System

Abstract

A system is provided.SOLUTION: A career development assistance system comprising: means for collecting profile information entered by a user; means for analyzing the collected profile information to identify a gap between the user's current skills and career goals; means for collecting market trends and industry news and extracting information relevant to the user's career goals; means for generating and providing personalized career advice to the user based on the analyzed data and extracted information; and means for periodically tracking the user's progress and providing feedback according to the progress.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] This paper aims to solve the problem of individual users finding the right career path. It also addresses the lack of a way to respond to rapidly changing market trends and industry news and provide users with relevant, up-to-date career advice. Furthermore, a system is needed to effectively track users' progress and provide appropriate feedback. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a career development support system including: means for collecting profile information entered by a user; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news and extracting information related to the user's career goals; means for generating and providing personalized career advice to the user based on the analyzed data and extracted information; and means for periodically tracking the user's progress and providing feedback according to the progress. Furthermore, by including means for suggesting educational resources suitable for the user based on the generated career advice and means for collecting feedback from the user and utilizing it to improve the system, more effective career support is provided.

[0006] "User" refers to an individual who uses this career development support system.

[0007] "Profile Information" refers to information entered by a user including past experience, current skill set, and career goals.

[0008] "Analysis" refers to the detailed analysis of collected profile information to identify gaps in a user's skills and career goals.

[0009] A "gap" refers to the difference between a user's current skills and the skills required for their desired career.

[0010] "Market Trends" refers to the latest changes and trends in a particular market.

[0011] "Industry News" refers to current information and events related to a particular industry.

[0012] "Advice" refers to personalized career guidance and recommendations provided to a User based on the analyzed data and extracted information.

[0013] "Educational Resources" means courses and training programs available to Users to improve their skills.

[0014] "Progress" refers to the activities and learning a user has undertaken towards their career goals.

[0015] "Feedback" refers to the evaluation and advice regarding progress provided by the system to the user.

[0016] "Real-time" refers to a situation in which information is collected, analyzed, and provided to users immediately.

[0017] "Career development support system" refers to an integrated system that includes the advice, educational resources, progress management, and feedback functions necessary to help users achieve their career goals. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The career development support system of the present invention is a system that provides personalized career advice by collecting and analyzing user profile information. Specific procedures for implementing this system are described below.

[0040] System Overview

[0041] This career development support system is a system in which a server, terminals, and users work together. The server performs the main data processing and analysis, and the terminals function as an interface with the users. Users access the system through their terminals and input the necessary information.

[0042] Collecting user profile information

[0043] The device displays a profile entry screen to the user, where the user enters information such as past experience, current skill set, and career goals. Once the entry is complete, the device sends this information to the server.

[0044] Analysis of user information

[0045] The server receives the user's profile information sent from the device. It then analyzes the collected information and identifies gaps between the user's current skills and their career goals. For example, if a user has a background as a "software engineer" and aims to become an "AI developer" in the future, the server will identify that they lack skills such as "machine learning" and "deep learning."

[0046] Market trends and industry news analysis

[0047] The server collects the latest market trends, industry news, and job information from the internet in real time. It analyzes this data and extracts information relevant to the user's career goals. For example, the server collects news such as "Demand for AI engineers is increasing" and prepares it to provide to the user.

[0048] Providing personalized career advice

[0049] The server generates personalized career advice for the user based on the analyzed data and extracted information. The generated advice is sent to the device, which then displays it to the user. For example, information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice" is displayed.

[0050] Continuous support and feedback

[0051] The server periodically tracks the user's progress. Each time the server receives progress information, such as "You've completed 50% of the Introduction to Machine Learning course," it generates real-time feedback and provides it to the user via their device, such as a message like "Great progress! Keep it up!"

[0052] The device also collects feedback from users and sends it to the server, which analyzes the feedback and uses it to improve the system.

[0053] Specific examples

[0054] Collecting and analyzing input information

[0055] The user enters, "5 years of software engineering experience, Python and Java skills, I want to become an AI developer." The server receives this information and determines that "machine learning" and "deep learning" are lacking.

[0056] Providing market trends

[0057] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[0058] Progress tracking and feedback

[0059] If a user starts the "Introduction to Machine Learning" course and achieves 50% progress, the server receives this data and generates a message saying "Great progress! Keep it up!" and displays it through the terminal.

[0060] In this way, this career development support system provides support tailored to the individual needs of each user, enabling companies to effectively support the career growth of their employees.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The device displays a profile entry screen to the user, which includes fields for entering the user's past experience, current skill set, and career goals.

[0064] Step 2:

[0065] Users input their background (e.g., 5 years of experience as a software engineer), skill set (e.g., Python, Java), and career goals (e.g., to become an AI developer).

[0066] Step 3:

[0067] The device collects all profile information entered by the user and sends it to the server, including the user's background, skill set, and career goals.

[0068] Step 4:

[0069] The server receives the user profile information sent from the terminal and stores it in a database.

[0070] Step 5:

[0071] The server analyzes the received profile information, including using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, it may determine that skills such as "machine learning" and "deep learning" are lacking.

[0072] Step 6:

[0073] The server collects the latest market trends, industry news, and job information from the internet. In this step, data is obtained using web scraping and APIs.

[0074] Step 7:

[0075] The server analyzes the collected market data and extracts information relevant to the user's career goals, such as information that there is a growing demand for AI engineers.

[0076] Step 8:

[0077] The server generates personalized career advice for the user based on the analyzed user information and market data, including recommended skill courses and related educational resources.

[0078] Step 9:

[0079] The server transmits the generated carrier advice to the terminal.

[0080] Step 10:

[0081] The device displays career advice sent from the server to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0082] Step 11:

[0083] The server collects user activity data to periodically check the user's progress, for example, the user has completed 50% of the introductory machine learning course.

[0084] Step 12:

[0085] The server generates real-time feedback based on collected activity data, such as messages like "Great progress, keep it up!"

[0086] Step 13:

[0087] The terminal displays the feedback generated from the server to the user.

[0088] Step 14:

[0089] The terminal collects feedback from the user and sends it back to the server, where it is used to help improve the system.

[0090] Step 15:

[0091] The server analyzes the collected feedback and identifies improvements to the system, thereby continually improving the user experience of the system.

[0092] With this processing flow, the career development support system provides support tailored to the individual needs of users, enabling companies to effectively support the career growth of their employees.

[0093] Example 1

[0094] 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."

[0095] Conventional career development support systems have difficulty providing appropriate career advice and educational resources to meet the individual needs of users, resulting in an inability to effectively support users' career growth. Furthermore, it is difficult to track users' progress and feedback in a timely manner and provide real-time support, which reduces the convenience and effectiveness of the system.

[0096] 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.

[0097] In this invention, the server includes: means for collecting profile information entered by the user; means for transmitting the collected profile information via the terminal to the server; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news from the Internet and extracting information related to the user's career goals; means for generating personalized career advice for the user based on the analyzed data and the extracted information; means for transmitting the generated advice to the terminal and displaying it to the user; means for periodically tracking the user's progress, generating feedback according to the progress, and providing it via the terminal; and means for collecting feedback from the user and using it to improve the system. This makes it possible to provide appropriate career advice and educational resources tailored to the individual needs of each user, and also makes it possible to track the user's progress and feedback in real time and provide prompt support.

[0098] A "User" is an individual who uses the system and enters profile information.

[0099] A "terminal" is a device used by a user, and is an interface through which profile information is entered and career advice is displayed via a web browser.

[0100] A "server" is a computer system that receives data sent from a terminal and performs analysis and data processing.

[0101] "Profile information" is information about a user's past experience, current skill set, and career goals.

[0102] "Collect" refers to the process of transmitting profile information entered by a user into a terminal to a server.

[0103] "Analysis" refers to the process of identifying gaps between the user's current skills and career goals based on the profile information received by the server.

[0104] "Market Trends" is information about the latest movements and trends in an industry or market.

[0105] "Industry news" refers to the latest happenings and reports in a particular field or industry.

[0106] "Personalized career advice" refers to career growth advice and suggestions that are customized to a user's individual needs.

[0107] "Progress" is information that indicates how far the user has progressed toward achieving their goal.

[0108] "Feedback" is information that provides responses and advice according to the user's progress.

[0109] "Educational Resources" means learning courses and materials offered to users to improve their skills and grow their careers.

[0110] "System improvement" is the process of improving the system's functions and services based on feedback collected from users.

[0111] The career development support system of the present invention is a system in which a server, a terminal, and a user work in cooperation with each other. A specific implementation method of this system will be described below.

[0112] System Overview

[0113] This career development support system is composed of a server that performs the main data processing and analysis, a terminal that functions as an interface with users, and users who provide information to the system.

[0114] Collecting user profile information

[0115] The terminal is a device connected to the Internet (e.g., a PC or a smartphone), and in this embodiment, a web browser (e.g., GOOGLE CHROME (registered trademark), Mozilla Firefox) is used. The terminal displays a screen for the user to enter profile information using an HTML form. The user enters information such as past experience, current skill set, and career goals. Once the information has been entered, the terminal collects this information using JavaScript (registered trademark), converts it to JSON format, and sends it to the server using HTTPS communication.

[0116] Analysis of user information

[0117] The server parses the received user profile information in JSON format. The server receives the data using a Python framework (e.g., Flask, Django) and converts the information into a data frame using the Pandas library. Based on this data frame, the server uses the Scikit-learn library to identify gaps between the user's current skills and their career goals. For example, if a user has a background as a "software engineer" and aspires to become an "AI developer," the server will identify a lack of skills such as "machine learning" and "deep learning."

[0118] Market trends and industry news analysis

[0119] The server uses web scraping technology to collect market trends and industry news from the Internet. Specifically, it uses Python's Beautiful Soup and Selenium libraries. The collected data is analyzed to identify information relevant to the user's career goals. For example, it extracts and analyzes news that "demand for AI engineers is increasing."

[0120] Providing personalized career advice

[0121] The server generates personalized career advice for the user based on the analyzed data and extracted information. For example, it might provide the user with information such as "Recommended courses: Introduction to Machine Learning, Deep Learning Practice." The generated advice is converted to JSON format and sent to the device. The device parses it using JavaScript and displays it to the user using HTML.

[0122] Continuous support and feedback

[0123] The server periodically receives data to track the user's progress. Each time a user reports their learning progress, the server analyzes the data and generates real-time feedback. For example, if the server receives progress information such as "You've completed 50% of the introductory machine learning course," it generates feedback such as "Great progress! Keep it up!" and provides it to the user via their device. The device also collects feedback from the user and sends it to the server. The server uses the collected feedback to improve the system.

[0124] Specific examples

[0125] The user enters information as follows:

[0126] Experience: "5 years of software engineering experience"

[0127] Skills: Python and Java

[0128] Goal: "I want to become an AI developer"

[0129] The server receives this information and determines that there is a lack of "machine learning" or "deep learning."

[0130] The server finds a news item saying, "Demand for AI engineers will increase by 20% by 2025," extracts a list of related courses (e.g., "Introduction to Machine Learning" and "Deep Learning in Practice"), and sends it to the device, which then displays it to the user.

[0131] If a user "completes 50% of the introductory machine learning course," the server receives this progress information, generates feedback to the user via the device, saying, "Great progress! Keep it up!"

[0132] In this way, the system provides career advice tailored to the user's individual needs and effectively supports the user's career growth through continuous support and feedback.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] The device displays a screen where the user can enter profile information. Input fields include background, skills, career goals, etc. After entering the information, the user presses the "Submit" button. Specifically, the information is collected using an HTML form, and then compiled using JavaScript and converted to JSON format.

[0136] Input: User's background, skills, and career goals

[0137] Data processing: Convert information into JSON format using JavaScript

[0138] Output: Profile information in JSON format

[0139] Step 2:

[0140] The device sends the collected profile information in JSON format to the server using HTTPS, which uses encrypted communication to ensure security.

[0141] Input: Profile information in JSON format converted on the device

[0142] Data transmission: Secure data transmission via HTTPS

[0143] Output: Profile information sent to the server

[0144] Step 3:

[0145] The server receives the JSON-formatted profile information using a Python framework (such as Flask or Django), converts it into a data frame using the Pandas library, and organizes the information by background, skills, and career goals.

[0146] Input: Profile information in JSON format

[0147] Data processing: Convert to a data frame using Pandas and organize the information

[0148] Output: A neat data frame

[0149] Step 4:

[0150] The server uses Scikit-learn to analyze the data and identify gaps between the user's current skills and their career goals. For example, it uses a linear regression model to analyze how well the user's skills match their career goals.

[0151] Input: A cleaned data frame

[0152] Data Computing: Skills and Career Goal Gap Analysis with Scikit-Learn

[0153] Output: User skill gap analysis results

[0154] Step 5:

[0155] The server uses web scraping with Beautiful Soup and Selenium to gather market trends and industry news from the internet, which is then analyzed to identify information relevant to the user's career goals.

[0156] Input: Market trends, industry news webpage URL

[0157] Data collection: Data collection through web scraping

[0158] Output: Analyzable market trend data, industry news data

[0159] Step 6:

[0160] The server generates personalized career advice based on the analyzed user profile information and market trend data, using natural language generation (NLG) technology to create advice in a user-friendly format.

[0161] Input: User skill gap analysis results, market trend data

[0162] Data Generation: Generating Career Advice with NLG

[0163] Output: Personalized career advice (e.g., "Introduction to Machine Learning Course")

[0164] Step 7:

[0165] The server converts the generated advice into JSON format and sends it to the device, which then parses the received JSON data using JavaScript and displays it to the user in HTML.

[0166] Input: Generated career advice

[0167] Data conversion and transmission: Converted to JSON format and transmitted via HTTPS

[0168] Output: Personalized career advice displayed to the user

[0169] Step 8:

[0170] The server periodically receives data to track the user's progress in the course of study selected by the user. The user enters progress data and sends it to the server.

[0171] Input: User progress data

[0172] Data calculation: Analysis of progress data

[0173] Output: Feedback based on progress

[0174] Step 9:

[0175] The server generates real-time feedback based on the progress data, using natural language generation technology, and sends it to the device, where it is displayed to the user.

[0176] Input: User progress data

[0177] Data generation: Feedback generated based on progress

[0178] Output: Feedback that is displayed to the user (e.g., "Great progress! Keep it up!")

[0179] Step 10:

[0180] The device collects feedback from users and sends it to the server, which analyzes it and uses it to improve the system.

[0181] Input: User feedback

[0182] Data collection and analysis: Feedback analysis

[0183] Output: System improvement proposals

[0184] In this step, the career development support system can provide individual career advice to users and provide continuous progress tracking and feedback, thereby effectively supporting users' career growth.

[0185] (Application example 1)

[0186] 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."

[0187] Traditional career development support systems provide personalized advice by collecting and analyzing user profile information, but most of this information is static and lacks specific support for how users should actually improve their skills. Furthermore, they lack the ability to track users' learning progress in real time and provide appropriate feedback, resulting in insufficient motivation and support for users to continue developing. As a result, it is difficult to efficiently bridge the gap between users' career goals and their actual skill development. Furthermore, they are unable to provide the latest learning content based on market trends and industry news in real time, making it difficult for users to utilize the latest information to advance their learning.

[0188] 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.

[0189] In this invention, the server includes means for collecting profile information entered by the user, means for analyzing the collected profile information to identify gaps between the user's current skills and career goals, means for collecting market trends and industry news and extracting information related to the user's career goals, means for generating and providing personalized career advice to the user based on the analyzed data and the extracted information, means for periodically tracking the user's progress and providing feedback according to the progress, and means for delivering learning content. This allows the user to improve their skills in real time while receiving learning content based on the latest market trends. In addition, the ability to receive feedback according to the user's progress reinforces continuous growth and motivation for learning.

[0190] A "collection instrument" is a device or software that transmits user-entered profile information to the system and stores that information in a database.

[0191] The "analyzing and identifying means" refers to algorithms or programs that process the collected profile information and identify gaps between the user's current skills and career goals.

[0192] "Market trend and industry news gathering means" means a device or software that obtains the latest market and industry trends from the Internet or other data sources and stores them in a database.

[0193] "Means for extracting information" are algorithms or programs that extract useful information related to the user's career goals from collected market trends and industry news.

[0194] The "means for generating and providing personalized career advice" is a system for creating career advice suited to the individual needs of a user based on the analyzed data and extracted information, and providing the advice to the user.

[0195] "Means for regular progress tracking and feedback" refers to a system that continuously monitors the progress of a user's learning or training and provides the user with advice and encouraging messages based on that progress.

[0196] A "means for delivering learning content" is a system that provides users with educational materials (videos, articles, interactive tutorials, etc.) via the Internet to learn the skills they need.

[0197] System Overview

[0198] This career development support system is a system in which a server, terminals, and users work together. The server performs the main data processing and analysis, while the terminals are used to interact with users. Users access the system via their smartphones and input and receive the necessary information.

[0199] Collecting user profile information

[0200] Users use an application on their smartphone to input information such as their past experience, current skill set, and career goals, which is then sent in real time to a server and stored in a database.

[0201] Analysis of user information

[0202] The server analyzes the user's profile information using Python and machine learning libraries such as Scikit-learn and TENSORFLOW®. This identifies gaps between the user's current skills and their career goals. For example, if a user enters "5 years of software engineering experience, Python and Java skills, and aspirations to become an AI developer," the server determines that "machine learning" and "deep learning" are lacking.

[0203] Market trends and industry news analysis

[0204] The server collects the latest market trends, industry news, and job information from the Internet in real time. It uses Python's Beautiful Soup and Requests to scrape the necessary information and stores it in a database. This allows it to extract information related to the user's career goals. For example, it collects news such as "Demand for AI engineers will increase by 20% by 2025" and prepares it to be provided to the user.

[0205] Providing personalized career advice

[0206] The server generates personalized career advice for the user based on the analyzed data and extracted information. The advice is sent to the device and displayed to the user via a smartphone app. For example, it displays information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0207] Continuous support and feedback

[0208] The server periodically tracks the user's progress. If a user starts the "Introduction to Machine Learning" course and achieves 50% progress, the server receives this data and generates a message saying "Great progress! Keep it up!", which is displayed on the device. The device also collects feedback from the user and sends it to the server. The server analyzes the collected feedback and uses it to improve the system.

[0209] Delivery of learning content

[0210] The server uses cloud storage such as AWS (registered trademark) S3 to deliver learning content to users. Users can receive videos, articles, and interactive tutorials in streaming format through the app, allowing users to always learn the latest information and technologies.

[0211] Specific examples

[0212] Profile input prompt

[0213] "Please tell us about your current occupation, past experience, skills, and future career goals."

[0214] Market trend extraction

[0215] For example, it obtains information from the internet that "demand for AI engineers is increasing," and based on that, recommends related learning videos.

[0216] "A recent report predicts that demand for AI engineers will increase by 20% by 2025. Check out this educational video."

[0217] In this way, the career development support system provides powerful support for users to continuously improve their skills and achieve their career goals by providing learning content tailored to their individual needs, tracking their progress, and collecting feedback.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] User enters profile information

[0221] Users access the application using their smartphones and enter information such as their past experience, current skill set, career goals, etc. Once the information is complete, the entered profile information is sent to the server.

[0222] Input: User profile information (background, skills, career goals)

[0223] Output: Profile information sent to the server

[0224] Step 2:

[0225] The server analyzes the profile information

[0226] The server receives the collected profile information and analyzes it using Python, Scikit-learn, and TensorFlow to identify gaps between the user's current skills and their career goals.

[0227] Input: User profile information

[0228] Output: Analysis results (skill gap identification)

[0229] Step 3:

[0230] Server collects market trends and industry news

[0231] The server uses Python's Beautiful Soup and Requests to scrape the latest market trends and industry news from the internet and store that information in a database.

[0232] Input: Market trends and industry news from the internet

[0233] Output: Market trends and industry news stored in a database

[0234] Step 4:

[0235] The server extracts the information

[0236] The server extracts information relevant to the user's career goals from the collected market trends and industry news using analytical algorithms to identify relevant information.

[0237] Input: Market trends and industry news stored in a database

[0238] Output: Extracted relevant information

[0239] Step 5:

[0240] The server generates personalized career advice

[0241] The server generates personalized career advice for the user based on the analysis results and the extracted information, and the generated career advice is sent to the terminal.

[0242] Input: Analysis results, extracted related information

[0243] Output: Personalized career advice

[0244] Step 6:

[0245] The device displays career advice

[0246] The device receives personalized career advice sent from the server and displays it to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0247] Enter: personalized career advice

[0248] Output: Career advice displayed to the user

[0249] Step 7:

[0250] Enter user progress

[0251] Users enter their own learning progress through the application, and the entered progress information is sent to the server.

