system

Abstract

We provide the system. [Solution] A means of acquiring user data and analyzing that data based on the user's interests and past experiences, A means of using artificial intelligence technology to generate personalized career paths, A means to recommend online learning content suitable for the user and to track learning progress, A means of matching users with companies and coaching resources, A means of providing community support and networking features, A system that includes this.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, many individuals have difficulty clarifying their career paths and are facing challenges in finding learning content and coaching resources to acquire appropriate skills. There are also problems in that they cannot optimize the time and resources for learning necessary skills and cannot build appropriate employment opportunities and networks.

Means for Solving the Problems

[0005] This invention provides a system incorporating artificial intelligence that suggests the optimal career path based on the user's interests and past experiences. The system analyzes user data and generates a personalized learning plan. It also tracks learning progress and matches the user with the most suitable online learning content to support efficient skill acquisition. Furthermore, it provides comprehensive support for career development by matching users with companies and coaching resources, and offering community support and networking functions.

[0006] "User data" refers to information including an individual's interests, past experiences, and behavioral patterns, which is collected for career development and learning planning purposes.

[0007] "Analysis" refers to the process of processing user data to derive useful information, and is used to understand interests and skill sets.

[0008] A "personalized career path" is an optimal job path suggested based on each user's interests and skills, designed to support their future success.

[0009] "Artificial intelligence technology" refers to programming techniques that enable computers to mimic human intellectual behavior, and is used for data analysis and career path proposals.

[0010] "Online learning content" refers to educational materials and courses accessible over the internet, designed to help users learn new skills.

[0011] "Tracking" refers to the process of tracking a user's learning progress and activities, and is a means of monitoring the performance of individual users.

[0012] "Matching" refers to the process of providing users with appropriate employment opportunities, educational resources, or coaching based on their skills and interests.

[0013] "Community support" is a platform function that allows users to support each other, and is a means of promoting information sharing and networking. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system for supporting users' career development, and its main elements include processes such as user data collection and analysis, career path proposal, learning content provision, progress management, and matching and networking.

[0036] User data collection and analysis

[0037] First, the user registers with the system and provides profile information. The device sends this information to the server. The server works with specific data providers and platforms to collect user interest and behavior data and stores it in a database. This data is analyzed to gain detailed insights into the user's interests and skills. The server uses artificial intelligence technology to analyze this data and identify the user's past experiences and skill set.

[0038] Career path proposals

[0039] Based on the analyzed data, the server proposes the optimal career path for the user. It clarifies specific job duties, required skills, and the steps to achieving goals. This proposal is customized to match the user's individual needs and interests.

[0040] Provision of learning content

[0041] To help users progress through their learning based on a suggested career path, the server provides information on appropriate online courses and workshops. This content is displayed to the user depending on their device. Users learn at their own pace, and their progress is tracked by the server.

[0042] Progress management and matching

[0043] The server tracks the user's learning progress in real time and optimizes the learning plan as needed. It also suggests job opportunities and expert coaching opportunities based on the user's acquired skill set. The terminal notifies the user of these matching results.

[0044] Community support and networking

[0045] Users can join communities where they can interact with other users who are pursuing similar career goals. The server supports this networking functionality, providing an environment where users can help each other.

[0046] For example, a user who wants to venture into a specific technical field can have a specialized career path suggested by the server based on their past work experience data, and can exchange ideas with community members while utilizing online courses related to that field. In this way, users can achieve career development tailored to their individual needs.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] When a user logs in for the first time, they enter their personal information and professional profile into their device. The device then sends this information to the server.

[0050] Step 2:

[0051] The server collects additional behavioral data and interest-related information from relevant data providers based on the user's profile information.

[0052] Step 3:

[0053] The server uses artificial intelligence technology to analyze the collected data and identify the user's interests and skills. It also records the user's past work experience and skill set in a database.

[0054] Step 4:

[0055] Based on the analysis results, the server generates several career paths suitable for the user's interests and skills. Each path clearly indicates the required skills and recommended learning routes.

[0056] Step 5:

[0057] The device displays the user the career path and associated learning content sent from the server. The user selects the path that best matches their interests.

[0058] Step 6:

[0059] The server suggests online courses and workshops that correspond to the user's chosen career path and sets up a system to track learning progress. The terminal provides the user with a detailed learning schedule and content.

[0060] Step 7:

[0061] Users acquire skills using learning content provided via their devices. Progress is tracked in real time by the server, and appropriate feedback is provided from the server at each stage of learning.

[0062] Step 8:

[0063] The server analyzes the user's learning progress and matches them with appropriate companies and coaches. The terminal presents this information to the user and schedules interviews and coaching sessions.

[0064] Step 9:

[0065] Users can further their careers by participating in networking and community forums, exchanging ideas with other users, and organizing study sessions. The server provides a platform for this, supporting smooth communication.

[0066] (Example 1)

[0067] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] Conventional career development support systems have problems such as failing to adequately propose specific and optimal career paths tailored to each user's individual interests and skills, and not effectively managing learning progress or matching users with companies. Furthermore, their networking functions among users are limited, and necessary support and information exchange are not adequately provided. There is a need to solve these problems and provide personalized and effective career development support.

[0069] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0070] In this invention, the server includes means for acquiring user information and analyzing the information based on the user's interests and past activities, means for using machine learning techniques to generate personalized career paths, and means for suggesting optimal online learning information to the user and monitoring their learning progress. This enables the suggestion of individually optimized career paths, efficient learning support based on progress management, and matching with appropriate human resources.

[0071] "User information" refers to information about the user, including basic profile information, past activities, interests, and skills.

[0072] "Analysis" is the act of analyzing acquired data for a specific purpose and deriving meaningful results.

[0073] A "career path" is a plan that outlines the steps and skills needed to achieve goals in a specific occupation or industry.

[0074] "Machine learning technology" refers to the algorithms and processes that enable computers to automatically learn from data and generate predictions and suggestions.

[0075] "Online learning information" refers to information about learning resources such as courses, materials, and workshops that are accessible via the internet.

[0076] "Progress status" refers to the current level of achievement or progress made in the process toward a specific goal.

[0077] "Human resource development resources" refer to resources such as experts, courses, and programs provided to support users' capacity development and skill improvement.

[0078] "Community support" refers to activities and support that enable participants within a group with a specific purpose to interact with each other, share information, and help one another.

[0079] "Communication features" are functions designed to allow users to communicate with other people and exchange information.

[0080] This invention is a system that supports users' career development, and it functions through the respective roles of the server, terminal, and user. The server utilizes advanced machine learning technology to analyze user information and propose the optimal career path. Software such as Python, Scikit-learn, and TENSORFLOW® are used for the analysis. The server analyzes profile information and behavioral data received from the user and generates a detailed career path based on that data.

[0081] The terminal transmits user-inputted information to the server and visually presents the user with career paths and learning information received from the server. This terminal can use a web browser or mobile application as its user interface.

[0082] Users enter their profile information on the system and proceed with their learning according to the suggested career path. The server collects online learning information from relevant resources and provides it in an appropriately organized format. Users can check their learning progress on their device and set their next learning goals based on that progress.

[0083] For example, a user interested in data science can enter keywords such as "data science" and "machine learning" in their profile to receive suggestions for specialized career paths and related online courses. An example of a prompt for a generative AI model would be: "For a user aiming for a career in data science, suggest the next necessary skills and recommended courses."

[0084] With the above configuration, this system can provide personalized career development support tailored to the user's characteristics, enabling efficient and effective support for achieving goals.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user enters their profile information into the system. This information includes work history, skills, interests, and goals. The terminal sends the entered information to the server. The entered data is sent to the server in JSON format, and the server then stores it in its database.

[0088] Step 2:

[0089] The server retrieves user information from the database and inputs it into a machine learning model. This input data includes detailed information about the user's interests and past work experience. The server utilizes libraries such as Scikit-learn and TensorFlow to perform data analysis. This analysis outputs intermediate data for skill matching and career path generation.

[0090] Step 3:

[0091] The server generates the optimal career path for the user based on the analysis results. Specifically, it uses a generation AI model to take prompts such as "What types of jobs and skills are suitable for the user?" and designs a career path based on the answers. The output results are a list of job types and a list of required skill sets, which are sent to the terminal.

[0092] Step 4:

[0093] The terminal visualizes and displays job path information received from the server to the user on the screen. The user uses this information to select the next step. Specifically, when the user clicks on a job that interests them, detailed requirements and goals are displayed.

[0094] Step 5:

[0095] The server collects online learning information related to the career path selected by the user. The server uses APIs from educational platforms related to the specified field to retrieve appropriate course information. Career path information is used as input data, and a list of course information is sent to the terminal as output.

[0096] Step 6:

[0097] The device provides the user with received online learning information, and the user registers for courses of interest. During this process, the device provides the user with an interactive interface for tracking learning progress. Each time the user completes a lecture, progress information is sent to the server.

[0098] Step 7:

[0099] The server analyzes learning progress data and adjusts the learning plan as needed. Specifically, it analyzes the user's achievement and understanding, and proposes a new learning strategy using a generative AI model. Learning progress data is used as input data, and the adjusted learning plan is notified to the device as output.

[0100] Step 8:

[0101] Based on the server's suggestions, users proceed to the next learning step. They also interact with other users using community features as needed. Their activity history within the community is recorded on the server and can be used to help them in their future career development.

[0102] (Application Example 1)

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

[0104] In today's world, for individuals to effectively advance their careers, they need to make effective use of information and resources that match their interests and skills. However, with so much information available, finding the optimal learning and career path is difficult. Furthermore, it is necessary to efficiently acquire useful information, such as selecting products related to career goals and interacting with others who have similar goals.

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

[0106] In this invention, the server includes means for acquiring and analyzing user data, means for using artificial intelligence technology to generate personalized career paths, and means for presenting users with recommended products and services related to their career goals and displaying user reviews. This enables the suggestion of optimal career paths tailored to individual needs and the efficient use of related products and services.

[0107] "User data" refers to data that includes information such as an individual's interests, past experiences, and skills.

[0108] "Analysis" is the process of analyzing acquired data to gain useful insights.

[0109] A "career path" refers to the steps and direction an individual should follow to achieve their professional goals.

[0110] "Artificial intelligence technology" is a technology that enables machines to mimic human intelligence, analyze data, and generate meaningful results.

[0111] "Online learning information" refers to information about educational resources and materials provided via the internet.

[0112] "Progress tracking" is a process that monitors in real time the degree to which users are achieving their set learning and career goals.

[0113] "Network functionality" refers to a platform that allows users to interact with other users and share information.

[0114] "Recommended products and services" are products and services presented according to the user's career goals and learning progress.

[0115] "User reviews" refer to information compiled from ratings and comments from other users.

[0116] The system for realizing this invention consists of a server and a user terminal. The server first receives user data from the terminal and analyzes the data using artificial intelligence technology. Based on the results of this analysis, it generates a carrier path optimized for each individual user and identifies relevant online learning information and recommended products. The server then transmits this information to the user's terminal and presents it to the user.

[0117] The user's device displays the received information through an interface, allowing the user to use that information to advance their career development. The device also continuously tracks the user's learning progress and career interests, and periodically sends this information to the server.

[0118] The system utilizes cloud-based server technologies such as AWS® and Firebase in its hardware and software. Artificial intelligence frameworks like TensorFlow and PyTorch are used for data analysis. This ensures secure management of user data and enables advanced data analysis.

[0119] For example, if a user wants to improve their "digital marketing" skills, the server will generate a career path and present a list of relevant online courses and recommended products. This information provides the user with effective resources for learning.

[0120] Examples of prompts to input into a generative AI model:

[0121] Prompt: Please research "Top Online Digital Marketing Courses" and display their popularity rankings.

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The server receives user data (e.g., profile information, interests, past experiences) from the terminal. This data is stored securely with privacy in mind. The server then prepares to begin analysis based on the received data.

[0125] Step 2:

[0126] The server analyzes the received user data using artificial intelligence technology (e.g., generative AI models using TensorFlow or PyTorch). It analyzes the input data to identify the user's interests and skill set. This analysis generates the optimal career path for the user. Based on these results, it prepares data to provide individualized career paths.

[0127] Step 3:

[0128] Based on the analyzed data, the server lists online learning information and recommended products and services related to the user's career goals. For example, if the user is interested in "digital marketing," this process will select online courses and materials in that field. The server then prepares to send the selected information to the user's device.

[0129] Step 4:

[0130] The server sends selected online learning and product information to the user's device. The device receives this information and displays it clearly to the user through its interface. This display facilitates access to information that interests the user.

