System

Abstract

A system is provided.SOLUTION: A system comprising: means for authenticating login information of a user; means for interactively displaying questions to the user and collecting answers; means for transmitting the collected answer data to a server; means for analyzing the answer data and extracting characteristics of the user; means for generating a career plan based on the characteristics of the user; means for presenting the generated career plan and a list of necessary skills and experiences to the user; and means for collecting feedback from the user and reflecting the feedback in improvement of the system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Today, many students and working adults face difficulties in clearly mapping out their own career plans. This often leads to inappropriate career choices, resulting in dissatisfaction and reduced labor productivity. Another problem is the lack of tools for making specific career suggestions based on individual characteristics and values. Conventional career support systems lack detailed analysis of users' characteristics, making it difficult to make accurate career suggestions. Against this background, the present invention aims to provide a system that accurately analyzes users' characteristics and suggests optimal career plans. [Means for solving the problem]

[0005] The present invention provides a system including means for authenticating a user's login information, means for interactively displaying questions to the user and collecting answers, means for transmitting the collected answer data to a server, means for analyzing the answer data and extracting the user's characteristics, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, and means for collecting feedback from the user and reflecting it in improving the system. The present invention also includes means for analyzing the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that the user holds dear, and means for using a machine learning model when generating a career plan. This enables appropriate career suggestions for each user, enabling the formulation of specific and feasible career plans.

[0006] "User" refers to an individual who accesses the system and logs in to receive career plan proposals.

[0007] "Login Information" refers to authentication information such as ID and password used by a User to access the System.

[0008] "Authentication" refers to the process of verifying that a user's login information is correct and granting access to a system.

[0009] "Terminal" refers to a device, such as a computer or smartphone, that a user uses to access the system.

[0010] "Server" refers to a central computer that receives data sent from terminals and analyzes and processes it.

[0011] "Questions" refer to items that the system presents to the user in order to understand the user's characteristics and career aspirations.

[0012] An "answer" refers to information entered by a user in response to a question.

[0013] "Collect" refers to the process by which the system compiles and stores the answers provided by users.

[0014] "Analysis" refers to the process of extracting user characteristics from collected data and deriving meaningful information.

[0015] "Characteristics" refers to individual characteristics that influence career choices, such as a user's strengths, weaknesses, areas of expertise, areas of weakness, and values.

[0016] "Career plan" refers to the occupations and future career paths suggested by the system based on the user's characteristics.

[0017] "Skills" refer to the abilities and knowledge required to perform a particular job or task.

[0018] "Experience" refers to the past work history and learning history necessary to realize your career plan.

[0019] "Feedback" refers to the opinions and reactions that users provide regarding the proposed career plan. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] This invention is a system that allows users to draw up specific career plans for themselves. This system features a mechanism that allows users to interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values, and then analyzes the information and proposes an appropriate career plan.

[0042] In this system, the following processing is performed.

[0043] Log in and ask a question

[0044] A user logs in to a terminal

[0045] A user accesses the system and enters their login information. The terminal sends this information to the server, which performs authentication. If authentication is successful, the user's session begins.

[0046] The device displays a question to the user.

[0047] Once the server recognizes the user's login, the system prompts the user with a series of questions, including about past experiences, influences, strengths and weaknesses, values, and so on.

[0048] Collecting and submitting responses

[0049] The user answers the question

[0050] The user answers the questions displayed on the screen in detail, and once the answers are complete, the terminal collects them and sends them to the server.

[0051] The server receives the data

[0052] The server receives the user's response data sent from the device and stores it centrally.

[0053] Data analysis

[0054] The server analyzes the data

[0055] The server analyzes the collected data and extracts the user's characteristics, specifically, their strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[0056] The server generates a carrier plan.

[0057] Based on the analysis results, the server generates a career plan suited to the user's characteristics, including job type, required skills, recommended experience, etc. Furthermore, machine learning models are used to improve the accuracy of the career plan.

[0058] Career plan proposals

[0059] The server sends the proposal to the device

[0060] The server sends the generated career plan and a list of required skills and experience to the terminal.

[0061] The device displays suggestions to the user

[0062] The terminal displays the proposed career plan and a list of skills and experience to the user, allowing the user to confirm a specific career plan based on their own characteristics.

[0063] User Feedback

[0064] Users provide feedback

[0065] Users can enter feedback and follow-up questions about the proposed career plan into the device, including specific feedback such as "I'd like to know more about acquiring presentation skills."

[0066] The device collects feedback and sends it to the server

[0067] The device collects feedback and sends it to the server, which uses it to improve the system and reflect it in future interactions.

[0068] Specific examples

[0069] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server will analyze this and identify "software engineer" as a suitable career path. It will then create a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggest them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics.

[0070] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The user logs in to the terminal. The user enters their user ID and password on the system login screen and presses the login button.

[0074] Step 2:

[0075] The terminal sends the user's login information to the server. The terminal encrypts the entered user ID and password and sends them to the server.

[0076] Step 3:

[0077] The server authenticates the user. The server checks the user ID and password in the database and starts a session if they match. If they don't match, it returns an authentication error.

[0078] Step 4:

[0079] The terminal displays the authentication result to the user. If authentication is successful, the user can proceed to the next screen. If authentication is unsuccessful, a login error screen is displayed.

[0080] Step 5:

[0081] The server prepares a series of questions for the user and sends them to the device, including questions about past experiences, influences, strengths and weaknesses, values, etc.

[0082] Step 6:

[0083] The terminal displays questions to the user, who then answers the questions displayed on the screen one by one.

[0084] Step 7:

[0085] The user answers each question and enters it into the terminal, where each answer is temporarily saved.

[0086] Step 8:

[0087] The device sends the collected response data to the server. Once all questions have been answered, the data is sent to the server in one batch.

[0088] Step 9:

[0089] The server receives the response data and begins analyzing it. The server analyzes the response data and extracts the user's characteristics. Specifically, the server analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[0090] Step 10:

[0091] The server generates a career plan suited to the user. Based on the analysis results, it uses a machine learning model to propose the optimal career plan. This plan includes job types, required skills, recommended experience, etc.

[0092] Step 11:

[0093] The server transmits the generated career plan and a list of required skills and experiences to the terminal.

[0094] Step 12:

[0095] The device displays the career plan and skills / experience list to the user, who can then review the proposed content.

[0096] Step 13:

[0097] The user provides feedback by inputting their opinions and questions about the proposal into the terminal.

[0098] Step 14:

[0099] The device collects the feedback and sends it to the server.

[0100] Step 15:

[0101] The server receives and analyzes the feedback, which it uses as data to improve the system's algorithms and question flow based on the user's feedback.

[0102] Step 16:

[0103] The server then incorporates the feedback into the next session, allowing the system to continuously learn and adjust to provide users with more accurate career plans.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] In today's career development process, it is not easy for individual users to draw up specific career plans based on their own characteristics. Conventional systems have difficulty providing users with adequately customized suggestions, and do not adequately support users in choosing a career based on their own strengths, weaknesses, and values. Furthermore, they lack a mechanism for effectively incorporating feedback and improving the accuracy of the system.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] In this invention, the server includes a means for authenticating the user's login information, a means for displaying questions to the user in an interactive format and collecting answers, and a means for transmitting the collected answer data to the server, thereby enabling the generation of a highly accurate career plan based on the user's characteristics.

[0109] "Means for authenticating login information" refers to a technology that collates the authentication information entered by the user into the terminal with information in a database and establishes a user session.

[0110] "Means for displaying questions in an interactive format and collecting answers" refers to a technique for displaying questions sent from a server to a user on a terminal and collecting answers from the user as input.

[0111] The "means for transmitting collected response data to a server" refers to a technique for transmitting user responses from the terminal to a server and storing them safely.

[0112] "Means of analyzing response data and extracting user characteristics" refers to techniques that analyze collected data using machine learning models and natural language processing techniques to reveal characteristics such as users' strengths, weaknesses, areas of expertise, areas of weakness, and values.

[0113] The "means for generating a career plan" is a technology that creates a career plan that includes the most suitable job type, required skills, experience, etc. for the user based on the analysis results.

[0114] The "means for proposing a career plan and a list of required skills and experiences to a user" is a technology for displaying a generated career plan and its detailed requirements to a user.

[0115] "Means for collecting feedback from users and reflecting it in system improvements" refers to a technique for collecting opinions and additional questions from users and incorporating them into system analysis to improve functionality.

[0116] "Means for vectorizing input text and analyzing it with a classification algorithm" refers to a technology that converts text data such as user responses into numerical vectors and analyzes them using a classification algorithm.

[0117] "Means for creating a list of occupations using a recommendation engine" refers to a technology that uses machine learning technology to list appropriate occupations based on the characteristics of the user.

[0118] The present invention is a system that allows users to specifically draw up their own career plans, and that interactively collects information such as the user's past experiences, areas of strength and weakness, and values, and then analyzes the information to propose an appropriate career plan. An embodiment of this system will be described in detail below.

[0119] System configuration

[0120] The system mainly includes the following components:

[0121] 1. Terminal

[0122] 2. Server

[0123] 3. Database

[0124] 4. Machine Learning Models

[0125] 5. Communication Protocol

[0126] Software and hardware used

[0127] Terminal

[0128] Users access the system using devices such as PCs, tablets, and smartphones, and the devices communicate with the server via a web browser or native application.

[0129] server

[0130] The server is the main component for running the program and uses web frameworks such as Django and Spring. The server handles major processes such as user authentication, data analysis, and career plan generation.

[0131] Database

[0132] The database uses MySQL (registered trademark) or PostgreSQL to store various data such as user information, response data, and analysis results.

[0133] Machine learning models

[0134] The machine learning models used are Tensorflow (registered trademark) and PyTorch, which are used to analyze user data and generate career plans. Specifically, natural language processing technology is used to analyze user responses and extract characteristics.

[0135] communication protocol

[0136] The device and server send and receive data using a secure communication protocol such as HTTPS.

[0137] System action

[0138] User Login

[0139] The user enters their login information into the terminal, and the server authenticates them against the database. If authentication is successful, a session ID is generated and the user's session begins.

[0140] Asking questions and collecting answers

[0141] The server generates a series of questions for the user and sends them to the device, which then displays the questions on the user's screen and the user enters answers to these questions.The answers are then sent from the device to the server and stored in a database.

[0142] Data analysis

[0143] The server receives the collected response data and analyzes it using machine learning models and natural language processing technology to extract information such as the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that they hold dear.

[0144] Creating a career plan

[0145] The server generates an optimal career plan for the user based on the analysis results. This career plan includes recommended occupations, required skills and experience, etc. A recommendation engine is used to create a list of occupations that match the user's characteristics.

[0146] Career plan proposals

[0147] The server sends the generated career plan and a list of required skills and experience to the terminal, which then displays it to the user, who can then check the specific career plan based on their own characteristics.

[0148] User Feedback

[0149] Users input feedback and follow-up questions about their career plans into the device. Specific feedback includes, for example, "I'd like to know more about mastering presentation skills." The device collects the feedback and sends it to the server, which analyzes it and uses it to improve the system.

[0150] Examples of concrete examples and prompts

[0151] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server analyzes this and identifies "software engineer" as a suitable career path. It then generates a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggests them to the student via their device.

[0152] Prompt Sentence Examples

[0153] "Tell me about your past work experience"

[0154] "What events or people influenced you?"

[0155] "Please tell me your strengths and weaknesses."

[0156] "What are your strengths and weaknesses?"

[0157] "What values ​​do you hold dear?"

[0158] By using the above prompt sentences, detailed information can be collected from the user, and a more accurate career plan can be proposed.

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] Step 1: User Login

[0161] The user enters login information (username and password) into the terminal. The terminal sends this information to the server, which then authenticates it by checking it against information in a database. If authentication is successful, the server generates a session ID and the user's session begins. The server returns the authentication result and session ID to the terminal.

[0162] Input: Username, Password

[0163] Data processing: Verification of input information (database search)

[0164] Output: Authentication result (success / failure), session ID

[0165] Specific behavior:

[0166] The device obtains the user's authentication information from the input fields and sends it to the server in JSON format.

[0167] The server checks the received authentication information against the information in its database, and if it matches, the authentication is successful and a session ID is generated.

[0168] The server returns the authentication result and session ID to the terminal in JSON format.

[0169] The device receives the authentication result, and if successful, transitions to the home screen.

[0170] Step 2: Posing the Question

[0171] The server generates a series of questions for the user who has been successfully authenticated and sends them to the device. The device receives the questions and displays them on the user's screen. The questions can be a pre-defined set of fixed questions or dynamically generated questions using a generative AI model.

[0172] Input: Session ID

[0173] Data processing: Question list generation (fixed or dynamic generation)

[0174] Output: Question list

[0175] Specific behavior:

[0176] The server checks the user information based on the session ID and selects or generates an appropriate questionnaire.

[0177] A series of questions (e.g., "Tell me about your past work experience") is sent to the device in JSON format.

[0178] The terminal displays the received list of questions on the screen.

[0179] Step 3: Collect and submit responses

[0180] The user answers the questions displayed through the terminal in detail, and the terminal collects the user's answers and sends them to the server, where they are stored.

[0181] Input: User's answer

[0182] Data processing: Collection and transmission of response data (JSON format)

[0183] Output: Answer data saved on the server

[0184] Specific behavior:

[0185] The user inputs answers to questions displayed on the screen using text boxes and options.

[0186] The device sends the user's answers in JSON format to the server.

[0187] The server stores the received response data in a database.

[0188] Step 4: Analyze the data

[0189] The server analyzes the user response data stored in the database, using machine learning models (e.g., TensorFlow or PyTorch) and natural language processing techniques to extract user characteristics.

[0190] Input: Answer data

[0191] Data processing: Vectorization of text data, analysis using machine learning models

[0192] Output: User characteristics (strengths, weaknesses, areas of expertise, areas of weakness, values)

[0193] Specific behavior:

[0194] The server retrieves the user's response data from the database and performs preprocessing (such as text cleaning).

[0195] Load the machine learning model and input the preprocessed data into the model.

[0196] The model output is analyzed to extract user characteristics.

[0197] Step 5: Create a career plan

[0198] The server generates a career plan suitable for the user based on the analysis results, which includes job types, required skills, recommended experience, etc. A recommendation engine is used to create a list of occupations that match the user's characteristics.

[0199] Input: User characteristics

[0200] Data processing: Generating career plans (recommended by algorithms)

[0201] Output: Career plan (job type, skills, experience list)

[0202] Specific behavior:

[0203] The server generates a list of occupations using a recommendation engine based on the user's characteristic data.

[0204] The occupational list includes the required skills and recommended experience for each job.

[0205] The generated carrier plan is sent to the terminal in JSON format.

[0206] Step 6: Propose a career plan

[0207] The server sends the generated carrier plan to the terminal, which displays it to the user, allowing the user to check the specific carrier plan.

[0208] Input: Career plan

[0209] Data processing: Sending career plan (JSON format)

[0210] Output: Career plan displayed to the user

[0211] Specific behavior:

[0212] The server sends the generated carrier plan in JSON format to the terminal.

[0213] The terminal displays the received carrier plan on the screen so that the user can check the contents.

[0214] Step 7: User feedback

[0215] Users input feedback and follow-up questions about their career plans into the device, which collects the feedback and sends it to the server, which analyzes it and uses it to improve the system.

[0216] Input: Feedback

[0217] Data processing: collecting and sending feedback (JSON format)

[0218] Output: Feedback stored on the server

[0219] Specific behavior:

[0220] Users can enter feedback or follow-up questions about the career plan in the text box.

[0221] The device sends feedback in JSON format to the server.

[0222] The server stores the received feedback in a database and uses it for subsequent analysis and system improvement.

[0223] (Application example 1)

[0224] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0225] Currently, many people find it difficult to map out their own career plans. In particular, in the food delivery industry, short-term employment and limited opportunities for skill development make it difficult to create long-term career plans. As a result, declining worker satisfaction and declining labor productivity have become problems. For example, there is a need for a system that can clarify how to advance one's career by utilizing one's experience and abilities.