[0252] Input: Learning progress information

[0253] Output: Progress information sent to the server

[0254] Step 8:

[0255] The server tracks your progress and provides feedback

[0256] The server analyzes the progress information and generates feedback based on the progress, such as a message like "Great progress, keep it up" that is sent to the device.

[0257] Input: Learning progress information

[0258] Output: The generated feedback message

[0259] Step 9:

[0260] The device displays feedback

[0261] The terminal receives the feedback message sent from the server and displays it to the user.

[0262] Input: Feedback message

[0263] Output: A feedback message that is displayed to the user

[0264] Step 10:

[0265] The server distributes learning content

[0266] The server delivers learning content to users using cloud storage such as AWS S3, and users receive the content in streaming format via their devices.

[0267] Input: Learning content

[0268] Output: Learning content delivered to users

[0269] 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.

[0270] This invention is a career development support system that utilizes a user's profile information and emotional information to provide personalized career advice. The system supports users in achieving their goals, effectively manages their progress, and provides feedback according to the user's emotional state.

[0271] System Overview

[0272] The career development support system operates in cooperation with a server, terminals, users, and an emotion engine. The server processes and analyzes data, and the emotion engine recognizes the user's emotions. The terminals function as an interface with the users, and the users access the system through their terminals.

[0273] Collecting user profile information

[0274] The device displays a profile entry screen to the user, where the user enters information such as past experience, current skill set, and career goals. Once the entry is complete, the device sends this information to the server.

[0275] Analysis of user information

[0276] The server stores the user's profile information received from the device in a database. It then analyzes the user information using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, if a user is aiming to change careers from "software engineer" to "AI developer," it identifies the necessary skills in "machine learning" and "deep learning."

[0277] Market trends and industry news analysis

[0278] The server collects the latest market trends, industry news, and job information from the internet in real time. It analyzes the collected data and extracts information relevant to the user's career goals. For example, it collects news such as "Demand for AI engineers is increasing" and provides it to the user.

[0279] Providing personalized career advice

[0280] Based on the analyzed data and extracted information, the server generates personalized career advice and suitable educational resources for the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0281] The device displays the generated career advice and educational resources to the user, who can then create a study plan based on the displayed information.

[0282] Continuous support and feedback

[0283] The server periodically tracks the user's progress, for example, "I've completed 50% of the introductory machine learning course," and generates real-time feedback.

[0284] The device displays feedback sent by the server to the user, for example, a message like "Great progress, keep it up," and also collects feedback from the user and sends it to the server.

[0285] The server analyzes the collected feedback and identifies improvements to the system, and based on this feedback, improves the user experience of the system.

[0286] Emotion engine integration

[0287] The device collects emotion data from the user's facial expressions and voice, and sends the collected emotion data to the emotion engine.

[0288] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state in real time, for example, determining whether the user is excited or stressed.

[0289] The server then adjusts the content of career advice based on the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will provide gentle feedback to boost motivation or provide resources to help them relax.

[0290] The device works in conjunction with an emotion engine to display feedback based on the user's emotional state, such as a message like "Take a break, we have the resources to refresh you."

[0291] Specific examples

[0292] Collecting and analyzing input information

[0293] A user enters into the terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and identifies "machine learning" and "deep learning" as areas of deficiency.

[0294] Providing market trends

[0295] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[0296] Emotion Recognition and Feedback

[0297] When the user achieves progress, the device recognizes the user's emotional state, and the emotion engine sends data indicating that the user is satisfied to the server. Based on this emotional data, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[0298] In this way, this career development support system provides support that meets the individual needs and feelings of users, enabling companies to more effectively support the career growth of their employees.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] The device displays a profile entry screen to the user, which includes fields for entering past experience, current skill set, and career goals.

[0302] Step 2:

[0303] Users input their background (e.g., 5 years of experience as a software engineer), skill set (e.g., Python, Java), and career goals (e.g., to become an AI developer).

[0304] Step 3:

[0305] The device collects all profile information entered by the user and sends it to a server, including the user's background, skill set, and career goals.

[0306] Step 4:

[0307] The server receives the user profile information sent from the terminal and stores it in a database.

[0308] Step 5:

[0309] The server analyzes the received profile information, including using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, it may determine that there is a lack of skills in "machine learning" and "deep learning."

[0310] Step 6:

[0311] The server collects the latest market trends, industry news, and job information from the internet. In this step, data is obtained using web scraping and APIs.

[0312] Step 7:

[0313] The server analyzes the collected market data and extracts information relevant to the user's career goals, such as "demand for AI engineers is increasing."

[0314] Step 8:

[0315] The server generates personalized career advice for the user based on the analyzed user information and market data, including recommended skill courses and related educational resources.

[0316] Step 9:

[0317] The server transmits the generated carrier advice to the terminal.

[0318] Step 10:

[0319] The device displays career advice sent from the server to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0320] Step 11:

[0321] The terminal collects emotional data such as the user's facial expressions and voice and sends it to the emotion engine.

[0322] Step 12:

[0323] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state, for example, determining in real time whether the user is feeling stressed or happy.

[0324] Step 13:

[0325] The server adjusts the content of career advice based on the emotion data recognized by the emotion engine. For example, if the user is feeling stressed, the advice will be changed to provide resources for relaxation.

[0326] Step 14:

[0327] The device then displays tailored career advice sent from the server to the user, such as "You seem to be feeling a bit stressed. Try this meditation course to help you relax."

[0328] Step 15:

[0329] The server collects user learning activity data to periodically track the user's learning progress, for example, "I have completed 50% of the introductory machine learning course."

[0330] Step 16:

[0331] The server generates real-time feedback based on the progress data collected, such as positive feedback like "Great progress, keep it up!"

[0332] Step 17:

[0333] The terminal displays the feedback sent from the server to the user.

[0334] Step 18:

[0335] The terminal collects feedback from the user and sends it back to the server.

[0336] Step 19:

[0337] The server analyzes the collected user feedback and identifies improvements to the system, and based on this feedback, continually improves the user experience of the system.

[0338] With this processing flow, the career development support system provides support that meets the individual needs and feelings of users, enabling companies to effectively support the career growth of their employees.

[0339] Example 2

[0340] 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."

[0341] While current career development support systems have the ability to collect user profile information and provide advice based on career goals, they lack personalized feedback that takes into account the user's emotional state. They also lack mechanisms for providing real-time feedback based on the user's progress or the latest career advice based on market trends. As a result, these systems face challenges that make it difficult to maintain user motivation and achieve effective career development.

[0342] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0343] In this invention, the server includes: means for collecting profile information entered by a user; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news and extracting information related to the user's career goals; means for generating and providing personalized career advice to the user based on the analyzed data and extracted information; means for periodically tracking the user's progress and providing feedback according to the progress; means for collecting emotional data from the user's facial expressions and voice; and means for analyzing the collected emotional data to recognize the user's emotional state and adjust the content of the career advice. This enables personalized feedback that takes the user's emotional state into consideration, thereby supporting effective career development while maintaining the user's motivation.

[0344] "User" refers to an entity that accesses the system and enters their profile information and career goals.

[0345] "Profile information" refers to information such as a user's past work history, current skill set, and future career goals.

[0346] A "skills gap" refers to the lack of skills between a user's current skills and the skills needed to achieve their career goals.

[0347] "Market trends" refers to the current situation and future outlook, as well as supply and demand trends, in a particular industry or occupation.

[0348] "Industry News" refers to current information, events, or important announcements related to a particular industry.

[0349] "Personalized career advice" refers to specific advice and suggestions tailored to a user's individual situation and goals.

[0350] "Progress" refers to the achievements and progress a user makes in their learning or career development process.

[0351] "Feedback" refers to assessments and advice provided based on a user's progress and emotional state.

[0352] "Facial expression data" refers to information about the user's facial expressions.

[0353] "Voice data" refers to information about a user's voice and speaking style.

[0354] An "emotion engine" refers to technology or a device that has the ability to analyze collected facial expression and voice data to recognize the user's emotional state.

[0355] MODE FOR CARRYING OUT THE INVENTION

[0356] This invention is a career development support system that provides personalized career advice by utilizing user profile information and emotion information. The system includes a server, a terminal, a user, and an emotion engine, all of which operate in cooperation with each other.

[0357] System Configuration

[0358] server

[0359] The server is the main data processing device that analyzes users' profile information and generates career advice. The server is equipped with a database, a generative AI model (e.g., GPT-4® by OpenAI®), and web scraping technology that collects market trends and industry news from the Internet. The server also manages users' progress and generates feedback.

[0360] Terminal

[0361] The terminal is a device that functions as an interface with the user. It displays an input screen for the user to enter profile information and provides the user with career advice and feedback from the server. The terminal also has the function of collecting the user's facial expressions and voice using a webcam and microphone.

[0362] User

[0363] Users access the system through their terminals, enter their profile information and career goals, and plan their actions based on the career advice and feedback provided, and record their progress.

[0364] Overview of program processing

[0365] 1. Collecting User Profile Information

[0366] The terminal displays a profile entry screen to the user, where the user enters their past work history, current skill set, and future career goals. Once the entry is complete, the terminal sends this information to the server.

[0367] 2. Analysis of User Information

[0368] The server stores the user's profile information received from the device in a database. It then uses a generative AI model to analyze the data and identify skill gaps between the user's current skills and their career goals. For example, it identifies the skills of "machine learning" and "deep learning" that are lacking for users who want to become AI developers.

[0369] 3. Gathering market trends and industry news

[0370] The server collects the latest market trends, industry news, and job information from the internet, analyzes this data, and extracts information relevant to the user's career goals. For example, it collects news that "demand for AI engineers is increasing" and provides it to the user.

[0371] 4. Generating personalized career advice

[0372] The server generates personalized career advice for the user based on the analyzed data and extracted information, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0373] 5. Continuous support and feedback

[0374] The server periodically tracks the user's learning progress and generates feedback based on the progress (e.g., "You have completed 50% of the introductory machine learning course"), which the device displays to the user.

[0375] 6. Emotion recognition and feedback regulation

[0376] The device collects the user's facial expressions and voice and sends them to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The analysis results are sent to the server, which then adjusts the content of career advice and feedback based on this information. For example, if the user is feeling stressed, it can provide relaxation resources and gentle feedback.

[0377] Specific examples

[0378] For example, if a user types into a terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer," the server receives this information and uses a generative AI model to identify skills the user lacks (such as "machine learning" or "deep learning").

[0379] Next, the server collects relevant market trends from the internet and extracts news such as "Demand for AI engineers will increase by 20% by 2025." Based on this, it generates career advice such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice," and provides it to the user via their device.

[0380] Furthermore, when a user achieves progress, the device recognizes the user's facial expressions and voice, and the emotion engine sends data indicating "satisfied" to the server. Based on this, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[0381] Example prompt sentence:

[0382] "I've been working as a software engineer for the past five years. I have skills in Python and Java, but I want to become an AI developer. I'd like some advice on what skills I should learn as my next step."

[0383] In this way, this career development support system can provide support tailored to the individual needs and emotional state of the user, and support more effective career growth.

[0384] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0385] Step 1:

[0386] Collecting user profile information

[0387] Input: The user enters their profile information (past work experience, current skill set, career goals) into the device.

[0388] How it works: The device displays a profile entry screen for the user, where the user enters information such as "5 years of software engineering experience, Python and Java skills, and aspirations to become an AI developer."

[0389] Output: The entered profile information is sent to the server.

[0390] Step 2:

[0391] Analysis of user information

[0392] Input: User profile information sent from the device.

[0393] How it works: The server stores the received profile information in a database. It then uses a generative AI model (e.g., GPT-4) to analyze the user information and identify skill gaps between their current skills and their career goals. For example, the server might identify a lack of skills in "machine learning" and "deep learning."

[0394] Output: Data about the user's skill gaps.

[0395] Step 3:

[0396] Gathering market trends and industry news

[0397] Input: Market trends, industry news, and job listings collected by the server from the internet.

[0398] How it works: The server uses web scraping technology to gather the latest market trends and industry news in real time, such as the news that "demand for AI engineers will increase by 20% by 2025."

[0399] Output: Collected market trends and industry news data.

[0400] Step 4:

[0401] Generate personalized career advice

[0402] Input: User information analysis results and market trend data.

[0403] How it works: The server generates personalized career advice for the user based on the analyzed data and extracted information, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0404] Output: Generated career advice and educational resource suggestions.

[0405] Step 5:

[0406] Providing career advice

[0407] Input: Career advice information sent to the device.

[0408] Operation: The device displays the career advice sent to the user, who can then create a study plan based on the information displayed.

[0409] Output: Career advice displayed to the user.

[0410] Step 6:

[0411] Continuous support and progress feedback

[0412] Input: User progress data.

[0413] How it works: The server periodically tracks the user's learning and career development progress and generates feedback based on their progress (e.g., "You've completed 50% of the Introduction to Machine Learning course"). The device displays the feedback sent by the server to the user (e.g., "Great progress! Keep it up!").

[0414] Output: Progress feedback displayed to the user.

[0415] Step 7:

[0416] Emotion recognition and feedback regulation

[0417] Input: User's facial and voice data.

[0418] How it works: The device uses a webcam and microphone to collect the user's facial expressions and voice. The emotion engine analyzes the collected data and recognizes the user's emotional state in real time (e.g., excitement, stress). The analysis results are sent to a server, which then adjusts the content of career advice and feedback based on the user's emotional state. For example, if the user is feeling stressed, it will provide relaxation resources and gentle feedback.

[0419] Output: Tailored career advice and feedback based on the user's emotional state.

[0420] (Application example 2)

[0421] 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."

[0422] Although conventional career development support systems provide career advice based on users' profile information, they are unable to provide feedback or recommendations that take into account the user's real-time emotional state, which means that they are unable to fully motivate users.In addition, the technology for collecting and analyzing emotional data in real time has not been properly integrated, leaving room for improvement in the user experience.

[0423] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting profile information entered by the user, means for analyzing the collected profile information to identify a gap between the user's current abilities and their goals, means for collecting market trends and industry news and extracting information related to the user's goals, means for generating and providing personalized advice to the user based on the analyzed data and the extracted information, means for periodically tracking the user's progress and providing feedback according to the progress, means for collecting the user's emotional state in real time using a smart device, and means for adjusting feedback and recommendations based on the collected emotional information. This makes it possible to provide feedback according to the user's real-time emotional state, thereby increasing the user's motivation and enabling more effective career development support.

[0424] "Profile information" is information such as past career history, current abilities, and goals entered by the user himself / herself.

[0425] "Analysis" is the process of analyzing the collected data to identify gaps between the user's current abilities and goals, as well as necessary information.

[0426] "Market trends" are information about current trends and future developments in a particular industry or field.

[0427] "Industry News" refers to current events and information related to a particular industry.

[0428] "Personalized advice" refers to customized recommendations or instructions determined based on a user's individual profile information and analytics data.

[0429] "Progress" is a measure of how far a user has progressed toward a particular goal.

[0430] "Feedback" refers to evaluations and advice provided to users regarding their actions and progress.

[0431] A "smart device" refers to an electronic device that can connect to the Internet and collect data using various sensors.

[0432] An "emotional state" is an emotion or feeling that a user is experiencing, such as stress or happiness.

[0433] "Real-time" means that data is processed almost instantly, without any delay.

[0434] To implement the present invention, the following configuration is necessary.

[0435] Overall system configuration

[0436] The career development support system consists of the following main components:

[0437] 1. Server

[0438] 2. Devices (smartphones, smart glasses, etc.)

[0439] 3. Users

[0440] 4. Emotion Engine

[0441] Collection of profile information

[0442] The device displays a profile entry screen to the user, where the user enters profile information such as past experience, current skill set, and goals. The profile information entered by the user is sent from the device to the server. At this stage, a simple prompt message like the following is displayed:

[0443] "Hello! Please enter your profile information, such as your past purchase history, preferred brands and styles, and budget."

[0444] Analyzing user information and identifying career goals

[0445] The server stores the user's profile information received from the device in a database and uses the generative AI model to analyze the gap between the user's current abilities and their goals. For example, if a user is aiming to change careers from "software engineer" to "AI developer," the server will identify the missing skills of "machine learning" and "deep learning."

[0446] Gathering market trends and industry news

[0447] The server collects the latest market trends, industry news, job listings, etc. from the internet in real time and extracts information relevant to the user's goals. For example, it provides the user with news that "demand for AI engineers is increasing."

[0448] Providing personalized advice

[0449] The server generates personalized advice for the user based on the analyzed data and extracted information, and provides it to the user via the device. Appropriate educational resources are also suggested at this stage. For example, information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice" is generated.

[0450] Progress Tracking and Feedback

[0451] The server periodically tracks the user's progress and provides feedback based on that progress, for example, if the progress information is "You've completed 50% of the Introduction to Machine Learning course," the message displayed is "Great progress! Keep it up!"

[0452] Emotion engine integration

[0453] The terminal uses a smart device to capture the user's facial expressions and voice and transmits them to the emotion engine, which analyzes the collected emotion data and recognizes the user's emotional state in real time. For example, it can determine whether the user is excited or stressed.

[0454] Adjusting feedback based on emotional state

[0455] The server adjusts the content of feedback and recommendations based on the emotional data recognized by the emotion engine. For example, if a user is feeling stressed, it can provide gentle feedback to increase motivation or provide resources to help them relax. This makes it possible to more effectively support the user's career development.

[0456] Specific examples

[0457] Collection of profile information

[0458] A user types into a terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and identifies a lack of necessary "machine learning" and "deep learning" skills.

[0459] Providing market trends

[0460] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[0461] Emotion Recognition and Feedback

[0462] When the user achieves progress, the device recognizes the user's emotional state, and the emotion engine sends data indicating that the user is satisfied to the server. Based on this emotional data, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[0463] In this way, the career development support system utilizes the user's profile information and emotional information collected in real time to provide personalized career advice.

[0464] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0465] Step 1:

[0466] The user inputs profile information into the terminal. Specifically, the user inputs information such as past experience, current skill set, and career goals into an input screen displayed on the terminal. The input data is then sent to the server by the terminal.

[0467] input:

[0468] User profile information (background, skill set, career goals)

[0469] output:

[0470] User profile data sent to the server

[0471] Data processing:

[0472] The device formats the data entered by the user and sends it to the server via the API.

[0473] Step 2:

[0474] The server stores the received user profile information and analyzes it using the generative AI model. The analysis results identify gaps between the user's current abilities and their career goals. For example, it identifies the skills they lack to become an AI developer.

[0475] input:

[0476] User profile data sent from your device

[0477] output:

[0478] Skills gap identification results

[0479] Data processing:

[0480] It uses a generative AI model to compare a user's current skills with their career goals and identify skill gaps.

[0481] Step 3:

[0482] The server collects and analyzes the latest market trends, industry news, and job information from the internet, extracting information relevant to the user's career goals, such as demand trends in the AI ​​industry and useful educational courses.

[0483] input:

[0484] Market trends, industry news, and job listings collected from the internet

[0485] output:

[0486] Extracted related information

[0487] Data processing:

[0488] It uses cloud-based web crawlers and natural language processing (NLP) algorithms to extract relevant information.

[0489] Step 4:

[0490] The server generates personalized advice for the user based on the analyzed data and extracted information and provides it via the device, such as suggesting suitable learning courses or career paths for the user.

[0491] input:

[0492] Skills gap identification results

[0493] Extracted related information

[0494] output:

[0495] Personalized advice

[0496] Data processing:

[0497] Based on analytical data, we generate optimal career advice and learning resources.

[0498] Step 5:

[0499] The server periodically tracks the user's progress and provides feedback based on the progress, for example by sending motivational messages based on the progress made.

[0500] input:

[0501] User progress data

[0502] output:

[0503] Progress Feedback

[0504] Data processing:

[0505] Aggregate progress data and generate motivational feedback.

[0506] Step 6:

[0507] The terminal collects the user's emotional state in real time using a smart device, capturing facial expressions and voice using the camera and microphone of smart glasses or a smartphone, and sending the captured data to the emotion engine.

[0508] input:

[0509] User's facial expression data, voice data

[0510] output:

[0511] Real-time emotion data

[0512] Data processing:

[0513] It uses a camera and microphone to capture data and send it to the emotion engine.