[0131] Step 5:

[0132] Based on the information received, users begin learning or purchasing products. The device tracks the user's learning progress and periodically sends progress data to the server. This allows the server to monitor the user's learning achievements.

[0133] Step 6:

[0134] The server analyzes the user's learning progress data and optimizes the learning plan. If necessary, it generates data to re-recommend more appropriate online courses and supplementary materials. It then prepares to present this updated information to the user again.

[0135] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0136] This invention is a career development support system that combines an emotion engine that recognizes user emotions, and includes processes such as user data collection and analysis, career path proposal, learning content provision, progress management, emotion recognition, and matching and networking.

[0137] User data collection and analysis

[0138] First, users register with the system and enter their profile information and work history. The terminal sends this information to the server. The server collaborates with various data providers to collect additional behavioral and interest data. This data is analyzed using artificial intelligence technology to understand the user's past experience and skills.

[0139] Emotion recognition by an emotion engine

[0140] The device uses an emotion engine to recognize the user's emotions based on voice, facial expressions, and input data as the user interacts with the interface. The server analyzes this emotion data to determine the user's current emotional state. This information is used to suggest career paths and adjust learning content.

[0141] Proposing career paths and providing learning content

[0142] The server suggests the optimal career path for the user based on analyzed data and emotional information. The terminal displays the suggested career path and related learning content to the user. The learning content is appropriately adjusted based on the user's motivation and emotional state.

[0143] Progress management and optimization of learning plans

[0144] The server monitors the user's learning progress and dynamically optimizes the learning plan by taking sentiment data into account. This makes the user's learning experience more personalized.

[0145] Matching and Networking

[0146] The server facilitates matching users with suitable companies and coaches based on their acquired skills and motivation levels. The terminal notifies users of these matching results and supports the smooth progress of interviews and coaching sessions.

[0147] For example, if a user who wants to acquire new project management skills is detected as "stressed" by the emotion engine, the server will provide simple tasks or relaxation-related content to alleviate that stress. In this way, users can progress with their learning and career development while receiving appropriate support tailored to their emotional state.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] The user logs into the system and enters their personal information and work history into a terminal. The terminal then sends this information to the server.

[0151] Step 2:

[0152] The server collects additional user data from the specified data provider and analyzes the data to reveal the user's interests and past experiences.

[0153] Step 3:

[0154] The device transmits voice input and facial expression data to the emotion engine through an interface that interacts with the user. The emotion engine processes this data to recognize the user's emotions.

[0155] Step 4:

[0156] The server analyzes the user's emotional data, recognized by the emotion engine, to obtain information necessary for suggesting career paths and adjusting learning content.

[0157] Step 5:

[0158] The server generates an optimal career path considering the user's skills, interests, and emotional data. The terminal displays the career path generated by the server to the user.

[0159] Step 6:

[0160] The user selects a career path from the presented options that best suits their goals. The device then displays online learning content related to the selected career path.

[0161] Step 7:

[0162] The server tracks the progress of online learning content and dynamically optimizes the learning plan based on the user's understanding and emotions. The device then presents the user with a learning schedule adjusted accordingly.

[0163] Step 8:

[0164] The server analyzes the user's progress and emotional state to identify appropriate coaching opportunities and job postings, and then matches the user with them. The terminal notifies the user of the matching results and assists with necessary procedures and scheduling.

[0165] Step 9:

[0166] Users enhance their networking by accessing community forums to share knowledge and receive support from other users. The server supports these networking activities and guides users to new resources and opportunities as needed.

[0167] (Example 2)

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

[0169] It is difficult for users to find personalized learning paths while receiving various information and support in their career development. In particular, there is a need to propose the optimal career path while considering the user's emotional state, dynamically adjust learning content, and ensure appropriate matching.

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

[0171] In this invention, the server includes means for using technology to recognize and evaluate user emotional information in real time, means for using knowledge processing technology to generate personalized career paths, and means for dynamically adjusting learning content according to the user's emotional state. This enables the provision of individualized career suggestions and learning content that take user emotions into consideration, as well as optimal matching.

[0172] "User data" refers to information such as an individual's work history, interests, and behavior, and is used to form a user profile.

[0173] "Knowledge processing technology" refers to algorithms and methods that analyze large amounts of information to gain insights from the data, and is used to provide personalized information that is relevant to the user.

[0174] "Emotional information" refers to information indicating the emotional state obtained from the user's voice, facial expressions, and input data, and is used to detect the user's psychological state.

[0175] "Learning resources" refer to educational materials and content that users use to improve their skills or gain knowledge, and include online courses and training materials.

[0176] "Networking features" refer to mechanisms that allow users to interact and connect with other users and experts, and are intended to stimulate communication.

[0177] "Mentoring resources" refer to a group of supporters, including experts such as coaches and mentors, who contribute to improving users' skills and maintaining their motivation.

[0178] "Personalization" refers to the process of optimizing the experience based on the individual user's characteristics and needs, in order to provide users with the most relevant information and content.

[0179] This invention is a system designed to support users' career development. During initial registration, users input their work history and personal profile information via a terminal. The terminal sends this information to a server, which then collaborates with data providers to collect additional information.

[0180] The server analyzes the collected user data using knowledge processing techniques. This technique is based on machine learning algorithms and utilizes Python-based libraries and tools. This makes it possible to understand and analyze the user's past experiences and current skill set in detail.

[0181] The system employs an emotion engine to recognize and evaluate user emotional information in real time. The device uses its camera and microphone to capture user facial expressions and voice data for the emotion engine. The server processes this emotional information to understand the user's emotional state. Based on this information, the server uses a generative AI model to propose a personalized career path.

[0182] Furthermore, the server suggests learning resources tailored to the user's emotional state and monitors their progress. For example, if a user wants to learn new management skills, the server dynamically adjusts the learning content, such as presenting content that alleviates the tension derived from their emotions, thereby enabling more effective learning.

[0183] Furthermore, the server matches users with industry institutions and mentoring resources based on their skill sets and motivations. This process uses a notification function on the device to inform users of the matching results and provide networking opportunities.

[0184] As a concrete example, a prompt message such as, "Consider the user's current emotional state and suggest the optimal career path and relevant learning content," is input into the AI ​​model, and the system derives a career scenario suitable for the user.

[0185] This enables users to experience personalized, emotion-based learning and career development. The system aims to motivate users and support them in pursuing better career paths.

[0186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0187] Step 1:

[0188] Users input data such as profile information and work history through their terminal. This input data is then sent to the server by the terminal. Specifically, the user enters information using the keyboard and clicks the "Send" button, sending the data to the server via the HTTPS protocol. The server receives this data and stores it in its database.

[0189] Step 2:

[0190] The server collects additional user data from external sources and via APIs, based on the registered data. This external information request utilizes the user's interests and past experiences, and the collected data is stored in the server's database. The server sends API requests and receives data in JSON format.

[0191] Step 3:

[0192] The server analyzes user data stored in the database using knowledge processing techniques. Input data includes work history and interests, and machine learning algorithms are used to analyze this data and generate insights into the user's skills and past experiences. The server uses Python®-based libraries for data analysis.

[0193] Step 4:

[0194] The device uses an emotion engine to recognize the user's emotions. When the user interacts with the interface, it uses the camera and microphone to capture facial expressions and audio data, which are then sent to the server. The server analyzes this data to determine the user's emotional state. Emotion analysis is performed in real time using an emotion analysis model.

[0195] Step 5:

[0196] The server uses a generative AI model to generate the optimal career path for the user based on the analyzed data and sentiment information. The input includes analysis results and sentiment data, and the output is a personalized career path. By inputting prompts into the generative AI model, a suitable career scenario is calculated.

[0197] Step 6:

[0198] The device presents the user with a career path received from the server and associated learning resources. The learning content is dynamically adjusted according to the user's emotional state. For example, the learning experience is tailored by suggesting content that alleviates tension based on the identified level of stress.

[0199] Step 7:

[0200] The server matches the user with appropriate industry institutions and mentoring resources based on the user's skills and sentiment data. The output is matching information with the resources and coaches selected as suitable. The matching algorithm is executed, and the terminal notifies the user of the results.

[0201] Step 8:

[0202] The server automatically generates and optimizes educational plans based on the user's learning progress and understanding. Input includes learning history and outcome data, and output proposes an improved educational plan. The server evaluates learning outcomes and uses AI technology to generate the next optimal learning step.

[0203] (Application Example 2)

[0204] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0205] For modern consumers and workers, receiving appropriate information and support based on their emotions is a crucial factor in purchasing decisions and career development. However, traditional systems have struggled to accurately recognize users' emotions and provide information accordingly. Furthermore, matching and networking functions with users are limited, making the provision of personalized services a challenge.

[0206] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0207] In this invention, the server includes means for acquiring user data and analyzing the data based on the user's emotions and past experiences, means for using artificial intelligence technology to generate a personalized career path, and means for recognizing the customer's emotions of interest and anxiety and providing information accordingly. This makes it possible to provide personalized services based on the user's emotions.

[0208] "User data" refers to a dataset that includes an individual's profile information, work history, behavioral data, and information about their interests.

[0209] "Emotion" refers to an individual's psychological state, analyzed from their voice, facial expressions, and behavioral patterns.

[0210] "Analysis" is the process of scrutinizing acquired data using artificial intelligence technology and extracting information relevant to a specific purpose.

[0211] A "personalized career path" is a career or learning path created according to the individual user's characteristics and goals.

[0212] "Artificial intelligence technology" refers to computer processing technology that uses techniques such as machine learning and natural language processing to analyze data and make decisions.

[0213] "Interest" refers to an individual's active interest in or involvement with a particular subject or activity.

[0214] "Anxiety" refers to the concerns and worries that an individual has about uncertain or unknown situations.

[0215] "Information provision" refers to activities that present useful data and content to users.

[0216] A "business entity" is a legal entity or organization that engages in specific business activities.

[0217] "Mentoring resources" refer to professional support that provides knowledge and skills to help users grow and achieve their goals.

[0218] "Communication support" refers to services and functions that facilitate the sharing of information and opinions among individuals and organizations, and the building of relationships.

[0219] To implement this invention, a smart device owned by the user (e.g., a smartphone or smart glasses) and a system using a cloud server are required. The user first installs a dedicated application on their smart device. This application uses the device's camera and microphone to acquire voice and facial expression data in real time.

[0220] The server converts the audio data received from the user into text using Google® Speech-to-Text and simultaneously analyzes the user's emotions by performing facial recognition using Microsoft® Azure® Face API. This emotional data is analyzed by a generative AI model and used to provide the user with optimal information and suggest career paths. Specifically, if a user is feeling anxious in front of a product, detailed information and reviews of that product are immediately provided.

[0221] The system as a whole collects users' past profile information and behavioral data, and based on this, artificial intelligence technology proposes personalized career paths to users. Learning content is dynamically adjusted based on the user's emotion recognition, and is designed to optimize learning progress. It also facilitates matching users with organizations and mentoring resources that match their skills, and enhances support for interaction.

[0222] For example, suppose a user is considering purchasing a new technology product in a physical store. If the emotion engine detects anxiety, the server can immediately present usage examples and reviews of the product to help alleviate the user's concerns. An example of a prompt would be, "When a user is in a technology product section, advise them based on their emotional state what information would increase their willingness to purchase." By providing feedback based on the user's emotions in this way, the purchasing process and career development become more personalized and effective.

[0223] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0224] Step 1:

[0225] The device acquires voice and facial expression data from the user. It collects this data in real time using a camera and microphone. Audio files and video footage are generated as input data.

[0226] Step 2:

[0227] The device sends the acquired audio data to the Google Speech-to-Text service, where it is converted into text. The input is audio data, and the output is text data. This conversion makes the audio information into a format that can be parsed.

[0228] Step 3:

[0229] The device sends video data to the Microsoft Azure Face API, which analyzes facial expressions to estimate the user's emotions. The input is video data, and the output is the estimated emotion category. This allows for obtaining visual emotion metrics.

[0230] Step 4:

[0231] The server receives text data and sentiment data, and uses a generative AI model to analyze the user's current emotional state. The input is text data and sentiment categories, and the output is data indicating the user's emotional state. This prepares the server for making decisions based on the user's emotions.

[0232] Step 5:

[0233] The server provides users with the most relevant information and career paths based on their analyzed emotional state. If a user is experiencing anxiety, it generates product information and reviews tailored to that situation and sends them to the device. The input is emotional state data, and the output is informational content.

[0234] Step 6:

[0235] The device presents information received from the server to the user. This information is displayed on the screen or as audio guidance to support the user's purchasing and learning decisions. Input is informational content from the server, and output is presented to the user as visual or auditory feedback.