[0226] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0227] In this invention, the server includes means for authenticating the user's login information, means for interactively displaying questions to the user and collecting answers, means for transmitting the collected answer data to the server, means for analyzing the answer data and extracting characteristics of the user, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, means for collecting feedback from the user and reflecting it in improving the system, and means for taking into account specific skills and experience in delivery work when generating a career plan by identifying needs and suggesting suitable jobs.

[0228] This allows users to draw up concrete and realistic career plans, and in the food delivery industry in particular, it makes it possible to clarify suitable jobs and ways to improve skills, which is expected to result in improved worker satisfaction and labor productivity.

[0229] "Login information" means authentication information for a user to access a system, and typically includes a user ID and password.

[0230] "Dialogue" refers to a method by which a user interacts with a system, often in the form of questions and answers.

[0231] "Response data" is a collection of information entered interactively by the user, and includes specific responses to questions.

[0232] A "server" is a computer system that stores and processes data and is responsible for managing and analyzing data from users.

[0233] "User characteristics" refers to the individual characteristics of a user, such as the user's strengths, weaknesses, areas of expertise, areas of weakness, and values.

[0234] A "career plan" refers to a plan that clarifies the career goals, skills, and experience a user wants to achieve in the future.

[0235] A "skills and experience list" is a list of the skills and achievements required to advance in a particular career.

[0236] "Feedback" refers to opinions and requests for improvement that users provide to the system, and is used to improve the system's performance.

[0237] "System improvement" is the process of improving the functionality and performance of software based on collected feedback.

[0238] "Needs" refer to specific wants and desires that users currently have, and in the career context, this includes suitable jobs and opportunities for skill development.

[0239] "Job" refers to a specific occupation or job content, including the tasks and roles a user performs.

[0240] "Delivery work" refers to the occupation of delivering goods or services to customers, including the specific tasks and duties that occur along the way.

[0241] "Skills and experience" refers to the techniques required to perform a particular job and the knowledge and skills acquired through actual work.

[0242] A "machine learning model" is an algorithm that analyzes large amounts of data, learns patterns and trends, and makes future predictions.

[0243] The system for implementing this invention authenticates the user's login information, collects and analyzes the user's characteristics by interactively asking and answering questions, and generates and proposes an appropriate career plan. This system mainly depends on the interaction between the server, the terminal, and the user.

[0244] System configuration

[0245] The system includes the following main components:

[0246] 1. Login authentication module:

[0247] The server receives the user's login information (user ID, password, etc.) and performs authentication. If this authentication is successful, the user's session begins.

[0248] 2. Interactive question display module:

[0249] After authentication, the server prompts the device with a series of questions, including past experiences, strengths and weaknesses, areas of expertise, areas of weakness, and values ​​that are important to the user.

[0250] 3. Response collection and submission module:

[0251] Users answer questions through their terminals, and the answer data is sent to a server where it is managed centrally.

[0252] 4. Data Analysis Module:

[0253] The server analyzes the collected response data and extracts user characteristics, using a machine learning model for highly accurate analysis.

[0254] 5. Career plan generation module:

[0255] Based on the extracted user characteristics, the server generates a unique career plan, taking into account the user's skills and experience in delivery work in particular, and proposes a plan that includes suitable jobs and ways to improve skills.

[0256] 6. Suggestion and Feedback Collection Module:

[0257] The generated career plan and list of required skills and experience are sent to the terminal and presented to the user, who can then input feedback on the plan, which is used to improve the system.

[0258] Hardware and software used

[0259] Server: a computer system that stores and processes data

[0260] Terminal: A device (such as a smartphone or computer) through which a user interacts with the system.

[0261] Programming language: Python

[0262] Framework:Flask

[0263] Process example

[0264] For example, suppose a user with 10 years of experience working in the food delivery industry answers, "I'm mainly good at deliveries within a city. I also have some experience managing a team." Based on that data, the server proposes a career plan for the user as a "Delivery Operations Manager" and lists the required skills (e.g., Team Leadership, Logistics Management, Fleet Management).

[0265] Prompt Sentence Examples

[0266] The user can provide specific information to the system through prompts such as:

[0267] "I have been working in delivery jobs for 10 years. I am good at mainly intra-city deliveries and have some experience in team management. Please suggest the best career plan for me."

[0268] Such prompts enable the system to accurately understand the user's characteristics and propose an appropriate career plan.

[0269] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0270] Step 1:

[0271] A user accesses the system using a terminal and enters login information (user ID and password). The terminal sends this information to the server, which authenticates the login information. If authentication is successful, the user's session begins.

[0272] Input: User ID, Password

[0273] Data processing: The server checks the authentication information

[0274] Output: Session ID upon successful login

[0275] Step 2:

[0276] The server recognizes the user's login and prompts the terminal with a series of questions, including past experience, strengths and weaknesses, areas of expertise, areas of weakness, and values.

[0277] Input: None (after login authentication)

[0278] Data processing: The server selects the questions

[0279] Output: List of questions

[0280] Step 3:

[0281] The user answers the questions displayed on the terminal in detail, and the user's answer data is collected on the terminal and sent to the server.

[0282] Input: User's answer

[0283] Data processing: The device collects the answers and converts them into a data structure.

[0284] Output: Response data

[0285] Step 4:

[0286] The server receives the collected response data and stores it centrally. The server then analyzes the response data and extracts user characteristics. This analysis is performed with high accuracy using machine learning models.

[0287] Input: Answer data

[0288] Data processing: Data analysis using machine learning models

[0289] Output: User characteristics data

[0290] Step 5:

[0291] Based on the analysis results, the server generates a career plan suited to the user's characteristics. The plan takes into account the skills and experience of delivery work. A list of required skills and experience is also generated.

[0292] Input: User characteristics data

[0293] Data processing: Career planning

[0294] Output: Career plan, skills and experience list

[0295] Step 6:

[0296] The server sends the generated career plan and skill / experience list to the terminal and presents it to the user, who then checks the proposal and enters feedback.

[0297] Input: Career plan, skills and experience list

[0298] Data processing: None (Suggestion display)

[0299] Output: Feedback data

[0300] Step 7:

[0301] The device collects feedback from users and sends it to the server, which stores the feedback and reflects it in subsequent plan generation to improve the system.

[0302] Input: Feedback data

[0303] Data processing: Analysis and storage of feedback

[0304] Output: Improved system data

[0305] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0306] This invention is a system that allows users to draw up specific career plans based on their own characteristic data, including their own emotions. This system features a mechanism in which users interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values ​​they hold dear, and then combines this information with an emotion engine that recognizes the user's emotions to analyze the information and propose an appropriate career plan.

[0307] In this system, the following processing is performed.

[0308] Log in and ask a question

[0309] A user logs in to a terminal

[0310] A user accesses the system and enters their login information. The terminal sends this information to the server, which performs authentication. If authentication is successful, the user's session begins.

[0311] The device displays a question to the user.

[0312] Once the server recognizes the user's login, the system prompts the user with a series of questions, including about past experiences, influences, strengths and weaknesses, values, and so on.

[0313] Emotion recognition by emotion engine

[0314] The device recognizes the user's emotions

[0315] While answering questions, the emotion engine recognizes emotions from the user's facial expressions and voice in real time, and this emotional data is used to adjust the content and order of questions.

[0316] Collecting and submitting responses

[0317] The user answers the question

[0318] The user answers the questions displayed on the screen in detail. Once the answer is complete, the device collects the answer and sends it to the server. At the same time, emotional data about the user's response is also collected.

[0319] The server receives the data

[0320] The server receives the user's response data and emotion data sent from the device and stores them centrally.

[0321] Data analysis

[0322] The server analyzes the data

[0323] The server analyzes the collected data and extracts the user's characteristics, such as their strengths, weaknesses, areas of expertise, areas of weakness, and values, and also incorporates emotional data.

[0324] The server generates a carrier plan.

[0325] Based on the analysis results, the server generates a career plan suited to the user's characteristics and emotions, including job type, required skills, recommended experience, etc. Furthermore, machine learning models are used to improve the accuracy of the career plan generation.

[0326] Career plan proposals

[0327] The server sends the proposal to the device

[0328] The server sends the generated career plan and a list of required skills and experience to the terminal.

[0329] The device displays suggestions to the user

[0330] The terminal displays the proposed career plan and a list of skills and experiences to the user, allowing the user to confirm a specific career plan based on their own characteristics and feelings.

[0331] User Feedback

[0332] Users provide feedback

[0333] The user can then input feedback or additional questions about the proposed career plan into the device, and the emotion engine will recognize the user's emotions and collect the data.

[0334] The device collects feedback and sends it to the server

[0335] The device collects feedback and sends it to the server, which uses it to improve the system and reflect it in future interactions.

[0336] Specific examples

[0337] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server analyzes this and identifies "software engineer" as a suitable career path. Furthermore, the emotion engine collects emotional data from the student's facial expressions, such as excitement and interest, and incorporates this into the plan generation process, providing a more accurate career plan. Next, it creates a list of required skills, such as advanced programming skills, database management, team collaboration, and presentation skills, and suggests them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics and emotional data.

[0338] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[0339] The processing flow will be explained below.

[0340] Step 1:

[0341] The user logs in to the terminal. The user enters their user ID and password on the system login screen and presses the login button.

[0342] Step 2:

[0343] The terminal sends the user's login information to the server. The terminal encrypts the entered user ID and password and sends them to the server.

[0344] Step 3:

[0345] The server authenticates the user. The server checks the user ID and password in the database and starts a session if they match. If they don't match, it returns an authentication error.

[0346] Step 4:

[0347] The terminal displays the authentication result to the user. If authentication is successful, the user can proceed to the next screen. If authentication is unsuccessful, a login error screen is displayed.

[0348] Step 5:

[0349] The server prepares a series of questions for the user and sends them to the device, including questions about past experiences, influences, strengths and weaknesses, values, etc.

[0350] Step 6:

[0351] The device displays a question to the user, and the emotion engine is activated to recognize emotions from the user's facial expressions and voice in real time.

[0352] Step 7:

[0353] The user answers each question and enters it into the terminal. The emotion engine recognizes and simultaneously collects the emotional data of the user when answering.

[0354] Step 8:

[0355] The device sends the collected response data and emotion data to the server. Once all questions have been answered, the data is sent to the server in one batch.

[0356] Step 9:

[0357] The server receives the response data and emotion data and begins analysis. The server analyzes the response data and extracts the user's characteristics. Specifically, it analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc. At the same time, it takes emotion data into consideration.

[0358] Step 10:

[0359] The server generates a career plan suited to the user. Based on the analysis results, a machine learning model is used to propose the optimal career plan. This plan includes information such as job type, required skills, and recommended experience. The user's emotional data is also reflected in the plan generation.

[0360] Step 11:

[0361] The server transmits the generated career plan and a list of required skills and experiences to the terminal.

[0362] Step 12:

[0363] The device displays the career plan and skills / experience list to the user, who can then review the proposed content.

[0364] Step 13:

[0365] The user provides feedback by inputting their opinions or questions about the proposal into the device. At this time, the emotion engine recognizes the user's emotions in real time and collects the data.

[0366] Step 14:

[0367] The device collects feedback and emotion data and sends it to a server.

[0368] Step 15:

[0369] The server receives and analyzes the feedback and emotional data, and uses it to improve the system's algorithms and question flow.

[0370] Step 16:

[0371] The server then incorporates feedback and sentiment data into the next session to improve the system, allowing it to continuously learn and adjust to provide users with more accurate career plans.

[0372] Example 2

[0373] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0374] Previous career plan proposal systems were based solely on user response data and did not take into account the user's emotions or immediate reactions. As a result, it was difficult to generate a career plan that fully reflected the user's characteristics and intentions. Furthermore, the system lacked a mechanism for incorporating feedback into system improvements, making continuous system optimization difficult.

[0375] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for authenticating the user's login information, means for displaying questions to the user in an interactive format and collecting answers, means for transmitting the collected answer data to the server, means for recognizing emotion data accompanying the collected answer data, means for analyzing the answers and emotion data and extracting user characteristics, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, and means for collecting feedback from the user and reflecting the feedback in improving the system. This enables the generation of a highly accurate career plan that incorporates the user's emotions and immediate reactions. Furthermore, the system can be continuously improved based on the feedback.

[0376] "Means for authenticating login information" refers to the functionality that verifies the authentication information entered by the user and confirms the user's access privileges.

[0377] "Means for interactively displaying questions and collecting answers" refers to the functionality of displaying a series of questions to a user and receiving the user's answers through an interface.

[0378] The "means for transmitting collected response data to a server" refers to a function for transmitting the response data collected from users to a server via a network.

[0379] "Means for recognizing emotional data accompanying collected response data" refers to a function that analyzes the user's facial expressions and voice while answering questions and recognizes their emotional state in real time.

[0380] "Means of analyzing response and emotional data and extracting user characteristics" refers to the function of analyzing and extracting a user's characteristics, strengths, weaknesses, areas of expertise and weaknesses, values, etc. based on the acquired response data and emotional data.

[0381] "Means for generating a career plan based on the characteristics of the user" refers to a function for generating an optimal career plan based on analyzed characteristic information of the user.

[0382] "Means for proposing the generated career plan and a list of required skills and experience to the user" refers to a function for presenting the generated career plan and the required skills and experience to the user.

[0383] "Means for collecting feedback from users and reflecting it in improving the system" refers to the function of collecting feedback provided by users and improving and optimizing the system based on that feedback.

[0384] This invention is a system that allows users to draw up specific career plans based on their own characteristic data, including their own emotions. This system features a mechanism in which users interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values ​​they hold dear, and then combines this information with an emotion engine that recognizes the user's emotions to analyze the information and propose an appropriate career plan.

[0385] The system includes a means for a user to authenticate by logging in using a terminal and transmitting login information to a server, which then displays a series of questions to the user on the terminal, to which the user enters answers.

[0386] The device is equipped with a built-in camera and microphone, and while the user is answering questions, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. This data is collected along with the answers and sent from the device to the server.

[0387] The server analyzes the collected response data and sentiment data to extract user characteristics. This can be done using Python data analysis tools such as Pandas and NumPy. A career plan is then generated based on the analysis results. Machine learning libraries such as Scikit-learn and TensorFlow are used for generation.

[0388] The generated career plan and a list of the skills and experience required for it are sent from the server to the terminal and displayed to the user. The user can review the displayed career plan and enter feedback or additional questions. The terminal collects this feedback and sends it back to the server. The server can analyze this feedback and reflect it in improving the system.

[0389] As a concrete example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won an award at a hackathon," the server will analyze this and identify "software engineer" as a suitable career path. Furthermore, the emotion engine collects emotional data from the student's facial expressions, such as excitement and interest at the time of the answer, and incorporates this into the plan generation process, providing a more accurate career plan. Next, the system creates a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggests them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics and emotional data.

[0390] Prompt Sentence Examples

[0391] "What about past experiences?"

[0392] "What influenced you?"

[0393] "Please tell me your strengths and weaknesses."

[0394] "What are your strengths and weaknesses?"

[0395] "What values ​​do you hold dear?"

[0396] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[0397] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0398] Step 1:

[0399] The user enters login information. The user enters a username and password on the terminal's login screen. The terminal sends the entered information to the server. The server compares the information with the user information stored in the database and performs authentication. If authentication is successful, the user's session begins.

[0400] Input: Username and Password

[0401] Output: Session started

[0402] Step 2:

[0403] The server sends a list of questions to the terminal, which then displays the questions to the user in an interactive format. The server generates a series of questions and sends them to the terminal, which receives them and displays them to the user in sequence.

[0404] Input: Question List

[0405] Output: Question printed to terminal

[0406] Step 3:

[0407] The user answers the questions. The user types in an answer to each question displayed on the device. For example, in response to the question "What is your past experience?", the user types in "I took a programming class and won a prize at a hackathon."