[0514] Step 7:

[0515] The emotion engine analyzes the collected emotion data to recognize the user's emotional state, for example, to determine whether the user is stressed or happy.

[0516] input:

[0517] Facial expression data, voice data

[0518] output:

[0519] Emotion recognition results

[0520] Data processing:

[0521] Emotional states are recognized using image analysis and audio analysis technology.

[0522] Step 8:

[0523] The server tailors feedback and recommendations based on the emotional data recognized by the emotion engine, for example, providing relaxation resources if the user is feeling stressed.

[0524] input:

[0525] Emotion recognition results

[0526] output:

[0527] Tailored feedback and recommendations

[0528] Data processing:

[0529] The feedback content is adjusted based on emotional data, and messages are generated to maintain the user's motivation.

[0530] 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.

[0531] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0532] 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.

[0533] [Second embodiment]

[0534] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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).

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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."

[0546] The career development support system of the present invention is a system that provides personalized career advice by collecting and analyzing user profile information. Specific procedures for implementing this system are described below.

[0547] System Overview

[0548] This career development support system is a system in which a server, terminals, and users work together. The server performs the main data processing and analysis, and the terminals function as an interface with the users. Users access the system through their terminals and input the necessary information.

[0549] Collecting user profile information

[0550] The device displays a profile entry screen to the user, where the user enters information such as past experience, current skill set, and career goals. Once the entry is complete, the device sends this information to the server.

[0551] Analysis of user information

[0552] The server receives the user's profile information sent from the device. It then analyzes the collected information and identifies gaps between the user's current skills and their career goals. For example, if a user has a background as a "software engineer" and aims to become an "AI developer" in the future, the server will identify that they lack skills such as "machine learning" and "deep learning."

[0553] Market trends and industry news analysis

[0554] The server collects the latest market trends, industry news, and job information from the internet in real time. It analyzes this data and extracts information relevant to the user's career goals. For example, the server collects news such as "Demand for AI engineers is increasing" and prepares it to provide to the user.

[0555] Providing personalized career advice

[0556] The server generates personalized career advice for the user based on the analyzed data and extracted information. The generated advice is sent to the device, which then displays it to the user. For example, information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice" is displayed.

[0557] Continuous support and feedback

[0558] The server periodically tracks the user's progress. Each time the server receives progress information, such as "You've completed 50% of the Introduction to Machine Learning course," it generates real-time feedback and provides it to the user via their device, such as a message like "Great progress! Keep it up!"

[0559] The device also collects feedback from users and sends it to the server, which analyzes the feedback and uses it to improve the system.

[0560] Specific examples

[0561] Collecting and analyzing input information

[0562] A user enters "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and determines that "machine learning" and "deep learning" are lacking.

[0563] Providing market trends

[0564] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[0565] Progress tracking and feedback

[0566] If a user starts the "Introduction to Machine Learning" course and achieves 50% progress, the server receives this data and generates a message saying "Great progress! Keep it up!" and displays it through the terminal.

[0567] In this way, this career development support system provides support tailored to the individual needs of each user, enabling companies to effectively support the career growth of their employees.

[0568] The processing flow will be explained below.

[0569] Step 1:

[0570] The device displays a profile entry screen to the user, which includes fields for entering the user's past experience, current skill set, and career goals.

[0571] Step 2:

[0572] Users input their background (e.g., 5 years of experience as a software engineer), skill set (e.g., Python, Java), and career goals (e.g., to become an AI developer).

[0573] Step 3:

[0574] The device collects all profile information entered by the user and sends it to the server, including the user's background, skill set, and career goals.

[0575] Step 4:

[0576] The server receives the user profile information sent from the terminal and stores it in a database.

[0577] Step 5:

[0578] The server analyzes the received profile information, including using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, it may determine that skills such as "machine learning" and "deep learning" are lacking.

[0579] Step 6:

[0580] The server collects the latest market trends, industry news, and job information from the internet. In this step, data is obtained using web scraping and APIs.

[0581] Step 7:

[0582] The server analyzes the collected market data and extracts information relevant to the user's career goals, such as information that there is a growing demand for AI engineers.

[0583] Step 8:

[0584] The server generates personalized career advice for the user based on the analyzed user information and market data, including recommended skill courses and related educational resources.

[0585] Step 9:

[0586] The server transmits the generated carrier advice to the terminal.

[0587] Step 10:

[0588] The device displays career advice sent from the server to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0589] Step 11:

[0590] The server collects user activity data to periodically check the user's progress, for example, the user has completed 50% of the introductory machine learning course.

[0591] Step 12:

[0592] The server generates real-time feedback based on collected activity data, such as messages like "Great progress, keep it up!"

[0593] Step 13:

[0594] The terminal displays the feedback generated from the server to the user.

[0595] Step 14:

[0596] The terminal collects feedback from the user and sends it back to the server, where it is used to help improve the system.

[0597] Step 15:

[0598] The server analyzes the collected feedback and identifies improvements to the system, thereby continually improving the user experience of the system.

[0599] With this processing flow, the career development support system provides support tailored to the individual needs of users, enabling companies to effectively support the career growth of their employees.

[0600] Example 1

[0601] 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."

[0602] Conventional career development support systems have difficulty providing appropriate career advice and educational resources to meet the individual needs of users, resulting in an inability to effectively support users' career growth. Furthermore, it is difficult to track users' progress and feedback in a timely manner and provide real-time support, which reduces the convenience and effectiveness of the system.

[0603] 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.

[0604] In this invention, the server includes: means for collecting profile information entered by the user; means for transmitting the collected profile information via the terminal to the server; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news from the Internet and extracting information related to the user's career goals; means for generating personalized career advice for the user based on the analyzed data and the extracted information; means for transmitting the generated advice to the terminal and displaying it to the user; means for periodically tracking the user's progress, generating feedback according to the progress, and providing it via the terminal; and means for collecting feedback from the user and using it to improve the system. This makes it possible to provide appropriate career advice and educational resources tailored to the individual needs of each user, and also makes it possible to track the user's progress and feedback in real time and provide prompt support.

[0605] A "User" is an individual who uses the system and enters profile information.

[0606] A "terminal" is a device used by a user, and is an interface through which profile information is entered and career advice is displayed via a web browser.

[0607] A "server" is a computer system that receives data sent from a terminal and performs analysis and data processing.

[0608] "Profile information" is information about a user's past experience, current skill set, and career goals.

[0609] "Collect" refers to the process of transmitting profile information entered by a user into a terminal to a server.

[0610] "Analysis" refers to the process of identifying gaps between the user's current skills and career goals based on the profile information received by the server.

[0611] "Market Trends" is information about the latest movements and trends in an industry or market.

[0612] "Industry news" refers to the latest happenings and reports in a particular field or industry.

[0613] "Personalized career advice" refers to career growth advice and suggestions that are customized to a user's individual needs.

[0614] "Progress" is information that indicates how far the user has progressed toward achieving their goal.

[0615] "Feedback" is information that provides responses and advice according to the user's progress.

[0616] "Educational Resources" means learning courses and materials offered to users to improve their skills and grow their careers.

[0617] "System improvement" is the process of improving the system's functions and services based on feedback collected from users.

[0618] The career development support system of the present invention is a system in which a server, a terminal, and a user work in cooperation with each other. A specific implementation method of this system will be described below.

[0619] System Overview

[0620] This career development support system is composed of a server that performs the main data processing and analysis, a terminal that functions as an interface with users, and users who provide information to the system.

[0621] Collecting user profile information

[0622] The terminal is a device connected to the Internet (e.g., a PC or a smartphone), and in this embodiment, a web browser (e.g., Google (registered trademark) Chrome, Mozilla Firefox) is used. The terminal displays a screen for the user to enter profile information using an HTML form. The user enters information such as past experience, current skill set, and career goals. Once the information has been entered, the terminal collects this information using JavaScript, converts it into JSON format, and sends it to the server using HTTPS communication.

[0623] Analysis of user information

[0624] The server parses the received user profile information in JSON format. The server receives the data using a Python framework (e.g., Flask, Django) and converts the information into a data frame using the Pandas library. Based on this data frame, the server uses the Scikit-learn library to identify gaps between the user's current skills and their career goals. For example, if a user has a background as a "software engineer" and aspires to become an "AI developer," the server will identify a lack of skills such as "machine learning" and "deep learning."

[0625] Market trends and industry news analysis

[0626] The server uses web scraping technology to collect market trends and industry news from the Internet. Specifically, it uses Python's Beautiful Soup and Selenium libraries. The collected data is analyzed to identify information relevant to the user's career goals. For example, it extracts and analyzes news that "demand for AI engineers is increasing."

[0627] Providing personalized career advice

[0628] The server generates personalized career advice for the user based on the analyzed data and extracted information. For example, it might provide the user with information such as "Recommended courses: Introduction to Machine Learning, Deep Learning Practice." The generated advice is converted to JSON format and sent to the device. The device parses it using JavaScript and displays it to the user using HTML.

[0629] Continuous support and feedback

[0630] The server periodically receives data to track the user's progress. Each time a user reports their learning progress, the server analyzes the data and generates real-time feedback. For example, if the server receives progress information such as "You've completed 50% of the introductory machine learning course," it generates feedback such as "Great progress! Keep it up!" and provides it to the user via their device. The device also collects feedback from the user and sends it to the server. The server uses the collected feedback to improve the system.

[0631] Specific examples

[0632] The user enters information as follows:

[0633] Experience: "5 years of software engineering experience"

[0634] Skills: Python and Java

[0635] Goal: "I want to become an AI developer"

[0636] The server receives this information and determines that there is a lack of "machine learning" or "deep learning."

[0637] The server finds a news item saying, "Demand for AI engineers will increase by 20% by 2025," extracts a list of related courses (e.g., "Introduction to Machine Learning" and "Deep Learning in Practice"), and sends it to the device, which then displays it to the user.

[0638] If a user "completes 50% of the introductory machine learning course," the server receives this progress information, generates feedback to the user via the device, saying, "Great progress! Keep it up!"

[0639] In this way, the system provides career advice tailored to the user's individual needs and effectively supports the user's career growth through continuous support and feedback.

[0640] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0641] Step 1:

[0642] The device displays a screen where the user can enter profile information. Input fields include background, skills, career goals, etc. After entering the information, the user presses the "Submit" button. Specifically, the information is collected using an HTML form, and then compiled using JavaScript and converted to JSON format.

[0643] Input: User's background, skills, and career goals

[0644] Data processing: Convert information into JSON format using JavaScript

[0645] Output: Profile information in JSON format

[0646] Step 2:

[0647] The device sends the collected profile information in JSON format to the server using HTTPS, which uses encrypted communication to ensure security.

[0648] Input: Profile information in JSON format converted on the device

[0649] Data transmission: Secure data transmission via HTTPS

[0650] Output: Profile information sent to the server

[0651] Step 3:

[0652] The server receives the JSON-formatted profile information using a Python framework (such as Flask or Django), converts it into a data frame using the Pandas library, and organizes the information by background, skills, and career goals.

[0653] Input: Profile information in JSON format

[0654] Data processing: Convert to a data frame using Pandas and organize the information

[0655] Output: A neat data frame

[0656] Step 4:

[0657] The server uses Scikit-learn to analyze the data and identify gaps between the user's current skills and their career goals. For example, it uses a linear regression model to analyze how well the user's skills match their career goals.

[0658] Input: A cleaned data frame

[0659] Data Computing: Skills and Career Goal Gap Analysis with Scikit-Learn

[0660] Output: User skill gap analysis results

[0661] Step 5:

[0662] The server uses web scraping with Beautiful Soup and Selenium to gather market trends and industry news from the internet, which is then analyzed to identify information relevant to the user's career goals.

[0663] Input: Market trends, industry news webpage URL

[0664] Data collection: Data collection through web scraping

[0665] Output: Analyzable market trend data, industry news data

[0666] Step 6:

[0667] The server generates personalized career advice based on the analyzed user profile information and market trend data, using natural language generation (NLG) technology to create advice in a user-friendly format.

[0668] Input: User skill gap analysis results, market trend data

[0669] Data Generation: Generating Career Advice with NLG

[0670] Output: Personalized career advice (e.g., "Introduction to Machine Learning Course")

[0671] Step 7:

[0672] The server converts the generated advice into JSON format and sends it to the device, which then parses the received JSON data using JavaScript and displays it to the user in HTML.

[0673] Input: Generated career advice

[0674] Data conversion and transmission: Converted to JSON format and transmitted via HTTPS

[0675] Output: Personalized career advice displayed to the user

[0676] Step 8:

[0677] The server periodically receives data to track the user's progress in the course of study selected by the user. The user enters progress data and sends it to the server.

[0678] Input: User progress data

[0679] Data calculation: Analysis of progress data

[0680] Output: Feedback based on progress

[0681] Step 9:

[0682] The server generates real-time feedback based on the progress data, using natural language generation technology, and sends it to the device, where it is displayed to the user.

[0683] Input: User progress data

[0684] Data generation: Feedback generated based on progress

[0685] Output: Feedback that is displayed to the user (e.g., "Great progress! Keep it up!")

[0686] Step 10:

[0687] The device collects feedback from users and sends it to the server, which analyzes it and uses it to improve the system.

[0688] Input: User feedback

[0689] Data collection and analysis: Feedback analysis

[0690] Output: System improvement proposals

[0691] In this step, the career development support system can provide individual career advice to users and provide continuous progress tracking and feedback, thereby effectively supporting users' career growth.

[0692] (Application example 1)

[0693] 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."

[0694] Traditional career development support systems provide personalized advice by collecting and analyzing user profile information, but most of this information is static and lacks specific support for how users should actually improve their skills. Furthermore, they lack the ability to track users' learning progress in real time and provide appropriate feedback, resulting in insufficient motivation and support for users to continue developing. As a result, it is difficult to efficiently bridge the gap between users' career goals and their actual skill development. Furthermore, they are unable to provide the latest learning content based on market trends and industry news in real time, making it difficult for users to utilize the latest information to advance their learning.

[0695] 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.

[0696] In this invention, the server includes means for collecting profile information entered by the user, means for analyzing the collected profile information to identify gaps between the user's current skills and career goals, means for collecting market trends and industry news and extracting information related to the user's career goals, means for generating and providing personalized career advice to the user based on the analyzed data and the extracted information, means for periodically tracking the user's progress and providing feedback according to the progress, and means for delivering learning content. This allows the user to improve their skills in real time while receiving learning content based on the latest market trends. In addition, the ability to receive feedback according to the user's progress reinforces continuous growth and motivation for learning.

[0697] A "collection instrument" is a device or software that transmits user-entered profile information to the system and stores that information in a database.

[0698] The "analyzing and identifying means" refers to algorithms or programs that process the collected profile information and identify gaps between the user's current skills and career goals.

[0699] "Market trend and industry news gathering means" means a device or software that obtains the latest market and industry trends from the Internet or other data sources and stores them in a database.

[0700] "Means for extracting information" are algorithms or programs that extract useful information related to the user's career goals from collected market trends and industry news.

[0701] The "means for generating and providing personalized career advice" is a system for creating career advice suited to the individual needs of a user based on the analyzed data and extracted information, and providing the advice to the user.

[0702] "Means for regular progress tracking and feedback" refers to a system that continuously monitors the progress of a user's learning or training and provides the user with advice and encouraging messages based on that progress.

[0703] A "means for delivering learning content" is a system that provides users with educational materials (videos, articles, interactive tutorials, etc.) via the Internet to learn the skills they need.

[0704] System Overview

[0705] This career development support system is a system in which a server, terminals, and users work together. The server performs the main data processing and analysis, while the terminals are used to interact with users. Users access the system via their smartphones and input and receive the necessary information.

[0706] Collecting user profile information

[0707] Users use an application on their smartphone to input information such as their past experience, current skill set, and career goals, which is then sent in real time to a server and stored in a database.

[0708] Analysis of user information

[0709] The server analyzes the user's profile information using Python and machine learning libraries such as Scikit-learn and TensorFlow. This identifies gaps between the user's current skills and their career goals. For example, if a user enters "5 years of software engineering experience, Python and Java skills, and aspirations to become an AI developer," the server will determine that "machine learning" and "deep learning" are lacking.

[0710] Market trends and industry news analysis

[0711] The server collects the latest market trends, industry news, and job information from the Internet in real time. It uses Python's Beautiful Soup and Requests to scrape the necessary information and stores it in a database. This allows it to extract information related to the user's career goals. For example, it collects news such as "Demand for AI engineers will increase by 20% by 2025" and prepares it to be provided to the user.

[0712] Providing personalized career advice

[0713] The server generates personalized career advice for the user based on the analyzed data and extracted information. The advice is sent to the device and displayed to the user via a smartphone app. For example, it displays information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0714] Continuous support and feedback

[0715] The server periodically tracks the user's progress. If a user starts the "Introduction to Machine Learning" course and achieves 50% progress, the server receives this data and generates a message saying "Great progress! Keep it up!", which is displayed on the device. The device also collects feedback from the user and sends it to the server. The server analyzes the collected feedback and uses it to improve the system.

[0716] Delivery of learning content

[0717] The server uses cloud storage such as AWS S3 to deliver learning content to users. Users can receive videos, articles, and interactive tutorials in streaming format through the app, allowing users to always learn the latest information and technologies.

[0718] Specific examples

[0719] Profile input prompt

[0720] "Please tell us about your current occupation, past experience, skills, and future career goals."

[0721] Market trend extraction

[0722] For example, it obtains information from the internet that "demand for AI engineers is increasing," and based on that, recommends related learning videos.

[0723] "A recent report predicts that demand for AI engineers will increase by 20% by 2025. Check out this educational video."

[0724] In this way, the career development support system provides powerful support for users to continuously improve their skills and achieve their career goals by providing learning content tailored to their individual needs, tracking their progress, and collecting feedback.

[0725] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0726] Step 1:

[0727] User enters profile information

[0728] Users access the application using their smartphones and enter information such as their past experience, current skill set, career goals, etc. Once the information is complete, the entered profile information is sent to the server.

[0729] Input: User profile information (background, skills, career goals)

[0730] Output: Profile information sent to the server

[0731] Step 2:

[0732] The server analyzes the profile information

[0733] The server receives the collected profile information and analyzes it using Python, Scikit-learn, and TensorFlow to identify gaps between the user's current skills and their career goals.

[0734] Input: User profile information

[0735] Output: Analysis results (skill gap identification)

[0736] Step 3:

[0737] Server collects market trends and industry news

[0738] The server uses Python's Beautiful Soup and Requests to scrape the latest market trends and industry news from the internet and store that information in a database.

[0739] Input: Market trends and industry news from the internet

[0740] Output: Market trends and industry news stored in a database

[0741] Step 4:

[0742] The server extracts the information

[0743] The server extracts information relevant to the user's career goals from the collected market trends and industry news using analytical algorithms to identify relevant information.

[0744] Input: Market trends and industry news stored in a database

[0745] Output: Extracted relevant information

[0746] Step 5:

[0747] The server generates personalized career advice

[0748] The server generates personalized career advice for the user based on the analysis results and the extracted information, and the generated career advice is sent to the terminal.

[0749] Input: Analysis results, extracted related information

[0750] Output: Personalized career advice

[0751] Step 6:

[0752] The device displays career advice

[0753] The device receives personalized career advice sent from the server and displays it to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0754] Enter: personalized career advice

[0755] Output: Career advice displayed to the user

[0756] Step 7:

[0757] Enter user progress

[0758] Users enter their own learning progress through the application, and the entered progress information is sent to the server.

[0759] Input: Learning progress information

[0760] Output: Progress information sent to the server

[0761] Step 8:

[0762] The server tracks your progress and provides feedback

[0763] The server analyzes the progress information and generates feedback based on the progress, such as a message like "Great progress, keep it up" that is sent to the device.

[0764] Input: Learning progress information

[0765] Output: The generated feedback message

[0766] Step 9:

[0767] The device displays feedback

[0768] The terminal receives the feedback message sent from the server and displays it to the user.

[0769] Input: Feedback message

[0770] Output: A feedback message that is displayed to the user

[0771] Step 10:

[0772] The server distributes learning content

[0773] The server delivers learning content to users using cloud storage such as AWS S3, and users receive the content in streaming format via their devices.

[0774] Input: Learning content

[0775] Output: Learning content delivered to users

[0776] 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.

[0777] This invention is a career development support system that utilizes a user's profile information and emotional information to provide personalized career advice. The system supports users in achieving their goals, effectively manages their progress, and provides feedback according to the user's emotional state.