[0236] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0237] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0238] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0239] [Second Embodiment]

[0240] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0241] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0242] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0243] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0244] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0245] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0246] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0247] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0248] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0249] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0250] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0251] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0252] This invention is a system for supporting users' career development, and its main elements include processes such as user data collection and analysis, career path proposal, learning content provision, progress management, and matching and networking.

[0253] User data collection and analysis

[0254] First, the user registers with the system and provides profile information. The device sends this information to the server. The server works with specific data providers and platforms to collect user interest and behavior data and stores it in a database. This data is analyzed to gain detailed insights into the user's interests and skills. The server uses artificial intelligence technology to analyze this data and identify the user's past experiences and skill set.

[0255] Career path proposals

[0256] Based on the analyzed data, the server proposes the optimal career path for the user. It clarifies specific job duties, required skills, and the steps to achieving goals. This proposal is customized to match the user's individual needs and interests.

[0257] Provision of learning content

[0258] To help users progress through their learning based on a suggested career path, the server provides information on appropriate online courses and workshops. This content is displayed to the user depending on their device. Users learn at their own pace, and their progress is tracked by the server.

[0259] Progress management and matching

[0260] The server tracks the user's learning progress in real time and optimizes the learning plan as needed. It also suggests job opportunities and expert coaching opportunities based on the user's acquired skill set. The terminal notifies the user of these matching results.

[0261] Community support and networking

[0262] Users can join communities where they can interact with other users who are pursuing similar career goals. The server supports this networking functionality, providing an environment where users can help each other.

[0263] For example, a user who wants to venture into a specific technical field can have a specialized career path suggested by the server based on their past work experience data, and can exchange ideas with community members while utilizing online courses related to that field. In this way, users can achieve career development tailored to their individual needs.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] When a user logs in for the first time, they enter their personal information and professional profile into their device. The device then sends this information to the server.

[0267] Step 2:

[0268] The server collects additional behavioral data and interest-related information from relevant data providers based on the user's profile information.

[0269] Step 3:

[0270] The server uses artificial intelligence technology to analyze the collected data and identify the user's interests and skills. It also records the user's past work experience and skill set in a database.

[0271] Step 4:

[0272] Based on the analysis results, the server generates several career paths suitable for the user's interests and skills. Each path clearly indicates the required skills and recommended learning routes.

[0273] Step 5:

[0274] The terminal displays the carrier path sent from the server and the accompanying learning content to the user. The user selects the path that best suits their interests.

[0275] Step 6:

[0276] The server proposes online courses and workshops corresponding to the carrier path selected by the user and sets up tracking of the learning progress. The terminal provides the user with a detailed learning schedule and content.

[0277] Step 7:

[0278] The user uses the learning content provided via the terminal to acquire skills. The progress is tracked in real time by the server, and appropriate feedback is provided by the server at each stage of learning.

[0279] Step 8:

[0280] The server analyzes the user's learning progress and matches with appropriate companies or coaches. The terminal presents this information to the user and sets up the schedule for interviews and coaching sessions.

[0281] Step 9:

[0282] The user participates in networking and community forums, holds opinion exchanges and study sessions with other users to further career advancement. The server provides a platform for this and supports smooth communication.

[0283] (Example 1)

[0284] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In conventional career formation support systems, there are problems such as insufficient proposal of specific and optimal career paths according to individual interests and skills of users, and ineffective management of learning progress and matching with companies. Furthermore, the networking function between users is limited, and necessary support and information exchange are not sufficiently provided. There is a need to solve such problems and provide individualized and effective career formation support.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0287] In this invention, the server includes means for acquiring user information and analyzing information based on the user's interests and past activities, means for using machine learning techniques to generate an individualized career path, and means for proposing optimal online learning information to the user and monitoring the progress of learning. Thereby, it becomes possible to propose an individually optimized career path, provide efficient learning support based on progress management, and match with appropriate human resources.

[0288] "User information" is information including basic profiles, past activities, interests, skills, etc. regarding the user.

[0289] "Analysis" is an act of analyzing information for a specific purpose based on the acquired data and deriving meaningful results.

[0290] "Career path" is a plan showing the steps and skills required to achieve goals in a specific occupation or industry.

[0291] "Machine learning technology" refers to technologies including algorithms and processes for a computer to automatically learn based on data and generate predictions and proposals.

[0292] "Online learning information" refers to information about learning resources such as courses, materials, and workshops that are accessible via the internet.

[0293] "Progress status" refers to the current level of achievement or progress made in the process toward a specific goal.

[0294] "Human resource development resources" refer to resources such as experts, courses, and programs provided to support users' capacity development and skill improvement.

[0295] "Community support" refers to activities and support that enable participants within a group with a specific purpose to interact with each other, share information, and help one another.

[0296] "Communication features" are functions designed to allow users to communicate with other people and exchange information.

[0297] This invention is a system that supports users' career development, and it functions through the respective roles of the server, terminal, and user. The server utilizes advanced machine learning technology to analyze user information and propose the optimal career path. Software such as Python, Scikit-learn, and TensorFlow are used for the analysis. The server analyzes profile information and behavioral data received from the user and generates a detailed career path based on that data.

[0298] The terminal transmits user-inputted information to the server and visually presents the user with career paths and learning information received from the server. This terminal can use a web browser or mobile application as its user interface.

[0299] Users enter their profile information on the system and proceed with their learning according to the suggested career path. The server collects online learning information from relevant resources and provides it in an appropriately organized format. Users can check their learning progress on their device and set their next learning goals based on that progress.

[0300] For example, a user interested in data science can enter keywords such as "data science" and "machine learning" in their profile to receive suggestions for specialized career paths and related online courses. An example of a prompt for a generative AI model would be: "For a user aiming for a career in data science, suggest the next necessary skills and recommended courses."

[0301] With the above configuration, this system can provide personalized career development support tailored to the user's characteristics, enabling efficient and effective support for achieving goals.

[0302] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0303] Step 1:

[0304] The user enters their profile information into the system. This information includes work history, skills, interests, and goals. The terminal sends the entered information to the server. The entered data is sent to the server in JSON format, and the server then stores it in its database.

[0305] Step 2:

[0306] The server retrieves user information from the database and inputs it into the machine learning model. The input data here is detailed information regarding the user's interests and past work experience. The server utilizes libraries such as Scikit-learn and TensorFlow for data analysis. Through this analysis, intermediate data for user skill matching and career path generation is outputted.

[0307] Step 3:

[0308] Based on the analysis results, the server generates an optimal career path for the user. Specifically, using a generative AI model, it inputs a prompt such as "What job types and skills are suitable for the user?" and designs the career path based on the answer. The output results are a list of job types and a list of required skill sets, which are then sent to the terminal.

[0309] Step 4:

[0310] The terminal visualizes the career path information received from the server for the user and presents it on the screen. The user selects the next step based on this. As a specific operation, when the user clicks on a job type they are interested in, detailed requirements and goals are displayed.

[0311] Step 5:

[0312] The server collects online learning information related to the career path selected by the user. The server utilizes the API of an educational platform related to the specified field to obtain appropriate course information. The career path information is used as input data, and a list of course information is sent to the terminal as output.

[0313] Step 6:

[0314] The device provides the user with received online learning information, and the user registers for courses of interest. During this process, the device provides the user with an interactive interface for tracking learning progress. Each time the user completes a lecture, progress information is sent to the server.

[0315] Step 7:

[0316] The server analyzes learning progress data and adjusts the learning plan as needed. Specifically, it analyzes the user's achievement and understanding, and proposes a new learning strategy using a generative AI model. Learning progress data is used as input data, and the adjusted learning plan is notified to the device as output.

[0317] Step 8:

[0318] Based on the server's suggestions, users proceed to the next learning step. They also interact with other users using community features as needed. Their activity history within the community is recorded on the server and can be used to help them in their future career development.

[0319] (Application Example 1)

[0320] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0321] In today's world, for individuals to effectively advance their careers, they need to make effective use of information and resources that match their interests and skills. However, with so much information available, finding the optimal learning and career path is difficult. Furthermore, it is necessary to efficiently acquire useful information, such as selecting products related to career goals and interacting with others who have similar goals.

[0322] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0323] In this invention, the server includes means for acquiring and analyzing user data, means for using artificial intelligence technology to generate personalized career paths, and means for presenting users with recommended products and services related to their career goals and displaying user reviews. This enables the suggestion of optimal career paths tailored to individual needs and the efficient use of related products and services.

[0324] "User data" refers to data that includes information such as an individual's interests, past experiences, and skills.

[0325] "Analysis" is the process of analyzing acquired data to gain useful insights.

[0326] A "career path" refers to the steps and direction an individual should follow to achieve their professional goals.

[0327] "Artificial intelligence technology" is a technology that enables machines to mimic human intelligence, analyze data, and generate meaningful results.

[0328] "Online learning information" refers to information about educational resources and materials provided via the internet.

[0329] "Progress tracking" is a process that monitors in real time the degree to which users are achieving their set learning and career goals.

[0330] "Network functionality" refers to a platform that allows users to interact with other users and share information.

[0331] "Recommended products and services" are products and services presented according to the user's career goals and learning progress.

[0332] "User reviews" refer to information compiled from ratings and comments from other users.

[0333] The system for realizing this invention consists of a server and a user terminal. The server first receives user data from the terminal and analyzes the data using artificial intelligence technology. Based on the results of this analysis, it generates a carrier path optimized for each individual user and identifies relevant online learning information and recommended products. The server then transmits this information to the user's terminal and presents it to the user.

[0334] The user's device displays the received information through an interface, allowing the user to use that information to advance their career development. The device also continuously tracks the user's learning progress and career interests, and periodically sends this information to the server.

[0335] The system utilizes cloud-based server technologies such as AWS and Firebase for its hardware and software. Artificial intelligence frameworks like TensorFlow and PyTorch are used for data analysis. This ensures secure management of user data and enables advanced data analysis.

[0336] For example, if a user wants to improve their "digital marketing" skills, the server will generate a career path and present a list of relevant online courses and recommended products. This information provides the user with effective resources for learning.

[0337] Examples of prompts to input into a generative AI model:

[0338] Prompt: Please research "Top Online Digital Marketing Courses" and display their popularity rankings.

[0339] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0340] Step 1:

[0341] The server receives user data (e.g., profile information, interests, past experiences) from the terminal. This data is stored securely with privacy in mind. The server then prepares to begin analysis based on the received data.

[0342] Step 2:

[0343] The server analyzes the received user data using artificial intelligence technology (e.g., generative AI models using TensorFlow or PyTorch). It analyzes the input data to identify the user's interests and skill set. This analysis generates the optimal career path for the user. Based on these results, it prepares data to provide individualized career paths.

[0344] Step 3:

[0345] Based on the analyzed data, the server lists online learning information and recommended products and services related to the user's career goals. For example, if the user is interested in "digital marketing," this process will select online courses and materials in that field. The server then prepares to send the selected information to the user's device.

[0346] Step 4:

[0347] The server sends selected online learning and product information to the user's device. The device receives this information and displays it clearly to the user through its interface. This display facilitates access to information that interests the user.

[0348] Step 5:

[0349] Based on the information received, users begin learning or purchasing products. The device tracks the user's learning progress and periodically sends progress data to the server. This allows the server to monitor the user's learning achievements.

[0350] Step 6:

[0351] The server analyzes the user's learning progress data and optimizes the learning plan. If necessary, it generates data to re-recommend more appropriate online courses and supplementary materials. It then prepares to present this updated information to the user again.

[0352] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0353] This invention is a career development support system that combines an emotion engine that recognizes user emotions, and includes processes such as user data collection and analysis, career path proposal, learning content provision, progress management, emotion recognition, and matching and networking.

[0354] User data collection and analysis

[0355] First, users register with the system and enter their profile information and work history. The terminal sends this information to the server. The server collaborates with various data providers to collect additional behavioral and interest data. This data is analyzed using artificial intelligence technology to understand the user's past experience and skills.

[0356] Emotion recognition by an emotion engine

[0357] The device uses an emotion engine to recognize the user's emotions based on voice, facial expressions, and input data as the user interacts with the interface. The server analyzes this emotion data to determine the user's current emotional state. This information is used to suggest career paths and adjust learning content.

[0358] Proposing career paths and providing learning content

[0359] The server suggests the optimal career path for the user based on analyzed data and emotional information. The terminal displays the suggested career path and related learning content to the user. The learning content is appropriately adjusted based on the user's motivation and emotional state.