[0408] Input: The user's answer to the question displayed

[0409] Output: User response data

[0410] Step 4:

[0411] The device recognizes the user's emotions. While the user is entering their answers, the device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time, and recognizes the emotional data using an emotion engine.

[0412] Input: User's facial expressions and voice

[0413] Output: Emotion data

[0414] Step 5:

[0415] The device sends the answers and emotion data to the server. Once the user has answered all the questions, the device sends the answer data and emotion data to the server. The data is encrypted before being sent.

[0416] Input: User response data and emotion data

[0417] Output: Data sent to the server

[0418] Step 6:

[0419] The server analyzes the response data and emotion data. Using Python's Pandas and NumPy, the server analyzes the received data and extracts the user's characteristics. Specifically, it analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[0420] Input: User response data and emotion data

[0421] Output: User characteristics data

[0422] Step 7:

[0423] The server generates a career plan based on the user's characteristics. Based on the analysis results, the server uses a machine learning model (such as Scikit-learn or TensorFlow) to generate an optimal career plan. The career plan includes recommended job types, required skills, and experience.

[0424] Input: User characteristics data

[0425] Output: Career plan

[0426] Step 8:

[0427] The server sends the career plan to the terminal. The generated career plan and the accompanying skill and experience list are then sent from the server to the terminal.

[0428] Input: Career plan

[0429] Output: Data sent to the terminal

[0430] Step 9:

[0431] The terminal displays the career plan to the user. The terminal displays the received career plan and skill list to the user.

[0432] Input: Data sent from the server

[0433] Output: Career plan displayed to the user

[0434] Step 10:

[0435] The user provides feedback on the career plan. The user checks the displayed career plan and enters feedback or additional questions into the terminal. For example, the user enters a comment such as, "I'm interested in this job, but are there any other options?"

[0436] Input: User feedback on career plans

[0437] Output: User feedback data

[0438] Step 11:

[0439] The device sends the feedback to the server. The device collects the feedback data entered by the user and sends it to the server. The data is encrypted before being sent.

[0440] Input: User feedback data

[0441] Output: Feedback data sent to the server

[0442] Step 12:

[0443] The server analyzes the feedback and reflects it in improvements to the system. The server analyzes the received feedback and analyzes its content. The system is improved based on the analysis results and reflected in future interactions.

[0444] Input: Feedback data

[0445] Output: System improvement data

[0446] (Application example 2)

[0447] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0448] Conventional career plan generation systems make suggestions based on the user's characteristics and experience, but because they do not take emotional data into account, they are unable to reflect the user's psychological state or emotional satisfaction. Furthermore, because they do not consider optimizing specific work schedules based on the proposed career plan, it is difficult to provide an efficient work schedule based on the user's aptitude and emotions. This has limited the improvement of employee satisfaction and productivity.

[0449] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0450] In this invention, the server includes means for authenticating user login information, means for displaying questions to the user in an interactive format and collecting answers, means for transmitting the collected answer data and emotion data to the server, means for analyzing the answer data and emotion data and extracting user characteristics, means for generating a career plan based on the user's characteristics and emotion data, means for proposing the generated career plan and a list of required skills and experience to the user, means for optimizing employee work schedules based on the proposed career plan, and means for collecting feedback from users and reflecting it in improving the system. This makes it possible to provide highly accurate career plans based on the user's emotions and characteristics and to optimize employee work schedules.

[0451] "Login Information" means authentication data for a user to access a system.

[0452] "Dialogue" refers to a format in which the user and the system communicate with each other through questions and answers.

[0453] "Answer data" refers to the response information provided by a user in response to a question presented by the system.

[0454] "Emotion data" is information indicating the emotional state of the user obtained by analyzing the user's facial expressions and voice.

[0455] A "server" is a computer system for receiving and processing data sent by users.

[0456] "Analysis" is the process of extracting and analyzing specific information from collected data.

[0457] "Characteristics" refers to individual characteristics of a user, such as strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that are important to the user.

[0458] A "career plan" is a plan for career choices and career activities that is proposed based on the user's characteristics and emotional data.

[0459] "Skills" are the techniques and abilities required to perform a specific job or task.

[0460] A "work schedule" is a plan that specifies what tasks an employee will perform at what time.

[0461] "Feedback" refers to users providing their opinions and impressions about the proposed career plan and use of the system.

[0462] "Optimization" is the process of improving plans and operations to be efficient and effective according to a purpose.

[0463] The system for realizing this application example is implemented as a smartphone application for optimizing employees' career plans and work schedules. Specific embodiments for carrying out the invention are described in detail below.

[0464] The system consists of the following main components:

[0465] 1. User authentication:

[0466] When a user logs in to a smartphone application, the server authenticates the user's login information, using standard ID and password or biometric authentication such as fingerprint or face recognition.

[0467] 2. Interactive Q&A:

[0468] After successful authentication, the user is presented with a series of interactive questions, including past experiences, strengths and weaknesses, values, etc. The user then enters answers to these questions.

[0469] 3. Collecting Emotional Data:

[0470] While the user answers questions, the smartphone's camera and microphone are used to collect real-time emotional data, which is then analyzed using an emotion recognition engine such as Microsoft® Azure® Face API.

[0471] 4. Data transmission and analysis:

[0472] The collected response data and emotion data are sent to a server, which analyzes the data and extracts user characteristics. A machine learning model is applied to the analysis using Python and Scikit-learn.

[0473] 5. Career Plan and Schedule Generation:

[0474] The server generates an appropriate career plan based on the user's characteristics and emotional data. This career plan includes recommended job types, required skills, and experience. Furthermore, the server uses tools such as Google®'s OR-Tools to optimize the work schedule based on the proposed career plan.

[0475] 6. User Suggestions and Feedback:

[0476] The generated career plan and optimized work schedule are presented to the user via a smartphone application, and the user can provide feedback, which is then sent to the server, which then uses this feedback to improve the system.

[0477] Specific examples include the following cases:

[0478] An employee logs in and answers questions about their past strengths, values, and past accomplishments. For example, they might provide an answer like, "I took a programming class and won a hackathon." Emotional data, such as excitement and interest, is collected from their facial expressions while answering.

[0479] The server analyzes this data and suggests "software engineer" as the best career plan for the employee, listing necessary skills such as advanced programming skills and teamwork ability.

[0480] Based on the proposal, employees' daily schedules will be optimized to consist of one hour of welding work and two hours of quality control training.

[0481] Additionally, specific prompts are generated using a generative AI model, such as:

[0482] Example prompt: "Analyze user Chris's past experience (welding), and suggest a schedule for today based on his strengths (welding) and interests (quality control). For example, suggest one hour of welding and two hours of quality control training. Optimize the schedule so that Chris shows positive emotions while working."

[0483] This will enable the provision of highly accurate career plans based on users' emotions and characteristics, as well as the optimization of employee work schedules.

[0484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0485] Step 1:

[0486] A user logs in to an application on their smartphone.

[0487] Specific operation: The user enters login information using ID and password, or fingerprint or facial recognition.

[0488] Input: User authentication information (ID, password, biometric information)

[0489] Output: Authentication result (success or failure)

[0490] Data processing: The app device sends the entered authentication information to the server, which then performs the authentication process. The server compares the information with the authentication information in its database and generates an authentication result.

[0491] Step 2:

[0492] After successful login, the terminal interactively displays questions to the user and collects answers.

[0493] Specific actions: Questions should be about past experiences, strengths, weaknesses, values, and strengths.

[0494] Input: User response data

[0495] Output: User response data collection results

[0496] Data processing: The device collects the user's answers as text data according to a format. The collected data is temporarily stored on the device.

[0497] Step 3:

[0498] The device collects the user's emotional data.

[0499] Specific operation: While answering questions, the user's facial expressions and voice are recorded in real time through the device's camera and microphone, and analyzed using an emotion recognition engine (such as Microsoft Azure Face API).

[0500] Input: User's facial expression data, voice data

[0501] Output: Emotion data (anger, joy, sadness, surprise, etc.)

[0502] Data processing: The acquired image and audio data is input into an emotion recognition engine, and emotion data is generated as an analysis result.

[0503] Step 4:

[0504] The terminal transmits the collected response data and emotion data to the server.

[0505] Specific operation: The device sends the temporarily stored data to the server using HTTPS communication.

[0506] Input: User response data, emotion data

[0507] Output: Data sent to the server

[0508] Data processing: Data is compressed, encrypted, and securely sent to the server.

[0509] Step 5:

[0510] The server analyzes the collected data.

[0511] Specific operation: The server analyzes the response data and sentiment data and extracts user characteristics using a machine learning model (Python, Scikit-learn).

[0512] Input: User response data, emotion data

[0513] Output: User characteristics data

[0514] Data processing: The server integrates the response data and emotion data stored in the database and runs an algorithm to extract features to analyze the user's characteristics.

[0515] Step 6:

[0516] The server generates a career plan based on the user's characteristics and optimizes the work schedule.

[0517] Specific operation: Using machine learning models (Python, Scikit-learn) and schedule optimization algorithms (Google OR-Tools), we generate career plans and work schedules that are suitable for users.

[0518] Input: User characteristic data

[0519] Output: Career plan, optimized work schedule

[0520] Data processing: The server inputs the characteristic data into a machine learning model and applies a schedule optimization algorithm based on the generated carrier plan.

[0521] Step 7:

[0522] The server transmits the generated career plan and work schedule to the terminal.

[0523] Specific operation: The server sends the proposed career plan, skill list, and optimized schedule to the terminal.

[0524] Input: Career plan, work schedule

[0525] Output: Result of sending the proposal data to the user

[0526] Data processing: The data is compressed and encrypted again and sent securely to the device.

[0527] Step 8:

[0528] The terminal displays the generated career plan and work schedule to the user.

[0529] Specific operation: A career plan, a list of required skills, and a work schedule are displayed on the terminal screen.

[0530] Input: Proposal data (career plan, skill list, work schedule)

[0531] Output: The result displayed to the user

[0532] Data processing: The received data is decompressed and decrypted, and presented visually to the user.

[0533] Step 9:

[0534] The user provides feedback.

[0535] Specific operation: The user enters feedback and questions about the proposed career plan and work schedule.

[0536] Input: User feedback data

[0537] Output: Feedback collection results

[0538] Data processing: The device collects feedback in text format and sends it to the server.

[0539] Step 10:

[0540] The server collects feedback and uses it to improve the system.

[0541] Specific operation: Analyze the feedback data and update the algorithm to reflect it in future career plan proposals.

[0542] Input: User feedback data

[0543] Output: System improvement information

[0544] Data processing: Feedback data is stored and analyzed to identify areas for improvement in the system.

[0545] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0546] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0547] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0548] [Second embodiment]

[0549] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0550] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0551] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0552] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0553] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0554] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0555] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0556] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0557] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0558] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0559] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0560] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0561] This invention is a system that allows users to draw up specific career plans for themselves. This system features a mechanism that allows users to interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values, and then analyzes the information and proposes an appropriate career plan.

[0562] In this system, the following processing is performed.

[0563] Log in and ask a question

[0564] A user logs in to a terminal

[0565] A user accesses the system and enters their login information. The terminal sends this information to the server, which performs authentication. If authentication is successful, the user's session begins.

[0566] The device displays a question to the user.

[0567] Once the server recognizes the user's login, the system prompts the user with a series of questions, including about past experiences, influences, strengths and weaknesses, values, and so on.

[0568] Collecting and submitting responses

[0569] The user answers the question

[0570] The user answers the questions displayed on the screen in detail, and once the answers are complete, the terminal collects them and sends them to the server.

[0571] The server receives the data

[0572] The server receives the user's response data sent from the device and stores it centrally.

[0573] Data analysis

[0574] The server analyzes the data

[0575] The server analyzes the collected data and extracts the user's characteristics, specifically, their strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[0576] The server generates a carrier plan.

[0577] Based on the analysis results, the server generates a career plan suited to the user's characteristics, including job type, required skills, recommended experience, etc. Furthermore, machine learning models are used to improve the accuracy of the career plan.

[0578] Career plan proposals

[0579] The server sends the proposal to the device

[0580] The server sends the generated career plan and a list of required skills and experience to the terminal.

[0581] The device displays suggestions to the user

[0582] The terminal displays the proposed career plan and a list of skills and experience to the user, allowing the user to confirm a specific career plan based on their own characteristics.

[0583] User Feedback

[0584] Users provide feedback

[0585] Users can enter feedback and follow-up questions about the proposed career plan into the device, including specific feedback such as "I'd like to know more about acquiring presentation skills."

[0586] The device collects feedback and sends it to the server

[0587] The device collects feedback and sends it to the server, which uses it to improve the system and reflect it in future interactions.

[0588] Specific examples

[0589] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server will analyze this and identify "software engineer" as a suitable career path. It will then create a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggest them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics.

[0590] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[0591] The processing flow will be explained below.

[0592] Step 1:

[0593] The user logs in to the terminal. The user enters their user ID and password on the system login screen and presses the login button.

[0594] Step 2:

[0595] The terminal sends the user's login information to the server. The terminal encrypts the entered user ID and password and sends them to the server.

[0596] Step 3:

[0597] The server authenticates the user. The server checks the user ID and password in the database and starts a session if they match. If they don't match, it returns an authentication error.

[0598] Step 4:

[0599] The terminal displays the authentication result to the user. If authentication is successful, the user can proceed to the next screen. If authentication is unsuccessful, a login error screen is displayed.

[0600] Step 5:

[0601] The server prepares a series of questions for the user and sends them to the device, including questions about past experiences, influences, strengths and weaknesses, values, etc.

[0602] Step 6:

[0603] The terminal displays questions to the user, who then answers the questions displayed on the screen one by one.

[0604] Step 7:

[0605] The user answers each question and enters it into the terminal, where each answer is temporarily saved.

[0606] Step 8:

[0607] The device sends the collected response data to the server. Once all questions have been answered, the data is sent to the server in one batch.

[0608] Step 9:

[0609] The server receives the response data and begins analyzing it. The server analyzes the response data and extracts the user's characteristics. Specifically, the server analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[0610] Step 10:

[0611] The server generates a career plan suited to the user. Based on the analysis results, it uses a machine learning model to propose the optimal career plan. This plan includes job types, required skills, recommended experience, etc.

[0612] Step 11:

[0613] The server transmits the generated career plan and a list of required skills and experiences to the terminal.

[0614] Step 12:

[0615] The device displays the career plan and skills / experience list to the user, who can then review the proposed content.

[0616] Step 13:

[0617] The user provides feedback by inputting their opinions and questions about the proposal into the terminal.

[0618] Step 14:

[0619] The device collects the feedback and sends it to the server.

[0620] Step 15:

[0621] The server receives and analyzes the feedback, which it uses as data to improve the system's algorithms and question flow based on the user's feedback.

[0622] Step 16:

[0623] The server then incorporates the feedback into the next session, allowing the system to continuously learn and adjust to provide users with more accurate career plans.

[0624] Example 1

[0625] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0626] In today's career development process, it is not easy for individual users to draw up specific career plans based on their own characteristics. Conventional systems have difficulty providing users with adequately customized suggestions, and do not adequately support users in choosing a career based on their own strengths, weaknesses, and values. Furthermore, they lack a mechanism for effectively incorporating feedback and improving the accuracy of the system.

[0627] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0628] In this invention, the server includes a means for authenticating the user's login information, a means for displaying questions to the user in an interactive format and collecting answers, and a means for transmitting the collected answer data to the server, thereby enabling the generation of a highly accurate career plan based on the user's characteristics.

[0629] "Means for authenticating login information" refers to a technology that collates the authentication information entered by the user into the terminal with information in a database and establishes a user session.

[0630] "Means for displaying questions in an interactive format and collecting answers" refers to a technique for displaying questions sent from a server to a user on a terminal and collecting answers from the user as input.