[0778] System Overview

[0779] The career development support system operates in cooperation with a server, terminals, users, and an emotion engine. The server processes and analyzes data, and the emotion engine recognizes the user's emotions. The terminals function as an interface with the users, and the users access the system through their terminals.

[0780] Collecting user profile information

[0781] The device displays a profile entry screen to the user, where the user enters information such as past experience, current skill set, and career goals. Once the entry is complete, the device sends this information to the server.

[0782] Analysis of user information

[0783] The server stores the user's profile information received from the device in a database. It then analyzes the user information using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, if a user is aiming to change careers from "software engineer" to "AI developer," it identifies the necessary skills in "machine learning" and "deep learning."

[0784] Market trends and industry news analysis

[0785] The server collects the latest market trends, industry news, and job information from the internet in real time. It analyzes the collected data and extracts information relevant to the user's career goals. For example, it collects news such as "Demand for AI engineers is increasing" and provides it to the user.

[0786] Providing personalized career advice

[0787] Based on the analyzed data and extracted information, the server generates personalized career advice and suitable educational resources for the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0788] The device displays the generated career advice and educational resources to the user, who can then create a study plan based on the displayed information.

[0789] Continuous support and feedback

[0790] The server periodically tracks the user's progress, for example, "I've completed 50% of the introductory machine learning course," and generates real-time feedback.

[0791] The device displays feedback sent by the server to the user, for example, a message like "Great progress, keep it up," and also collects feedback from the user and sends it to the server.

[0792] The server analyzes the collected feedback and identifies improvements to the system, and based on this feedback, improves the user experience of the system.

[0793] Emotion engine integration

[0794] The device collects emotion data from the user's facial expressions and voice, and sends the collected emotion data to the emotion engine.

[0795] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state in real time, for example, determining whether the user is excited or stressed.

[0796] The server then adjusts the content of career advice based on the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will provide gentle feedback to boost motivation or provide resources to help them relax.

[0797] The device works in conjunction with an emotion engine to display feedback based on the user's emotional state, such as a message like "Take a break, we have the resources to refresh you."

[0798] Specific examples

[0799] Collecting and analyzing input information

[0800] A user enters into the terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and identifies "machine learning" and "deep learning" as areas of deficiency.

[0801] Providing market trends

[0802] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[0803] Emotion Recognition and Feedback

[0804] When the user achieves progress, the device recognizes the user's emotional state, and the emotion engine sends data indicating that the user is satisfied to the server. Based on this emotional data, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[0805] In this way, this career development support system provides support that meets the individual needs and feelings of users, enabling companies to more effectively support the career growth of their employees.

[0806] The processing flow will be explained below.

[0807] Step 1:

[0808] The device displays a profile entry screen to the user, which includes fields for entering past experience, current skill set, and career goals.

[0809] Step 2:

[0810] Users input their background (e.g., 5 years of experience as a software engineer), skill set (e.g., Python, Java), and career goals (e.g., to become an AI developer).

[0811] Step 3:

[0812] The device collects all profile information entered by the user and sends it to a server, including the user's background, skill set, and career goals.

[0813] Step 4:

[0814] The server receives the user profile information sent from the terminal and stores it in a database.

[0815] Step 5:

[0816] The server analyzes the received profile information, including using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, it may determine that there is a lack of skills in "machine learning" and "deep learning."

[0817] Step 6:

[0818] The server collects the latest market trends, industry news, and job information from the internet. In this step, data is obtained using web scraping and APIs.

[0819] Step 7:

[0820] The server analyzes the collected market data and extracts information relevant to the user's career goals, such as "demand for AI engineers is increasing."

[0821] Step 8:

[0822] The server generates personalized career advice for the user based on the analyzed user information and market data, including recommended skill courses and related educational resources.

[0823] Step 9:

[0824] The server transmits the generated carrier advice to the terminal.

[0825] Step 10:

[0826] The device displays career advice sent from the server to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0827] Step 11:

[0828] The terminal collects emotional data such as the user's facial expressions and voice and sends it to the emotion engine.

[0829] Step 12:

[0830] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state, for example, determining in real time whether the user is feeling stressed or happy.

[0831] Step 13:

[0832] The server adjusts the content of career advice based on the emotion data recognized by the emotion engine. For example, if the user is feeling stressed, the advice will be changed to provide resources for relaxation.

[0833] Step 14:

[0834] The device then displays tailored career advice sent from the server to the user, such as "You seem to be feeling a bit stressed. Try this meditation course to help you relax."

[0835] Step 15:

[0836] The server collects user learning activity data to periodically track the user's learning progress, for example, "I have completed 50% of the introductory machine learning course."

[0837] Step 16:

[0838] The server generates real-time feedback based on the progress data collected, such as positive feedback like "Great progress, keep it up!"

[0839] Step 17:

[0840] The terminal displays the feedback sent from the server to the user.

[0841] Step 18:

[0842] The terminal collects feedback from the user and sends it back to the server.

[0843] Step 19:

[0844] The server analyzes the collected user feedback and identifies improvements to the system, and based on this feedback, continually improves the user experience of the system.

[0845] With this processing flow, the career development support system provides support that meets the individual needs and feelings of users, enabling companies to effectively support the career growth of their employees.

[0846] Example 2

[0847] 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."

[0848] While current career development support systems have the ability to collect user profile information and provide advice based on career goals, they lack personalized feedback that takes into account the user's emotional state. They also lack mechanisms for providing real-time feedback based on the user's progress or the latest career advice based on market trends. As a result, these systems face challenges that make it difficult to maintain user motivation and achieve effective career development.

[0849] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0850] In this invention, the server includes: means for collecting profile information entered by a user; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news and extracting information related to the user's career goals; means for generating and providing personalized career advice to the user based on the analyzed data and extracted information; means for periodically tracking the user's progress and providing feedback according to the progress; means for collecting emotional data from the user's facial expressions and voice; and means for analyzing the collected emotional data to recognize the user's emotional state and adjust the content of the career advice. This enables personalized feedback that takes the user's emotional state into consideration, thereby supporting effective career development while maintaining the user's motivation.

[0851] "User" refers to an entity that accesses the system and enters their profile information and career goals.

[0852] "Profile information" refers to information such as a user's past work history, current skill set, and future career goals.

[0853] A "skills gap" refers to the lack of skills between a user's current skills and the skills needed to achieve their career goals.

[0854] "Market trends" refers to the current situation and future outlook, as well as supply and demand trends, in a particular industry or occupation.

[0855] "Industry News" refers to current information, events, or important announcements related to a particular industry.

[0856] "Personalized career advice" refers to specific advice and suggestions tailored to a user's individual situation and goals.

[0857] "Progress" refers to the achievements and progress a user makes in their learning or career development process.

[0858] "Feedback" refers to assessments and advice provided based on a user's progress and emotional state.

[0859] "Facial expression data" refers to information about the user's facial expressions.

[0860] "Voice data" refers to information about a user's voice and speaking style.

[0861] An "emotion engine" refers to technology or a device that has the ability to analyze collected facial expression and voice data to recognize the user's emotional state.

[0862] MODE FOR CARRYING OUT THE INVENTION

[0863] This invention is a career development support system that provides personalized career advice by utilizing user profile information and emotion information. The system includes a server, a terminal, a user, and an emotion engine, all of which operate in cooperation with each other.

[0864] System Configuration

[0865] server

[0866] The server is the main data processing device that analyzes users' profile information and generates career advice. It is equipped with a database, generative AI models (e.g., OpenAI's GPT-4), and web scraping technology that collects market trends and industry news from the Internet. The server also manages users' progress and generates feedback.

[0867] Terminal

[0868] The terminal is a device that functions as an interface with the user. It displays an input screen for the user to enter profile information and provides the user with career advice and feedback from the server. The terminal also has the function of collecting the user's facial expressions and voice using a webcam and microphone.

[0869] User

[0870] Users access the system through their terminals, enter their profile information and career goals, and plan their actions based on the career advice and feedback provided, and record their progress.

[0871] Overview of program processing

[0872] 1. Collecting User Profile Information

[0873] The terminal displays a profile entry screen to the user, where the user enters their past work history, current skill set, and future career goals. Once the entry is complete, the terminal sends this information to the server.

[0874] 2. Analysis of User Information

[0875] The server stores the user's profile information received from the device in a database. It then uses a generative AI model to analyze the data and identify skill gaps between the user's current skills and their career goals. For example, it identifies the skills of "machine learning" and "deep learning" that are lacking for users who want to become AI developers.

[0876] 3. Gathering market trends and industry news

[0877] The server collects the latest market trends, industry news, and job information from the internet, analyzes this data, and extracts information relevant to the user's career goals. For example, it collects news that "demand for AI engineers is increasing" and provides it to the user.

[0878] 4. Generating personalized career advice

[0879] The server generates personalized career advice for the user based on the analyzed data and extracted information, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0880] 5. Continuous support and feedback

[0881] The server periodically tracks the user's learning progress and generates feedback based on the progress (e.g., "You have completed 50% of the introductory machine learning course"), which the device displays to the user.

[0882] 6. Emotion recognition and feedback regulation

[0883] The device collects the user's facial expressions and voice and sends them to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The analysis results are sent to the server, which then adjusts the content of career advice and feedback based on this information. For example, if the user is feeling stressed, it can provide relaxation resources and gentle feedback.

[0884] Specific examples

[0885] For example, if a user types into a terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer," the server receives this information and uses a generative AI model to identify skills the user lacks (such as "machine learning" or "deep learning").

[0886] Next, the server collects relevant market trends from the internet and extracts news such as "Demand for AI engineers will increase by 20% by 2025." Based on this, it generates career advice such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice," and provides it to the user via their device.

[0887] Furthermore, when a user achieves progress, the device recognizes the user's facial expressions and voice, and the emotion engine sends data indicating "satisfied" to the server. Based on this, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[0888] Example prompt sentence:

[0889] "I've been working as a software engineer for the past five years. I have skills in Python and Java, but I want to become an AI developer. I'd like some advice on what skills I should learn as my next step."

[0890] In this way, this career development support system can provide support tailored to the individual needs and emotional state of the user, and support more effective career growth.

[0891] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0892] Step 1:

[0893] Collecting user profile information

[0894] Input: The user enters their profile information (past work experience, current skill set, career goals) into the device.

[0895] How it works: The device displays a profile entry screen for the user, where the user enters information such as "5 years of software engineering experience, Python and Java skills, and aspirations to become an AI developer."

[0896] Output: The entered profile information is sent to the server.

[0897] Step 2:

[0898] Analysis of user information

[0899] Input: User profile information sent from the device.

[0900] How it works: The server stores the received profile information in a database. It then uses a generative AI model (e.g., GPT-4) to analyze the user information and identify skill gaps between their current skills and their career goals. For example, the server might identify a lack of skills in "machine learning" and "deep learning."

[0901] Output: Data about the user's skill gaps.

[0902] Step 3:

[0903] Gathering market trends and industry news

[0904] Input: Market trends, industry news, and job listings collected by the server from the internet.

[0905] How it works: The server uses web scraping technology to gather the latest market trends and industry news in real time, such as the news that "demand for AI engineers will increase by 20% by 2025."

[0906] Output: Collected market trends and industry news data.

[0907] Step 4:

[0908] Generate personalized career advice

[0909] Input: User information analysis results and market trend data.

[0910] How it works: The server generates personalized career advice for the user based on the analyzed data and extracted information, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[0911] Output: Generated career advice and educational resource suggestions.

[0912] Step 5:

[0913] Providing career advice

[0914] Input: Career advice information sent to the device.

[0915] Operation: The device displays the career advice sent to the user, who can then create a study plan based on the information displayed.

[0916] Output: Career advice displayed to the user.

[0917] Step 6:

[0918] Continuous support and progress feedback

[0919] Input: User progress data.

[0920] How it works: The server periodically tracks the user's learning and career development progress and generates feedback based on their progress (e.g., "You've completed 50% of the Introduction to Machine Learning course"). The device displays the feedback sent by the server to the user (e.g., "Great progress! Keep it up!").

[0921] Output: Progress feedback displayed to the user.

[0922] Step 7:

[0923] Emotion recognition and feedback regulation

[0924] Input: User's facial and voice data.

[0925] How it works: The device uses a webcam and microphone to collect the user's facial expressions and voice. The emotion engine analyzes the collected data and recognizes the user's emotional state in real time (e.g., excitement, stress). The analysis results are sent to a server, which then adjusts the content of career advice and feedback based on the user's emotional state. For example, if the user is feeling stressed, it will provide relaxation resources and gentle feedback.

[0926] Output: Tailored career advice and feedback based on the user's emotional state.

[0927] (Application example 2)

[0928] 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."

[0929] Although conventional career development support systems provide career advice based on users' profile information, they are unable to provide feedback or recommendations that take into account the user's real-time emotional state, which means that they are unable to fully motivate users.In addition, the technology for collecting and analyzing emotional data in real time has not been properly integrated, leaving room for improvement in the user experience.

[0930] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting profile information entered by the user, means for analyzing the collected profile information to identify a gap between the user's current abilities and their goals, means for collecting market trends and industry news and extracting information related to the user's goals, means for generating and providing personalized advice to the user based on the analyzed data and the extracted information, means for periodically tracking the user's progress and providing feedback according to the progress, means for collecting the user's emotional state in real time using a smart device, and means for adjusting feedback and recommendations based on the collected emotional information. This makes it possible to provide feedback according to the user's real-time emotional state, thereby increasing the user's motivation and enabling more effective career development support.

[0931] "Profile information" is information such as past career history, current abilities, and goals entered by the user himself / herself.

[0932] "Analysis" is the process of analyzing the collected data to identify gaps between the user's current abilities and goals, as well as necessary information.

[0933] "Market trends" are information about current trends and future developments in a particular industry or field.

[0934] "Industry News" refers to current events and information related to a particular industry.

[0935] "Personalized advice" refers to customized recommendations or instructions determined based on a user's individual profile information and analytics data.

[0936] "Progress" is a measure of how far a user has progressed toward a particular goal.

[0937] "Feedback" refers to evaluations and advice provided to users regarding their actions and progress.

[0938] A "smart device" refers to an electronic device that can connect to the Internet and collect data using various sensors.

[0939] An "emotional state" is an emotion or feeling that a user is experiencing, such as stress or happiness.

[0940] "Real-time" means that data is processed almost instantly, without any delay.

[0941] To implement the present invention, the following configuration is necessary.

[0942] Overall system configuration

[0943] The career development support system consists of the following main components:

[0944] 1. Server

[0945] 2. Devices (smartphones, smart glasses, etc.)

[0946] 3. Users

[0947] 4. Emotion Engine

[0948] Collection of profile information

[0949] The device displays a profile entry screen to the user, where the user enters profile information such as past experience, current skill set, and goals. The profile information entered by the user is sent from the device to the server. At this stage, a simple prompt message like the following is displayed:

[0950] "Hello! Please enter your profile information, such as your past purchase history, preferred brands and styles, and budget."

[0951] Analyzing user information and identifying career goals

[0952] The server stores the user's profile information received from the device in a database and uses the generative AI model to analyze the gap between the user's current abilities and their goals. For example, if a user is aiming to change careers from "software engineer" to "AI developer," the server will identify the missing skills of "machine learning" and "deep learning."

[0953] Gathering market trends and industry news

[0954] The server collects the latest market trends, industry news, job listings, etc. from the internet in real time and extracts information relevant to the user's goals. For example, it provides the user with news that "demand for AI engineers is increasing."

[0955] Providing personalized advice

[0956] The server generates personalized advice for the user based on the analyzed data and extracted information, and provides it to the user via the device. Appropriate educational resources are also suggested at this stage. For example, information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice" is generated.

[0957] Progress Tracking and Feedback

[0958] The server periodically tracks the user's progress and provides feedback based on that progress, for example, if the progress information is "You've completed 50% of the Introduction to Machine Learning course," the message displayed is "Great progress! Keep it up!"

[0959] Emotion engine integration

[0960] The terminal uses a smart device to capture the user's facial expressions and voice and transmits them to the emotion engine, which analyzes the collected emotion data and recognizes the user's emotional state in real time. For example, it can determine whether the user is excited or stressed.

[0961] Adjusting feedback based on emotional state

[0962] The server adjusts the content of feedback and recommendations based on the emotional data recognized by the emotion engine. For example, if a user is feeling stressed, it can provide gentle feedback to increase motivation or provide resources to help them relax. This makes it possible to more effectively support the user's career development.

[0963] Specific examples

[0964] Collection of profile information

[0965] A user types into a terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and identifies a lack of necessary "machine learning" and "deep learning" skills.

[0966] Providing market trends

[0967] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[0968] Emotion Recognition and Feedback

[0969] When the user achieves progress, the device recognizes the user's emotional state, and the emotion engine sends data indicating that the user is satisfied to the server. Based on this emotional data, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[0970] In this way, the career development support system utilizes the user's profile information and emotional information collected in real time to provide personalized career advice.

[0971] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0972] Step 1:

[0973] The user inputs profile information into the terminal. Specifically, the user inputs information such as past experience, current skill set, and career goals into an input screen displayed on the terminal. The input data is then sent to the server by the terminal.

[0974] input:

[0975] User profile information (background, skill set, career goals)

[0976] output:

[0977] User profile data sent to the server

[0978] Data processing:

[0979] The device formats the data entered by the user and sends it to the server via the API.

[0980] Step 2:

[0981] The server stores the received user profile information and analyzes it using the generative AI model. The analysis results identify gaps between the user's current abilities and their career goals. For example, it identifies the skills they lack to become an AI developer.

[0982] input:

[0983] User profile data sent from your device

[0984] output:

[0985] Skills gap identification results

[0986] Data processing:

[0987] It uses a generative AI model to compare a user's current skills with their career goals and identify skill gaps.

[0988] Step 3:

[0989] The server collects and analyzes the latest market trends, industry news, and job information from the internet, extracting information relevant to the user's career goals, such as demand trends in the AI ​​industry and useful educational courses.

[0990] input:

[0991] Market trends, industry news, and job listings collected from the internet

[0992] output:

[0993] Extracted related information

[0994] Data processing:

[0995] It uses cloud-based web crawlers and natural language processing (NLP) algorithms to extract relevant information.

[0996] Step 4:

[0997] The server generates personalized advice for the user based on the analyzed data and extracted information and provides it via the device, such as suggesting suitable learning courses or career paths for the user.

[0998] input:

[0999] Skills gap identification results

[1000] Extracted related information

[1001] output:

[1002] Personalized advice

[1003] Data processing:

[1004] Based on analytical data, we generate optimal career advice and learning resources.

[1005] Step 5:

[1006] The server periodically tracks the user's progress and provides feedback based on the progress, for example by sending motivational messages based on the progress made.

[1007] input:

[1008] User progress data

[1009] output:

[1010] Progress Feedback

[1011] Data processing:

[1012] Aggregate progress data and generate motivational feedback.

[1013] Step 6:

[1014] The terminal collects the user's emotional state in real time using a smart device, capturing facial expressions and voice using the camera and microphone of smart glasses or a smartphone, and sending the captured data to the emotion engine.

[1015] input:

[1016] User's facial expression data, voice data

[1017] output:

[1018] Real-time emotion data

[1019] Data processing:

[1020] It uses a camera and microphone to capture data and send it to the emotion engine.

[1021] Step 7:

[1022] The emotion engine analyzes the collected emotion data to recognize the user's emotional state, for example, to determine whether the user is stressed or happy.

[1023] input:

[1024] Facial expression data, voice data

[1025] output:

[1026] Emotion recognition results

[1027] Data processing:

[1028] Emotional states are recognized using image analysis and audio analysis technology.

[1029] Step 8:

[1030] The server tailors feedback and recommendations based on the emotional data recognized by the emotion engine, for example, providing relaxation resources if the user is feeling stressed.

[1031] input:

[1032] Emotion recognition results

[1033] output:

[1034] Tailored feedback and recommendations

[1035] Data processing:

[1036] The feedback content is adjusted based on emotional data, and messages are generated to maintain the user's motivation.

[1037] 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.

[1038] 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.

[1039] 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.

[1040] [Third embodiment]

[1041] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1042] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1043] 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).

[1044] 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.

[1045] 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.

[1046] 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).

[1047] 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.

[1048] 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.

[1049] 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.

[1050] 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.

[1051] 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.

[1052] 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."

[1053] The career development support system of the present invention is a system that provides personalized career advice by collecting and analyzing user profile information. Specific procedures for implementing this system are described below.