[0360] Progress management and optimization of learning plans

[0361] The server monitors the user's learning progress and dynamically optimizes the learning plan by taking sentiment data into account. This makes the user's learning experience more personalized.

[0362] Matching and Networking

[0363] The server facilitates matching users with suitable companies and coaches based on their acquired skills and motivation levels. The terminal notifies users of these matching results and supports the smooth progress of interviews and coaching sessions.

[0364] For example, if a user who wants to acquire new project management skills is detected as "stressed" by the emotion engine, the server will provide simple tasks or relaxation-related content to alleviate that stress. In this way, users can progress with their learning and career development while receiving appropriate support tailored to their emotional state.

[0365] The following describes the processing flow.

[0366] Step 1:

[0367] The user logs into the system and enters their personal information and work history into a terminal. The terminal then sends this information to the server.

[0368] Step 2:

[0369] The server collects additional user data from the specified data provider and analyzes the data to reveal the user's interests and past experiences.

[0370] Step 3:

[0371] The device transmits voice input and facial expression data to the emotion engine through an interface that interacts with the user. The emotion engine processes this data to recognize the user's emotions.

[0372] Step 4:

[0373] The server analyzes the user's emotional data, recognized by the emotion engine, to obtain information necessary for suggesting career paths and adjusting learning content.

[0374] Step 5:

[0375] The server generates an optimal career path considering the user's skills, interests, and emotional data. The terminal displays the career path generated by the server to the user.

[0376] Step 6:

[0377] The user selects a career path from the presented options that best suits their goals. The device then displays online learning content related to the selected career path.

[0378] Step 7:

[0379] The server tracks the progress of online learning content and dynamically optimizes the learning plan based on the user's understanding and emotions. The device then presents the user with a learning schedule adjusted accordingly.

[0380] Step 8:

[0381] The server analyzes the user's progress and emotional state to identify appropriate coaching opportunities and job postings, and then matches the user with them. The terminal notifies the user of the matching results and assists with necessary procedures and scheduling.

[0382] Step 9:

[0383] Users enhance their networking by accessing community forums to share knowledge and receive support from other users. The server supports these networking activities and guides users to new resources and opportunities as needed.

[0384] (Example 2)

[0385] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0386] It is difficult for users to find personalized learning paths while receiving various information and support in their career development. In particular, there is a need to propose the optimal career path while considering the user's emotional state, dynamically adjust learning content, and ensure appropriate matching.

[0387] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0388] In this invention, the server includes means for using technology to recognize and evaluate user emotional information in real time, means for using knowledge processing technology to generate personalized career paths, and means for dynamically adjusting learning content according to the user's emotional state. This enables the provision of individualized career suggestions and learning content that take user emotions into consideration, as well as optimal matching.

[0389] "User data" refers to information such as an individual's work history, interests, and behavior, and is used to form a user profile.

[0390] "Knowledge processing technology" refers to algorithms and methods that analyze large amounts of information to gain insights from the data, and is used to provide personalized information that is relevant to the user.

[0391] "Emotional information" refers to information indicating the emotional state obtained from the user's voice, facial expressions, and input data, and is used to detect the user's psychological state.

[0392] "Learning resources" refer to educational materials and content that users use to improve their skills or gain knowledge, and include online courses and training materials.

[0393] "Networking features" refer to mechanisms that allow users to interact and connect with other users and experts, and are intended to stimulate communication.

[0394] "Mentoring resources" refer to a group of supporters, including experts such as coaches and mentors, who contribute to improving users' skills and maintaining their motivation.

[0395] "Personalization" refers to the process of optimizing the experience based on the individual user's characteristics and needs, in order to provide users with the most relevant information and content.

[0396] This invention is a system designed to support users' career development. During initial registration, users input their work history and personal profile information via a terminal. The terminal sends this information to a server, which then collaborates with data providers to collect additional information.

[0397] The server analyzes the collected user data using knowledge processing techniques. This technique is based on machine learning algorithms and utilizes Python-based libraries and tools. This makes it possible to understand and analyze the user's past experiences and current skill set in detail.

[0398] The system employs an emotion engine to recognize and evaluate user emotional information in real time. The device uses its camera and microphone to capture user facial expressions and voice data for the emotion engine. The server processes this emotional information to understand the user's emotional state. Based on this information, the server uses a generative AI model to propose a personalized career path.

[0399] Furthermore, the server suggests learning resources tailored to the user's emotional state and monitors their progress. For example, if a user wants to learn new management skills, the server dynamically adjusts the learning content, such as presenting content that alleviates the tension derived from their emotions, thereby enabling more effective learning.

[0400] Furthermore, the server matches users with industry institutions and mentoring resources based on their skill sets and motivations. This process uses a notification function on the device to inform users of the matching results and provide networking opportunities.

[0401] As a concrete example, a prompt message such as, "Consider the user's current emotional state and suggest the optimal career path and relevant learning content," is input into the AI ​​model, and the system derives a career scenario suitable for the user.

[0402] This enables users to experience personalized, emotion-based learning and career development. The system aims to motivate users and support them in pursuing better career paths.

[0403] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0404] Step 1:

[0405] Users input data such as profile information and work history through their terminal. This input data is then sent to the server by the terminal. Specifically, the user enters information using the keyboard and clicks the "Send" button, sending the data to the server via the HTTPS protocol. The server receives this data and stores it in its database.

[0406] Step 2:

[0407] The server collects additional user data from external sources and via APIs, based on the registered data. This external information request utilizes the user's interests and past experiences, and the collected data is stored in the server's database. The server sends API requests and receives data in JSON format.

[0408] Step 3:

[0409] The server analyzes user data stored in the database using knowledge processing techniques. Input data includes work history and interests, and machine learning algorithms are used to analyze this data and generate insights into the user's skills and past experiences. The server uses Python-based libraries for data analysis.

[0410] Step 4:

[0411] The device uses an emotion engine to recognize the user's emotions. When the user interacts with the interface, it uses the camera and microphone to capture facial expressions and audio data, which are then sent to the server. The server analyzes this data to determine the user's emotional state. Emotion analysis is performed in real time using an emotion analysis model.

[0412] Step 5:

[0413] The server uses a generative AI model to generate the optimal career path for the user based on the analyzed data and sentiment information. The input includes analysis results and sentiment data, and the output is a personalized career path. By inputting prompts into the generative AI model, a suitable career scenario is calculated.

[0414] Step 6:

[0415] The device presents the user with a career path received from the server and associated learning resources. The learning content is dynamically adjusted according to the user's emotional state. For example, the learning experience is tailored by suggesting content that alleviates tension based on the identified level of stress.

[0416] Step 7:

[0417] The server matches the user with appropriate industry institutions and mentoring resources based on the user's skills and sentiment data. The output is matching information with the resources and coaches selected as suitable. The matching algorithm is executed, and the terminal notifies the user of the results.

[0418] Step 8:

[0419] The server automatically generates and optimizes educational plans based on the user's learning progress and understanding. Input includes learning history and outcome data, and output proposes an improved educational plan. The server evaluates learning outcomes and uses AI technology to generate the next optimal learning step.

[0420] (Application Example 2)

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

[0422] For modern consumers and workers, receiving appropriate information and support based on their emotions is a crucial factor in purchasing decisions and career development. However, traditional systems have struggled to accurately recognize users' emotions and provide information accordingly. Furthermore, matching and networking functions with users are limited, making the provision of personalized services a challenge.

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

[0424] In this invention, the server includes means for acquiring user data and analyzing the data based on the user's emotions and past experiences, means for using artificial intelligence technology to generate a personalized career path, and means for recognizing the customer's emotions of interest and anxiety and providing information accordingly. This makes it possible to provide personalized services based on the user's emotions.

[0425] "User data" refers to a dataset that includes an individual's profile information, work history, behavioral data, and information about their interests.

[0426] "Emotion" refers to an individual's psychological state, analyzed from their voice, facial expressions, and behavioral patterns.

[0427] "Analysis" is the process of scrutinizing acquired data using artificial intelligence technology and extracting information relevant to a specific purpose.

[0428] A "personalized career path" is a career or learning path created according to the individual user's characteristics and goals.

[0429] "Artificial intelligence technology" refers to computer processing technology that uses techniques such as machine learning and natural language processing to analyze data and make decisions.

[0430] "Interest" refers to an individual's active interest in or involvement with a particular subject or activity.

[0431] "Anxiety" refers to the concerns and worries that an individual has about uncertain or unknown situations.

[0432] "Information provision" refers to activities that present useful data and content to users.

[0433] A "business entity" is a legal entity or organization that engages in specific business activities.

[0434] "Mentoring resources" refer to professional support that provides knowledge and skills to help users grow and achieve their goals.

[0435] "Communication support" refers to services and functions that facilitate the sharing of information and opinions among individuals and organizations, and the building of relationships.

[0436] To implement this invention, a smart device owned by the user (e.g., a smartphone or smart glasses) and a system using a cloud server are required. The user first installs a dedicated application on their smart device. This application uses the device's camera and microphone to acquire voice and facial expression data in real time.

[0437] The server converts the audio data received from the user into text using Google Speech-to-Text and simultaneously analyzes the user's emotions by performing facial recognition using the Microsoft Azure Face API. This emotional data is then analyzed by a generative AI model and used to provide the user with optimal information and suggest career paths. Specifically, if a user is feeling anxious in front of a product, the server will immediately provide detailed information and reviews of that product.

[0438] The system as a whole collects users' past profile information and behavioral data, and based on this, artificial intelligence technology proposes personalized career paths to users. Learning content is dynamically adjusted based on the user's emotion recognition, and is designed to optimize learning progress. It also facilitates matching users with organizations and mentoring resources that match their skills, and enhances support for interaction.

[0439] For example, suppose a user is considering purchasing a new technology product in a physical store. If the emotion engine detects anxiety, the server can immediately present usage examples and reviews of the product to help alleviate the user's concerns. An example of a prompt would be, "When a user is in a technology product section, advise them based on their emotional state what information would increase their willingness to purchase." By providing feedback based on the user's emotions in this way, the purchasing process and career development become more personalized and effective.

[0440] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0441] Step 1:

[0442] The device acquires voice and facial expression data from the user. It collects this data in real time using a camera and microphone. Audio files and video footage are generated as input data.

[0443] Step 2:

[0444] The device sends the acquired audio data to the Google Speech-to-Text service, where it is converted into text. The input is audio data, and the output is text data. This conversion makes the audio information into a format that can be parsed.

[0445] Step 3:

[0446] The device sends video data to the Microsoft Azure Face API, which analyzes facial expressions to estimate the user's emotions. The input is video data, and the output is the estimated emotion category. This allows for obtaining visual emotion metrics.

[0447] Step 4:

[0448] The server receives text data and sentiment data, and uses a generative AI model to analyze the user's current emotional state. The input is text data and sentiment categories, and the output is data indicating the user's emotional state. This prepares the server for making decisions based on the user's emotions.

[0449] Step 5:

[0450] The server provides users with the most relevant information and career paths based on their analyzed emotional state. If a user is experiencing anxiety, it generates product information and reviews tailored to that situation and sends them to the device. The input is emotional state data, and the output is informational content.

[0451] Step 6:

[0452] The device presents information received from the server to the user. This information is displayed on the screen or as audio guidance to support the user's purchasing and learning decisions. Input is informational content from the server, and output is presented to the user as visual or auditory feedback.

[0453] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0454] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0455] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0456] [Third Embodiment]

[0457] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0458] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0459] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0460] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0461] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0462] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0463] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0464] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0465] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0466] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0467] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0468] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0469] This invention is a system for supporting users' career development, and its main elements include processes such as user data collection and analysis, career path proposal, learning content provision, progress management, and matching and networking.

[0470] User data collection and analysis

[0471] First, the user registers with the system and provides profile information. The device sends this information to the server. The server works with specific data providers and platforms to collect user interest and behavior data and stores it in a database. This data is analyzed to gain detailed insights into the user's interests and skills. The server uses artificial intelligence technology to analyze this data and identify the user's past experiences and skill set.

[0472] Career path proposals

[0473] Based on the analyzed data, the server proposes the optimal career path for the user. It clarifies specific job duties, required skills, and the steps to achieving goals. This proposal is customized to match the user's individual needs and interests.

[0474] Provision of learning content

[0475] To help users progress through their learning based on a suggested career path, the server provides information on appropriate online courses and workshops. This content is displayed to the user depending on their device. Users learn at their own pace, and their progress is tracked by the server.

[0476] Progress management and matching

[0477] The server tracks the user's learning progress in real time and optimizes the learning plan as needed. It also suggests job opportunities and expert coaching opportunities based on the user's acquired skill set. The terminal notifies the user of these matching results.