[0631] The "means for transmitting collected response data to a server" refers to a technique for transmitting user responses from the terminal to a server and storing them safely.

[0632] "Means of analyzing response data and extracting user characteristics" refers to techniques that analyze collected data using machine learning models and natural language processing techniques to reveal characteristics such as users' strengths, weaknesses, areas of expertise, areas of weakness, and values.

[0633] The "means for generating a career plan" is a technology that creates a career plan that includes the most suitable job type, required skills, experience, etc. for the user based on the analysis results.

[0634] The "means for proposing a career plan and a list of required skills and experiences to a user" is a technology for displaying a generated career plan and its detailed requirements to a user.

[0635] "Means for collecting feedback from users and reflecting it in system improvements" refers to a technique for collecting opinions and additional questions from users and incorporating them into system analysis to improve functionality.

[0636] "Means for vectorizing input text and analyzing it with a classification algorithm" refers to a technology that converts text data such as user responses into numerical vectors and analyzes them using a classification algorithm.

[0637] "Means for creating a list of occupations using a recommendation engine" refers to a technology that uses machine learning technology to list appropriate occupations based on the characteristics of the user.

[0638] The present invention is a system that allows users to specifically draw up their own career plans, and that interactively collects information such as the user's past experiences, areas of strength and weakness, and values, and then analyzes the information to propose an appropriate career plan. An embodiment of this system will be described in detail below.

[0639] System configuration

[0640] The system mainly includes the following components:

[0641] 1. Terminal

[0642] 2. Server

[0643] 3. Database

[0644] 4. Machine Learning Models

[0645] 5. Communication Protocol

[0646] Software and hardware used

[0647] Terminal

[0648] Users access the system using devices such as PCs, tablets, and smartphones, and the devices communicate with the server via a web browser or native application.

[0649] server

[0650] The server is the main component for running the program and uses web frameworks such as Django and Spring. The server handles major processes such as user authentication, data analysis, and career plan generation.

[0651] Database

[0652] MySQL and PostgreSQL are used as databases to store various data such as user information, response data, and analysis results.

[0653] Machine learning models

[0654] The machine learning model uses TensorFlow and PyTorch to analyze user data and generate career plans. Specifically, natural language processing technology is used to analyze user responses and extract characteristics.

[0655] communication protocol

[0656] The device and server send and receive data using a secure communication protocol such as HTTPS.

[0657] System action

[0658] User Login

[0659] The user enters their login information into the terminal, and the server authenticates them against the database. If authentication is successful, a session ID is generated and the user's session begins.

[0660] Asking questions and collecting answers

[0661] The server generates a series of questions for the user and sends them to the device, which then displays the questions on the user's screen and the user enters answers to these questions.The answers are then sent from the device to the server and stored in a database.

[0662] Data analysis

[0663] The server receives the collected response data and analyzes it using machine learning models and natural language processing technology to extract information such as the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that they hold dear.

[0664] Creating a career plan

[0665] The server generates an optimal career plan for the user based on the analysis results. This career plan includes recommended occupations, required skills and experience, etc. A recommendation engine is used to create a list of occupations that match the user's characteristics.

[0666] Career plan proposals

[0667] The server sends the generated career plan and a list of required skills and experience to the terminal, which then displays it to the user, who can then check the specific career plan based on their own characteristics.

[0668] User Feedback

[0669] Users input feedback and follow-up questions about their career plans into the device. Specific feedback includes, for example, "I'd like to know more about mastering presentation skills." The device collects the feedback and sends it to the server, which analyzes it and uses it to improve the system.

[0670] Examples of concrete examples and prompts

[0671] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server analyzes this and identifies "software engineer" as a suitable career path. It then generates a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggests them to the student via their device.

[0672] Prompt Sentence Examples

[0673] "Tell me about your past work experience"

[0674] "What events or people influenced you?"

[0675] "Please tell me your strengths and weaknesses."

[0676] "What are your strengths and weaknesses?"

[0677] "What values ​​do you hold dear?"

[0678] By using the above prompt sentences, detailed information can be collected from the user, and a more accurate career plan can be proposed.

[0679] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0680] Step 1: User Login

[0681] The user enters login information (username and password) into the terminal. The terminal sends this information to the server, which then authenticates it by checking it against information in a database. If authentication is successful, the server generates a session ID and the user's session begins. The server returns the authentication result and session ID to the terminal.

[0682] Input: Username, Password

[0683] Data processing: Verification of input information (database search)

[0684] Output: Authentication result (success / failure), session ID

[0685] Specific behavior:

[0686] The device obtains the user's authentication information from the input fields and sends it to the server in JSON format.

[0687] The server checks the received authentication information against the information in its database, and if it matches, the authentication is successful and a session ID is generated.

[0688] The server returns the authentication result and session ID to the terminal in JSON format.

[0689] The device receives the authentication result, and if successful, transitions to the home screen.

[0690] Step 2: Posing the Question

[0691] The server generates a series of questions for the user who has been successfully authenticated and sends them to the device. The device receives the questions and displays them on the user's screen. The questions can be a pre-defined set of fixed questions or dynamically generated questions using a generative AI model.

[0692] Input: Session ID

[0693] Data processing: Question list generation (fixed or dynamic generation)

[0694] Output: Question list

[0695] Specific behavior:

[0696] The server checks the user information based on the session ID and selects or generates an appropriate questionnaire.

[0697] A series of questions (e.g., "Tell me about your past work experience") is sent to the device in JSON format.

[0698] The terminal displays the received list of questions on the screen.

[0699] Step 3: Collect and submit responses

[0700] The user answers the questions displayed through the terminal in detail, and the terminal collects the user's answers and sends them to the server, where they are stored.

[0701] Input: User's answer

[0702] Data processing: Collection and transmission of response data (JSON format)

[0703] Output: Answer data saved on the server

[0704] Specific behavior:

[0705] The user inputs answers to questions displayed on the screen using text boxes and options.

[0706] The device sends the user's answers in JSON format to the server.

[0707] The server stores the received response data in a database.

[0708] Step 4: Analyze the data

[0709] The server analyzes the user response data stored in the database, using machine learning models (e.g., TensorFlow or PyTorch) and natural language processing techniques to extract user characteristics.

[0710] Input: Answer data

[0711] Data processing: Vectorization of text data, analysis using machine learning models

[0712] Output: User characteristics (strengths, weaknesses, areas of expertise, areas of weakness, values)

[0713] Specific behavior:

[0714] The server retrieves the user's response data from the database and performs preprocessing (such as text cleaning).

[0715] Load the machine learning model and input the preprocessed data into the model.

[0716] The model output is analyzed to extract user characteristics.

[0717] Step 5: Create a career plan

[0718] The server generates a career plan suitable for the user based on the analysis results, which includes job types, required skills, recommended experience, etc. A recommendation engine is used to create a list of occupations that match the user's characteristics.

[0719] Input: User characteristics

[0720] Data processing: Generating career plans (recommended by algorithms)

[0721] Output: Career plan (job type, skills, experience list)

[0722] Specific behavior:

[0723] The server generates a list of occupations using a recommendation engine based on the user's characteristic data.

[0724] The occupational list includes the required skills and recommended experience for each job.

[0725] The generated carrier plan is sent to the terminal in JSON format.

[0726] Step 6: Propose a career plan

[0727] The server sends the generated carrier plan to the terminal, which displays it to the user, allowing the user to check the specific carrier plan.

[0728] Input: Career plan

[0729] Data processing: Sending career plan (JSON format)

[0730] Output: Career plan displayed to the user

[0731] Specific behavior:

[0732] The server sends the generated carrier plan in JSON format to the terminal.

[0733] The terminal displays the received carrier plan on the screen so that the user can check the contents.

[0734] Step 7: User feedback

[0735] Users input feedback and follow-up questions about their career plans into the device, which collects the feedback and sends it to the server, which analyzes it and uses it to improve the system.

[0736] Input: Feedback

[0737] Data processing: collecting and sending feedback (JSON format)

[0738] Output: Feedback stored on the server

[0739] Specific behavior:

[0740] Users can enter feedback or follow-up questions about the career plan in the text box.

[0741] The device sends feedback in JSON format to the server.

[0742] The server stores the received feedback in a database and uses it for subsequent analysis and system improvement.

[0743] (Application example 1)

[0744] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0745] Currently, many people find it difficult to map out their own career plans. In particular, in the food delivery industry, short-term employment and limited opportunities for skill development make it difficult to create long-term career plans. As a result, declining worker satisfaction and declining labor productivity have become problems. For example, there is a need for a system that can clarify how to advance one's career by utilizing one's experience and abilities.

[0746] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0747] In this invention, the server includes means for authenticating the user's login information, means for interactively displaying questions to the user and collecting answers, means for transmitting the collected answer data to the server, means for analyzing the answer data and extracting characteristics of the user, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, means for collecting feedback from the user and reflecting it in improving the system, and means for taking into account specific skills and experience in delivery work when generating a career plan by identifying needs and suggesting suitable jobs.

[0748] This allows users to draw up concrete and realistic career plans, and in the food delivery industry in particular, it makes it possible to clarify suitable jobs and ways to improve skills, which is expected to result in improved worker satisfaction and labor productivity.

[0749] "Login information" means authentication information for a user to access a system, and typically includes a user ID and password.

[0750] "Dialogue" refers to a method by which a user interacts with a system, often in the form of questions and answers.

[0751] "Response data" is a collection of information entered interactively by the user, and includes specific responses to questions.

[0752] A "server" is a computer system that stores and processes data and is responsible for managing and analyzing data from users.

[0753] "User characteristics" refers to the individual characteristics of a user, such as the user's strengths, weaknesses, areas of expertise, areas of weakness, and values.

[0754] A "career plan" refers to a plan that clarifies the career goals, skills, and experience a user wants to achieve in the future.

[0755] A "skills and experience list" is a list of the skills and achievements required to advance in a particular career.

[0756] "Feedback" refers to opinions and requests for improvement that users provide to the system, and is used to improve the system's performance.

[0757] "System improvement" is the process of improving the functionality and performance of software based on collected feedback.

[0758] "Needs" refer to specific wants and desires that users currently have, and in the career context, this includes suitable jobs and opportunities for skill development.

[0759] "Job" refers to a specific occupation or job content, including the tasks and roles a user performs.

[0760] "Delivery work" refers to the occupation of delivering goods or services to customers, including the specific tasks and duties that occur along the way.

[0761] "Skills and experience" refers to the techniques required to perform a particular job and the knowledge and skills acquired through actual work.

[0762] A "machine learning model" is an algorithm that analyzes large amounts of data, learns patterns and trends, and makes future predictions.

[0763] The system for implementing this invention authenticates the user's login information, collects and analyzes the user's characteristics by interactively asking and answering questions, and generates and proposes an appropriate career plan. This system mainly depends on the interaction between the server, the terminal, and the user.

[0764] System configuration

[0765] The system includes the following main components:

[0766] 1. Login authentication module:

[0767] The server receives the user's login information (user ID, password, etc.) and performs authentication. If this authentication is successful, the user's session begins.

[0768] 2. Interactive question display module:

[0769] After authentication, the server prompts the device with a series of questions, including past experiences, strengths and weaknesses, areas of expertise, areas of weakness, and values ​​that are important to the user.

[0770] 3. Response collection and submission module:

[0771] Users answer questions through their terminals, and the answer data is sent to a server where it is managed centrally.

[0772] 4. Data Analysis Module:

[0773] The server analyzes the collected response data and extracts user characteristics, using a machine learning model for highly accurate analysis.

[0774] 5. Career plan generation module:

[0775] Based on the extracted user characteristics, the server generates a unique career plan, taking into account the user's skills and experience in delivery work in particular, and proposes a plan that includes suitable jobs and ways to improve skills.

[0776] 6. Suggestion and Feedback Collection Module:

[0777] The generated career plan and list of required skills and experience are sent to the terminal and presented to the user, who can then input feedback on the plan, which is used to improve the system.

[0778] Hardware and software used

[0779] Server: a computer system that stores and processes data

[0780] Terminal: A device (such as a smartphone or computer) through which a user interacts with the system.

[0781] Programming language: Python

[0782] Framework:Flask

[0783] Process example

[0784] For example, suppose a user with 10 years of experience working in the food delivery industry answers, "I'm mainly good at deliveries within a city. I also have some experience managing a team." Based on that data, the server proposes a career plan for the user as a "Delivery Operations Manager" and lists the required skills (e.g., Team Leadership, Logistics Management, Fleet Management).

[0785] Prompt Sentence Examples

[0786] The user can provide specific information to the system through prompts such as:

[0787] "I have been working in delivery jobs for 10 years. I am good at mainly intra-city deliveries and have some experience in team management. Please suggest the best career plan for me."

[0788] Such prompts enable the system to accurately understand the user's characteristics and propose an appropriate career plan.

[0789] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0790] Step 1:

[0791] A user accesses the system using a terminal and enters login information (user ID and password). The terminal sends this information to the server, which authenticates the login information. If authentication is successful, the user's session begins.

[0792] Input: User ID, Password

[0793] Data processing: The server checks the authentication information

[0794] Output: Session ID upon successful login

[0795] Step 2:

[0796] The server recognizes the user's login and prompts the terminal with a series of questions, including past experience, strengths and weaknesses, areas of expertise, areas of weakness, and values.

[0797] Input: None (after login authentication)

[0798] Data processing: The server selects the questions

[0799] Output: List of questions

[0800] Step 3:

[0801] The user answers the questions displayed on the terminal in detail, and the user's answer data is collected on the terminal and sent to the server.

[0802] Input: User's answer

[0803] Data processing: The device collects the answers and converts them into a data structure.

[0804] Output: Response data

[0805] Step 4:

[0806] The server receives the collected response data and stores it centrally. The server then analyzes the response data and extracts user characteristics. This analysis is performed with high accuracy using machine learning models.

[0807] Input: Answer data

[0808] Data processing: Data analysis using machine learning models

[0809] Output: User characteristics data

[0810] Step 5:

[0811] Based on the analysis results, the server generates a career plan suited to the user's characteristics. The plan takes into account the skills and experience of delivery work. A list of required skills and experience is also generated.

[0812] Input: User characteristics data

[0813] Data processing: Career planning

[0814] Output: Career plan, skills and experience list

[0815] Step 6:

[0816] The server sends the generated career plan and skill / experience list to the terminal and presents it to the user, who then checks the proposal and enters feedback.

[0817] Input: Career plan, skills and experience list

[0818] Data processing: None (Suggestion display)

[0819] Output: Feedback data

[0820] Step 7:

[0821] The device collects feedback from users and sends it to the server, which stores the feedback and reflects it in subsequent plan generation to improve the system.

[0822] Input: Feedback data

[0823] Data processing: Analysis and storage of feedback

[0824] Output: Improved system data

[0825] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0826] This invention is a system that allows users to draw up specific career plans based on their own characteristic data, including their own emotions. This system features a mechanism in which users interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values ​​they hold dear, and then combines this information with an emotion engine that recognizes the user's emotions to analyze the information and propose an appropriate career plan.

[0827] In this system, the following processing is performed.

[0828] Log in and ask a question

[0829] A user logs in to a terminal

[0830] A user accesses the system and enters their login information. The terminal sends this information to the server, which performs authentication. If authentication is successful, the user's session begins.

[0831] The device displays a question to the user.

[0832] Once the server recognizes the user's login, the system prompts the user with a series of questions, including about past experiences, influences, strengths and weaknesses, values, and so on.

[0833] Emotion recognition by emotion engine

[0834] The device recognizes the user's emotions

[0835] While answering questions, the emotion engine recognizes emotions from the user's facial expressions and voice in real time, and this emotional data is used to adjust the content and order of questions.

[0836] Collecting and submitting responses

[0837] The user answers the question

[0838] The user answers the questions displayed on the screen in detail. Once the answer is complete, the device collects the answer and sends it to the server. At the same time, emotional data about the user's response is also collected.