[1054] System Overview

[1055] This career development support system is a system in which a server, terminals, and users work together. The server performs the main data processing and analysis, and the terminals function as an interface with the users. Users access the system through their terminals and input the necessary information.

[1056] Collecting user profile information

[1057] The device displays a profile entry screen to the user, where the user enters information such as past experience, current skill set, and career goals. Once the entry is complete, the device sends this information to the server.

[1058] Analysis of user information

[1059] The server receives the user's profile information sent from the device. It then analyzes the collected information and identifies gaps between the user's current skills and their career goals. For example, if a user has a background as a "software engineer" and aims to become an "AI developer" in the future, the server will identify that they lack skills such as "machine learning" and "deep learning."

[1060] Market trends and industry news analysis

[1061] The server collects the latest market trends, industry news, and job information from the internet in real time. It analyzes this data and extracts information relevant to the user's career goals. For example, the server collects news such as "Demand for AI engineers is increasing" and prepares it to provide to the user.

[1062] Providing personalized career advice

[1063] The server generates personalized career advice for the user based on the analyzed data and extracted information. The generated advice is sent to the device, which then displays it to the user. For example, information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice" is displayed.

[1064] Continuous support and feedback

[1065] The server periodically tracks the user's progress. Each time the server receives progress information, such as "You've completed 50% of the Introduction to Machine Learning course," it generates real-time feedback and provides it to the user via their device, such as a message like "Great progress! Keep it up!"

[1066] The device also collects feedback from users and sends it to the server, which analyzes the feedback and uses it to improve the system.

[1067] Specific examples

[1068] Collecting and analyzing input information

[1069] A user enters "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and determines that "machine learning" and "deep learning" are lacking.

[1070] Providing market trends

[1071] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[1072] Progress tracking and feedback

[1073] If a user starts the "Introduction to Machine Learning" course and achieves 50% progress, the server receives this data and generates a message saying "Great progress! Keep it up!" and displays it through the terminal.

[1074] In this way, this career development support system provides support tailored to the individual needs of each user, enabling companies to effectively support the career growth of their employees.

[1075] The processing flow will be explained below.

[1076] Step 1:

[1077] The device displays a profile entry screen to the user, which includes fields for entering the user's past experience, current skill set, and career goals.

[1078] Step 2:

[1079] Users input their background (e.g., 5 years of experience as a software engineer), skill set (e.g., Python, Java), and career goals (e.g., to become an AI developer).

[1080] Step 3:

[1081] The device collects all profile information entered by the user and sends it to the server, including the user's background, skill set, and career goals.

[1082] Step 4:

[1083] The server receives the user profile information sent from the terminal and stores it in a database.

[1084] Step 5:

[1085] The server analyzes the received profile information, including using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, it may determine that skills such as "machine learning" and "deep learning" are lacking.

[1086] Step 6:

[1087] The server collects the latest market trends, industry news, and job information from the internet. In this step, data is obtained using web scraping and APIs.

[1088] Step 7:

[1089] The server analyzes the collected market data and extracts information relevant to the user's career goals, such as information that there is a growing demand for AI engineers.

[1090] Step 8:

[1091] The server generates personalized career advice for the user based on the analyzed user information and market data, including recommended skill courses and related educational resources.

[1092] Step 9:

[1093] The server transmits the generated carrier advice to the terminal.

[1094] Step 10:

[1095] The device displays career advice sent from the server to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1096] Step 11:

[1097] The server collects user activity data to periodically check the user's progress, for example, the user has completed 50% of the introductory machine learning course.

[1098] Step 12:

[1099] The server generates real-time feedback based on collected activity data, such as messages like "Great progress, keep it up!"

[1100] Step 13:

[1101] The terminal displays the feedback generated from the server to the user.

[1102] Step 14:

[1103] The terminal collects feedback from the user and sends it back to the server, where it is used to help improve the system.

[1104] Step 15:

[1105] The server analyzes the collected feedback and identifies improvements to the system, thereby continually improving the user experience of the system.

[1106] With this processing flow, the career development support system provides support tailored to the individual needs of users, enabling companies to effectively support the career growth of their employees.

[1107] Example 1

[1108] 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."

[1109] Conventional career development support systems have difficulty providing appropriate career advice and educational resources to meet the individual needs of users, resulting in an inability to effectively support users' career growth. Furthermore, it is difficult to track users' progress and feedback in a timely manner and provide real-time support, which reduces the convenience and effectiveness of the system.

[1110] 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.

[1111] In this invention, the server includes: means for collecting profile information entered by the user; means for transmitting the collected profile information via the terminal to the server; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news from the Internet and extracting information related to the user's career goals; means for generating personalized career advice for the user based on the analyzed data and the extracted information; means for transmitting the generated advice to the terminal and displaying it to the user; means for periodically tracking the user's progress, generating feedback according to the progress, and providing it via the terminal; and means for collecting feedback from the user and using it to improve the system. This makes it possible to provide appropriate career advice and educational resources tailored to the individual needs of each user, and also makes it possible to track the user's progress and feedback in real time and provide prompt support.

[1112] A "User" is an individual who uses the system and enters profile information.

[1113] A "terminal" is a device used by a user, and is an interface through which profile information is entered and career advice is displayed via a web browser.

[1114] A "server" is a computer system that receives data sent from a terminal and performs analysis and data processing.

[1115] "Profile information" is information about a user's past experience, current skill set, and career goals.

[1116] "Collect" refers to the process of transmitting profile information entered by a user into a terminal to a server.

[1117] "Analysis" refers to the process of identifying gaps between the user's current skills and career goals based on the profile information received by the server.

[1118] "Market Trends" is information about the latest movements and trends in an industry or market.

[1119] "Industry news" refers to the latest happenings and reports in a particular field or industry.

[1120] "Personalized career advice" refers to career growth advice and suggestions that are customized to a user's individual needs.

[1121] "Progress" is information that indicates how far the user has progressed toward achieving their goal.

[1122] "Feedback" is information that provides responses and advice according to the user's progress.

[1123] "Educational Resources" means learning courses and materials offered to users to improve their skills and grow their careers.

[1124] "System improvement" is the process of improving the system's functions and services based on feedback collected from users.

[1125] The career development support system of the present invention is a system in which a server, a terminal, and a user work in cooperation with each other. A specific implementation method of this system will be described below.

[1126] System Overview

[1127] This career development support system is composed of a server that performs the main data processing and analysis, a terminal that functions as an interface with users, and users who provide information to the system.

[1128] Collecting user profile information

[1129] The terminal is a device connected to the Internet (e.g., a PC or a smartphone), and in this embodiment, a web browser (e.g., Google Chrome or Mozilla Firefox) is used. The terminal displays a screen for the user to enter profile information using an HTML form. The user enters information such as past experience, current skill set, and career goals. Once the information has been entered, the terminal collects this information using JavaScript, converts it into JSON format, and sends it to the server using HTTPS communication.

[1130] Analysis of user information

[1131] The server parses the received user profile information in JSON format. The server receives the data using a Python framework (e.g., Flask, Django) and converts the information into a data frame using the Pandas library. Based on this data frame, the server uses the Scikit-learn library to identify gaps between the user's current skills and their career goals. For example, if a user has a background as a "software engineer" and aspires to become an "AI developer," the server will identify a lack of skills such as "machine learning" and "deep learning."

[1132] Market trends and industry news analysis

[1133] The server uses web scraping technology to collect market trends and industry news from the Internet. Specifically, it uses Python's Beautiful Soup and Selenium libraries. The collected data is analyzed to identify information relevant to the user's career goals. For example, it extracts and analyzes news that "demand for AI engineers is increasing."

[1134] Providing personalized career advice

[1135] The server generates personalized career advice for the user based on the analyzed data and extracted information. For example, it might provide the user with information such as "Recommended courses: Introduction to Machine Learning, Deep Learning Practice." The generated advice is converted to JSON format and sent to the device. The device parses it using JavaScript and displays it to the user using HTML.

[1136] Continuous support and feedback

[1137] The server periodically receives data to track the user's progress. Each time a user reports their learning progress, the server analyzes the data and generates real-time feedback. For example, if the server receives progress information such as "You've completed 50% of the introductory machine learning course," it generates feedback such as "Great progress! Keep it up!" and provides it to the user via their device. The device also collects feedback from the user and sends it to the server. The server uses the collected feedback to improve the system.

[1138] Specific examples

[1139] The user enters information as follows:

[1140] Experience: "5 years of software engineering experience"

[1141] Skills: Python and Java

[1142] Goal: "I want to become an AI developer"

[1143] The server receives this information and determines that there is a lack of "machine learning" or "deep learning."

[1144] The server finds a news item saying, "Demand for AI engineers will increase by 20% by 2025," extracts a list of related courses (e.g., "Introduction to Machine Learning" and "Deep Learning in Practice"), and sends it to the device, which then displays it to the user.

[1145] If a user "completes 50% of the introductory machine learning course," the server receives this progress information, generates feedback to the user via the device, saying, "Great progress! Keep it up!"

[1146] In this way, the system provides career advice tailored to the user's individual needs and effectively supports the user's career growth through continuous support and feedback.

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

[1148] Step 1:

[1149] The device displays a screen where the user can enter profile information. Input fields include background, skills, career goals, etc. After entering the information, the user presses the "Submit" button. Specifically, the information is collected using an HTML form, and then compiled using JavaScript and converted to JSON format.

[1150] Input: User's background, skills, and career goals

[1151] Data processing: Convert information into JSON format using JavaScript

[1152] Output: Profile information in JSON format

[1153] Step 2:

[1154] The device sends the collected profile information in JSON format to the server using HTTPS, which uses encrypted communication to ensure security.

[1155] Input: Profile information in JSON format converted on the device

[1156] Data transmission: Secure data transmission via HTTPS

[1157] Output: Profile information sent to the server

[1158] Step 3:

[1159] The server receives the JSON-formatted profile information using a Python framework (such as Flask or Django), converts it into a data frame using the Pandas library, and organizes the information by background, skills, and career goals.

[1160] Input: Profile information in JSON format

[1161] Data processing: Convert to a data frame using Pandas and organize the information

[1162] Output: A neat data frame

[1163] Step 4:

[1164] The server uses Scikit-learn to analyze the data and identify gaps between the user's current skills and their career goals. For example, it uses a linear regression model to analyze how well the user's skills match their career goals.

[1165] Input: A cleaned data frame

[1166] Data Computing: Skills and Career Goal Gap Analysis with Scikit-Learn

[1167] Output: User skill gap analysis results

[1168] Step 5:

[1169] The server uses web scraping with Beautiful Soup and Selenium to gather market trends and industry news from the internet, which is then analyzed to identify information relevant to the user's career goals.

[1170] Input: Market trends, industry news webpage URL

[1171] Data collection: Data collection through web scraping

[1172] Output: Analyzable market trend data, industry news data

[1173] Step 6:

[1174] The server generates personalized career advice based on the analyzed user profile information and market trend data, using natural language generation (NLG) technology to create advice in a user-friendly format.

[1175] Input: User skill gap analysis results, market trend data

[1176] Data Generation: Generating Career Advice with NLG

[1177] Output: Personalized career advice (e.g., "Introduction to Machine Learning Course")

[1178] Step 7:

[1179] The server converts the generated advice into JSON format and sends it to the device, which then parses the received JSON data using JavaScript and displays it to the user in HTML.

[1180] Input: Generated career advice

[1181] Data conversion and transmission: Converted to JSON format and transmitted via HTTPS

[1182] Output: Personalized career advice displayed to the user

[1183] Step 8:

[1184] The server periodically receives data to track the user's progress in the course of study selected by the user. The user enters progress data and sends it to the server.

[1185] Input: User progress data

[1186] Data calculation: Analysis of progress data

[1187] Output: Feedback based on progress

[1188] Step 9:

[1189] The server generates real-time feedback based on the progress data, using natural language generation technology, and sends it to the device, where it is displayed to the user.

[1190] Input: User progress data

[1191] Data generation: Feedback generated based on progress

[1192] Output: Feedback that is displayed to the user (e.g., "Great progress! Keep it up!")

[1193] Step 10:

[1194] The device collects feedback from users and sends it to the server, which analyzes it and uses it to improve the system.

[1195] Input: User feedback

[1196] Data collection and analysis: Feedback analysis

[1197] Output: System improvement proposals

[1198] In this step, the career development support system can provide individual career advice to users and provide continuous progress tracking and feedback, thereby effectively supporting users' career growth.

[1199] (Application example 1)

[1200] 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."

[1201] Traditional career development support systems provide personalized advice by collecting and analyzing user profile information, but most of this information is static and lacks specific support for how users should actually improve their skills. Furthermore, they lack the ability to track users' learning progress in real time and provide appropriate feedback, resulting in insufficient motivation and support for users to continue developing. As a result, it is difficult to efficiently bridge the gap between users' career goals and their actual skill development. Furthermore, they are unable to provide the latest learning content based on market trends and industry news in real time, making it difficult for users to utilize the latest information to advance their learning.

[1202] 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.

[1203] In this invention, the server includes means for collecting profile information entered by the user, means for analyzing the collected profile information to identify gaps between the user's current skills and career goals, means for collecting market trends and industry news and extracting information related to the user's career goals, means for generating and providing personalized career advice to the user based on the analyzed data and the extracted information, means for periodically tracking the user's progress and providing feedback according to the progress, and means for delivering learning content. This allows the user to improve their skills in real time while receiving learning content based on the latest market trends. In addition, the ability to receive feedback according to the user's progress reinforces continuous growth and motivation for learning.

[1204] A "collection instrument" is a device or software that transmits user-entered profile information to the system and stores that information in a database.

[1205] The "analyzing and identifying means" refers to algorithms or programs that process the collected profile information and identify gaps between the user's current skills and career goals.

[1206] "Market trend and industry news gathering means" means a device or software that obtains the latest market and industry trends from the Internet or other data sources and stores them in a database.

[1207] "Means for extracting information" are algorithms or programs that extract useful information related to the user's career goals from collected market trends and industry news.

[1208] The "means for generating and providing personalized career advice" is a system for creating career advice suited to the individual needs of a user based on the analyzed data and extracted information, and providing the advice to the user.

[1209] "Means for regular progress tracking and feedback" refers to a system that continuously monitors the progress of a user's learning or training and provides the user with advice and encouraging messages based on that progress.

[1210] A "means for delivering learning content" is a system that provides users with educational materials (videos, articles, interactive tutorials, etc.) via the Internet to learn the skills they need.

[1211] System Overview

[1212] This career development support system is a system in which a server, terminals, and users work together. The server performs the main data processing and analysis, while the terminals are used to interact with users. Users access the system via their smartphones and input and receive the necessary information.

[1213] Collecting user profile information

[1214] Users use an application on their smartphone to input information such as their past experience, current skill set, and career goals, which is then sent in real time to a server and stored in a database.

[1215] Analysis of user information

[1216] The server analyzes the user's profile information using Python and machine learning libraries such as Scikit-learn and TensorFlow. This identifies gaps between the user's current skills and their career goals. For example, if a user enters "5 years of software engineering experience, Python and Java skills, and aspirations to become an AI developer," the server will determine that "machine learning" and "deep learning" are lacking.

[1217] Market trends and industry news analysis

[1218] The server collects the latest market trends, industry news, and job information from the Internet in real time. It uses Python's Beautiful Soup and Requests to scrape the necessary information and stores it in a database. This allows it to extract information related to the user's career goals. For example, it collects news such as "Demand for AI engineers will increase by 20% by 2025" and prepares it to be provided to the user.

[1219] Providing personalized career advice

[1220] The server generates personalized career advice for the user based on the analyzed data and extracted information. The advice is sent to the device and displayed to the user via a smartphone app. For example, it displays information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1221] Continuous support and feedback

[1222] The server periodically tracks the user's progress. If a user starts the "Introduction to Machine Learning" course and achieves 50% progress, the server receives this data and generates a message saying "Great progress! Keep it up!", which is displayed on the device. The device also collects feedback from the user and sends it to the server. The server analyzes the collected feedback and uses it to improve the system.

[1223] Delivery of learning content

[1224] The server uses cloud storage such as AWS S3 to deliver learning content to users. Users can receive videos, articles, and interactive tutorials in streaming format through the app, allowing users to always learn the latest information and technologies.

[1225] Specific examples

[1226] Profile input prompt

[1227] "Please tell us about your current occupation, past experience, skills, and future career goals."

[1228] Market trend extraction

[1229] For example, it obtains information from the internet that "demand for AI engineers is increasing," and based on that, recommends related learning videos.

[1230] "A recent report predicts that demand for AI engineers will increase by 20% by 2025. Check out this educational video."

[1231] In this way, the career development support system provides powerful support for users to continuously improve their skills and achieve their career goals by providing learning content tailored to their individual needs, tracking their progress, and collecting feedback.

[1232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1233] Step 1:

[1234] User enters profile information

[1235] Users access the application using their smartphones and enter information such as their past experience, current skill set, career goals, etc. Once the information is complete, the entered profile information is sent to the server.

[1236] Input: User profile information (background, skills, career goals)

[1237] Output: Profile information sent to the server

[1238] Step 2:

[1239] The server analyzes the profile information

[1240] The server receives the collected profile information and analyzes it using Python, Scikit-learn, and TensorFlow to identify gaps between the user's current skills and their career goals.

[1241] Input: User profile information

[1242] Output: Analysis results (skill gap identification)

[1243] Step 3:

[1244] Server collects market trends and industry news

[1245] The server uses Python's Beautiful Soup and Requests to scrape the latest market trends and industry news from the internet and store that information in a database.

[1246] Input: Market trends and industry news from the internet

[1247] Output: Market trends and industry news stored in a database

[1248] Step 4:

[1249] The server extracts the information

[1250] The server extracts information relevant to the user's career goals from the collected market trends and industry news using analytical algorithms to identify relevant information.

[1251] Input: Market trends and industry news stored in a database

[1252] Output: Extracted relevant information

[1253] Step 5:

[1254] The server generates personalized career advice

[1255] The server generates personalized career advice for the user based on the analysis results and the extracted information, and the generated career advice is sent to the terminal.

[1256] Input: Analysis results, extracted related information

[1257] Output: Personalized career advice

[1258] Step 6:

[1259] The device displays career advice

[1260] The device receives personalized career advice sent from the server and displays it to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1261] Enter: personalized career advice

[1262] Output: Career advice displayed to the user

[1263] Step 7:

[1264] Enter user progress

[1265] Users enter their own learning progress through the application, and the entered progress information is sent to the server.

[1266] Input: Learning progress information

[1267] Output: Progress information sent to the server

[1268] Step 8:

[1269] The server tracks your progress and provides feedback

[1270] The server analyzes the progress information and generates feedback based on the progress, such as a message like "Great progress, keep it up" that is sent to the device.

[1271] Input: Learning progress information

[1272] Output: The generated feedback message

[1273] Step 9:

[1274] The device displays feedback

[1275] The terminal receives the feedback message sent from the server and displays it to the user.

[1276] Input: Feedback message

[1277] Output: A feedback message that is displayed to the user

[1278] Step 10:

[1279] The server distributes learning content

[1280] The server delivers learning content to users using cloud storage such as AWS S3, and users receive the content in streaming format via their devices.

[1281] Input: Learning content

[1282] Output: Learning content delivered to users

[1283] 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.

[1284] This invention is a career development support system that utilizes a user's profile information and emotional information to provide personalized career advice. The system supports users in achieving their goals, effectively manages their progress, and provides feedback according to the user's emotional state.

[1285] System Overview

[1286] The career development support system operates in cooperation with a server, terminals, users, and an emotion engine. The server processes and analyzes data, and the emotion engine recognizes the user's emotions. The terminals function as an interface with the users, and the users access the system through their terminals.

[1287] Collecting user profile information

[1288] The device displays a profile entry screen to the user, where the user enters information such as past experience, current skill set, and career goals. Once the entry is complete, the device sends this information to the server.

[1289] Analysis of user information

[1290] The server stores the user's profile information received from the device in a database. It then analyzes the user information using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, if a user is aiming to change careers from "software engineer" to "AI developer," it identifies the necessary skills in "machine learning" and "deep learning."

[1291] Market trends and industry news analysis

[1292] The server collects the latest market trends, industry news, and job information from the internet in real time. It analyzes the collected data and extracts information relevant to the user's career goals. For example, it collects news such as "Demand for AI engineers is increasing" and provides it to the user.

[1293] Providing personalized career advice

[1294] Based on the analyzed data and extracted information, the server generates personalized career advice and suitable educational resources for the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1295] The device displays the generated career advice and educational resources to the user, who can then create a study plan based on the displayed information.