[0478] Community support and networking

[0479] Users can join communities where they can interact with other users who are pursuing similar career goals. The server supports this networking functionality, providing an environment where users can help each other.

[0480] For example, a user who wants to venture into a specific technical field can have a specialized career path suggested by the server based on their past work experience data, and can exchange ideas with community members while utilizing online courses related to that field. In this way, users can achieve career development tailored to their individual needs.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] When a user logs in for the first time, they enter their personal information and professional profile into their device. The device then sends this information to the server.

[0484] Step 2:

[0485] The server collects additional behavioral data and interest-related information from relevant data providers based on the user's profile information.

[0486] Step 3:

[0487] The server uses artificial intelligence technology to analyze the collected data and identify the user's interests and skills. It also records the user's past work experience and skill set in a database.

[0488] Step 4:

[0489] Based on the analysis results, the server generates several career paths suitable for the user's interests and skills. Each path clearly indicates the required skills and recommended learning routes.

[0490] Step 5:

[0491] The device displays the user the career path and associated learning content sent from the server. The user selects the path that best matches their interests.

[0492] Step 6:

[0493] The server suggests online courses and workshops that correspond to the user's chosen career path and sets up a system to track learning progress. The terminal provides the user with a detailed learning schedule and content.

[0494] Step 7:

[0495] Users acquire skills using learning content provided via their devices. Progress is tracked in real time by the server, and appropriate feedback is provided from the server at each stage of learning.

[0496] Step 8:

[0497] The server analyzes the user's learning progress and matches them with appropriate companies and coaches. The terminal presents this information to the user and schedules interviews and coaching sessions.

[0498] Step 9:

[0499] Users can further their careers by participating in networking and community forums, exchanging ideas with other users, and organizing study sessions. The server provides a platform for this, supporting smooth communication.

[0500] (Example 1)

[0501] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0502] Conventional career development support systems have problems such as failing to adequately propose specific and optimal career paths tailored to each user's individual interests and skills, and not effectively managing learning progress or matching users with companies. Furthermore, their networking functions among users are limited, and necessary support and information exchange are not adequately provided. There is a need to solve these problems and provide personalized and effective career development support.

[0503] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0504] In this invention, the server includes means for acquiring user information and analyzing the information based on the user's interests and past activities, means for using machine learning techniques to generate personalized career paths, and means for suggesting optimal online learning information to the user and monitoring their learning progress. This enables the suggestion of individually optimized career paths, efficient learning support based on progress management, and matching with appropriate human resources.

[0505] "User information" refers to information about the user, including basic profile information, past activities, interests, and skills.

[0506] "Analysis" is the act of analyzing acquired data for a specific purpose and deriving meaningful results.

[0507] A "career path" is a plan that outlines the steps and skills needed to achieve goals in a specific occupation or industry.

[0508] "Machine learning technology" refers to the algorithms and processes that enable computers to automatically learn from data and generate predictions and suggestions.

[0509] "Online learning information" refers to information about learning resources such as courses, materials, and workshops that are accessible via the internet.

[0510] "Progress status" refers to the current level of achievement or progress made in the process toward a specific goal.

[0511] "Human resource development resources" refer to resources such as experts, courses, and programs provided to support users' capacity development and skill improvement.

[0512] "Community support" refers to activities and support that enable participants within a group with a specific purpose to interact with each other, share information, and help one another.

[0513] "Communication features" are functions designed to allow users to communicate with other people and exchange information.

[0514] This invention is a system that supports users' career development, and it functions through the respective roles of the server, terminal, and user. The server utilizes advanced machine learning technology to analyze user information and propose the optimal career path. Software such as Python, Scikit-learn, and TensorFlow are used for the analysis. The server analyzes profile information and behavioral data received from the user and generates a detailed career path based on that data.

[0515] The terminal transmits user-inputted information to the server and visually presents the user with career paths and learning information received from the server. This terminal can use a web browser or mobile application as its user interface.

[0516] Users enter their profile information on the system and proceed with their learning according to the suggested career path. The server collects online learning information from relevant resources and provides it in an appropriately organized format. Users can check their learning progress on their device and set their next learning goals based on that progress.

[0517] For example, a user interested in data science can enter keywords such as "data science" and "machine learning" in their profile to receive suggestions for specialized career paths and related online courses. An example of a prompt for a generative AI model would be: "For a user aiming for a career in data science, suggest the next necessary skills and recommended courses."

[0518] With the above configuration, this system can provide personalized career development support tailored to the user's characteristics, enabling efficient and effective support for achieving goals.

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

[0520] Step 1:

[0521] The user enters their profile information into the system. This information includes work history, skills, interests, and goals. The terminal sends the entered information to the server. The entered data is sent to the server in JSON format, and the server then stores it in its database.

[0522] Step 2:

[0523] The server retrieves user information from the database and inputs it into a machine learning model. This input data includes detailed information about the user's interests and past work experience. The server utilizes libraries such as Scikit-learn and TensorFlow to perform data analysis. This analysis outputs intermediate data for skill matching and career path generation.

[0524] Step 3:

[0525] The server generates the optimal career path for the user based on the analysis results. Specifically, it uses a generation AI model to take prompts such as "What types of jobs and skills are suitable for the user?" and designs a career path based on the answers. The output results are a list of job types and a list of required skill sets, which are sent to the terminal.

[0526] Step 4:

[0527] The terminal visualizes and displays job path information received from the server to the user on the screen. The user uses this information to select the next step. Specifically, when the user clicks on a job that interests them, detailed requirements and goals are displayed.

[0528] Step 5:

[0529] The server collects online learning information related to the career path selected by the user. The server uses APIs from educational platforms related to the specified field to retrieve appropriate course information. Career path information is used as input data, and a list of course information is sent to the terminal as output.

[0530] Step 6:

[0531] The device provides the user with received online learning information, and the user registers for courses of interest. During this process, the device provides the user with an interactive interface for tracking learning progress. Each time the user completes a lecture, progress information is sent to the server.

[0532] Step 7:

[0533] The server analyzes learning progress data and adjusts the learning plan as needed. Specifically, it analyzes the user's achievement and understanding, and proposes a new learning strategy using a generative AI model. Learning progress data is used as input data, and the adjusted learning plan is notified to the device as output.

[0534] Step 8:

[0535] Based on the server's suggestions, users proceed to the next learning step. They also interact with other users using community features as needed. Their activity history within the community is recorded on the server and can be used to help them in their future career development.

[0536] (Application Example 1)

[0537] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0538] In today's world, for individuals to effectively advance their careers, they need to make effective use of information and resources that match their interests and skills. However, with so much information available, finding the optimal learning and career path is difficult. Furthermore, it is necessary to efficiently acquire useful information, such as selecting products related to career goals and interacting with others who have similar goals.

[0539] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0540] In this invention, the server includes means for acquiring and analyzing user data, means for using artificial intelligence technology to generate personalized career paths, and means for presenting users with recommended products and services related to their career goals and displaying user reviews. This enables the suggestion of optimal career paths tailored to individual needs and the efficient use of related products and services.

[0541] "User data" refers to data that includes information such as an individual's interests, past experiences, and skills.

[0542] "Analysis" is the process of analyzing acquired data to gain useful insights.

[0543] A "career path" refers to the steps and direction an individual should follow to achieve their professional goals.

[0544] "Artificial intelligence technology" is a technology that enables machines to mimic human intelligence, analyze data, and generate meaningful results.

[0545] "Online learning information" refers to information about educational resources and materials provided via the internet.

[0546] "Progress tracking" is a process that monitors in real time the degree to which users are achieving their set learning and career goals.

[0547] "Network functionality" refers to a platform that allows users to interact with other users and share information.

[0548] "Recommended products and services" are products and services presented according to the user's career goals and learning progress.

[0549] "User reviews" refer to information compiled from ratings and comments from other users.

[0550] The system for realizing this invention consists of a server and a user terminal. The server first receives user data from the terminal and analyzes the data using artificial intelligence technology. Based on the results of this analysis, it generates a carrier path optimized for each individual user and identifies relevant online learning information and recommended products. The server then transmits this information to the user's terminal and presents it to the user.

[0551] The user's device displays the received information through an interface, allowing the user to use that information to advance their career development. The device also continuously tracks the user's learning progress and career interests, and periodically sends this information to the server.

[0552] The system utilizes cloud-based server technologies such as AWS and Firebase for its hardware and software. Artificial intelligence frameworks like TensorFlow and PyTorch are used for data analysis. This ensures secure management of user data and enables advanced data analysis.

[0553] For example, if a user wants to improve their "digital marketing" skills, the server will generate a career path and present a list of relevant online courses and recommended products. This information provides the user with effective resources for learning.

[0554] Examples of prompts to input into a generative AI model:

[0555] Prompt: Please research "Top Online Digital Marketing Courses" and display their popularity rankings.

[0556] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0557] Step 1:

[0558] The server receives user data (e.g., profile information, interests, past experiences) from the terminal. This data is stored securely with privacy in mind. The server then prepares to begin analysis based on the received data.

[0559] Step 2:

[0560] The server analyzes the received user data using artificial intelligence technology (e.g., generative AI models using TensorFlow or PyTorch). It analyzes the input data to identify the user's interests and skill set. This analysis generates the optimal career path for the user. Based on these results, it prepares data to provide individualized career paths.

[0561] Step 3:

[0562] Based on the analyzed data, the server lists online learning information and recommended products and services related to the user's career goals. For example, if the user is interested in "digital marketing," this process will select online courses and materials in that field. The server then prepares to send the selected information to the user's device.

[0563] Step 4:

[0564] The server sends selected online learning and product information to the user's device. The device receives this information and displays it clearly to the user through its interface. This display facilitates access to information that interests the user.

[0565] Step 5:

[0566] Based on the information received, users begin learning or purchasing products. The device tracks the user's learning progress and periodically sends progress data to the server. This allows the server to monitor the user's learning achievements.

[0567] Step 6:

[0568] The server analyzes the user's learning progress data and optimizes the learning plan. If necessary, it generates data to re-recommend more appropriate online courses and supplementary materials. It then prepares to present this updated information to the user again.

[0569] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0570] This invention is a career development support system that combines an emotion engine that recognizes user emotions, and includes processes such as user data collection and analysis, career path proposal, learning content provision, progress management, emotion recognition, and matching and networking.

[0571] User data collection and analysis

[0572] First, users register with the system and enter their profile information and work history. The terminal sends this information to the server. The server collaborates with various data providers to collect additional behavioral and interest data. This data is analyzed using artificial intelligence technology to understand the user's past experience and skills.

[0573] Emotion recognition by an emotion engine

[0574] The device uses an emotion engine to recognize the user's emotions based on voice, facial expressions, and input data as the user interacts with the interface. The server analyzes this emotion data to determine the user's current emotional state. This information is used to suggest career paths and adjust learning content.

[0575] Proposing career paths and providing learning content

[0576] The server suggests the optimal career path for the user based on analyzed data and emotional information. The terminal displays the suggested career path and related learning content to the user. The learning content is appropriately adjusted based on the user's motivation and emotional state.

[0577] Progress management and optimization of learning plans

[0578] The server monitors the user's learning progress and dynamically optimizes the learning plan by taking sentiment data into account. This makes the user's learning experience more personalized.

[0579] Matching and Networking

[0580] The server facilitates matching users with suitable companies and coaches based on their acquired skills and motivation levels. The terminal notifies users of these matching results and supports the smooth progress of interviews and coaching sessions.

[0581] For example, if a user who wants to acquire new project management skills is detected as "stressed" by the emotion engine, the server will provide simple tasks or relaxation-related content to alleviate that stress. In this way, users can progress with their learning and career development while receiving appropriate support tailored to their emotional state.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The user logs into the system and enters their personal information and work history into a terminal. The terminal then sends this information to the server.

[0585] Step 2:

[0586] The server collects additional user data from the specified data provider and analyzes the data to reveal the user's interests and past experiences.

[0587] Step 3:

[0588] The device transmits voice input and facial expression data to the emotion engine through an interface that interacts with the user. The emotion engine processes this data to recognize the user's emotions.

[0589] Step 4:

[0590] The server analyzes the user's emotional data, recognized by the emotion engine, to obtain information necessary for suggesting career paths and adjusting learning content.

[0591] Step 5:

[0592] The server generates an optimal career path considering the user's skills, interests, and emotional data. The terminal displays the career path generated by the server to the user.

[0593] Step 6:

[0594] The user selects a career path from the presented options that best suits their goals. The device then displays online learning content related to the selected career path.