[0839] The server receives the data

[0840] The server receives the user's response data and emotion data sent from the device and stores them centrally.

[0841] Data analysis

[0842] The server analyzes the data

[0843] The server analyzes the collected data and extracts the user's characteristics, such as their strengths, weaknesses, areas of expertise, areas of weakness, and values, and also incorporates emotional data.

[0844] The server generates a carrier plan.

[0845] Based on the analysis results, the server generates a career plan suited to the user's characteristics and emotions, including job type, required skills, recommended experience, etc. Furthermore, machine learning models are used to improve the accuracy of the career plan generation.

[0846] Career plan proposals

[0847] The server sends the proposal to the device

[0848] The server sends the generated career plan and a list of required skills and experience to the terminal.

[0849] The device displays suggestions to the user

[0850] The terminal displays the proposed career plan and a list of skills and experiences to the user, allowing the user to confirm a specific career plan based on their own characteristics and feelings.

[0851] User Feedback

[0852] Users provide feedback

[0853] The user can then input feedback or additional questions about the proposed career plan into the device, and the emotion engine will recognize the user's emotions and collect the data.

[0854] The device collects feedback and sends it to the server

[0855] The device collects feedback and sends it to the server, which uses it to improve the system and reflect it in future interactions.

[0856] Specific examples

[0857] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server analyzes this and identifies "software engineer" as a suitable career path. Furthermore, the emotion engine collects emotional data from the student's facial expressions, such as excitement and interest, and incorporates this into the plan generation process, providing a more accurate career plan. Next, it creates a list of required skills, such as advanced programming skills, database management, team collaboration, and presentation skills, and suggests them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics and emotional data.

[0858] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[0859] The processing flow will be explained below.

[0860] Step 1:

[0861] The user logs in to the terminal. The user enters their user ID and password on the system login screen and presses the login button.

[0862] Step 2:

[0863] The terminal sends the user's login information to the server. The terminal encrypts the entered user ID and password and sends them to the server.

[0864] Step 3:

[0865] The server authenticates the user. The server checks the user ID and password in the database and starts a session if they match. If they don't match, it returns an authentication error.

[0866] Step 4:

[0867] The terminal displays the authentication result to the user. If authentication is successful, the user can proceed to the next screen. If authentication is unsuccessful, a login error screen is displayed.

[0868] Step 5:

[0869] The server prepares a series of questions for the user and sends them to the device, including questions about past experiences, influences, strengths and weaknesses, values, etc.

[0870] Step 6:

[0871] The device displays a question to the user, and the emotion engine is activated to recognize emotions from the user's facial expressions and voice in real time.

[0872] Step 7:

[0873] The user answers each question and enters it into the terminal. The emotion engine recognizes and simultaneously collects the emotional data of the user when answering.

[0874] Step 8:

[0875] The device sends the collected response data and emotion data to the server. Once all questions have been answered, the data is sent to the server in one batch.

[0876] Step 9:

[0877] The server receives the response data and emotion data and begins analysis. The server analyzes the response data and extracts the user's characteristics. Specifically, it analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc. At the same time, it takes emotion data into consideration.

[0878] Step 10:

[0879] The server generates a career plan suited to the user. Based on the analysis results, a machine learning model is used to propose the optimal career plan. This plan includes information such as job type, required skills, and recommended experience. The user's emotional data is also reflected in the plan generation.

[0880] Step 11:

[0881] The server transmits the generated career plan and a list of required skills and experiences to the terminal.

[0882] Step 12:

[0883] The device displays the career plan and skills / experience list to the user, who can then review the proposed content.

[0884] Step 13:

[0885] The user provides feedback by inputting their opinions or questions about the proposal into the device. At this time, the emotion engine recognizes the user's emotions in real time and collects the data.

[0886] Step 14:

[0887] The device collects feedback and emotion data and sends it to a server.

[0888] Step 15:

[0889] The server receives and analyzes the feedback and emotional data, and uses it to improve the system's algorithms and question flow.

[0890] Step 16:

[0891] The server then incorporates feedback and sentiment data into the next session to improve the system, allowing it to continuously learn and adjust to provide users with more accurate career plans.

[0892] Example 2

[0893] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0894] Previous career plan proposal systems were based solely on user response data and did not take into account the user's emotions or immediate reactions. As a result, it was difficult to generate a career plan that fully reflected the user's characteristics and intentions. Furthermore, the system lacked a mechanism for incorporating feedback into system improvements, making continuous system optimization difficult.

[0895] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for authenticating the user's login information, means for displaying questions to the user in an interactive format and collecting answers, means for transmitting the collected answer data to the server, means for recognizing emotion data accompanying the collected answer data, means for analyzing the answers and emotion data and extracting user characteristics, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, and means for collecting feedback from the user and reflecting the feedback in improving the system. This enables the generation of a highly accurate career plan that incorporates the user's emotions and immediate reactions. Furthermore, the system can be continuously improved based on the feedback.

[0896] "Means for authenticating login information" refers to the functionality that verifies the authentication information entered by the user and confirms the user's access privileges.

[0897] "Means for interactively displaying questions and collecting answers" refers to the functionality of displaying a series of questions to a user and receiving the user's answers through an interface.

[0898] The "means for transmitting collected response data to a server" refers to a function for transmitting the response data collected from users to a server via a network.

[0899] "Means for recognizing emotional data accompanying collected response data" refers to a function that analyzes the user's facial expressions and voice while answering questions and recognizes their emotional state in real time.

[0900] "Means of analyzing response and emotional data and extracting user characteristics" refers to the function of analyzing and extracting a user's characteristics, strengths, weaknesses, areas of expertise and weaknesses, values, etc. based on the acquired response data and emotional data.

[0901] "Means for generating a career plan based on the characteristics of the user" refers to a function for generating an optimal career plan based on analyzed characteristic information of the user.

[0902] "Means for proposing the generated career plan and a list of required skills and experience to the user" refers to a function for presenting the generated career plan and the required skills and experience to the user.

[0903] "Means for collecting feedback from users and reflecting it in improving the system" refers to the function of collecting feedback provided by users and improving and optimizing the system based on that feedback.

[0904] This invention is a system that allows users to draw up specific career plans based on their own characteristic data, including their own emotions. This system features a mechanism in which users interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values ​​they hold dear, and then combines this information with an emotion engine that recognizes the user's emotions to analyze the information and propose an appropriate career plan.

[0905] The system includes a means for a user to authenticate by logging in using a terminal and transmitting login information to a server, which then displays a series of questions to the user on the terminal, to which the user enters answers.

[0906] The device is equipped with a built-in camera and microphone, and while the user is answering questions, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. This data is collected along with the answers and sent from the device to the server.

[0907] The server analyzes the collected response data and sentiment data to extract user characteristics. This can be done using Python data analysis tools such as Pandas and NumPy. A career plan is then generated based on the analysis results. Machine learning libraries such as Scikit-learn and TensorFlow are used for generation.

[0908] The generated career plan and a list of the skills and experience required for it are sent from the server to the terminal and displayed to the user. The user can review the displayed career plan and enter feedback or additional questions. The terminal collects this feedback and sends it back to the server. The server can analyze this feedback and reflect it in improving the system.

[0909] As a concrete example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won an award at a hackathon," the server will analyze this and identify "software engineer" as a suitable career path. Furthermore, the emotion engine collects emotional data from the student's facial expressions, such as excitement and interest at the time of the answer, and incorporates this into the plan generation process, providing a more accurate career plan. Next, the system creates a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggests them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics and emotional data.

[0910] Prompt Sentence Examples

[0911] "What about past experiences?"

[0912] "What influenced you?"

[0913] "Please tell me your strengths and weaknesses."

[0914] "What are your strengths and weaknesses?"

[0915] "What values ​​do you hold dear?"

[0916] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[0917] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0918] Step 1:

[0919] The user enters login information. The user enters a username and password on the terminal's login screen. The terminal sends the entered information to the server. The server compares the information with the user information stored in the database and performs authentication. If authentication is successful, the user's session begins.

[0920] Input: Username and Password

[0921] Output: Session started

[0922] Step 2:

[0923] The server sends a list of questions to the terminal, which then displays the questions to the user in an interactive format. The server generates a series of questions and sends them to the terminal, which receives them and displays them to the user in sequence.

[0924] Input: Question List

[0925] Output: Question printed to terminal

[0926] Step 3:

[0927] The user answers the questions. The user types in an answer to each question displayed on the device. For example, in response to the question "What is your past experience?", the user types in "I took a programming class and won a prize at a hackathon."

[0928] Input: The user's answer to the question displayed

[0929] Output: User response data

[0930] Step 4:

[0931] The device recognizes the user's emotions. While the user is entering their answers, the device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time, and recognizes the emotional data using an emotion engine.

[0932] Input: User's facial expressions and voice

[0933] Output: Emotion data

[0934] Step 5:

[0935] The device sends the answers and emotion data to the server. Once the user has answered all the questions, the device sends the answer data and emotion data to the server. The data is encrypted before being sent.

[0936] Input: User response data and emotion data

[0937] Output: Data sent to the server

[0938] Step 6:

[0939] The server analyzes the response data and emotion data. Using Python's Pandas and NumPy, the server analyzes the received data and extracts the user's characteristics. Specifically, it analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[0940] Input: User response data and emotion data

[0941] Output: User characteristics data

[0942] Step 7:

[0943] The server generates a career plan based on the user's characteristics. Based on the analysis results, the server uses a machine learning model (such as Scikit-learn or TensorFlow) to generate an optimal career plan. The career plan includes recommended job types, required skills, and experience.

[0944] Input: User characteristics data

[0945] Output: Career plan

[0946] Step 8:

[0947] The server sends the career plan to the terminal. The generated career plan and the accompanying skill and experience list are then sent from the server to the terminal.

[0948] Input: Career plan

[0949] Output: Data sent to the terminal

[0950] Step 9:

[0951] The terminal displays the career plan to the user. The terminal displays the received career plan and skill list to the user.

[0952] Input: Data sent from the server

[0953] Output: Career plan displayed to the user

[0954] Step 10:

[0955] The user provides feedback on the career plan. The user checks the displayed career plan and enters feedback or additional questions into the terminal. For example, the user enters a comment such as, "I'm interested in this job, but are there any other options?"

[0956] Input: User feedback on career plans

[0957] Output: User feedback data

[0958] Step 11:

[0959] The device sends the feedback to the server. The device collects the feedback data entered by the user and sends it to the server. The data is encrypted before being sent.

[0960] Input: User feedback data

[0961] Output: Feedback data sent to the server

[0962] Step 12:

[0963] The server analyzes the feedback and reflects it in improvements to the system. The server analyzes the received feedback and analyzes its content. The system is improved based on the analysis results and reflected in future interactions.

[0964] Input: Feedback data

[0965] Output: System improvement data

[0966] (Application example 2)

[0967] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0968] Conventional career plan generation systems make suggestions based on the user's characteristics and experience, but because they do not take emotional data into account, they are unable to reflect the user's psychological state or emotional satisfaction. Furthermore, because they do not consider optimizing specific work schedules based on the proposed career plan, it is difficult to provide an efficient work schedule based on the user's aptitude and emotions. This has limited the improvement of employee satisfaction and productivity.

[0969] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0970] In this invention, the server includes means for authenticating user login information, means for displaying questions to the user in an interactive format and collecting answers, means for transmitting the collected answer data and emotion data to the server, means for analyzing the answer data and emotion data and extracting user characteristics, means for generating a career plan based on the user's characteristics and emotion data, means for proposing the generated career plan and a list of required skills and experience to the user, means for optimizing employee work schedules based on the proposed career plan, and means for collecting feedback from users and reflecting it in improving the system. This makes it possible to provide highly accurate career plans based on the user's emotions and characteristics and to optimize employee work schedules.

[0971] "Login Information" means authentication data for a user to access a system.

[0972] "Dialogue" refers to a format in which the user and the system communicate with each other through questions and answers.

[0973] "Answer data" refers to the response information provided by a user in response to a question presented by the system.

[0974] "Emotion data" is information indicating the emotional state of the user obtained by analyzing the user's facial expressions and voice.

[0975] A "server" is a computer system for receiving and processing data sent by users.

[0976] "Analysis" is the process of extracting and analyzing specific information from collected data.

[0977] "Characteristics" refers to individual characteristics of a user, such as strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that are important to the user.

[0978] A "career plan" is a plan for career choices and career activities that is proposed based on the user's characteristics and emotional data.

[0979] "Skills" are the techniques and abilities required to perform a specific job or task.

[0980] A "work schedule" is a plan that specifies what tasks an employee will perform at what time.

[0981] "Feedback" refers to users providing their opinions and impressions about the proposed career plan and use of the system.

[0982] "Optimization" is the process of improving plans and operations to be efficient and effective according to a purpose.

[0983] The system for realizing this application example is implemented as a smartphone application for optimizing employees' career plans and work schedules. Specific embodiments for carrying out the invention are described in detail below.

[0984] The system consists of the following main components:

[0985] 1. User authentication:

[0986] When a user logs in to a smartphone application, the server authenticates the user's login information, using standard ID and password or biometric authentication such as fingerprint or face recognition.

[0987] 2. Interactive Q&A:

[0988] After successful authentication, the user is presented with a series of interactive questions, including past experiences, strengths and weaknesses, values, etc. The user then enters answers to these questions.

[0989] 3. Collecting Emotional Data:

[0990] While the user answers questions, the smartphone's camera and microphone are used to collect real-time emotional data, which is then analyzed using an emotion recognition engine such as the Microsoft Azure Face API.

[0991] 4. Data transmission and analysis:

[0992] The collected response data and emotion data are sent to a server, which analyzes the data and extracts user characteristics. A machine learning model is applied to the analysis using Python and Scikit-learn.

[0993] 5. Career Plan and Schedule Generation:

[0994] The server generates an appropriate career plan based on the user's characteristics and emotional data. This career plan includes recommended job types, required skills, and experience. Furthermore, using tools such as Google's OR-Tools, the server optimizes the work schedule based on the proposed career plan.

[0995] 6. User Suggestions and Feedback:

[0996] The generated career plan and optimized work schedule are presented to the user via a smartphone application, and the user can provide feedback, which is then sent to the server, which then uses this feedback to improve the system.

[0997] Specific examples include the following cases:

[0998] An employee logs in and answers questions about their past strengths, values, and past accomplishments. For example, they might provide an answer like, "I took a programming class and won a hackathon." Emotional data, such as excitement and interest, is collected from their facial expressions while answering.

[0999] The server analyzes this data and suggests "software engineer" as the best career plan for the employee, listing necessary skills such as advanced programming skills and teamwork ability.

[1000] Based on the proposal, employees' daily schedules will be optimized to consist of one hour of welding work and two hours of quality control training.

[1001] Additionally, specific prompts are generated using a generative AI model, such as:

[1002] Example prompt: "Analyze user Chris's past experience (welding), and suggest a schedule for today based on his strengths (welding) and interests (quality control). For example, suggest one hour of welding and two hours of quality control training. Optimize the schedule so that Chris shows positive emotions while working."

[1003] This will enable the provision of highly accurate career plans based on users' emotions and characteristics, as well as the optimization of employee work schedules.

[1004] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1005] Step 1:

[1006] A user logs in to an application on their smartphone.

[1007] Specific operation: The user enters login information using ID and password, or fingerprint or facial recognition.

[1008] Input: User authentication information (ID, password, biometric information)

[1009] Output: Authentication result (success or failure)

[1010] Data processing: The app device sends the entered authentication information to the server, which then performs the authentication process. The server compares the information with the authentication information in its database and generates an authentication result.

[1011] Step 2:

[1012] After successful login, the terminal interactively displays questions to the user and collects answers.

[1013] Specific actions: Questions should be about past experiences, strengths, weaknesses, values, and strengths.