[1296] Continuous support and feedback

[1297] The server periodically tracks the user's progress, for example, "I've completed 50% of the introductory machine learning course," and generates real-time feedback.

[1298] The device displays feedback sent by the server to the user, for example, a message like "Great progress, keep it up," and also collects feedback from the user and sends it to the server.

[1299] The server analyzes the collected feedback and identifies improvements to the system, and based on this feedback, improves the user experience of the system.

[1300] Emotion engine integration

[1301] The device collects emotion data from the user's facial expressions and voice, and sends the collected emotion data to the emotion engine.

[1302] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state in real time, for example, determining whether the user is excited or stressed.

[1303] The server then adjusts the content of career advice based on the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will provide gentle feedback to boost motivation or provide resources to help them relax.

[1304] The device works in conjunction with an emotion engine to display feedback based on the user's emotional state, such as a message like "Take a break, we have the resources to refresh you."

[1305] Specific examples

[1306] Collecting and analyzing input information

[1307] A user enters into the terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and identifies "machine learning" and "deep learning" as areas of deficiency.

[1308] Providing market trends

[1309] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[1310] Emotion Recognition and Feedback

[1311] When the user achieves progress, the device recognizes the user's emotional state, and the emotion engine sends data indicating that the user is satisfied to the server. Based on this emotional data, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[1312] In this way, this career development support system provides support that meets the individual needs and feelings of users, enabling companies to more effectively support the career growth of their employees.

[1313] The processing flow will be explained below.

[1314] Step 1:

[1315] The device displays a profile entry screen to the user, which includes fields for entering past experience, current skill set, and career goals.

[1316] Step 2:

[1317] Users input their background (e.g., 5 years of experience as a software engineer), skill set (e.g., Python, Java), and career goals (e.g., to become an AI developer).

[1318] Step 3:

[1319] The device collects all profile information entered by the user and sends it to a server, including the user's background, skill set, and career goals.

[1320] Step 4:

[1321] The server receives the user profile information sent from the terminal and stores it in a database.

[1322] Step 5:

[1323] The server analyzes the received profile information, including using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, it may determine that there is a lack of skills in "machine learning" and "deep learning."

[1324] Step 6:

[1325] The server collects the latest market trends, industry news, and job information from the internet. In this step, data is obtained using web scraping and APIs.

[1326] Step 7:

[1327] The server analyzes the collected market data and extracts information relevant to the user's career goals, such as "demand for AI engineers is increasing."

[1328] Step 8:

[1329] The server generates personalized career advice for the user based on the analyzed user information and market data, including recommended skill courses and related educational resources.

[1330] Step 9:

[1331] The server transmits the generated carrier advice to the terminal.

[1332] Step 10:

[1333] The device displays career advice sent from the server to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1334] Step 11:

[1335] The terminal collects emotional data such as the user's facial expressions and voice and sends it to the emotion engine.

[1336] Step 12:

[1337] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state, for example, determining in real time whether the user is feeling stressed or happy.

[1338] Step 13:

[1339] The server adjusts the content of career advice based on the emotion data recognized by the emotion engine. For example, if the user is feeling stressed, the advice will be changed to provide resources for relaxation.

[1340] Step 14:

[1341] The device then displays tailored career advice sent from the server to the user, such as "You seem to be feeling a bit stressed. Try this meditation course to help you relax."

[1342] Step 15:

[1343] The server collects user learning activity data to periodically track the user's learning progress, for example, "I have completed 50% of the introductory machine learning course."

[1344] Step 16:

[1345] The server generates real-time feedback based on the progress data collected, such as positive feedback like "Great progress, keep it up!"

[1346] Step 17:

[1347] The terminal displays the feedback sent from the server to the user.

[1348] Step 18:

[1349] The terminal collects feedback from the user and sends it back to the server.

[1350] Step 19:

[1351] The server analyzes the collected user feedback and identifies improvements to the system, and based on this feedback, continually improves the user experience of the system.

[1352] With this processing flow, the career development support system provides support that meets the individual needs and feelings of users, enabling companies to effectively support the career growth of their employees.

[1353] Example 2

[1354] 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."

[1355] While current career development support systems have the ability to collect user profile information and provide advice based on career goals, they lack personalized feedback that takes into account the user's emotional state. They also lack mechanisms for providing real-time feedback based on the user's progress or the latest career advice based on market trends. As a result, these systems face challenges that make it difficult to maintain user motivation and achieve effective career development.

[1356] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1357] In this invention, the server includes: means for collecting profile information entered by a user; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news and extracting information related to the user's career goals; means for generating and providing personalized career advice to the user based on the analyzed data and extracted information; means for periodically tracking the user's progress and providing feedback according to the progress; means for collecting emotional data from the user's facial expressions and voice; and means for analyzing the collected emotional data to recognize the user's emotional state and adjust the content of the career advice. This enables personalized feedback that takes the user's emotional state into consideration, thereby supporting effective career development while maintaining the user's motivation.

[1358] "User" refers to an entity that accesses the system and enters their profile information and career goals.

[1359] "Profile information" refers to information such as a user's past work history, current skill set, and future career goals.

[1360] A "skills gap" refers to the lack of skills between a user's current skills and the skills needed to achieve their career goals.

[1361] "Market trends" refers to the current situation and future outlook, as well as supply and demand trends, in a particular industry or occupation.

[1362] "Industry News" refers to current information, events, or important announcements related to a particular industry.

[1363] "Personalized career advice" refers to specific advice and suggestions tailored to a user's individual situation and goals.

[1364] "Progress" refers to the achievements and progress a user makes in their learning or career development process.

[1365] "Feedback" refers to assessments and advice provided based on a user's progress and emotional state.

[1366] "Facial expression data" refers to information about the user's facial expressions.

[1367] "Voice data" refers to information about a user's voice and speaking style.

[1368] An "emotion engine" refers to technology or a device that has the ability to analyze collected facial expression and voice data to recognize the user's emotional state.

[1369] MODE FOR CARRYING OUT THE INVENTION

[1370] This invention is a career development support system that provides personalized career advice by utilizing user profile information and emotion information. The system includes a server, a terminal, a user, and an emotion engine, all of which operate in cooperation with each other.

[1371] System Configuration

[1372] server

[1373] The server is the main data processing device that analyzes users' profile information and generates career advice. It is equipped with a database, generative AI models (e.g., OpenAI's GPT-4), and web scraping technology that collects market trends and industry news from the Internet. The server also manages users' progress and generates feedback.

[1374] Terminal

[1375] The terminal is a device that functions as an interface with the user. It displays an input screen for the user to enter profile information and provides the user with career advice and feedback from the server. The terminal also has the function of collecting the user's facial expressions and voice using a webcam and microphone.

[1376] User

[1377] Users access the system through their terminals, enter their profile information and career goals, and plan their actions based on the career advice and feedback provided, and record their progress.

[1378] Overview of program processing

[1379] 1. Collecting User Profile Information

[1380] The terminal displays a profile entry screen to the user, where the user enters their past work history, current skill set, and future career goals. Once the entry is complete, the terminal sends this information to the server.

[1381] 2. Analysis of User Information

[1382] The server stores the user's profile information received from the device in a database. It then uses a generative AI model to analyze the data and identify skill gaps between the user's current skills and their career goals. For example, it identifies the skills of "machine learning" and "deep learning" that are lacking for users who want to become AI developers.

[1383] 3. Gathering market trends and industry news

[1384] The server collects the latest market trends, industry news, and job information from the internet, analyzes this data, and extracts information relevant to the user's career goals. For example, it collects news that "demand for AI engineers is increasing" and provides it to the user.

[1385] 4. Generating personalized career advice

[1386] The server generates personalized career advice for the user based on the analyzed data and extracted information, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1387] 5. Continuous support and feedback

[1388] The server periodically tracks the user's learning progress and generates feedback based on the progress (e.g., "You have completed 50% of the introductory machine learning course"), which the device displays to the user.

[1389] 6. Emotion recognition and feedback regulation

[1390] The device collects the user's facial expressions and voice and sends them to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The analysis results are sent to the server, which then adjusts the content of career advice and feedback based on this information. For example, if the user is feeling stressed, it can provide relaxation resources and gentle feedback.

[1391] Specific examples

[1392] For example, if a user types into a terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer," the server receives this information and uses a generative AI model to identify skills the user lacks (such as "machine learning" or "deep learning").

[1393] Next, the server collects relevant market trends from the internet and extracts news such as "Demand for AI engineers will increase by 20% by 2025." Based on this, it generates career advice such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice," and provides it to the user via their device.

[1394] Furthermore, when a user achieves progress, the device recognizes the user's facial expressions and voice, and the emotion engine sends data indicating "satisfied" to the server. Based on this, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[1395] Example prompt sentence:

[1396] "I've been working as a software engineer for the past five years. I have skills in Python and Java, but I want to become an AI developer. I'd like some advice on what skills I should learn as my next step."

[1397] In this way, this career development support system can provide support tailored to the individual needs and emotional state of the user, and support more effective career growth.

[1398] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1399] Step 1:

[1400] Collecting user profile information

[1401] Input: The user enters their profile information (past work experience, current skill set, career goals) into the device.

[1402] How it works: The device displays a profile entry screen for the user, where the user enters information such as "5 years of software engineering experience, Python and Java skills, and aspirations to become an AI developer."

[1403] Output: The entered profile information is sent to the server.

[1404] Step 2:

[1405] Analysis of user information

[1406] Input: User profile information sent from the device.

[1407] How it works: The server stores the received profile information in a database. It then uses a generative AI model (e.g., GPT-4) to analyze the user information and identify skill gaps between their current skills and their career goals. For example, the server might identify a lack of skills in "machine learning" and "deep learning."

[1408] Output: Data about the user's skill gaps.

[1409] Step 3:

[1410] Gathering market trends and industry news

[1411] Input: Market trends, industry news, and job listings collected by the server from the internet.

[1412] How it works: The server uses web scraping technology to gather the latest market trends and industry news in real time, such as the news that "demand for AI engineers will increase by 20% by 2025."

[1413] Output: Collected market trends and industry news data.

[1414] Step 4:

[1415] Generate personalized career advice

[1416] Input: User information analysis results and market trend data.

[1417] How it works: The server generates personalized career advice for the user based on the analyzed data and extracted information, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1418] Output: Generated career advice and educational resource suggestions.

[1419] Step 5:

[1420] Providing career advice

[1421] Input: Career advice information sent to the device.

[1422] Operation: The device displays the career advice sent to the user, who can then create a study plan based on the information displayed.

[1423] Output: Career advice displayed to the user.

[1424] Step 6:

[1425] Continuous support and progress feedback

[1426] Input: User progress data.

[1427] How it works: The server periodically tracks the user's learning and career development progress and generates feedback based on their progress (e.g., "You've completed 50% of the Introduction to Machine Learning course"). The device displays the feedback sent by the server to the user (e.g., "Great progress! Keep it up!").

[1428] Output: Progress feedback displayed to the user.

[1429] Step 7:

[1430] Emotion recognition and feedback regulation

[1431] Input: User's facial and voice data.

[1432] How it works: The device uses a webcam and microphone to collect the user's facial expressions and voice. The emotion engine analyzes the collected data and recognizes the user's emotional state in real time (e.g., excitement, stress). The analysis results are sent to a server, which then adjusts the content of career advice and feedback based on the user's emotional state. For example, if the user is feeling stressed, it will provide relaxation resources and gentle feedback.

[1433] Output: Tailored career advice and feedback based on the user's emotional state.

[1434] (Application example 2)

[1435] 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."

[1436] Although conventional career development support systems provide career advice based on users' profile information, they are unable to provide feedback or recommendations that take into account the user's real-time emotional state, which means that they are unable to fully motivate users.In addition, the technology for collecting and analyzing emotional data in real time has not been properly integrated, leaving room for improvement in the user experience.

[1437] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting profile information entered by the user, means for analyzing the collected profile information to identify a gap between the user's current abilities and their goals, means for collecting market trends and industry news and extracting information related to the user's goals, means for generating and providing personalized advice to the user based on the analyzed data and the extracted information, means for periodically tracking the user's progress and providing feedback according to the progress, means for collecting the user's emotional state in real time using a smart device, and means for adjusting feedback and recommendations based on the collected emotional information. This makes it possible to provide feedback according to the user's real-time emotional state, thereby increasing the user's motivation and enabling more effective career development support.

[1438] "Profile information" is information such as past career history, current abilities, and goals entered by the user himself / herself.

[1439] "Analysis" is the process of analyzing the collected data to identify gaps between the user's current abilities and goals, as well as necessary information.

[1440] "Market trends" are information about current trends and future developments in a particular industry or field.

[1441] "Industry News" refers to current events and information related to a particular industry.

[1442] "Personalized advice" refers to customized recommendations or instructions determined based on a user's individual profile information and analytics data.

[1443] "Progress" is a measure of how far a user has progressed toward a particular goal.

[1444] "Feedback" refers to evaluations and advice provided to users regarding their actions and progress.

[1445] A "smart device" refers to an electronic device that can connect to the Internet and collect data using various sensors.

[1446] An "emotional state" is an emotion or feeling that a user is experiencing, such as stress or happiness.

[1447] "Real-time" means that data is processed almost instantly, without any delay.

[1448] To implement the present invention, the following configuration is necessary.

[1449] Overall system configuration

[1450] The career development support system consists of the following main components:

[1451] 1. Server

[1452] 2. Devices (smartphones, smart glasses, etc.)

[1453] 3. Users

[1454] 4. Emotion Engine

[1455] Collection of profile information

[1456] The device displays a profile entry screen to the user, where the user enters profile information such as past experience, current skill set, and goals. The profile information entered by the user is sent from the device to the server. At this stage, a simple prompt message like the following is displayed:

[1457] "Hello! Please enter your profile information, such as your past purchase history, preferred brands and styles, and budget."

[1458] Analyzing user information and identifying career goals

[1459] The server stores the user's profile information received from the device in a database and uses the generative AI model to analyze the gap between the user's current abilities and their goals. For example, if a user is aiming to change careers from "software engineer" to "AI developer," the server will identify the missing skills of "machine learning" and "deep learning."

[1460] Gathering market trends and industry news

[1461] The server collects the latest market trends, industry news, job listings, etc. from the internet in real time and extracts information relevant to the user's goals. For example, it provides the user with news that "demand for AI engineers is increasing."

[1462] Providing personalized advice

[1463] The server generates personalized advice for the user based on the analyzed data and extracted information, and provides it to the user via the device. Appropriate educational resources are also suggested at this stage. For example, information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice" is generated.

[1464] Progress Tracking and Feedback

[1465] The server periodically tracks the user's progress and provides feedback based on that progress, for example, if the progress information is "You've completed 50% of the Introduction to Machine Learning course," the message displayed is "Great progress! Keep it up!"

[1466] Emotion engine integration

[1467] The terminal uses a smart device to capture the user's facial expressions and voice and transmits them to the emotion engine, which analyzes the collected emotion data and recognizes the user's emotional state in real time. For example, it can determine whether the user is excited or stressed.

[1468] Adjusting feedback based on emotional state

[1469] The server adjusts the content of feedback and recommendations based on the emotional data recognized by the emotion engine. For example, if a user is feeling stressed, it can provide gentle feedback to increase motivation or provide resources to help them relax. This makes it possible to more effectively support the user's career development.

[1470] Specific examples

[1471] Collection of profile information

[1472] A user types into a terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and identifies a lack of necessary "machine learning" and "deep learning" skills.

[1473] Providing market trends

[1474] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[1475] Emotion Recognition and Feedback

[1476] When the user achieves progress, the device recognizes the user's emotional state, and the emotion engine sends data indicating that the user is satisfied to the server. Based on this emotional data, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[1477] In this way, the career development support system utilizes the user's profile information and emotional information collected in real time to provide personalized career advice.

[1478] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1479] Step 1:

[1480] The user inputs profile information into the terminal. Specifically, the user inputs information such as past experience, current skill set, and career goals into an input screen displayed on the terminal. The input data is then sent to the server by the terminal.

[1481] input:

[1482] User profile information (background, skill set, career goals)

[1483] output:

[1484] User profile data sent to the server

[1485] Data processing:

[1486] The device formats the data entered by the user and sends it to the server via the API.

[1487] Step 2:

[1488] The server stores the received user profile information and analyzes it using the generative AI model. The analysis results identify gaps between the user's current abilities and their career goals. For example, it identifies the skills they lack to become an AI developer.

[1489] input:

[1490] User profile data sent from your device

[1491] output:

[1492] Skills gap identification results

[1493] Data processing:

[1494] It uses a generative AI model to compare a user's current skills with their career goals and identify skill gaps.

[1495] Step 3:

[1496] The server collects and analyzes the latest market trends, industry news, and job information from the internet, extracting information relevant to the user's career goals, such as demand trends in the AI ​​industry and useful educational courses.

[1497] input:

[1498] Market trends, industry news, and job listings collected from the internet

[1499] output:

[1500] Extracted related information

[1501] Data processing:

[1502] It uses cloud-based web crawlers and natural language processing (NLP) algorithms to extract relevant information.

[1503] Step 4:

[1504] The server generates personalized advice for the user based on the analyzed data and extracted information and provides it via the device, such as suggesting suitable learning courses or career paths for the user.

[1505] input:

[1506] Skills gap identification results

[1507] Extracted related information

[1508] output:

[1509] Personalized advice

[1510] Data processing:

[1511] Based on analytical data, we generate optimal career advice and learning resources.

[1512] Step 5:

[1513] The server periodically tracks the user's progress and provides feedback based on the progress, for example by sending motivational messages based on the progress made.

[1514] input:

[1515] User progress data

[1516] output:

[1517] Progress Feedback

[1518] Data processing:

[1519] Aggregate progress data and generate motivational feedback.

[1520] Step 6:

[1521] The terminal collects the user's emotional state in real time using a smart device, capturing facial expressions and voice using the camera and microphone of smart glasses or a smartphone, and sending the captured data to the emotion engine.

[1522] input:

[1523] User's facial expression data, voice data

[1524] output:

[1525] Real-time emotion data

[1526] Data processing:

[1527] It uses a camera and microphone to capture data and send it to the emotion engine.

[1528] Step 7:

[1529] The emotion engine analyzes the collected emotion data to recognize the user's emotional state, for example, to determine whether the user is stressed or happy.

[1530] input:

[1531] Facial expression data, voice data

[1532] output:

[1533] Emotion recognition results

[1534] Data processing:

[1535] Emotional states are recognized using image analysis and audio analysis technology.

[1536] Step 8:

[1537] The server tailors feedback and recommendations based on the emotional data recognized by the emotion engine, for example, providing relaxation resources if the user is feeling stressed.

[1538] input:

[1539] Emotion recognition results

[1540] output:

[1541] Tailored feedback and recommendations

[1542] Data processing:

[1543] The feedback content is adjusted based on emotional data, and messages are generated to maintain the user's motivation.

[1544] 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.

[1545] 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.

[1546] 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.

[1547] [Fourth embodiment]

[1548] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1549] 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.

[1550] 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).

[1551] 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.

[1552] 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.

[1553] 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).

[1554] 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.

[1555] 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.

[1556] 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.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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."

[1561] The career development support system of the present invention is a system that provides personalized career advice by collecting and analyzing user profile information. Specific procedures for implementing this system are described below.

[1562] System Overview

[1563] This career development support system is a system in which a server, terminals, and users work together. The server performs the main data processing and analysis, and the terminals function as an interface with the users. Users access the system through their terminals and input the necessary information.

[1564] Collecting user profile information

[1565] The device displays a profile entry screen to the user, where the user enters information such as past experience, current skill set, and career goals. Once the entry is complete, the device sends this information to the server.

[1566] Analysis of user information

[1567] The server receives the user's profile information sent from the device. It then analyzes the collected information and identifies gaps between the user's current skills and their career goals. For example, if a user has a background as a "software engineer" and aims to become an "AI developer" in the future, the server will identify that they lack skills such as "machine learning" and "deep learning."

[1568] Market trends and industry news analysis

[1569] The server collects the latest market trends, industry news, and job information from the internet in real time. It analyzes this data and extracts information relevant to the user's career goals. For example, the server collects news such as "Demand for AI engineers is increasing" and prepares it to provide to the user.

[1570] Providing personalized career advice

[1571] The server generates personalized career advice for the user based on the analyzed data and extracted information. The generated advice is sent to the device, which then displays it to the user. For example, information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice" is displayed.