[0595] Step 7:

[0596] The server tracks the progress of online learning content and dynamically optimizes the learning plan based on the user's understanding and emotions. The device then presents the user with a learning schedule adjusted accordingly.

[0597] Step 8:

[0598] The server analyzes the user's progress and emotional state to identify appropriate coaching opportunities and job postings, and then matches the user with them. The terminal notifies the user of the matching results and assists with necessary procedures and scheduling.

[0599] Step 9:

[0600] Users enhance their networking by accessing community forums to share knowledge and receive support from other users. The server supports these networking activities and guides users to new resources and opportunities as needed.

[0601] (Example 2)

[0602] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0603] It is difficult for users to find personalized learning paths while receiving various information and support in their career development. In particular, there is a need to propose the optimal career path while considering the user's emotional state, dynamically adjust learning content, and ensure appropriate matching.

[0604] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0605] In this invention, the server includes means for using technology to recognize and evaluate user emotional information in real time, means for using knowledge processing technology to generate personalized career paths, and means for dynamically adjusting learning content according to the user's emotional state. This enables the provision of individualized career suggestions and learning content that take user emotions into consideration, as well as optimal matching.

[0606] "User data" refers to information such as an individual's work history, interests, and behavior, and is used to form a user profile.

[0607] "Knowledge processing technology" refers to algorithms and methods that analyze large amounts of information to gain insights from the data, and is used to provide personalized information that is relevant to the user.

[0608] "Emotional information" refers to information indicating the emotional state obtained from the user's voice, facial expressions, and input data, and is used to detect the user's psychological state.

[0609] "Learning resources" refer to educational materials and content that users use to improve their skills or gain knowledge, and include online courses and training materials.

[0610] "Networking features" refer to mechanisms that allow users to interact and connect with other users and experts, and are intended to stimulate communication.

[0611] "Mentoring resources" refer to a group of supporters, including experts such as coaches and mentors, who contribute to improving users' skills and maintaining their motivation.

[0612] "Personalization" refers to the process of optimizing the experience based on the individual user's characteristics and needs, in order to provide users with the most relevant information and content.

[0613] This invention is a system designed to support users' career development. During initial registration, users input their work history and personal profile information via a terminal. The terminal sends this information to a server, which then collaborates with data providers to collect additional information.

[0614] The server analyzes the collected user data using knowledge processing techniques. This technique is based on machine learning algorithms and utilizes Python-based libraries and tools. This makes it possible to understand and analyze the user's past experiences and current skill set in detail.

[0615] The system employs an emotion engine to recognize and evaluate user emotional information in real time. The device uses its camera and microphone to capture user facial expressions and voice data for the emotion engine. The server processes this emotional information to understand the user's emotional state. Based on this information, the server uses a generative AI model to propose a personalized career path.

[0616] Furthermore, the server suggests learning resources tailored to the user's emotional state and monitors their progress. For example, if a user wants to learn new management skills, the server dynamically adjusts the learning content, such as presenting content that alleviates the tension derived from their emotions, thereby enabling more effective learning.

[0617] Furthermore, the server matches users with industry institutions and mentoring resources based on their skill sets and motivations. This process uses a notification function on the device to inform users of the matching results and provide networking opportunities.

[0618] As a concrete example, a prompt message such as, "Consider the user's current emotional state and suggest the optimal career path and relevant learning content," is input into the AI ​​model, and the system derives a career scenario suitable for the user.

[0619] This enables users to experience personalized, emotion-based learning and career development. The system aims to motivate users and support them in pursuing better career paths.

[0620] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0621] Step 1:

[0622] Users input data such as profile information and work history through their terminal. This input data is then sent to the server by the terminal. Specifically, the user enters information using the keyboard and clicks the "Send" button, sending the data to the server via the HTTPS protocol. The server receives this data and stores it in its database.

[0623] Step 2:

[0624] The server collects additional user data from external sources and via APIs, based on the registered data. This external information request utilizes the user's interests and past experiences, and the collected data is stored in the server's database. The server sends API requests and receives data in JSON format.

[0625] Step 3:

[0626] The server analyzes user data stored in the database using knowledge processing techniques. Input data includes work history and interests, and machine learning algorithms are used to analyze this data and generate insights into the user's skills and past experiences. The server uses Python-based libraries for data analysis.

[0627] Step 4:

[0628] The device uses an emotion engine to recognize the user's emotions. When the user interacts with the interface, it uses the camera and microphone to capture facial expressions and audio data, which are then sent to the server. The server analyzes this data to determine the user's emotional state. Emotion analysis is performed in real time using an emotion analysis model.

[0629] Step 5:

[0630] The server uses a generative AI model to generate the optimal career path for the user based on the analyzed data and sentiment information. The input includes analysis results and sentiment data, and the output is a personalized career path. By inputting prompts into the generative AI model, a suitable career scenario is calculated.

[0631] Step 6:

[0632] The device presents the user with a career path received from the server and associated learning resources. The learning content is dynamically adjusted according to the user's emotional state. For example, the learning experience is tailored by suggesting content that alleviates tension based on the identified level of stress.

[0633] Step 7:

[0634] The server matches the user with appropriate industry institutions and mentoring resources based on the user's skills and sentiment data. The output is matching information with the resources and coaches selected as suitable. The matching algorithm is executed, and the terminal notifies the user of the results.

[0635] Step 8:

[0636] The server automatically generates and optimizes educational plans based on the user's learning progress and understanding. Input includes learning history and outcome data, and output proposes an improved educational plan. The server evaluates learning outcomes and uses AI technology to generate the next optimal learning step.

[0637] (Application Example 2)

[0638] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0639] For modern consumers and workers, receiving appropriate information and support based on their emotions is a crucial factor in purchasing decisions and career development. However, traditional systems have struggled to accurately recognize users' emotions and provide information accordingly. Furthermore, matching and networking functions with users are limited, making the provision of personalized services a challenge.

[0640] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0641] In this invention, the server includes means for acquiring user data and analyzing the data based on the user's emotions and past experiences, means for using artificial intelligence technology to generate a personalized career path, and means for recognizing the customer's emotions of interest and anxiety and providing information accordingly. This makes it possible to provide personalized services based on the user's emotions.

[0642] "User data" refers to a dataset that includes an individual's profile information, work history, behavioral data, and information about their interests.

[0643] "Emotion" refers to an individual's psychological state, analyzed from their voice, facial expressions, and behavioral patterns.

[0644] "Analysis" is the process of scrutinizing acquired data using artificial intelligence technology and extracting information relevant to a specific purpose.

[0645] A "personalized career path" is a career or learning path created according to the individual user's characteristics and goals.

[0646] "Artificial intelligence technology" refers to computer processing technology that uses techniques such as machine learning and natural language processing to analyze data and make decisions.

[0647] "Interest" refers to an individual's active interest in or involvement with a particular subject or activity.

[0648] "Anxiety" refers to the concerns and worries that an individual has about uncertain or unknown situations.

[0649] "Information provision" refers to activities that present useful data and content to users.

[0650] A "business entity" is a legal entity or organization that engages in specific business activities.

[0651] "Mentoring resources" refer to professional support that provides knowledge and skills to help users grow and achieve their goals.

[0652] "Communication support" refers to services and functions that facilitate the sharing of information and opinions among individuals and organizations, and the building of relationships.

[0653] To implement this invention, a smart device owned by the user (e.g., a smartphone or smart glasses) and a system using a cloud server are required. The user first installs a dedicated application on their smart device. This application uses the device's camera and microphone to acquire voice and facial expression data in real time.

[0654] The server converts the audio data received from the user into text using Google Speech-to-Text and simultaneously analyzes the user's emotions by performing facial recognition using the Microsoft Azure Face API. This emotional data is then analyzed by a generative AI model and used to provide the user with optimal information and suggest career paths. Specifically, if a user is feeling anxious in front of a product, the server will immediately provide detailed information and reviews of that product.

[0655] The system as a whole collects users' past profile information and behavioral data, and based on this, artificial intelligence technology proposes personalized career paths to users. Learning content is dynamically adjusted based on the user's emotion recognition, and is designed to optimize learning progress. It also facilitates matching users with organizations and mentoring resources that match their skills, and enhances support for interaction.

[0656] For example, suppose a user is considering purchasing a new technology product in a physical store. If the emotion engine detects anxiety, the server can immediately present usage examples and reviews of the product to help alleviate the user's concerns. An example of a prompt would be, "When a user is in a technology product section, advise them based on their emotional state what information would increase their willingness to purchase." By providing feedback based on the user's emotions in this way, the purchasing process and career development become more personalized and effective.

[0657] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0658] Step 1:

[0659] The device acquires voice and facial expression data from the user. It collects this data in real time using a camera and microphone. Audio files and video footage are generated as input data.

[0660] Step 2:

[0661] The device sends the acquired audio data to the Google Speech-to-Text service, where it is converted into text. The input is audio data, and the output is text data. This conversion makes the audio information into a format that can be parsed.

[0662] Step 3:

[0663] The device sends video data to the Microsoft Azure Face API, which analyzes facial expressions to estimate the user's emotions. The input is video data, and the output is the estimated emotion category. This allows for obtaining visual emotion metrics.

[0664] Step 4:

[0665] The server receives text data and sentiment data, and uses a generative AI model to analyze the user's current emotional state. The input is text data and sentiment categories, and the output is data indicating the user's emotional state. This prepares the server for making decisions based on the user's emotions.

[0666] Step 5:

[0667] The server provides users with the most relevant information and career paths based on their analyzed emotional state. If a user is experiencing anxiety, it generates product information and reviews tailored to that situation and sends them to the device. The input is emotional state data, and the output is informational content.

[0668] Step 6:

[0669] The device presents information received from the server to the user. This information is displayed on the screen or as audio guidance to support the user's purchasing and learning decisions. Input is informational content from the server, and output is presented to the user as visual or auditory feedback.

[0670] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0671] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0672] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0673] [Fourth Embodiment]

[0674] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0675] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0676] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0677] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0678] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0679] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0680] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0681] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors in the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0682] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0683] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0684] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0685] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0686] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] This invention is a system for supporting users' career development, and its main elements include processes such as user data collection and analysis, career path proposal, learning content provision, progress management, and matching and networking.

[0688] User data collection and analysis

[0689] First, the user registers with the system and provides profile information. The device sends this information to the server. The server works with specific data providers and platforms to collect user interest and behavior data and stores it in a database. This data is analyzed to gain detailed insights into the user's interests and skills. The server uses artificial intelligence technology to analyze this data and identify the user's past experiences and skill set.

[0690] Career path proposals

[0691] Based on the analyzed data, the server proposes the optimal career path for the user. It clarifies specific job duties, required skills, and the steps to achieving goals. This proposal is customized to match the user's individual needs and interests.

[0692] Provision of learning content

[0693] To help users progress through their learning based on a suggested career path, the server provides information on appropriate online courses and workshops. This content is displayed to the user depending on their device. Users learn at their own pace, and their progress is tracked by the server.

[0694] Progress management and matching

[0695] The server tracks the user's learning progress in real time and optimizes the learning plan as needed. It also suggests job opportunities and expert coaching opportunities based on the user's acquired skill set. The terminal notifies the user of these matching results.

[0696] Community support and networking

[0697] Users can join communities where they can interact with other users who are pursuing similar career goals. The server supports this networking functionality, providing an environment where users can help each other.

[0698] For example, a user who wants to venture into a specific technical field can have a specialized career path suggested by the server based on their past work experience data, and can exchange ideas with community members while utilizing online courses related to that field. In this way, users can achieve career development tailored to their individual needs.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] When a user logs in for the first time, they enter their personal information and professional profile into their device. The device then sends this information to the server.

[0702] Step 2:

[0703] The server collects additional behavioral data and interest-related information from relevant data providers based on the user's profile information.

[0704] Step 3:

[0705] The server uses artificial intelligence technology to analyze the collected data and identify the user's interests and skills. It also records the user's past work experience and skill set in a database.

[0706] Step 4:

[0707] Based on the analysis results, the server generates several career paths suitable for the user's interests and skills. Each path clearly indicates the required skills and recommended learning routes.

[0708] Step 5:

[0709] The device displays the user the career path and associated learning content sent from the server. The user selects the path that best matches their interests.

[0710] Step 6:

[0711] The server suggests online courses and workshops that correspond to the user's chosen career path and sets up a system to track learning progress. The terminal provides the user with a detailed learning schedule and content.

[0712] Step 7:

[0713] Users acquire skills using learning content provided via their devices. Progress is tracked in real time by the server, and appropriate feedback is provided from the server at each stage of learning.