[1014] Input: User response data

[1015] Output: User response data collection results

[1016] Data processing: The device collects the user's answers as text data according to a format. The collected data is temporarily stored on the device.

[1017] Step 3:

[1018] The device collects the user's emotional data.

[1019] Specific operation: While answering questions, the user's facial expressions and voice are recorded in real time through the device's camera and microphone, and analyzed using an emotion recognition engine (such as Microsoft Azure Face API).

[1020] Input: User's facial expression data, voice data

[1021] Output: Emotion data (anger, joy, sadness, surprise, etc.)

[1022] Data processing: The acquired image and audio data is input into an emotion recognition engine, and emotion data is generated as an analysis result.

[1023] Step 4:

[1024] The terminal transmits the collected response data and emotion data to the server.

[1025] Specific operation: The device sends the temporarily stored data to the server using HTTPS communication.

[1026] Input: User response data, emotion data

[1027] Output: Data sent to the server

[1028] Data processing: Data is compressed, encrypted, and securely sent to the server.

[1029] Step 5:

[1030] The server analyzes the collected data.

[1031] Specific operation: The server analyzes the response data and sentiment data and extracts user characteristics using a machine learning model (Python, Scikit-learn).

[1032] Input: User response data, emotion data

[1033] Output: User characteristics data

[1034] Data processing: The server integrates the response data and emotion data stored in the database and runs an algorithm to extract features to analyze the user's characteristics.

[1035] Step 6:

[1036] The server generates a career plan based on the user's characteristics and optimizes the work schedule.

[1037] Specific operation: Using machine learning models (Python, Scikit-learn) and schedule optimization algorithms (Google OR-Tools), we generate career plans and work schedules that are suitable for users.

[1038] Input: User characteristic data

[1039] Output: Career plan, optimized work schedule

[1040] Data processing: The server inputs the characteristic data into a machine learning model and applies a schedule optimization algorithm based on the generated carrier plan.

[1041] Step 7:

[1042] The server transmits the generated career plan and work schedule to the terminal.

[1043] Specific operation: The server sends the proposed career plan, skill list, and optimized schedule to the terminal.

[1044] Input: Career plan, work schedule

[1045] Output: Result of sending the proposal data to the user

[1046] Data processing: The data is compressed and encrypted again and sent securely to the device.

[1047] Step 8:

[1048] The terminal displays the generated career plan and work schedule to the user.

[1049] Specific operation: A career plan, a list of required skills, and a work schedule are displayed on the terminal screen.

[1050] Input: Proposal data (career plan, skill list, work schedule)

[1051] Output: The result displayed to the user

[1052] Data processing: The received data is decompressed and decrypted, and presented visually to the user.

[1053] Step 9:

[1054] The user provides feedback.

[1055] Specific operation: The user enters feedback and questions about the proposed career plan and work schedule.

[1056] Input: User feedback data

[1057] Output: Feedback collection results

[1058] Data processing: The device collects feedback in text format and sends it to the server.

[1059] Step 10:

[1060] The server collects feedback and uses it to improve the system.

[1061] Specific operation: Analyze the feedback data and update the algorithm to reflect it in future career plan proposals.

[1062] Input: User feedback data

[1063] Output: System improvement information

[1064] Data processing: Feedback data is stored and analyzed to identify areas for improvement in the system.

[1065] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1066] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1067] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1068] [Third embodiment]

[1069] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1070] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1071] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1072] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1073] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1074] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1075] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1076] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1077] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1078] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1079] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1080] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1081] This invention is a system that allows users to draw up specific career plans for themselves. This system features a mechanism that allows users to interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values, and then analyzes the information and proposes an appropriate career plan.

[1082] In this system, the following processing is performed.

[1083] Log in and ask a question

[1084] A user logs in to a terminal

[1085] A user accesses the system and enters their login information. The terminal sends this information to the server, which performs authentication. If authentication is successful, the user's session begins.

[1086] The device displays a question to the user.

[1087] Once the server recognizes the user's login, the system prompts the user with a series of questions, including about past experiences, influences, strengths and weaknesses, values, and so on.

[1088] Collecting and submitting responses

[1089] The user answers the question

[1090] The user answers the questions displayed on the screen in detail, and once the answers are complete, the terminal collects them and sends them to the server.

[1091] The server receives the data

[1092] The server receives the user's response data sent from the device and stores it centrally.

[1093] Data analysis

[1094] The server analyzes the data

[1095] The server analyzes the collected data and extracts the user's characteristics, specifically, their strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[1096] The server generates a carrier plan.

[1097] Based on the analysis results, the server generates a career plan suited to the user's characteristics, including job type, required skills, recommended experience, etc. Furthermore, machine learning models are used to improve the accuracy of the career plan.

[1098] Career plan proposals

[1099] The server sends the proposal to the device

[1100] The server sends the generated career plan and a list of required skills and experience to the terminal.

[1101] The device displays suggestions to the user

[1102] The terminal displays the proposed career plan and a list of skills and experience to the user, allowing the user to confirm a specific career plan based on their own characteristics.

[1103] User Feedback

[1104] Users provide feedback

[1105] Users can enter feedback and follow-up questions about the proposed career plan into the device, including specific feedback such as "I'd like to know more about acquiring presentation skills."

[1106] The device collects feedback and sends it to the server

[1107] The device collects feedback and sends it to the server, which uses it to improve the system and reflect it in future interactions.

[1108] Specific examples

[1109] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server will analyze this and identify "software engineer" as a suitable career path. It will then create a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggest them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics.

[1110] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[1111] The processing flow will be explained below.

[1112] Step 1:

[1113] The user logs in to the terminal. The user enters their user ID and password on the system login screen and presses the login button.

[1114] Step 2:

[1115] The terminal sends the user's login information to the server. The terminal encrypts the entered user ID and password and sends them to the server.

[1116] Step 3:

[1117] The server authenticates the user. The server checks the user ID and password in the database and starts a session if they match. If they don't match, it returns an authentication error.

[1118] Step 4:

[1119] The terminal displays the authentication result to the user. If authentication is successful, the user can proceed to the next screen. If authentication is unsuccessful, a login error screen is displayed.

[1120] Step 5:

[1121] The server prepares a series of questions for the user and sends them to the device, including questions about past experiences, influences, strengths and weaknesses, values, etc.

[1122] Step 6:

[1123] The terminal displays questions to the user, who then answers the questions displayed on the screen one by one.

[1124] Step 7:

[1125] The user answers each question and enters it into the terminal, where each answer is temporarily saved.

[1126] Step 8:

[1127] The device sends the collected response data to the server. Once all questions have been answered, the data is sent to the server in one batch.

[1128] Step 9:

[1129] The server receives the response data and begins analyzing it. The server analyzes the response data and extracts the user's characteristics. Specifically, the server analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[1130] Step 10:

[1131] The server generates a career plan suited to the user. Based on the analysis results, it uses a machine learning model to propose the optimal career plan. This plan includes job types, required skills, recommended experience, etc.

[1132] Step 11:

[1133] The server transmits the generated career plan and a list of required skills and experiences to the terminal.

[1134] Step 12:

[1135] The device displays the career plan and skills / experience list to the user, who can then review the proposed content.

[1136] Step 13:

[1137] The user provides feedback by inputting their opinions and questions about the proposal into the terminal.

[1138] Step 14:

[1139] The device collects the feedback and sends it to the server.

[1140] Step 15:

[1141] The server receives and analyzes the feedback, which it uses as data to improve the system's algorithms and question flow based on the user's feedback.

[1142] Step 16:

[1143] The server then incorporates the feedback into the next session, allowing the system to continuously learn and adjust to provide users with more accurate career plans.

[1144] Example 1

[1145] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1146] In today's career development process, it is not easy for individual users to draw up specific career plans based on their own characteristics. Conventional systems have difficulty providing users with adequately customized suggestions, and do not adequately support users in choosing a career based on their own strengths, weaknesses, and values. Furthermore, they lack a mechanism for effectively incorporating feedback and improving the accuracy of the system.

[1147] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1148] In this invention, the server includes a means for authenticating the user's login information, a means for displaying questions to the user in an interactive format and collecting answers, and a means for transmitting the collected answer data to the server, thereby enabling the generation of a highly accurate career plan based on the user's characteristics.

[1149] "Means for authenticating login information" refers to a technology that collates the authentication information entered by the user into the terminal with information in a database and establishes a user session.

[1150] "Means for displaying questions in an interactive format and collecting answers" refers to a technique for displaying questions sent from a server to a user on a terminal and collecting answers from the user as input.

[1151] The "means for transmitting collected response data to a server" refers to a technique for transmitting user responses from the terminal to a server and storing them safely.

[1152] "Means of analyzing response data and extracting user characteristics" refers to techniques that analyze collected data using machine learning models and natural language processing techniques to reveal characteristics such as users' strengths, weaknesses, areas of expertise, areas of weakness, and values.

[1153] The "means for generating a career plan" is a technology that creates a career plan that includes the most suitable job type, required skills, experience, etc. for the user based on the analysis results.

[1154] The "means for proposing a career plan and a list of required skills and experiences to a user" is a technology for displaying a generated career plan and its detailed requirements to a user.

[1155] "Means for collecting feedback from users and reflecting it in system improvements" refers to a technique for collecting opinions and additional questions from users and incorporating them into system analysis to improve functionality.

[1156] "Means for vectorizing input text and analyzing it with a classification algorithm" refers to a technology that converts text data such as user responses into numerical vectors and analyzes them using a classification algorithm.

[1157] "Means for creating a list of occupations using a recommendation engine" refers to a technology that uses machine learning technology to list appropriate occupations based on the characteristics of the user.

[1158] The present invention is a system that allows users to specifically draw up their own career plans, and that interactively collects information such as the user's past experiences, areas of strength and weakness, and values, and then analyzes the information to propose an appropriate career plan. An embodiment of this system will be described in detail below.

[1159] System configuration

[1160] The system mainly includes the following components:

[1161] 1. Terminal

[1162] 2. Server

[1163] 3. Database

[1164] 4. Machine Learning Models

[1165] 5. Communication Protocol

[1166] Software and hardware used

[1167] Terminal

[1168] Users access the system using devices such as PCs, tablets, and smartphones, and the devices communicate with the server via a web browser or native application.

[1169] server

[1170] The server is the main component for running the program and uses web frameworks such as Django and Spring. The server handles major processes such as user authentication, data analysis, and career plan generation.

[1171] Database

[1172] MySQL and PostgreSQL are used as databases to store various data such as user information, response data, and analysis results.

[1173] Machine learning models

[1174] The machine learning model uses TensorFlow and PyTorch to analyze user data and generate career plans. Specifically, natural language processing technology is used to analyze user responses and extract characteristics.

[1175] communication protocol

[1176] The device and server send and receive data using a secure communication protocol such as HTTPS.

[1177] System action

[1178] User Login

[1179] The user enters their login information into the terminal, and the server authenticates them against the database. If authentication is successful, a session ID is generated and the user's session begins.

[1180] Asking questions and collecting answers

[1181] The server generates a series of questions for the user and sends them to the device, which then displays the questions on the user's screen and the user enters answers to these questions.The answers are then sent from the device to the server and stored in a database.

[1182] Data analysis

[1183] The server receives the collected response data and analyzes it using machine learning models and natural language processing technology to extract information such as the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that they hold dear.

[1184] Creating a career plan

[1185] The server generates an optimal career plan for the user based on the analysis results. This career plan includes recommended occupations, required skills and experience, etc. A recommendation engine is used to create a list of occupations that match the user's characteristics.

[1186] Career plan proposals

[1187] The server sends the generated career plan and a list of required skills and experience to the terminal, which then displays it to the user, who can then check the specific career plan based on their own characteristics.

[1188] User Feedback

[1189] Users input feedback and follow-up questions about their career plans into the device. Specific feedback includes, for example, "I'd like to know more about mastering presentation skills." The device collects the feedback and sends it to the server, which analyzes it and uses it to improve the system.

[1190] Examples of concrete examples and prompts

[1191] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server analyzes this and identifies "software engineer" as a suitable career path. It then generates a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggests them to the student via their device.

[1192] Prompt Sentence Examples

[1193] "Tell me about your past work experience"

[1194] "What events or people influenced you?"

[1195] "Please tell me your strengths and weaknesses."

[1196] "What are your strengths and weaknesses?"

[1197] "What values ​​do you hold dear?"

[1198] By using the above prompt sentences, detailed information can be collected from the user, and a more accurate career plan can be proposed.

[1199] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1200] Step 1: User Login

[1201] The user enters login information (username and password) into the terminal. The terminal sends this information to the server, which then authenticates it by checking it against information in a database. If authentication is successful, the server generates a session ID and the user's session begins. The server returns the authentication result and session ID to the terminal.

[1202] Input: Username, Password

[1203] Data processing: Verification of input information (database search)

[1204] Output: Authentication result (success / failure), session ID

[1205] Specific behavior:

[1206] The device obtains the user's authentication information from the input fields and sends it to the server in JSON format.

[1207] The server checks the received authentication information against the information in its database, and if it matches, the authentication is successful and a session ID is generated.

[1208] The server returns the authentication result and session ID to the terminal in JSON format.

[1209] The device receives the authentication result, and if successful, transitions to the home screen.

[1210] Step 2: Posing the Question

[1211] The server generates a series of questions for the user who has been successfully authenticated and sends them to the device. The device receives the questions and displays them on the user's screen. The questions can be a pre-defined set of fixed questions or dynamically generated questions using a generative AI model.

[1212] Input: Session ID

[1213] Data processing: Question list generation (fixed or dynamic generation)

[1214] Output: Question list

[1215] Specific behavior:

[1216] The server checks the user information based on the session ID and selects or generates an appropriate questionnaire.

[1217] A series of questions (e.g., "Tell me about your past work experience") is sent to the device in JSON format.

[1218] The terminal displays the received list of questions on the screen.

[1219] Step 3: Collect and submit responses

[1220] The user answers the questions displayed through the terminal in detail, and the terminal collects the user's answers and sends them to the server, where they are stored.

[1221] Input: User's answer

[1222] Data processing: Collection and transmission of response data (JSON format)

[1223] Output: Answer data saved on the server

[1224] Specific behavior:

[1225] The user inputs answers to questions displayed on the screen using text boxes and options.

[1226] The device sends the user's answers in JSON format to the server.

[1227] The server stores the received response data in a database.

[1228] Step 4: Analyze the data

[1229] The server analyzes the user response data stored in the database, using machine learning models (e.g., TensorFlow or PyTorch) and natural language processing techniques to extract user characteristics.

[1230] Input: Answer data

[1231] Data processing: Vectorization of text data, analysis using machine learning models

[1232] Output: User characteristics (strengths, weaknesses, areas of expertise, areas of weakness, values)

[1233] Specific behavior:

[1234] The server retrieves the user's response data from the database and performs preprocessing (such as text cleaning).

[1235] Load the machine learning model and input the preprocessed data into the model.

[1236] The model output is analyzed to extract user characteristics.

[1237] Step 5: Create a career plan

[1238] The server generates a career plan suitable for the user based on the analysis results, which includes job types, required skills, recommended experience, etc. A recommendation engine is used to create a list of occupations that match the user's characteristics.

[1239] Input: User characteristics

[1240] Data processing: Generating career plans (recommended by algorithms)

[1241] Output: Career plan (job type, skills, experience list)

[1242] Specific behavior:

[1243] The server generates a list of occupations using a recommendation engine based on the user's characteristic data.

[1244] The occupational list includes the required skills and recommended experience for each job.

[1245] The generated carrier plan is sent to the terminal in JSON format.

[1246] Step 6: Propose a career plan

[1247] The server sends the generated carrier plan to the terminal, which displays it to the user, allowing the user to check the specific carrier plan.

[1248] Input: Career plan

[1249] Data processing: Sending career plan (JSON format)

[1250] Output: Career plan displayed to the user

[1251] Specific behavior:

[1252] The server sends the generated carrier plan in JSON format to the terminal.