[1572] Continuous support and feedback

[1573] The server periodically tracks the user's progress. Each time the server receives progress information, such as "You've completed 50% of the Introduction to Machine Learning course," it generates real-time feedback and provides it to the user via their device, such as a message like "Great progress! Keep it up!"

[1574] The device also collects feedback from users and sends it to the server, which analyzes the feedback and uses it to improve the system.

[1575] Specific examples

[1576] Collecting and analyzing input information

[1577] A user enters "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and determines that "machine learning" and "deep learning" are lacking.

[1578] Providing market trends

[1579] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[1580] Progress tracking and feedback

[1581] If a user starts the "Introduction to Machine Learning" course and achieves 50% progress, the server receives this data and generates a message saying "Great progress! Keep it up!" and displays it through the terminal.

[1582] In this way, this career development support system provides support tailored to the individual needs of each user, enabling companies to effectively support the career growth of their employees.

[1583] The processing flow will be explained below.

[1584] Step 1:

[1585] The device displays a profile entry screen to the user, which includes fields for entering the user's past experience, current skill set, and career goals.

[1586] Step 2:

[1587] Users input their background (e.g., 5 years of experience as a software engineer), skill set (e.g., Python, Java), and career goals (e.g., to become an AI developer).

[1588] Step 3:

[1589] The device collects all profile information entered by the user and sends it to the server, including the user's background, skill set, and career goals.

[1590] Step 4:

[1591] The server receives the user profile information sent from the terminal and stores it in a database.

[1592] Step 5:

[1593] The server analyzes the received profile information, including using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, it may determine that skills such as "machine learning" and "deep learning" are lacking.

[1594] Step 6:

[1595] The server collects the latest market trends, industry news, and job information from the internet. In this step, data is obtained using web scraping and APIs.

[1596] Step 7:

[1597] The server analyzes the collected market data and extracts information relevant to the user's career goals, such as information that there is a growing demand for AI engineers.

[1598] Step 8:

[1599] The server generates personalized career advice for the user based on the analyzed user information and market data, including recommended skill courses and related educational resources.

[1600] Step 9:

[1601] The server transmits the generated carrier advice to the terminal.

[1602] Step 10:

[1603] The device displays career advice sent from the server to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1604] Step 11:

[1605] The server collects user activity data to periodically check the user's progress, for example, the user has completed 50% of the introductory machine learning course.

[1606] Step 12:

[1607] The server generates real-time feedback based on collected activity data, such as messages like "Great progress, keep it up!"

[1608] Step 13:

[1609] The terminal displays the feedback generated from the server to the user.

[1610] Step 14:

[1611] The terminal collects feedback from the user and sends it back to the server, where it is used to help improve the system.

[1612] Step 15:

[1613] The server analyzes the collected feedback and identifies improvements to the system, thereby continually improving the user experience of the system.

[1614] With this processing flow, the career development support system provides support tailored to the individual needs of users, enabling companies to effectively support the career growth of their employees.

[1615] Example 1

[1616] 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."

[1617] Conventional career development support systems have difficulty providing appropriate career advice and educational resources to meet the individual needs of users, resulting in an inability to effectively support users' career growth. Furthermore, it is difficult to track users' progress and feedback in a timely manner and provide real-time support, which reduces the convenience and effectiveness of the system.

[1618] 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.

[1619] In this invention, the server includes: means for collecting profile information entered by the user; means for transmitting the collected profile information via the terminal to the server; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news from the Internet and extracting information related to the user's career goals; means for generating personalized career advice for the user based on the analyzed data and the extracted information; means for transmitting the generated advice to the terminal and displaying it to the user; means for periodically tracking the user's progress, generating feedback according to the progress, and providing it via the terminal; and means for collecting feedback from the user and using it to improve the system. This makes it possible to provide appropriate career advice and educational resources tailored to the individual needs of each user, and also makes it possible to track the user's progress and feedback in real time and provide prompt support.

[1620] A "User" is an individual who uses the system and enters profile information.

[1621] A "terminal" is a device used by a user, and is an interface through which profile information is entered and career advice is displayed via a web browser.

[1622] A "server" is a computer system that receives data sent from a terminal and performs analysis and data processing.

[1623] "Profile information" is information about a user's past experience, current skill set, and career goals.

[1624] "Collect" refers to the process of transmitting profile information entered by a user into a terminal to a server.

[1625] "Analysis" refers to the process of identifying gaps between the user's current skills and career goals based on the profile information received by the server.

[1626] "Market Trends" is information about the latest movements and trends in an industry or market.

[1627] "Industry news" refers to the latest happenings and reports in a particular field or industry.

[1628] "Personalized career advice" refers to career growth advice and suggestions that are customized to a user's individual needs.

[1629] "Progress" is information that indicates how far the user has progressed toward achieving their goal.

[1630] "Feedback" is information that provides responses and advice according to the user's progress.

[1631] "Educational Resources" means learning courses and materials offered to users to improve their skills and grow their careers.

[1632] "System improvement" is the process of improving the system's functions and services based on feedback collected from users.

[1633] The career development support system of the present invention is a system in which a server, a terminal, and a user work in cooperation with each other. A specific implementation method of this system will be described below.

[1634] System Overview

[1635] This career development support system is composed of a server that performs the main data processing and analysis, a terminal that functions as an interface with users, and users who provide information to the system.

[1636] Collecting user profile information

[1637] The terminal is a device connected to the Internet (e.g., a PC or a smartphone), and in this embodiment, a web browser (e.g., Google Chrome or Mozilla Firefox) is used. The terminal displays a screen for the user to enter profile information using an HTML form. The user enters information such as past experience, current skill set, and career goals. Once the information has been entered, the terminal collects this information using JavaScript, converts it into JSON format, and sends it to the server using HTTPS communication.

[1638] Analysis of user information

[1639] The server parses the received user profile information in JSON format. The server receives the data using a Python framework (e.g., Flask, Django) and converts the information into a data frame using the Pandas library. Based on this data frame, the server uses the Scikit-learn library to identify gaps between the user's current skills and their career goals. For example, if a user has a background as a "software engineer" and aspires to become an "AI developer," the server will identify a lack of skills such as "machine learning" and "deep learning."

[1640] Market trends and industry news analysis

[1641] The server uses web scraping technology to collect market trends and industry news from the Internet. Specifically, it uses Python's Beautiful Soup and Selenium libraries. The collected data is analyzed to identify information relevant to the user's career goals. For example, it extracts and analyzes news that "demand for AI engineers is increasing."

[1642] Providing personalized career advice

[1643] The server generates personalized career advice for the user based on the analyzed data and extracted information. For example, it might provide the user with information such as "Recommended courses: Introduction to Machine Learning, Deep Learning Practice." The generated advice is converted to JSON format and sent to the device. The device parses it using JavaScript and displays it to the user using HTML.

[1644] Continuous support and feedback

[1645] The server periodically receives data to track the user's progress. Each time a user reports their learning progress, the server analyzes the data and generates real-time feedback. For example, if the server receives progress information such as "You've completed 50% of the introductory machine learning course," it generates feedback such as "Great progress! Keep it up!" and provides it to the user via their device. The device also collects feedback from the user and sends it to the server. The server uses the collected feedback to improve the system.

[1646] Specific examples

[1647] The user enters information as follows:

[1648] Experience: "5 years of software engineering experience"

[1649] Skills: Python and Java

[1650] Goal: "I want to become an AI developer"

[1651] The server receives this information and determines that there is a lack of "machine learning" or "deep learning."

[1652] The server finds a news item saying, "Demand for AI engineers will increase by 20% by 2025," extracts a list of related courses (e.g., "Introduction to Machine Learning" and "Deep Learning in Practice"), and sends it to the device, which then displays it to the user.

[1653] If a user "completes 50% of the introductory machine learning course," the server receives this progress information, generates feedback to the user via the device, saying, "Great progress! Keep it up!"

[1654] In this way, the system provides career advice tailored to the user's individual needs and effectively supports the user's career growth through continuous support and feedback.

[1655] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1656] Step 1:

[1657] The device displays a screen where the user can enter profile information. Input fields include background, skills, career goals, etc. After entering the information, the user presses the "Submit" button. Specifically, the information is collected using an HTML form, and then compiled using JavaScript and converted to JSON format.

[1658] Input: User's background, skills, and career goals

[1659] Data processing: Convert information into JSON format using JavaScript

[1660] Output: Profile information in JSON format

[1661] Step 2:

[1662] The device sends the collected profile information in JSON format to the server using HTTPS, which uses encrypted communication to ensure security.

[1663] Input: Profile information in JSON format converted on the device

[1664] Data transmission: Secure data transmission via HTTPS

[1665] Output: Profile information sent to the server

[1666] Step 3:

[1667] The server receives the JSON-formatted profile information using a Python framework (such as Flask or Django), converts it into a data frame using the Pandas library, and organizes the information by background, skills, and career goals.

[1668] Input: Profile information in JSON format

[1669] Data processing: Convert to a data frame using Pandas and organize the information

[1670] Output: A neat data frame

[1671] Step 4:

[1672] The server uses Scikit-learn to analyze the data and identify gaps between the user's current skills and their career goals. For example, it uses a linear regression model to analyze how well the user's skills match their career goals.

[1673] Input: A cleaned data frame

[1674] Data Computing: Skills and Career Goal Gap Analysis with Scikit-Learn

[1675] Output: User skill gap analysis results

[1676] Step 5:

[1677] The server uses web scraping with Beautiful Soup and Selenium to gather market trends and industry news from the internet, which is then analyzed to identify information relevant to the user's career goals.

[1678] Input: Market trends, industry news webpage URL

[1679] Data collection: Data collection through web scraping

[1680] Output: Analyzable market trend data, industry news data

[1681] Step 6:

[1682] The server generates personalized career advice based on the analyzed user profile information and market trend data, using natural language generation (NLG) technology to create advice in a user-friendly format.

[1683] Input: User skill gap analysis results, market trend data

[1684] Data Generation: Generating Career Advice with NLG

[1685] Output: Personalized career advice (e.g., "Introduction to Machine Learning Course")

[1686] Step 7:

[1687] The server converts the generated advice into JSON format and sends it to the device, which then parses the received JSON data using JavaScript and displays it to the user in HTML.

[1688] Input: Generated career advice

[1689] Data conversion and transmission: Converted to JSON format and transmitted via HTTPS

[1690] Output: Personalized career advice displayed to the user

[1691] Step 8:

[1692] The server periodically receives data to track the user's progress in the course of study selected by the user. The user enters progress data and sends it to the server.

[1693] Input: User progress data

[1694] Data calculation: Analysis of progress data

[1695] Output: Feedback based on progress

[1696] Step 9:

[1697] The server generates real-time feedback based on the progress data, using natural language generation technology, and sends it to the device, where it is displayed to the user.

[1698] Input: User progress data

[1699] Data generation: Feedback generated based on progress

[1700] Output: Feedback that is displayed to the user (e.g., "Great progress! Keep it up!")

[1701] Step 10:

[1702] The device collects feedback from users and sends it to the server, which analyzes it and uses it to improve the system.

[1703] Input: User feedback

[1704] Data collection and analysis: Feedback analysis

[1705] Output: System improvement proposals

[1706] In this step, the career development support system can provide individual career advice to users and provide continuous progress tracking and feedback, thereby effectively supporting users' career growth.

[1707] (Application example 1)

[1708] 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."

[1709] Traditional career development support systems provide personalized advice by collecting and analyzing user profile information, but most of this information is static and lacks specific support for how users should actually improve their skills. Furthermore, they lack the ability to track users' learning progress in real time and provide appropriate feedback, resulting in insufficient motivation and support for users to continue developing. As a result, it is difficult to efficiently bridge the gap between users' career goals and their actual skill development. Furthermore, they are unable to provide the latest learning content based on market trends and industry news in real time, making it difficult for users to utilize the latest information to advance their learning.

[1710] 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.

[1711] In this invention, the server includes means for collecting profile information entered by the user, means for analyzing the collected profile information to identify gaps between the user's current skills and career goals, means for collecting market trends and industry news and extracting information related to the user's career goals, means for generating and providing personalized career advice to the user based on the analyzed data and the extracted information, means for periodically tracking the user's progress and providing feedback according to the progress, and means for delivering learning content. This allows the user to improve their skills in real time while receiving learning content based on the latest market trends. In addition, the ability to receive feedback according to the user's progress reinforces continuous growth and motivation for learning.

[1712] A "collection instrument" is a device or software that transmits user-entered profile information to the system and stores that information in a database.

[1713] The "analyzing and identifying means" refers to algorithms or programs that process the collected profile information and identify gaps between the user's current skills and career goals.

[1714] "Market trend and industry news gathering means" means a device or software that obtains the latest market and industry trends from the Internet or other data sources and stores them in a database.

[1715] "Means for extracting information" are algorithms or programs that extract useful information related to the user's career goals from collected market trends and industry news.

[1716] The "means for generating and providing personalized career advice" is a system for creating career advice suited to the individual needs of a user based on the analyzed data and extracted information, and providing the advice to the user.

[1717] "Means for regular progress tracking and feedback" refers to a system that continuously monitors the progress of a user's learning or training and provides the user with advice and encouraging messages based on that progress.

[1718] A "means for delivering learning content" is a system that provides users with educational materials (videos, articles, interactive tutorials, etc.) via the Internet to learn the skills they need.

[1719] System Overview

[1720] This career development support system is a system in which a server, terminals, and users work together. The server performs the main data processing and analysis, while the terminals are used to interact with users. Users access the system via their smartphones and input and receive the necessary information.

[1721] Collecting user profile information

[1722] Users use an application on their smartphone to input information such as their past experience, current skill set, and career goals, which is then sent in real time to a server and stored in a database.

[1723] Analysis of user information

[1724] The server analyzes the user's profile information using Python and machine learning libraries such as Scikit-learn and TensorFlow. This identifies gaps between the user's current skills and their career goals. For example, if a user enters "5 years of software engineering experience, Python and Java skills, and aspirations to become an AI developer," the server will determine that "machine learning" and "deep learning" are lacking.

[1725] Market trends and industry news analysis

[1726] The server collects the latest market trends, industry news, and job information from the Internet in real time. It uses Python's Beautiful Soup and Requests to scrape the necessary information and stores it in a database. This allows it to extract information related to the user's career goals. For example, it collects news such as "Demand for AI engineers will increase by 20% by 2025" and prepares it to be provided to the user.

[1727] Providing personalized career advice

[1728] The server generates personalized career advice for the user based on the analyzed data and extracted information. The advice is sent to the device and displayed to the user via a smartphone app. For example, it displays information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1729] Continuous support and feedback

[1730] The server periodically tracks the user's progress. If a user starts the "Introduction to Machine Learning" course and achieves 50% progress, the server receives this data and generates a message saying "Great progress! Keep it up!", which is displayed on the device. The device also collects feedback from the user and sends it to the server. The server analyzes the collected feedback and uses it to improve the system.

[1731] Delivery of learning content

[1732] The server uses cloud storage such as AWS S3 to deliver learning content to users. Users can receive videos, articles, and interactive tutorials in streaming format through the app, allowing users to always learn the latest information and technologies.

[1733] Specific examples

[1734] Profile input prompt

[1735] "Please tell us about your current occupation, past experience, skills, and future career goals."

[1736] Market trend extraction

[1737] For example, it obtains information from the internet that "demand for AI engineers is increasing," and based on that, recommends related learning videos.

[1738] "A recent report predicts that demand for AI engineers will increase by 20% by 2025. Check out this educational video."

[1739] In this way, the career development support system provides powerful support for users to continuously improve their skills and achieve their career goals by providing learning content tailored to their individual needs, tracking their progress, and collecting feedback.

[1740] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1741] Step 1:

[1742] User enters profile information

[1743] Users access the application using their smartphones and enter information such as their past experience, current skill set, career goals, etc. Once the information is complete, the entered profile information is sent to the server.

[1744] Input: User profile information (background, skills, career goals)

[1745] Output: Profile information sent to the server

[1746] Step 2:

[1747] The server analyzes the profile information

[1748] The server receives the collected profile information and analyzes it using Python, Scikit-learn, and TensorFlow to identify gaps between the user's current skills and their career goals.

[1749] Input: User profile information

[1750] Output: Analysis results (skill gap identification)

[1751] Step 3:

[1752] Server collects market trends and industry news

[1753] The server uses Python's Beautiful Soup and Requests to scrape the latest market trends and industry news from the internet and store that information in a database.

[1754] Input: Market trends and industry news from the internet

[1755] Output: Market trends and industry news stored in a database

[1756] Step 4:

[1757] The server extracts the information

[1758] The server extracts information relevant to the user's career goals from the collected market trends and industry news using analytical algorithms to identify relevant information.

[1759] Input: Market trends and industry news stored in a database

[1760] Output: Extracted relevant information

[1761] Step 5:

[1762] The server generates personalized career advice

[1763] The server generates personalized career advice for the user based on the analysis results and the extracted information, and the generated career advice is sent to the terminal.

[1764] Input: Analysis results, extracted related information

[1765] Output: Personalized career advice

[1766] Step 6:

[1767] The device displays career advice

[1768] The device receives personalized career advice sent from the server and displays it to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1769] Enter: personalized career advice

[1770] Output: Career advice displayed to the user

[1771] Step 7:

[1772] Enter user progress

[1773] Users enter their own learning progress through the application, and the entered progress information is sent to the server.

[1774] Input: Learning progress information

[1775] Output: Progress information sent to the server

[1776] Step 8:

[1777] The server tracks your progress and provides feedback

[1778] The server analyzes the progress information and generates feedback based on the progress, such as a message like "Great progress, keep it up" that is sent to the device.

[1779] Input: Learning progress information

[1780] Output: The generated feedback message

[1781] Step 9:

[1782] The device displays feedback

[1783] The terminal receives the feedback message sent from the server and displays it to the user.

[1784] Input: Feedback message

[1785] Output: A feedback message that is displayed to the user

[1786] Step 10:

[1787] The server distributes learning content

[1788] The server delivers learning content to users using cloud storage such as AWS S3, and users receive the content in streaming format via their devices.

[1789] Input: Learning content

[1790] Output: Learning content delivered to users

[1791] 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.

[1792] This invention is a career development support system that utilizes a user's profile information and emotional information to provide personalized career advice. The system supports users in achieving their goals, effectively manages their progress, and provides feedback according to the user's emotional state.

[1793] System Overview

[1794] The career development support system operates in cooperation with a server, terminals, users, and an emotion engine. The server processes and analyzes data, and the emotion engine recognizes the user's emotions. The terminals function as an interface with the users, and the users access the system through their terminals.

[1795] Collecting user profile information

[1796] The device displays a profile entry screen to the user, where the user enters information such as past experience, current skill set, and career goals. Once the entry is complete, the device sends this information to the server.

[1797] Analysis of user information

[1798] The server stores the user's profile information received from the device in a database. It then analyzes the user information using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, if a user is aiming to change careers from "software engineer" to "AI developer," it identifies the necessary skills in "machine learning" and "deep learning."

[1799] Market trends and industry news analysis

[1800] The server collects the latest market trends, industry news, and job information from the internet in real time. It analyzes the collected data and extracts information relevant to the user's career goals. For example, it collects news such as "Demand for AI engineers is increasing" and provides it to the user.

[1801] Providing personalized career advice

[1802] Based on the analyzed data and extracted information, the server generates personalized career advice and suitable educational resources for the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1803] The device displays the generated career advice and educational resources to the user, who can then create a study plan based on the displayed information.

[1804] Continuous support and feedback

[1805] The server periodically tracks the user's progress, for example, "I've completed 50% of the introductory machine learning course," and generates real-time feedback.

[1806] The device displays feedback sent by the server to the user, for example, a message like "Great progress, keep it up," and also collects feedback from the user and sends it to the server.

[1807] The server analyzes the collected feedback and identifies improvements to the system, and based on this feedback, improves the user experience of the system.

[1808] Emotion engine integration

[1809] The device collects emotion data from the user's facial expressions and voice, and sends the collected emotion data to the emotion engine.

[1810] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state in real time, for example, determining whether the user is excited or stressed.

[1811] The server then adjusts the content of career advice based on the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will provide gentle feedback to boost motivation or provide resources to help them relax.

[1812] The device works in conjunction with an emotion engine to display feedback based on the user's emotional state, such as a message like "Take a break, we have the resources to refresh you."

[1813] Specific examples

[1814] Collecting and analyzing input information

[1815] A user enters into the terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and identifies "machine learning" and "deep learning" as areas of deficiency.