[0714] Step 8:

[0715] The server analyzes the user's learning progress and matches them with appropriate companies and coaches. The terminal presents this information to the user and schedules interviews and coaching sessions.

[0716] Step 9:

[0717] Users can further their careers by participating in networking and community forums, exchanging ideas with other users, and organizing study sessions. The server provides a platform for this, supporting smooth communication.

[0718] (Example 1)

[0719] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0720] Conventional career development support systems have problems such as failing to adequately propose specific and optimal career paths tailored to each user's individual interests and skills, and not effectively managing learning progress or matching users with companies. Furthermore, their networking functions among users are limited, and necessary support and information exchange are not adequately provided. There is a need to solve these problems and provide personalized and effective career development support.

[0721] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0722] In this invention, the server includes means for acquiring user information and analyzing the information based on the user's interests and past activities, means for using machine learning techniques to generate personalized career paths, and means for suggesting optimal online learning information to the user and monitoring their learning progress. This enables the suggestion of individually optimized career paths, efficient learning support based on progress management, and matching with appropriate human resources.

[0723] "User information" refers to information about the user, including basic profile information, past activities, interests, and skills.

[0724] "Analysis" is the act of analyzing acquired data for a specific purpose and deriving meaningful results.

[0725] A "career path" is a plan that outlines the steps and skills needed to achieve goals in a specific occupation or industry.

[0726] "Machine learning technology" refers to the algorithms and processes that enable computers to automatically learn from data and generate predictions and suggestions.

[0727] "Online learning information" refers to information about learning resources such as courses, materials, and workshops that are accessible via the internet.

[0728] "Progress status" refers to the current level of achievement or progress made in the process toward a specific goal.

[0729] "Human resource development resources" refer to resources such as experts, courses, and programs provided to support users' capacity development and skill improvement.

[0730] "Community support" refers to activities and support that enable participants within a group with a specific purpose to interact with each other, share information, and help one another.

[0731] "Communication features" are functions designed to allow users to communicate with other people and exchange information.

[0732] This invention is a system that supports users' career development, and it functions through the respective roles of the server, terminal, and user. The server utilizes advanced machine learning technology to analyze user information and propose the optimal career path. Software such as Python, Scikit-learn, and TensorFlow are used for the analysis. The server analyzes profile information and behavioral data received from the user and generates a detailed career path based on that data.

[0733] The terminal transmits user-inputted information to the server and visually presents the user with career paths and learning information received from the server. This terminal can use a web browser or mobile application as its user interface.

[0734] Users enter their profile information on the system and proceed with their learning according to the suggested career path. The server collects online learning information from relevant resources and provides it in an appropriately organized format. Users can check their learning progress on their device and set their next learning goals based on that progress.

[0735] For example, a user interested in data science can enter keywords such as "data science" and "machine learning" in their profile to receive suggestions for specialized career paths and related online courses. An example of a prompt for a generative AI model would be: "For a user aiming for a career in data science, suggest the next necessary skills and recommended courses."

[0736] With the above configuration, this system can provide personalized career development support tailored to the user's characteristics, enabling efficient and effective support for achieving goals.

[0737] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0738] Step 1:

[0739] The user enters their profile information into the system. This information includes work history, skills, interests, and goals. The terminal sends the entered information to the server. The entered data is sent to the server in JSON format, and the server then stores it in its database.

[0740] Step 2:

[0741] The server retrieves user information from the database and inputs it into a machine learning model. This input data includes detailed information about the user's interests and past work experience. The server utilizes libraries such as Scikit-learn and TensorFlow to perform data analysis. This analysis outputs intermediate data for skill matching and career path generation.

[0742] Step 3:

[0743] The server generates the optimal career path for the user based on the analysis results. Specifically, it uses a generation AI model to take prompts such as "What types of jobs and skills are suitable for the user?" and designs a career path based on the answers. The output results are a list of job types and a list of required skill sets, which are sent to the terminal.

[0744] Step 4:

[0745] The terminal visualizes and displays job path information received from the server to the user on the screen. The user uses this information to select the next step. Specifically, when the user clicks on a job that interests them, detailed requirements and goals are displayed.

[0746] Step 5:

[0747] The server collects online learning information related to the career path selected by the user. The server uses APIs from educational platforms related to the specified field to retrieve appropriate course information. Career path information is used as input data, and a list of course information is sent to the terminal as output.

[0748] Step 6:

[0749] The device provides the user with received online learning information, and the user registers for courses of interest. During this process, the device provides the user with an interactive interface for tracking learning progress. Each time the user completes a lecture, progress information is sent to the server.

[0750] Step 7:

[0751] The server analyzes learning progress data and adjusts the learning plan as needed. Specifically, it analyzes the user's achievement and understanding, and proposes a new learning strategy using a generative AI model. Learning progress data is used as input data, and the adjusted learning plan is notified to the device as output.

[0752] Step 8:

[0753] Based on the server's suggestions, users proceed to the next learning step. They also interact with other users using community features as needed. Their activity history within the community is recorded on the server and can be used to help them in their future career development.

[0754] (Application Example 1)

[0755] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] In today's world, for individuals to effectively advance their careers, they need to make effective use of information and resources that match their interests and skills. However, with so much information available, finding the optimal learning and career path is difficult. Furthermore, it is necessary to efficiently acquire useful information, such as selecting products related to career goals and interacting with others who have similar goals.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0758] In this invention, the server includes means for acquiring and analyzing user data, means for using artificial intelligence technology to generate personalized career paths, and means for presenting users with recommended products and services related to their career goals and displaying user reviews. This enables the suggestion of optimal career paths tailored to individual needs and the efficient use of related products and services.

[0759] "User data" refers to data that includes information such as an individual's interests, past experiences, and skills.

[0760] "Analysis" is the process of analyzing acquired data to gain useful insights.

[0761] A "career path" refers to the steps and direction an individual should follow to achieve their professional goals.

[0762] "Artificial intelligence technology" is a technology that enables machines to mimic human intelligence, analyze data, and generate meaningful results.

[0763] "Online learning information" refers to information about educational resources and materials provided via the internet.

[0764] "Progress tracking" is a process that monitors in real time the degree to which users are achieving their set learning and career goals.

[0765] "Network functionality" refers to a platform that allows users to interact with other users and share information.

[0766] "Recommended products and services" are products and services presented according to the user's career goals and learning progress.

[0767] "User reviews" refer to information compiled from ratings and comments from other users.

[0768] The system for realizing this invention consists of a server and a user terminal. The server first receives user data from the terminal and analyzes the data using artificial intelligence technology. Based on the results of this analysis, it generates a carrier path optimized for each individual user and identifies relevant online learning information and recommended products. The server then transmits this information to the user's terminal and presents it to the user.

[0769] The user's device displays the received information through an interface, allowing the user to use that information to advance their career development. The device also continuously tracks the user's learning progress and career interests, and periodically sends this information to the server.

[0770] The system utilizes cloud-based server technologies such as AWS and Firebase for its hardware and software. Artificial intelligence frameworks like TensorFlow and PyTorch are used for data analysis. This ensures secure management of user data and enables advanced data analysis.

[0771] For example, if a user wants to improve their "digital marketing" skills, the server will generate a career path and present a list of relevant online courses and recommended products. This information provides the user with effective resources for learning.

[0772] Examples of prompts to input into a generative AI model:

[0773] Prompt: Please research "Top Online Digital Marketing Courses" and display their popularity rankings.

[0774] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0775] Step 1:

[0776] The server receives user data (e.g., profile information, interests, past experiences) from the terminal. This data is stored securely with privacy in mind. The server then prepares to begin analysis based on the received data.

[0777] Step 2:

[0778] The server analyzes the received user data using artificial intelligence technology (e.g., generative AI models using TensorFlow or PyTorch). It analyzes the input data to identify the user's interests and skill set. This analysis generates the optimal career path for the user. Based on these results, it prepares data to provide individualized career paths.

[0779] Step 3:

[0780] Based on the analyzed data, the server lists online learning information and recommended products and services related to the user's career goals. For example, if the user is interested in "digital marketing," this process will select online courses and materials in that field. The server then prepares to send the selected information to the user's device.

[0781] Step 4:

[0782] The server sends selected online learning and product information to the user's device. The device receives this information and displays it clearly to the user through its interface. This display facilitates access to information that interests the user.

[0783] Step 5:

[0784] Based on the information received, users begin learning or purchasing products. The device tracks the user's learning progress and periodically sends progress data to the server. This allows the server to monitor the user's learning achievements.

[0785] Step 6:

[0786] The server analyzes the user's learning progress data and optimizes the learning plan. If necessary, it generates data to re-recommend more appropriate online courses and supplementary materials. It then prepares to present this updated information to the user again.

[0787] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0788] This invention is a career development support system that combines an emotion engine that recognizes user emotions, and includes processes such as user data collection and analysis, career path proposal, learning content provision, progress management, emotion recognition, and matching and networking.

[0789] User data collection and analysis

[0790] First, users register with the system and enter their profile information and work history. The terminal sends this information to the server. The server collaborates with various data providers to collect additional behavioral and interest data. This data is analyzed using artificial intelligence technology to understand the user's past experience and skills.

[0791] Emotion recognition by an emotion engine

[0792] The device uses an emotion engine to recognize the user's emotions based on voice, facial expressions, and input data as the user interacts with the interface. The server analyzes this emotion data to determine the user's current emotional state. This information is used to suggest career paths and adjust learning content.

[0793] Proposing career paths and providing learning content

[0794] The server suggests the optimal career path for the user based on analyzed data and emotional information. The terminal displays the suggested career path and related learning content to the user. The learning content is appropriately adjusted based on the user's motivation and emotional state.

[0795] Progress management and optimization of learning plans

[0796] The server monitors the user's learning progress and dynamically optimizes the learning plan by taking sentiment data into account. This makes the user's learning experience more personalized.

[0797] Matching and Networking

[0798] The server facilitates matching users with suitable companies and coaches based on their acquired skills and motivation levels. The terminal notifies users of these matching results and supports the smooth progress of interviews and coaching sessions.

[0799] For example, if a user who wants to acquire new project management skills is detected as "stressed" by the emotion engine, the server will provide simple tasks or relaxation-related content to alleviate that stress. In this way, users can progress with their learning and career development while receiving appropriate support tailored to their emotional state.

[0800] The following describes the processing flow.

[0801] Step 1:

[0802] The user logs into the system and enters their personal information and work history into a terminal. The terminal then sends this information to the server.

[0803] Step 2:

[0804] The server collects additional user data from the specified data provider and analyzes the data to reveal the user's interests and past experiences.

[0805] Step 3:

[0806] The device transmits voice input and facial expression data to the emotion engine through an interface that interacts with the user. The emotion engine processes this data to recognize the user's emotions.

[0807] Step 4:

[0808] The server analyzes the user's emotional data, recognized by the emotion engine, to obtain information necessary for suggesting career paths and adjusting learning content.

[0809] Step 5:

[0810] The server generates an optimal career path considering the user's skills, interests, and emotional data. The terminal displays the career path generated by the server to the user.

[0811] Step 6:

[0812] The user selects a career path from the presented options that best suits their goals. The device then displays online learning content related to the selected career path.

[0813] Step 7:

[0814] The server tracks the progress of online learning content and dynamically optimizes the learning plan based on the user's understanding and emotions. The device then presents the user with a learning schedule adjusted accordingly.

[0815] Step 8:

[0816] The server analyzes the user's progress and emotional state to identify appropriate coaching opportunities and job postings, and then matches the user with them. The terminal notifies the user of the matching results and assists with necessary procedures and scheduling.

[0817] Step 9:

[0818] Users enhance their networking by accessing community forums to share knowledge and receive support from other users. The server supports these networking activities and guides users to new resources and opportunities as needed.

[0819] (Example 2)

[0820] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0821] It is difficult for users to find personalized learning paths while receiving various information and support in their career development. In particular, there is a need to propose the optimal career path while considering the user's emotional state, dynamically adjust learning content, and ensure appropriate matching.

[0822] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0823] In this invention, the server includes means for using technology to recognize and evaluate user emotional information in real time, means for using knowledge processing technology to generate personalized career paths, and means for dynamically adjusting learning content according to the user's emotional state. This enables the provision of individualized career suggestions and learning content that take user emotions into consideration, as well as optimal matching.

[0824] "User data" refers to information such as an individual's work history, interests, and behavior, and is used to form a user profile.

[0825] "Knowledge processing technology" refers to algorithms and methods that analyze large amounts of information to gain insights from the data, and is used to provide personalized information that is relevant to the user.

[0826] "Emotional information" refers to information indicating the emotional state obtained from the user's voice, facial expressions, and input data, and is used to detect the user's psychological state.