[1253] The terminal displays the received carrier plan on the screen so that the user can check the contents.

[1254] Step 7: User feedback

[1255] Users input feedback and follow-up questions about their career plans into the device, which collects the feedback and sends it to the server, which analyzes it and uses it to improve the system.

[1256] Input: Feedback

[1257] Data processing: collecting and sending feedback (JSON format)

[1258] Output: Feedback stored on the server

[1259] Specific behavior:

[1260] Users can enter feedback or follow-up questions about the career plan in the text box.

[1261] The device sends feedback in JSON format to the server.

[1262] The server stores the received feedback in a database and uses it for subsequent analysis and system improvement.

[1263] (Application example 1)

[1264] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1265] Currently, many people find it difficult to map out their own career plans. In particular, in the food delivery industry, short-term employment and limited opportunities for skill development make it difficult to create long-term career plans. As a result, declining worker satisfaction and declining labor productivity have become problems. For example, there is a need for a system that can clarify how to advance one's career by utilizing one's experience and abilities.

[1266] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1267] In this invention, the server includes means for authenticating the user's login information, means for interactively displaying questions to the user and collecting answers, means for transmitting the collected answer data to the server, means for analyzing the answer data and extracting characteristics of the user, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, means for collecting feedback from the user and reflecting it in improving the system, and means for taking into account specific skills and experience in delivery work when generating a career plan by identifying needs and suggesting suitable jobs.

[1268] This allows users to draw up concrete and realistic career plans, and in the food delivery industry in particular, it makes it possible to clarify suitable jobs and ways to improve skills, which is expected to result in improved worker satisfaction and labor productivity.

[1269] "Login information" means authentication information for a user to access a system, and typically includes a user ID and password.

[1270] "Dialogue" refers to a method by which a user interacts with a system, often in the form of questions and answers.

[1271] "Response data" is a collection of information entered interactively by the user, and includes specific responses to questions.

[1272] A "server" is a computer system that stores and processes data and is responsible for managing and analyzing data from users.

[1273] "User characteristics" refers to the individual characteristics of a user, such as the user's strengths, weaknesses, areas of expertise, areas of weakness, and values.

[1274] A "career plan" refers to a plan that clarifies the career goals, skills, and experience a user wants to achieve in the future.

[1275] A "skills and experience list" is a list of the skills and achievements required to advance in a particular career.

[1276] "Feedback" refers to opinions and requests for improvement that users provide to the system, and is used to improve the system's performance.

[1277] "System improvement" is the process of improving the functionality and performance of software based on collected feedback.

[1278] "Needs" refer to specific wants and desires that users currently have, and in the career context, this includes suitable jobs and opportunities for skill development.

[1279] "Job" refers to a specific occupation or job content, including the tasks and roles a user performs.

[1280] "Delivery work" refers to the occupation of delivering goods or services to customers, including the specific tasks and duties that occur along the way.

[1281] "Skills and experience" refers to the techniques required to perform a particular job and the knowledge and skills acquired through actual work.

[1282] A "machine learning model" is an algorithm that analyzes large amounts of data, learns patterns and trends, and makes future predictions.

[1283] The system for implementing this invention authenticates the user's login information, collects and analyzes the user's characteristics by interactively asking and answering questions, and generates and proposes an appropriate career plan. This system mainly depends on the interaction between the server, the terminal, and the user.

[1284] System configuration

[1285] The system includes the following main components:

[1286] 1. Login authentication module:

[1287] The server receives the user's login information (user ID, password, etc.) and performs authentication. If this authentication is successful, the user's session begins.

[1288] 2. Interactive question display module:

[1289] After authentication, the server prompts the device with a series of questions, including past experiences, strengths and weaknesses, areas of expertise, areas of weakness, and values ​​that are important to the user.

[1290] 3. Response collection and submission module:

[1291] Users answer questions through their terminals, and the answer data is sent to a server where it is managed centrally.

[1292] 4. Data Analysis Module:

[1293] The server analyzes the collected response data and extracts user characteristics, using a machine learning model for highly accurate analysis.

[1294] 5. Career plan generation module:

[1295] Based on the extracted user characteristics, the server generates a unique career plan, taking into account the user's skills and experience in delivery work in particular, and proposes a plan that includes suitable jobs and ways to improve skills.

[1296] 6. Suggestion and Feedback Collection Module:

[1297] The generated career plan and list of required skills and experience are sent to the terminal and presented to the user, who can then input feedback on the plan, which is used to improve the system.

[1298] Hardware and software used

[1299] Server: a computer system that stores and processes data

[1300] Terminal: A device (such as a smartphone or computer) through which a user interacts with the system.

[1301] Programming language: Python

[1302] Framework:Flask

[1303] Process example

[1304] For example, suppose a user with 10 years of experience working in the food delivery industry answers, "I'm mainly good at deliveries within a city. I also have some experience managing a team." Based on that data, the server proposes a career plan for the user as a "Delivery Operations Manager" and lists the required skills (e.g., Team Leadership, Logistics Management, Fleet Management).

[1305] Prompt Sentence Examples

[1306] The user can provide specific information to the system through prompts such as:

[1307] "I have been working in delivery jobs for 10 years. I am good at mainly intra-city deliveries and have some experience in team management. Please suggest the best career plan for me."

[1308] Such prompts enable the system to accurately understand the user's characteristics and propose an appropriate career plan.

[1309] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1310] Step 1:

[1311] A user accesses the system using a terminal and enters login information (user ID and password). The terminal sends this information to the server, which authenticates the login information. If authentication is successful, the user's session begins.

[1312] Input: User ID, Password

[1313] Data processing: The server checks the authentication information

[1314] Output: Session ID upon successful login

[1315] Step 2:

[1316] The server recognizes the user's login and prompts the terminal with a series of questions, including past experience, strengths and weaknesses, areas of expertise, areas of weakness, and values.

[1317] Input: None (after login authentication)

[1318] Data processing: The server selects the questions

[1319] Output: List of questions

[1320] Step 3:

[1321] The user answers the questions displayed on the terminal in detail, and the user's answer data is collected on the terminal and sent to the server.

[1322] Input: User's answer

[1323] Data processing: The device collects the answers and converts them into a data structure.

[1324] Output: Response data

[1325] Step 4:

[1326] The server receives the collected response data and stores it centrally. The server then analyzes the response data and extracts user characteristics. This analysis is performed with high accuracy using machine learning models.

[1327] Input: Answer data

[1328] Data processing: Data analysis using machine learning models

[1329] Output: User characteristics data

[1330] Step 5:

[1331] Based on the analysis results, the server generates a career plan suited to the user's characteristics. The plan takes into account the skills and experience of delivery work. A list of required skills and experience is also generated.

[1332] Input: User characteristics data

[1333] Data processing: Career planning

[1334] Output: Career plan, skills and experience list

[1335] Step 6:

[1336] The server sends the generated career plan and skill / experience list to the terminal and presents it to the user, who then checks the proposal and enters feedback.

[1337] Input: Career plan, skills and experience list

[1338] Data processing: None (Suggestion display)

[1339] Output: Feedback data

[1340] Step 7:

[1341] The device collects feedback from users and sends it to the server, which stores the feedback and reflects it in subsequent plan generation to improve the system.

[1342] Input: Feedback data

[1343] Data processing: Analysis and storage of feedback

[1344] Output: Improved system data

[1345] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1346] This invention is a system that allows users to draw up specific career plans based on their own characteristic data, including their own emotions. This system features a mechanism in which users interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values ​​they hold dear, and then combines this information with an emotion engine that recognizes the user's emotions to analyze the information and propose an appropriate career plan.

[1347] In this system, the following processing is performed.

[1348] Log in and ask a question

[1349] A user logs in to a terminal

[1350] A user accesses the system and enters their login information. The terminal sends this information to the server, which performs authentication. If authentication is successful, the user's session begins.

[1351] The device displays a question to the user.

[1352] Once the server recognizes the user's login, the system prompts the user with a series of questions, including about past experiences, influences, strengths and weaknesses, values, and so on.

[1353] Emotion recognition by emotion engine

[1354] The device recognizes the user's emotions

[1355] While answering questions, the emotion engine recognizes emotions from the user's facial expressions and voice in real time, and this emotional data is used to adjust the content and order of questions.

[1356] Collecting and submitting responses

[1357] The user answers the question

[1358] The user answers the questions displayed on the screen in detail. Once the answer is complete, the device collects the answer and sends it to the server. At the same time, emotional data about the user's response is also collected.

[1359] The server receives the data

[1360] The server receives the user's response data and emotion data sent from the device and stores them centrally.

[1361] Data analysis

[1362] The server analyzes the data

[1363] The server analyzes the collected data and extracts the user's characteristics, such as their strengths, weaknesses, areas of expertise, areas of weakness, and values, and also incorporates emotional data.

[1364] The server generates a carrier plan.

[1365] Based on the analysis results, the server generates a career plan suited to the user's characteristics and emotions, including job type, required skills, recommended experience, etc. Furthermore, machine learning models are used to improve the accuracy of the career plan generation.

[1366] Career plan proposals

[1367] The server sends the proposal to the device

[1368] The server sends the generated career plan and a list of required skills and experience to the terminal.

[1369] The device displays suggestions to the user

[1370] The terminal displays the proposed career plan and a list of skills and experiences to the user, allowing the user to confirm a specific career plan based on their own characteristics and feelings.

[1371] User Feedback

[1372] Users provide feedback

[1373] The user can then input feedback or additional questions about the proposed career plan into the device, and the emotion engine will recognize the user's emotions and collect the data.

[1374] The device collects feedback and sends it to the server

[1375] The device collects feedback and sends it to the server, which uses it to improve the system and reflect it in future interactions.

[1376] Specific examples

[1377] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server analyzes this and identifies "software engineer" as a suitable career path. Furthermore, the emotion engine collects emotional data from the student's facial expressions, such as excitement and interest, and incorporates this into the plan generation process, providing a more accurate career plan. Next, it creates a list of required skills, such as advanced programming skills, database management, team collaboration, and presentation skills, and suggests them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics and emotional data.

[1378] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[1379] The processing flow will be explained below.

[1380] Step 1:

[1381] The user logs in to the terminal. The user enters their user ID and password on the system login screen and presses the login button.

[1382] Step 2:

[1383] The terminal sends the user's login information to the server. The terminal encrypts the entered user ID and password and sends them to the server.

[1384] Step 3:

[1385] The server authenticates the user. The server checks the user ID and password in the database and starts a session if they match. If they don't match, it returns an authentication error.

[1386] Step 4:

[1387] The terminal displays the authentication result to the user. If authentication is successful, the user can proceed to the next screen. If authentication is unsuccessful, a login error screen is displayed.

[1388] Step 5:

[1389] The server prepares a series of questions for the user and sends them to the device, including questions about past experiences, influences, strengths and weaknesses, values, etc.

[1390] Step 6:

[1391] The device displays a question to the user, and the emotion engine is activated to recognize emotions from the user's facial expressions and voice in real time.

[1392] Step 7:

[1393] The user answers each question and enters it into the terminal. The emotion engine recognizes and simultaneously collects the emotional data of the user when answering.

[1394] Step 8:

[1395] The device sends the collected response data and emotion data to the server. Once all questions have been answered, the data is sent to the server in one batch.

[1396] Step 9:

[1397] The server receives the response data and emotion data and begins analysis. The server analyzes the response data and extracts the user's characteristics. Specifically, it analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc. At the same time, it takes emotion data into consideration.

[1398] Step 10:

[1399] The server generates a career plan suited to the user. Based on the analysis results, a machine learning model is used to propose the optimal career plan. This plan includes information such as job type, required skills, and recommended experience. The user's emotional data is also reflected in the plan generation.

[1400] Step 11:

[1401] The server transmits the generated career plan and a list of required skills and experiences to the terminal.

[1402] Step 12:

[1403] The device displays the career plan and skills / experience list to the user, who can then review the proposed content.

[1404] Step 13:

[1405] The user provides feedback by inputting their opinions or questions about the proposal into the device. At this time, the emotion engine recognizes the user's emotions in real time and collects the data.

[1406] Step 14:

[1407] The device collects feedback and emotion data and sends it to a server.

[1408] Step 15:

[1409] The server receives and analyzes the feedback and emotional data, and uses it to improve the system's algorithms and question flow.

[1410] Step 16:

[1411] The server then incorporates feedback and sentiment data into the next session to improve the system, allowing it to continuously learn and adjust to provide users with more accurate career plans.

[1412] Example 2

[1413] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1414] Previous career plan proposal systems were based solely on user response data and did not take into account the user's emotions or immediate reactions. As a result, it was difficult to generate a career plan that fully reflected the user's characteristics and intentions. Furthermore, the system lacked a mechanism for incorporating feedback into system improvements, making continuous system optimization difficult.

[1415] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for authenticating the user's login information, means for displaying questions to the user in an interactive format and collecting answers, means for transmitting the collected answer data to the server, means for recognizing emotion data accompanying the collected answer data, means for analyzing the answers and emotion data and extracting user characteristics, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, and means for collecting feedback from the user and reflecting the feedback in improving the system. This enables the generation of a highly accurate career plan that incorporates the user's emotions and immediate reactions. Furthermore, the system can be continuously improved based on the feedback.

[1416] "Means for authenticating login information" refers to the functionality that verifies the authentication information entered by the user and confirms the user's access privileges.

[1417] "Means for interactively displaying questions and collecting answers" refers to the functionality of displaying a series of questions to a user and receiving the user's answers through an interface.

[1418] The "means for transmitting collected response data to a server" refers to a function for transmitting the response data collected from users to a server via a network.

[1419] "Means for recognizing emotional data accompanying collected response data" refers to a function that analyzes the user's facial expressions and voice while answering questions and recognizes their emotional state in real time.

[1420] "Means of analyzing response and emotional data and extracting user characteristics" refers to the function of analyzing and extracting a user's characteristics, strengths, weaknesses, areas of expertise and weaknesses, values, etc. based on the acquired response data and emotional data.

[1421] "Means for generating a career plan based on the characteristics of the user" refers to a function for generating an optimal career plan based on analyzed characteristic information of the user.

[1422] "Means for proposing the generated career plan and a list of required skills and experience to the user" refers to a function for presenting the generated career plan and the required skills and experience to the user.

[1423] "Means for collecting feedback from users and reflecting it in improving the system" refers to the function of collecting feedback provided by users and improving and optimizing the system based on that feedback.

[1424] This invention is a system that allows users to draw up specific career plans based on their own characteristic data, including their own emotions. This system features a mechanism in which users interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values ​​they hold dear, and then combines this information with an emotion engine that recognizes the user's emotions to analyze the information and propose an appropriate career plan.

[1425] The system includes a means for a user to authenticate by logging in using a terminal and transmitting login information to a server, which then displays a series of questions to the user on the terminal, to which the user enters answers.

[1426] The device is equipped with a built-in camera and microphone, and while the user is answering questions, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. This data is collected along with the answers and sent from the device to the server.

[1427] The server analyzes the collected response data and sentiment data to extract user characteristics. This can be done using Python data analysis tools such as Pandas and NumPy. A career plan is then generated based on the analysis results. Machine learning libraries such as Scikit-learn and TensorFlow are used for generation.

[1428] The generated career plan and a list of the skills and experience required for it are sent from the server to the terminal and displayed to the user. The user can review the displayed career plan and enter feedback or additional questions. The terminal collects this feedback and sends it back to the server. The server can analyze this feedback and reflect it in improving the system.

[1429] As a concrete example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won an award at a hackathon," the server will analyze this and identify "software engineer" as a suitable career path. Furthermore, the emotion engine collects emotional data from the student's facial expressions, such as excitement and interest at the time of the answer, and incorporates this into the plan generation process, providing a more accurate career plan. Next, the system creates a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggests them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics and emotional data.