[1816] Providing market trends

[1817] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[1818] Emotion Recognition and Feedback

[1819] When the user achieves progress, the device recognizes the user's emotional state, and the emotion engine sends data indicating that the user is satisfied to the server. Based on this emotional data, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[1820] In this way, this career development support system provides support that meets the individual needs and feelings of users, enabling companies to more effectively support the career growth of their employees.

[1821] The processing flow will be explained below.

[1822] Step 1:

[1823] The device displays a profile entry screen to the user, which includes fields for entering past experience, current skill set, and career goals.

[1824] Step 2:

[1825] Users input their background (e.g., 5 years of experience as a software engineer), skill set (e.g., Python, Java), and career goals (e.g., to become an AI developer).

[1826] Step 3:

[1827] The device collects all profile information entered by the user and sends it to a server, including the user's background, skill set, and career goals.

[1828] Step 4:

[1829] The server receives the user profile information sent from the terminal and stores it in a database.

[1830] Step 5:

[1831] The server analyzes the received profile information, including using a generative AI model to identify skill gaps between the user's current skills and their career goals. For example, it may determine that there is a lack of skills in "machine learning" and "deep learning."

[1832] Step 6:

[1833] The server collects the latest market trends, industry news, and job information from the internet. In this step, data is obtained using web scraping and APIs.

[1834] Step 7:

[1835] The server analyzes the collected market data and extracts information relevant to the user's career goals, such as "demand for AI engineers is increasing."

[1836] Step 8:

[1837] The server generates personalized career advice for the user based on the analyzed user information and market data, including recommended skill courses and related educational resources.

[1838] Step 9:

[1839] The server transmits the generated carrier advice to the terminal.

[1840] Step 10:

[1841] The device displays career advice sent from the server to the user, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1842] Step 11:

[1843] The terminal collects emotional data such as the user's facial expressions and voice and sends it to the emotion engine.

[1844] Step 12:

[1845] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state, for example, determining in real time whether the user is feeling stressed or happy.

[1846] Step 13:

[1847] The server adjusts the content of career advice based on the emotion data recognized by the emotion engine. For example, if the user is feeling stressed, the advice will be changed to provide resources for relaxation.

[1848] Step 14:

[1849] The device then displays tailored career advice sent from the server to the user, such as "You seem to be feeling a bit stressed. Try this meditation course to help you relax."

[1850] Step 15:

[1851] The server collects user learning activity data to periodically track the user's learning progress, for example, "I have completed 50% of the introductory machine learning course."

[1852] Step 16:

[1853] The server generates real-time feedback based on the progress data collected, such as positive feedback like "Great progress, keep it up!"

[1854] Step 17:

[1855] The terminal displays the feedback sent from the server to the user.

[1856] Step 18:

[1857] The terminal collects feedback from the user and sends it back to the server.

[1858] Step 19:

[1859] The server analyzes the collected user feedback and identifies improvements to the system, and based on this feedback, continually improves the user experience of the system.

[1860] With this processing flow, the career development support system provides support that meets the individual needs and feelings of users, enabling companies to effectively support the career growth of their employees.

[1861] Example 2

[1862] 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."

[1863] While current career development support systems have the ability to collect user profile information and provide advice based on career goals, they lack personalized feedback that takes into account the user's emotional state. They also lack mechanisms for providing real-time feedback based on the user's progress or the latest career advice based on market trends. As a result, these systems face challenges that make it difficult to maintain user motivation and achieve effective career development.

[1864] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1865] In this invention, the server includes: means for collecting profile information entered by a user; means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news and extracting information related to the user's career goals; means for generating and providing personalized career advice to the user based on the analyzed data and extracted information; means for periodically tracking the user's progress and providing feedback according to the progress; means for collecting emotional data from the user's facial expressions and voice; and means for analyzing the collected emotional data to recognize the user's emotional state and adjust the content of the career advice. This enables personalized feedback that takes the user's emotional state into consideration, thereby supporting effective career development while maintaining the user's motivation.

[1866] "User" refers to an entity that accesses the system and enters their profile information and career goals.

[1867] "Profile information" refers to information such as a user's past work history, current skill set, and future career goals.

[1868] A "skills gap" refers to the lack of skills between a user's current skills and the skills needed to achieve their career goals.

[1869] "Market trends" refers to the current situation and future outlook, as well as supply and demand trends, in a particular industry or occupation.

[1870] "Industry News" refers to current information, events, or important announcements related to a particular industry.

[1871] "Personalized career advice" refers to specific advice and suggestions tailored to a user's individual situation and goals.

[1872] "Progress" refers to the achievements and progress a user makes in their learning or career development process.

[1873] "Feedback" refers to assessments and advice provided based on a user's progress and emotional state.

[1874] "Facial expression data" refers to information about the user's facial expressions.

[1875] "Voice data" refers to information about a user's voice and speaking style.

[1876] An "emotion engine" refers to technology or a device that has the ability to analyze collected facial expression and voice data to recognize the user's emotional state.

[1877] MODE FOR CARRYING OUT THE INVENTION

[1878] This invention is a career development support system that provides personalized career advice by utilizing user profile information and emotion information. The system includes a server, a terminal, a user, and an emotion engine, all of which operate in cooperation with each other.

[1879] System Configuration

[1880] server

[1881] The server is the main data processing device that analyzes users' profile information and generates career advice. It is equipped with a database, generative AI models (e.g., OpenAI's GPT-4), and web scraping technology that collects market trends and industry news from the Internet. The server also manages users' progress and generates feedback.

[1882] Terminal

[1883] The terminal is a device that functions as an interface with the user. It displays an input screen for the user to enter profile information and provides the user with career advice and feedback from the server. The terminal also has the function of collecting the user's facial expressions and voice using a webcam and microphone.

[1884] User

[1885] Users access the system through their terminals, enter their profile information and career goals, and plan their actions based on the career advice and feedback provided, and record their progress.

[1886] Overview of program processing

[1887] 1. Collecting User Profile Information

[1888] The terminal displays a profile entry screen to the user, where the user enters their past work history, current skill set, and future career goals. Once the entry is complete, the terminal sends this information to the server.

[1889] 2. Analysis of User Information

[1890] The server stores the user's profile information received from the device in a database. It then uses a generative AI model to analyze the data and identify skill gaps between the user's current skills and their career goals. For example, it identifies the skills of "machine learning" and "deep learning" that are lacking for users who want to become AI developers.

[1891] 3. Gathering market trends and industry news

[1892] The server collects the latest market trends, industry news, and job information from the internet, analyzes this data, and extracts information relevant to the user's career goals. For example, it collects news that "demand for AI engineers is increasing" and provides it to the user.

[1893] 4. Generating personalized career advice

[1894] The server generates personalized career advice for the user based on the analyzed data and extracted information, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1895] 5. Continuous support and feedback

[1896] The server periodically tracks the user's learning progress and generates feedback based on the progress (e.g., "You have completed 50% of the introductory machine learning course"), which the device displays to the user.

[1897] 6. Emotion recognition and feedback regulation

[1898] The device collects the user's facial expressions and voice and sends them to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state. The analysis results are sent to the server, which then adjusts the content of career advice and feedback based on this information. For example, if the user is feeling stressed, it can provide relaxation resources and gentle feedback.

[1899] Specific examples

[1900] For example, if a user types into a terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer," the server receives this information and uses a generative AI model to identify skills the user lacks (such as "machine learning" or "deep learning").

[1901] Next, the server collects relevant market trends from the internet and extracts news such as "Demand for AI engineers will increase by 20% by 2025." Based on this, it generates career advice such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice," and provides it to the user via their device.

[1902] Furthermore, when a user achieves progress, the device recognizes the user's facial expressions and voice, and the emotion engine sends data indicating "satisfied" to the server. Based on this, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[1903] Example prompt sentence:

[1904] "I've been working as a software engineer for the past five years. I have skills in Python and Java, but I want to become an AI developer. I'd like some advice on what skills I should learn as my next step."

[1905] In this way, this career development support system can provide support tailored to the individual needs and emotional state of the user, and support more effective career growth.

[1906] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1907] Step 1:

[1908] Collecting user profile information

[1909] Input: The user enters their profile information (past work experience, current skill set, career goals) into the device.

[1910] How it works: The device displays a profile entry screen for the user, where the user enters information such as "5 years of software engineering experience, Python and Java skills, and aspirations to become an AI developer."

[1911] Output: The entered profile information is sent to the server.

[1912] Step 2:

[1913] Analysis of user information

[1914] Input: User profile information sent from the device.

[1915] How it works: The server stores the received profile information in a database. It then uses a generative AI model (e.g., GPT-4) to analyze the user information and identify skill gaps between their current skills and their career goals. For example, the server might identify a lack of skills in "machine learning" and "deep learning."

[1916] Output: Data about the user's skill gaps.

[1917] Step 3:

[1918] Gathering market trends and industry news

[1919] Input: Market trends, industry news, and job listings collected by the server from the internet.

[1920] How it works: The server uses web scraping technology to gather the latest market trends and industry news in real time, such as the news that "demand for AI engineers will increase by 20% by 2025."

[1921] Output: Collected market trends and industry news data.

[1922] Step 4:

[1923] Generate personalized career advice

[1924] Input: User information analysis results and market trend data.

[1925] How it works: The server generates personalized career advice for the user based on the analyzed data and extracted information, such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice."

[1926] Output: Generated career advice and educational resource suggestions.

[1927] Step 5:

[1928] Providing career advice

[1929] Input: Career advice information sent to the device.

[1930] Operation: The device displays the career advice sent to the user, who can then create a study plan based on the information displayed.

[1931] Output: Career advice displayed to the user.

[1932] Step 6:

[1933] Continuous support and progress feedback

[1934] Input: User progress data.

[1935] How it works: The server periodically tracks the user's learning and career development progress and generates feedback based on their progress (e.g., "You've completed 50% of the Introduction to Machine Learning course"). The device displays the feedback sent by the server to the user (e.g., "Great progress! Keep it up!").

[1936] Output: Progress feedback displayed to the user.

[1937] Step 7:

[1938] Emotion recognition and feedback regulation

[1939] Input: User's facial and voice data.

[1940] How it works: The device uses a webcam and microphone to collect the user's facial expressions and voice. The emotion engine analyzes the collected data and recognizes the user's emotional state in real time (e.g., excitement, stress). The analysis results are sent to a server, which then adjusts the content of career advice and feedback based on the user's emotional state. For example, if the user is feeling stressed, it will provide relaxation resources and gentle feedback.

[1941] Output: Tailored career advice and feedback based on the user's emotional state.

[1942] (Application example 2)

[1943] 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."

[1944] Although conventional career development support systems provide career advice based on users' profile information, they are unable to provide feedback or recommendations that take into account the user's real-time emotional state, which means that they are unable to fully motivate users.In addition, the technology for collecting and analyzing emotional data in real time has not been properly integrated, leaving room for improvement in the user experience.

[1945] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting profile information entered by the user, means for analyzing the collected profile information to identify a gap between the user's current abilities and their goals, means for collecting market trends and industry news and extracting information related to the user's goals, means for generating and providing personalized advice to the user based on the analyzed data and the extracted information, means for periodically tracking the user's progress and providing feedback according to the progress, means for collecting the user's emotional state in real time using a smart device, and means for adjusting feedback and recommendations based on the collected emotional information. This makes it possible to provide feedback according to the user's real-time emotional state, thereby increasing the user's motivation and enabling more effective career development support.

[1946] "Profile information" is information such as past career history, current abilities, and goals entered by the user himself / herself.

[1947] "Analysis" is the process of analyzing the collected data to identify gaps between the user's current abilities and goals, as well as necessary information.

[1948] "Market trends" are information about current trends and future developments in a particular industry or field.

[1949] "Industry News" refers to current events and information related to a particular industry.

[1950] "Personalized advice" refers to customized recommendations or instructions determined based on a user's individual profile information and analytics data.

[1951] "Progress" is a measure of how far a user has progressed toward a particular goal.

[1952] "Feedback" refers to evaluations and advice provided to users regarding their actions and progress.

[1953] A "smart device" refers to an electronic device that can connect to the Internet and collect data using various sensors.

[1954] An "emotional state" is an emotion or feeling that a user is experiencing, such as stress or happiness.

[1955] "Real-time" means that data is processed almost instantly, without any delay.

[1956] To implement the present invention, the following configuration is necessary.

[1957] Overall system configuration

[1958] The career development support system consists of the following main components:

[1959] 1. Server

[1960] 2. Devices (smartphones, smart glasses, etc.)

[1961] 3. Users

[1962] 4. Emotion Engine

[1963] Collection of profile information

[1964] The device displays a profile entry screen to the user, where the user enters profile information such as past experience, current skill set, and goals. The profile information entered by the user is sent from the device to the server. At this stage, a simple prompt message like the following is displayed:

[1965] "Hello! Please enter your profile information, such as your past purchase history, preferred brands and styles, and budget."

[1966] Analyzing user information and identifying career goals

[1967] The server stores the user's profile information received from the device in a database and uses the generative AI model to analyze the gap between the user's current abilities and their goals. For example, if a user is aiming to change careers from "software engineer" to "AI developer," the server will identify the missing skills of "machine learning" and "deep learning."

[1968] Gathering market trends and industry news

[1969] The server collects the latest market trends, industry news, job listings, etc. from the internet in real time and extracts information relevant to the user's goals. For example, it provides the user with news that "demand for AI engineers is increasing."

[1970] Providing personalized advice

[1971] The server generates personalized advice for the user based on the analyzed data and extracted information, and provides it to the user via the device. Appropriate educational resources are also suggested at this stage. For example, information such as "Recommended courses for you: Introduction to Machine Learning, Deep Learning Practice" is generated.

[1972] Progress Tracking and Feedback

[1973] The server periodically tracks the user's progress and provides feedback based on that progress, for example, if the progress information is "You've completed 50% of the Introduction to Machine Learning course," the message displayed is "Great progress! Keep it up!"

[1974] Emotion engine integration

[1975] The terminal uses a smart device to capture the user's facial expressions and voice and transmits them to the emotion engine, which analyzes the collected emotion data and recognizes the user's emotional state in real time. For example, it can determine whether the user is excited or stressed.

[1976] Adjusting feedback based on emotional state

[1977] The server adjusts the content of feedback and recommendations based on the emotional data recognized by the emotion engine. For example, if a user is feeling stressed, it can provide gentle feedback to increase motivation or provide resources to help them relax. This makes it possible to more effectively support the user's career development.

[1978] Specific examples

[1979] Collection of profile information

[1980] A user types into a terminal, "5 years of software engineering experience, Python and Java skills, want to become an AI developer." The server receives this information and identifies a lack of necessary "machine learning" and "deep learning" skills.

[1981] Providing market trends

[1982] The server extracts the news item "Demand for AI engineers will increase by 20% by 2025" and sends it to the device along with a list of available courses, which the device then displays to the user.

[1983] Emotion Recognition and Feedback

[1984] When the user achieves progress, the device recognizes the user's emotional state, and the emotion engine sends data indicating that the user is satisfied to the server. Based on this emotional data, the server generates feedback such as "Great progress! Keep it up!" and provides it via the device.

[1985] In this way, the career development support system utilizes the user's profile information and emotional information collected in real time to provide personalized career advice.

[1986] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1987] Step 1:

[1988] The user inputs profile information into the terminal. Specifically, the user inputs information such as past experience, current skill set, and career goals into an input screen displayed on the terminal. The input data is then sent to the server by the terminal.

[1989] input:

[1990] User profile information (background, skill set, career goals)

[1991] output:

[1992] User profile data sent to the server

[1993] Data processing:

[1994] The device formats the data entered by the user and sends it to the server via the API.

[1995] Step 2:

[1996] The server stores the received user profile information and analyzes it using the generative AI model. The analysis results identify gaps between the user's current abilities and their career goals. For example, it identifies the skills they lack to become an AI developer.

[1997] input:

[1998] User profile data sent from your device

[1999] output:

[2000] Skills gap identification results

[2001] Data processing:

[2002] It uses a generative AI model to compare a user's current skills with their career goals and identify skill gaps.

[2003] Step 3:

[2004] The server collects and analyzes the latest market trends, industry news, and job information from the internet, extracting information relevant to the user's career goals, such as demand trends in the AI ​​industry and useful educational courses.

[2005] input:

[2006] Market trends, industry news, and job listings collected from the internet

[2007] output:

[2008] Extracted related information

[2009] Data processing:

[2010] It uses cloud-based web crawlers and natural language processing (NLP) algorithms to extract relevant information.

[2011] Step 4:

[2012] The server generates personalized advice for the user based on the analyzed data and extracted information and provides it via the device, such as suggesting suitable learning courses or career paths for the user.

[2013] input:

[2014] Skills gap identification results

[2015] Extracted related information

[2016] output:

[2017] Personalized advice

[2018] Data processing:

[2019] Based on analytical data, we generate optimal career advice and learning resources.

[2020] Step 5:

[2021] The server periodically tracks the user's progress and provides feedback based on the progress, for example by sending motivational messages based on the progress made.

[2022] input:

[2023] User progress data

[2024] output:

[2025] Progress Feedback

[2026] Data processing:

[2027] Aggregate progress data and generate motivational feedback.

[2028] Step 6:

[2029] The terminal collects the user's emotional state in real time using a smart device, capturing facial expressions and voice using the camera and microphone of smart glasses or a smartphone, and sending the captured data to the emotion engine.

[2030] input:

[2031] User's facial expression data, voice data

[2032] output:

[2033] Real-time emotion data

[2034] Data processing:

[2035] It uses a camera and microphone to capture data and send it to the emotion engine.

[2036] Step 7:

[2037] The emotion engine analyzes the collected emotion data to recognize the user's emotional state, for example, to determine whether the user is stressed or happy.

[2038] input:

[2039] Facial expression data, voice data

[2040] output:

[2041] Emotion recognition results

[2042] Data processing:

[2043] Emotional states are recognized using image analysis and audio analysis technology.

[2044] Step 8:

[2045] The server tailors feedback and recommendations based on the emotional data recognized by the emotion engine, for example, providing relaxation resources if the user is feeling stressed.

[2046] input:

[2047] Emotion recognition results

[2048] output:

[2049] Tailored feedback and recommendations

[2050] Data processing:

[2051] The feedback content is adjusted based on emotional data, and messages are generated to maintain the user's motivation.

[2052] 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.

[2053] 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.

[2054] 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 robot 414.

[2055] 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.

[2056] FIG. 9 is a diagram illustrating 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 actions 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.

[2057] 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.

[2058] 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).

[2059] 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.

[2060] 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."

[2061] 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.

[2062] 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).

[2063] 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.

[2064] 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.

[2065] 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.

[2066] 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.

[2067] 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.

[2068] 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.

[2069] 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.

[2070] 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.

[2071] 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.

[2072] 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.

[2073] The following is further disclosed regarding the above embodiment.

[2074] (Claim 1)

[2075] a means for collecting user-entered profile information;

[2076] a means for analyzing the collected profile information to identify gaps between the user's current skills and career goals;

[2077] means for collecting market trends and industry news and extracting information relevant to the user's career goals;

[2078] means for generating and providing personalized career advice to the user based on the analyzed data and extracted information;

[2079] a means for periodically tracking the user's progress and providing feedback accordingly;

[2080] A career development support system including:

[2081] (Claim 2)

[2082] 2. The career development support system according to claim 1, further comprising means for suggesting suitable educational resources to the user based on the generated career advice.

[2083] (Claim 3)

[2084] 2. The career development support system according to claim 1, further comprising means for collecting feedback from users and utilizing the feedback to improve the system.

[2085] "Example 1"

[2086] (Claim 1)

[2087] a means for collecting user-entered profile information;

[2088] means for transmitting the collected profile information via the terminal to a server;

[2089] a means for analyzing the collected profile information to identify gaps between the user's current skills and career goals;

[2090] means for collecting market ...

Claims

1. a means for collecting user-entered profile information; a means for analyzing the collected profile information to identify gaps between the user's current skills and career goals; means for collecting market trends and industry news and extracting information relevant to the user's career goals; means for generating and providing personalized career advice to the user based on the analyzed data and extracted information; a means for periodically tracking the user's progress and providing feedback accordingly; A career development support system including:

2. 2. The career development support system according to claim 1, further comprising means for suggesting suitable educational resources to the user based on the generated career advice.

3. 2. The career development support system according to claim 1, further comprising means for collecting feedback from users and utilizing the feedback to improve the system.

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