[0827] "Learning resources" refer to educational materials and content that users use to improve their skills or gain knowledge, and include online courses and training materials.

[0828] "Networking features" refer to mechanisms that allow users to interact and connect with other users and experts, and are intended to stimulate communication.

[0829] "Mentoring resources" refer to a group of supporters, including experts such as coaches and mentors, who contribute to improving users' skills and maintaining their motivation.

[0830] "Personalization" refers to the process of optimizing the experience based on the individual user's characteristics and needs, in order to provide users with the most relevant information and content.

[0831] This invention is a system designed to support users' career development. During initial registration, users input their work history and personal profile information via a terminal. The terminal sends this information to a server, which then collaborates with data providers to collect additional information.

[0832] The server analyzes the collected user data using knowledge processing techniques. This technique is based on machine learning algorithms and utilizes Python-based libraries and tools. This makes it possible to understand and analyze the user's past experiences and current skill set in detail.

[0833] The system employs an emotion engine to recognize and evaluate user emotional information in real time. The device uses its camera and microphone to capture user facial expressions and voice data for the emotion engine. The server processes this emotional information to understand the user's emotional state. Based on this information, the server uses a generative AI model to propose a personalized career path.

[0834] Furthermore, the server suggests learning resources tailored to the user's emotional state and monitors their progress. For example, if a user wants to learn new management skills, the server dynamically adjusts the learning content, such as presenting content that alleviates the tension derived from their emotions, thereby enabling more effective learning.

[0835] Furthermore, the server matches users with industry institutions and mentoring resources based on their skill sets and motivations. This process uses a notification function on the device to inform users of the matching results and provide networking opportunities.

[0836] As a concrete example, a prompt message such as, "Consider the user's current emotional state and suggest the optimal career path and relevant learning content," is input into the AI ​​model, and the system derives a career scenario suitable for the user.

[0837] This enables users to experience personalized, emotion-based learning and career development. The system aims to motivate users and support them in pursuing better career paths.

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

[0839] Step 1:

[0840] Users input data such as profile information and work history through their terminal. This input data is then sent to the server by the terminal. Specifically, the user enters information using the keyboard and clicks the "Send" button, sending the data to the server via the HTTPS protocol. The server receives this data and stores it in its database.

[0841] Step 2:

[0842] The server collects additional user data from external sources and via APIs, based on the registered data. This external information request utilizes the user's interests and past experiences, and the collected data is stored in the server's database. The server sends API requests and receives data in JSON format.

[0843] Step 3:

[0844] The server analyzes user data stored in the database using knowledge processing techniques. Input data includes work history and interests, and machine learning algorithms are used to analyze this data and generate insights into the user's skills and past experiences. The server uses Python-based libraries for data analysis.

[0845] Step 4:

[0846] The device uses an emotion engine to recognize the user's emotions. When the user interacts with the interface, it uses the camera and microphone to capture facial expressions and audio data, which are then sent to the server. The server analyzes this data to determine the user's emotional state. Emotion analysis is performed in real time using an emotion analysis model.

[0847] Step 5:

[0848] The server uses a generative AI model to generate the optimal career path for the user based on the analyzed data and sentiment information. The input includes analysis results and sentiment data, and the output is a personalized career path. By inputting prompts into the generative AI model, a suitable career scenario is calculated.

[0849] Step 6:

[0850] The device presents the user with a career path received from the server and associated learning resources. The learning content is dynamically adjusted according to the user's emotional state. For example, the learning experience is tailored by suggesting content that alleviates tension based on the identified level of stress.

[0851] Step 7:

[0852] The server matches the user with appropriate industry institutions and mentoring resources based on the user's skills and sentiment data. The output is matching information with the resources and coaches selected as suitable. The matching algorithm is executed, and the terminal notifies the user of the results.

[0853] Step 8:

[0854] The server automatically generates and optimizes educational plans based on the user's learning progress and understanding. Input includes learning history and outcome data, and output proposes an improved educational plan. The server evaluates learning outcomes and uses AI technology to generate the next optimal learning step.

[0855] (Application Example 2)

[0856] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0857] For modern consumers and workers, receiving appropriate information and support based on their emotions is a crucial factor in purchasing decisions and career development. However, traditional systems have struggled to accurately recognize users' emotions and provide information accordingly. Furthermore, matching and networking functions with users are limited, making the provision of personalized services a challenge.

[0858] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0859] In this invention, the server includes means for acquiring user data and analyzing the data based on the user's emotions and past experiences, means for using artificial intelligence technology to generate a personalized career path, and means for recognizing the customer's emotions of interest and anxiety and providing information accordingly. This makes it possible to provide personalized services based on the user's emotions.

[0860] "User data" refers to a dataset that includes an individual's profile information, work history, behavioral data, and information about their interests.

[0861] "Emotion" refers to an individual's psychological state, analyzed from their voice, facial expressions, and behavioral patterns.

[0862] "Analysis" is the process of scrutinizing acquired data using artificial intelligence technology and extracting information relevant to a specific purpose.

[0863] A "personalized career path" is a career or learning path created according to the individual user's characteristics and goals.

[0864] "Artificial intelligence technology" refers to computer processing technology that uses techniques such as machine learning and natural language processing to analyze data and make decisions.

[0865] "Interest" refers to an individual's active interest in or involvement with a particular subject or activity.

[0866] "Anxiety" refers to the concerns and worries that an individual has about uncertain or unknown situations.

[0867] "Information provision" refers to activities that present useful data and content to users.

[0868] A "business entity" is a legal entity or organization that engages in specific business activities.

[0869] "Mentoring resources" refer to professional support that provides knowledge and skills to help users grow and achieve their goals.

[0870] "Communication support" refers to services and functions that facilitate the sharing of information and opinions among individuals and organizations, and the building of relationships.

[0871] To implement this invention, a smart device owned by the user (e.g., a smartphone or smart glasses) and a system using a cloud server are required. The user first installs a dedicated application on their smart device. This application uses the device's camera and microphone to acquire voice and facial expression data in real time.

[0872] The server converts the audio data received from the user into text using Google Speech-to-Text and simultaneously analyzes the user's emotions by performing facial recognition using the Microsoft Azure Face API. This emotional data is then analyzed by a generative AI model and used to provide the user with optimal information and suggest career paths. Specifically, if a user is feeling anxious in front of a product, the server will immediately provide detailed information and reviews of that product.

[0873] The system as a whole collects users' past profile information and behavioral data, and based on this, artificial intelligence technology proposes personalized career paths to users. Learning content is dynamically adjusted based on the user's emotion recognition, and is designed to optimize learning progress. It also facilitates matching users with organizations and mentoring resources that match their skills, and enhances support for interaction.

[0874] For example, suppose a user is considering purchasing a new technology product in a physical store. If the emotion engine detects anxiety, the server can immediately present usage examples and reviews of the product to help alleviate the user's concerns. An example of a prompt would be, "When a user is in a technology product section, advise them based on their emotional state what information would increase their willingness to purchase." By providing feedback based on the user's emotions in this way, the purchasing process and career development become more personalized and effective.

[0875] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0876] Step 1:

[0877] The device acquires voice and facial expression data from the user. It collects this data in real time using a camera and microphone. Audio files and video footage are generated as input data.

[0878] Step 2:

[0879] The device sends the acquired audio data to the Google Speech-to-Text service, where it is converted into text. The input is audio data, and the output is text data. This conversion makes the audio information into a format that can be parsed.

[0880] Step 3:

[0881] The device sends video data to the Microsoft Azure Face API, which analyzes facial expressions to estimate the user's emotions. The input is video data, and the output is the estimated emotion category. This allows for obtaining visual emotion metrics.

[0882] Step 4:

[0883] The server receives text data and sentiment data, and uses a generative AI model to analyze the user's current emotional state. The input is text data and sentiment categories, and the output is data indicating the user's emotional state. This prepares the server for making decisions based on the user's emotions.

[0884] Step 5:

[0885] The server provides users with the most relevant information and career paths based on their analyzed emotional state. If a user is experiencing anxiety, it generates product information and reviews tailored to that situation and sends them to the device. The input is emotional state data, and the output is informational content.

[0886] Step 6:

[0887] The device presents information received from the server to the user. This information is displayed on the screen or as audio guidance to support the user's purchasing and learning decisions. Input is informational content from the server, and output is presented to the user as visual or auditory feedback.

[0888] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0889] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0890] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0891] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0892] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0893] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0894] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0895] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0896] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0897] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0898] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0899] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0900] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0901] 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.

[0902] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0903] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0904] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0905] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0906] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0907] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0908] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0909] The following is further disclosed regarding the embodiments described above.

[0910] (Claim 1)

[0911] A means of acquiring user data and analyzing that data based on the user's interests and past experiences,

[0912] A means of using artificial intelligence technology to generate personalized career paths,

[0913] A means to recommend online learning content suitable for the user and to track learning progress,

[0914] A means of matching users with companies and coaching resources,

[0915] A means of providing community support and networking features,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, which collects user data from a specific user data provider.

[0919] (Claim 3)

[0920] The system according to claim 1, comprising a function for automatically generating and optimizing a learning plan based on learning progress and level of understanding.

[0921] "Example 1"

[0922] (Claim 1)

[0923] A means of acquiring user information and analyzing that information based on the user's interests and past activities,

[0924] A means of using machine learning techniques to generate individualized career paths,

[0925] A means of suggesting the most suitable online learning information to users and monitoring their learning progress,

[0926] A means of connecting users with organizational and human resource development resources,

[0927] Means of providing community support and interaction functions,

[0928] A means of generating career paths based on data analysis and proposing learning steps based on set goals,

[0929] Based on the acquired learning history, the next learning objectives will be set, and the means for acquiring related information will be established.

[0930] A means of proposing networking events to users and promoting interaction,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] The system according to claim 1 for collecting user information from specific information providers.

[0934] (Claim 3)

[0935] The system according to claim 1, comprising a function for automatically creating and optimizing an educational plan based on learning progress and level of understanding.

[0936] "Application Example 1"

[0937] (Claim 1)

[0938] A means of acquiring user data and analyzing that data based on the user's interests and past experiences,

[0939] A means of using artificial intelligence technology to generate personalized career paths,

[0940] A means to recommend online learning information suitable for the user and to track their progress,

[0941] A means of matching users with companies and expert resources,

[0942] Means for providing network functionality,

[0943] A means of presenting users with recommended products and services related to their career goals and displaying user reviews,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1 for collecting data from a specific information provider.

[0947] (Claim 3)

[0948] The system according to claim 1, comprising a function for automatically generating and optimizing a plan based on progress and understanding.

[0949] "Example 2 of combining an emotion engine"

[0950] (Claim 1)

[0951] A means of acquiring user data and analyzing the information based on the user's interests and past experiences,

[0952] A means of using technology to recognize and evaluate user emotional information in real time,

[0953] A means of using knowledge processing techniques to generate personalized career paths,

[0954] A means of recommending learning resources suitable for the user and monitoring learning progress,

[0955] A means of dynamically adjusting learning content according to the user's emotional state,

[0956] A means of matching users with industry institutions and leadership resources,

[0957] Means for providing cooperative support and networking functions,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, which collects user data from a specific source.

[0961] (Claim 3)

[0962] The system according to claim 1, comprising a function for automatically generating and optimizing an educational plan based on learning progress and level of understanding.

[0963] "Application example 2 when combining with an emotional engine"

[0964] (Claim 1)

[0965] A means of acquiring user data and analyzing that data based on the user's emotions and past experiences,

[0966] A means of using artificial intelligence technology to generate personalized career paths,

[0967] A means of recognizing customers' interests and anxieties and providing information accordingly,

[0968] A means to recommend online learning content suitable for the user and to track learning progress,

[0969] A means of matching users with business entities and coaching resources,

[0970] Means of providing support for interaction and networking functions,

[0971] A system that includes this.

[0972] (Claim 2)

[0973] The system according to claim 1, which collects user data from specific data providers.

[0974] (Claim 3)

[0975] The system according to claim 1, comprising a function for automatically generating and optimizing a learning plan based on learning progress and level of understanding. [Explanation of Symbols]

[0976] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring user data and analyzing that data based on the user's interests and past experiences, A means of using artificial intelligence technology to generate personalized career paths, A means to recommend online learning content suitable for the user and to track learning progress, A means of matching users with companies and coaching resources, A means of providing community support and networking features, A system that includes this.

2. The system according to claim 1, which collects user data from a specific user data provider.

3. The system according to claim 1, comprising a function to automatically generate and optimize a learning plan based on learning progress and level of understanding.

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