A means for generating a career path using a generative AI model based on the extracted information; and

[2056] means for transmitting the generated career path to a user terminal and displaying the same;

[2057] A means for receiving feedback from users and generating information again;

[2058] A system including:

[2059] (Claim 2)

[2060] The system of claim 1, wherein the information input by the user includes at least basic information, a skill set, a work history, and an area of ​​interest.

[2061] (Claim 3)

[2062] The system of claim 1, wherein the generative AI model uses natural language processing technology.

[2063] (Claim 4)

[2064] 4. The system of claim 3, wherein said pre-processing includes means for detecting and correcting deficiencies and inconsistencies in data received from a user.

[2065] (Claim 5)

[2066] The system of claim 4, wherein the user terminal includes means for transmitting basic information, skill set, work history, and areas of interest input by the user to the server, and means for receiving the generated career path from the server and displaying it to the user.

[2067] (Claim 6)

[2068] 6. The system of claim 5, wherein the feedback includes a means for a user to request more detailed information about a particular career path.

[2069] (Claim 7)

[2070] 7. The system of claim 6, wherein the generative AI model includes means for analyzing user feedback and generating prompt sentences for generating new information.

[2071] "Application Example 1"

[2072] (Claim 1)

[2073] means for receiving and preprocessing user input information;

[2074] means for extracting useful information from the preprocessed information;

[2075] A means for generating a career path using a generative AI model based on the extracted information; and

[2076] means for transmitting the generated career path to a user terminal and displaying the same;

[2077] A means for receiving feedback from users and generating information again;

[2078] A means to suggest the most suitable job titles, training programs, and related qualifications based on user information;

[2079] A system including:

[2080] (Claim 2)

[2081] The system of claim 1 , wherein the information input by the user includes at least a skill set, work experience, and areas of interest.

[2082] (Claim 3)

[2083] The system of claim 1 , wherein the generative AI model uses natural language processing.

[2084] "Example 2: Combining Emotion Engines"

[2085] (Claim 1)

[2086] means for receiving and preprocessing user input information and emotion data;

[2087] means for extracting useful information from the pre-processed information and emotion data and recognizing emotions;

[2088] A means for generating a career path using a generative AI model based on the extracted information and recognized emotion data; and

[2089] means for transmitting the generated career path to a user terminal and displaying the same;

[2090] A means for receiving feedback from users and generating information again;

[2091] A system including:

[2092] (Claim 2)

[2093] The system of claim 1 , wherein the information input by the user includes at least a skill set, work experience, areas of interest, and emotional data.

[2094] (Claim 3)

[2095] The system of claim 1 , wherein the generative AI model uses natural language processing.

[2096] "Application example 2 when combining emotion engines"

[2097] (Claim 1)

[2098] means for receiving and preprocessing user input information;

[2099] means for extracting useful information from the preprocessed information;

[2100] A means for generating a career path using a generative AI model based on the extracted information and user emotion data;

[2101] means for transmitting the generated career path to a user terminal and displaying the same;

[2102] A means for acquiring facial expression and voice data of a user and extracting emotion data using an emotion recognition engine;

[2103] A means for receiving feedback from users and generating information again;

[2104] ...

[2105] A system including:

[2106] (Claim 2)

[2107] The system of claim 1 , wherein the information input by the user includes at least a skill set, work experie...

Claims

1. means for receiving and preprocessing user input information; means for extracting useful information from the preprocessed information; A means for generating a career path using a generative AI model based on the extracted information; and means for transmitting the generated career path to a user terminal and displaying the same; A means for receiving feedback from users and generating information again; A system including:

2. The system of claim 1 , wherein the user-entered information includes at least a skill set, work experience, and areas of interest.

3. The system of claim 1 , wherein the generative AI model uses natural language processing.

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