[1430] Prompt Sentence Examples

[1431] "What about past experiences?"

[1432] "What influenced you?"

[1433] "Please tell me your strengths and weaknesses."

[1434] "What are your strengths and weaknesses?"

[1435] "What values ​​do you hold dear?"

[1436] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[1437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1438] Step 1:

[1439] The user enters login information. The user enters a username and password on the terminal's login screen. The terminal sends the entered information to the server. The server compares the information with the user information stored in the database and performs authentication. If authentication is successful, the user's session begins.

[1440] Input: Username and Password

[1441] Output: Session started

[1442] Step 2:

[1443] The server sends a list of questions to the terminal, which then displays the questions to the user in an interactive format. The server generates a series of questions and sends them to the terminal, which receives them and displays them to the user in sequence.

[1444] Input: Question List

[1445] Output: Question printed to terminal

[1446] Step 3:

[1447] The user answers the questions. The user types in an answer to each question displayed on the device. For example, in response to the question "What is your past experience?", the user types in "I took a programming class and won a prize at a hackathon."

[1448] Input: The user's answer to the question displayed

[1449] Output: User response data

[1450] Step 4:

[1451] The device recognizes the user's emotions. While the user is entering their answers, the device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time, and recognizes the emotional data using an emotion engine.

[1452] Input: User's facial expressions and voice

[1453] Output: Emotion data

[1454] Step 5:

[1455] The device sends the answers and emotion data to the server. Once the user has answered all the questions, the device sends the answer data and emotion data to the server. The data is encrypted before being sent.

[1456] Input: User response data and emotion data

[1457] Output: Data sent to the server

[1458] Step 6:

[1459] The server analyzes the response data and emotion data. Using Python's Pandas and NumPy, the server analyzes the received data and extracts the user's characteristics. Specifically, it analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[1460] Input: User response data and emotion data

[1461] Output: User characteristics data

[1462] Step 7:

[1463] The server generates a career plan based on the user's characteristics. Based on the analysis results, the server uses a machine learning model (such as Scikit-learn or TensorFlow) to generate an optimal career plan. The career plan includes recommended job types, required skills, and experience.

[1464] Input: User characteristics data

[1465] Output: Career plan

[1466] Step 8:

[1467] The server sends the career plan to the terminal. The generated career plan and the accompanying skill and experience list are then sent from the server to the terminal.

[1468] Input: Career plan

[1469] Output: Data sent to the terminal

[1470] Step 9:

[1471] The terminal displays the career plan to the user. The terminal displays the received career plan and skill list to the user.

[1472] Input: Data sent from the server

[1473] Output: Career plan displayed to the user

[1474] Step 10:

[1475] The user provides feedback on the career plan. The user checks the displayed career plan and enters feedback or additional questions into the terminal. For example, the user enters a comment such as, "I'm interested in this job, but are there any other options?"

[1476] Input: User feedback on career plans

[1477] Output: User feedback data

[1478] Step 11:

[1479] The device sends the feedback to the server. The device collects the feedback data entered by the user and sends it to the server. The data is encrypted before being sent.

[1480] Input: User feedback data

[1481] Output: Feedback data sent to the server

[1482] Step 12:

[1483] The server analyzes the feedback and reflects it in improvements to the system. The server analyzes the received feedback and analyzes its content. The system is improved based on the analysis results and reflected in future interactions.

[1484] Input: Feedback data

[1485] Output: System improvement data

[1486] (Application example 2)

[1487] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1488] Conventional career plan generation systems make suggestions based on the user's characteristics and experience, but because they do not take emotional data into account, they are unable to reflect the user's psychological state or emotional satisfaction. Furthermore, because they do not consider optimizing specific work schedules based on the proposed career plan, it is difficult to provide an efficient work schedule based on the user's aptitude and emotions. This has limited the improvement of employee satisfaction and productivity.

[1489] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1490] In this invention, the server includes means for authenticating user login information, means for displaying questions to the user in an interactive format and collecting answers, means for transmitting the collected answer data and emotion data to the server, means for analyzing the answer data and emotion data and extracting user characteristics, means for generating a career plan based on the user's characteristics and emotion data, means for proposing the generated career plan and a list of required skills and experience to the user, means for optimizing employee work schedules based on the proposed career plan, and means for collecting feedback from users and reflecting it in improving the system. This makes it possible to provide highly accurate career plans based on the user's emotions and characteristics and to optimize employee work schedules.

[1491] "Login Information" means authentication data for a user to access a system.

[1492] "Dialogue" refers to a format in which the user and the system communicate with each other through questions and answers.

[1493] "Answer data" refers to the response information provided by a user in response to a question presented by the system.

[1494] "Emotion data" is information indicating the emotional state of the user obtained by analyzing the user's facial expressions and voice.

[1495] A "server" is a computer system for receiving and processing data sent by users.

[1496] "Analysis" is the process of extracting and analyzing specific information from collected data.

[1497] "Characteristics" refers to individual characteristics of a user, such as strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that are important to the user.

[1498] A "career plan" is a plan for career choices and career activities that is proposed based on the user's characteristics and emotional data.

[1499] "Skills" are the techniques and abilities required to perform a specific job or task.

[1500] A "work schedule" is a plan that specifies what tasks an employee will perform at what time.

[1501] "Feedback" refers to users providing their opinions and impressions about the proposed career plan and use of the system.

[1502] "Optimization" is the process of improving plans and operations to be efficient and effective according to a purpose.

[1503] The system for realizing this application example is implemented as a smartphone application for optimizing employees' career plans and work schedules. Specific embodiments for carrying out the invention are described in detail below.

[1504] The system consists of the following main components:

[1505] 1. User authentication:

[1506] When a user logs in to a smartphone application, the server authenticates the user's login information, using standard ID and password or biometric authentication such as fingerprint or face recognition.

[1507] 2. Interactive Q&A:

[1508] After successful authentication, the user is presented with a series of interactive questions, including past experiences, strengths and weaknesses, values, etc. The user then enters answers to these questions.

[1509] 3. Collecting Emotional Data:

[1510] While the user answers questions, the smartphone's camera and microphone are used to collect real-time emotional data, which is then analyzed using an emotion recognition engine such as the Microsoft Azure Face API.

[1511] 4. Data transmission and analysis:

[1512] The collected response data and emotion data are sent to a server, which analyzes the data and extracts user characteristics. A machine learning model is applied to the analysis using Python and Scikit-learn.

[1513] 5. Career Plan and Schedule Generation:

[1514] The server generates an appropriate career plan based on the user's characteristics and emotional data. This career plan includes recommended job types, required skills, and experience. Furthermore, using tools such as Google's OR-Tools, the server optimizes the work schedule based on the proposed career plan.

[1515] 6. User Suggestions and Feedback:

[1516] The generated career plan and optimized work schedule are presented to the user via a smartphone application, and the user can provide feedback, which is then sent to the server, which then uses this feedback to improve the system.

[1517] Specific examples include the following cases:

[1518] An employee logs in and answers questions about their past strengths, values, and past accomplishments. For example, they might provide an answer like, "I took a programming class and won a hackathon." Emotional data, such as excitement and interest, is collected from their facial expressions while answering.

[1519] The server analyzes this data and suggests "software engineer" as the best career plan for the employee, listing necessary skills such as advanced programming skills and teamwork ability.

[1520] Based on the proposal, employees' daily schedules will be optimized to consist of one hour of welding work and two hours of quality control training.

[1521] Additionally, specific prompts are generated using a generative AI model, such as:

[1522] Example prompt: "Analyze user Chris's past experience (welding), and suggest a schedule for today based on his strengths (welding) and interests (quality control). For example, suggest one hour of welding and two hours of quality control training. Optimize the schedule so that Chris shows positive emotions while working."

[1523] This will enable the provision of highly accurate career plans based on users' emotions and characteristics, as well as the optimization of employee work schedules.

[1524] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1525] Step 1:

[1526] A user logs in to an application on their smartphone.

[1527] Specific operation: The user enters login information using ID and password, or fingerprint or facial recognition.

[1528] Input: User authentication information (ID, password, biometric information)

[1529] Output: Authentication result (success or failure)

[1530] Data processing: The app device sends the entered authentication information to the server, which then performs the authentication process. The server compares the information with the authentication information in its database and generates an authentication result.

[1531] Step 2:

[1532] After successful login, the terminal interactively displays questions to the user and collects answers.

[1533] Specific actions: Questions should be about past experiences, strengths, weaknesses, values, and strengths.

[1534] Input: User response data

[1535] Output: User response data collection results

[1536] Data processing: The device collects the user's answers as text data according to a format. The collected data is temporarily stored on the device.

[1537] Step 3:

[1538] The device collects the user's emotional data.

[1539] Specific operation: While answering questions, the user's facial expressions and voice are recorded in real time through the device's camera and microphone, and analyzed using an emotion recognition engine (such as Microsoft Azure Face API).

[1540] Input: User's facial expression data, voice data

[1541] Output: Emotion data (anger, joy, sadness, surprise, etc.)

[1542] Data processing: The acquired image and audio data is input into an emotion recognition engine, and emotion data is generated as an analysis result.

[1543] Step 4:

[1544] The terminal transmits the collected response data and emotion data to the server.

[1545] Specific operation: The device sends the temporarily stored data to the server using HTTPS communication.

[1546] Input: User response data, emotion data

[1547] Output: Data sent to the server

[1548] Data processing: Data is compressed, encrypted, and securely sent to the server.

[1549] Step 5:

[1550] The server analyzes the collected data.

[1551] Specific operation: The server analyzes the response data and sentiment data and extracts user characteristics using a machine learning model (Python, Scikit-learn).

[1552] Input: User response data, emotion data

[1553] Output: User characteristics data

[1554] Data processing: The server integrates the response data and emotion data stored in the database and runs an algorithm to extract features to analyze the user's characteristics.

[1555] Step 6:

[1556] The server generates a career plan based on the user's characteristics and optimizes the work schedule.

[1557] Specific operation: Using machine learning models (Python, Scikit-learn) and schedule optimization algorithms (Google OR-Tools), we generate career plans and work schedules that are suitable for users.

[1558] Input: User characteristic data

[1559] Output: Career plan, optimized work schedule

[1560] Data processing: The server inputs the characteristic data into a machine learning model and applies a schedule optimization algorithm based on the generated carrier plan.

[1561] Step 7:

[1562] The server transmits the generated career plan and work schedule to the terminal.

[1563] Specific operation: The server sends the proposed career plan, skill list, and optimized schedule to the terminal.

[1564] Input: Career plan, work schedule

[1565] Output: Result of sending the proposal data to the user

[1566] Data processing: The data is compressed and encrypted again and sent securely to the device.

[1567] Step 8:

[1568] The terminal displays the generated career plan and work schedule to the user.

[1569] Specific operation: A career plan, a list of required skills, and a work schedule are displayed on the terminal screen.

[1570] Input: Proposal data (career plan, skill list, work schedule)

[1571] Output: The result displayed to the user

[1572] Data processing: The received data is decompressed and decrypted, and presented visually to the user.

[1573] Step 9:

[1574] The user provides feedback.

[1575] Specific operation: The user enters feedback and questions about the proposed career plan and work schedule.

[1576] Input: User feedback data

[1577] Output: Feedback collection results

[1578] Data processing: The device collects feedback in text format and sends it to the server.

[1579] Step 10:

[1580] The server collects feedback and uses it to improve the system.

[1581] Specific operation: Analyze the feedback data and update the algorithm to reflect it in future career plan proposals.

[1582] Input: User feedback data

[1583] Output: System improvement information

[1584] Data processing: Feedback data is stored and analyzed to identify areas for improvement in the system.

[1585] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1586] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1587] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1588] [Fourth embodiment]

[1589] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1590] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1591] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1592] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1593] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1595] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1596] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1597] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1598] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1599] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1600] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1601] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1602] This invention is a system that allows users to draw up specific career plans for themselves. This system features a mechanism that allows users to interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values, and then analyzes the information and proposes an appropriate career plan.

[1603] In this system, the following processing is performed.

[1604] Log in and ask a question

[1605] A user logs in to a terminal

[1606] A user accesses the system and enters their login information. The terminal sends this information to the server, which performs authentication. If authentication is successful, the user's session begins.

[1607] The device displays a question to the user.

[1608] Once the server recognizes the user's login, the system prompts the user with a series of questions, including about past experiences, influences, strengths and weaknesses, values, and so on.

[1609] Collecting and submitting responses

[1610] The user answers the question

[1611] The user answers the questions displayed on the screen in detail, and once the answers are complete, the terminal collects them and sends them to the server.

[1612] The server receives the data

[1613] The server receives the user's response data sent from the device and stores it centrally.

[1614] Data analysis

[1615] The server analyzes the data

[1616] The server analyzes the collected data and extracts the user's characteristics, specifically, their strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[1617] The server generates a carrier plan.

[1618] Based on the analysis results, the server generates a career plan suited to the user's characteristics, including job type, required skills, recommended experience, etc. Furthermore, machine learning models are used to improve the accuracy of the career plan.

[1619] Career plan proposals

[1620] The server sends the proposal to the device

[1621] The server sends the generated career plan and a list of required skills and experience to the terminal.

[1622] The device displays suggestions to the user

[1623] The terminal displays the proposed career plan and a list of skills and experience to the user, allowing the user to confirm a specific career plan based on their own characteristics.

[1624] User Feedback

[1625] Users provide feedback

[1626] Users can enter feedback and follow-up questions about the proposed career plan into the device, including specific feedback such as "I'd like to know more about acquiring presentation skills."

[1627] The device collects feedback and sends it to the server

[1628] The device collects feedback and sends it to the server, which uses it to improve the system and reflect it in future interactions.

[1629] Specific examples

[1630] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server will analyze this and identify "software engineer" as a suitable career path. It will then create a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggest them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics.

[1631] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[1632] The processing flow will be explained below.

[1633] Step 1:

[1634] The user logs in to the terminal. The user enters their user ID and password on the system login screen and presses the login button.

[1635] Step 2:

[1636] The terminal sends the user's login information to the server. The terminal encrypts the entered user ID and password and sends them to the server.

[1637] Step 3:

[1638] The server authenticates the user. The server checks the user ID and password in the database and starts a session if they match. If they don't match, it returns an authentication error.

[1639] Step 4:

[1640] The terminal displays the authentication result to the user. If authentication is successful, the user can proceed to the next screen. If authentication is unsuccessful, a login error screen is displayed.

[1641] Step 5:

[1642] The server prepares a series of questions for the user and sends them to the device, including questions about past experiences, influences, strengths and weaknesses, values, etc.

[1643] Step 6:

[1644] The terminal displays questions to the user, who then answers the questions displayed on the screen one by one.

[1645] Step 7:

[1646] The user answers each question and enters it into the terminal, where each answer is temporarily saved.

[1647] Step 8:

[1648] The device sends the collected response data to the server. Once all questions have been answered, the data is sent to the server in one batch.

[1649] Step 9:

[1650] The server receives the response data and begins analyzing it. The server analyzes the response data and extracts the user's characteristics. Specifically, the server analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[1651] Step 10:

[1652] The server generates a career plan suited to the user. Based on the analysis results, it uses a machine learning model to propose the optimal career plan. This plan includes job types, required skills, recommended experience, etc.

[1653] Step 11:

[1654] The server transmits the generated career plan and a list of required skills and experiences to the terminal.

[1655] Step 12:

[1656] The device displays the career plan and skills / experience list to the user, who can then review the proposed content.

[1657] Step 13:

[1658] The user provides feedback by inputting their opinions and questions about the proposal into the terminal.

[1659] Step 14:

[1660] The device collects the feedback and sends it to the server.

[1661] Step 15:

[1662] The server receives and analyzes the feedback, which it uses as data to improve the system's algorithms and question flow based on the user's feedback.

[1663] Step 16:

[1664] The server then incorporates the feedback into the next session, allowing the system to continuously learn and adjust to provide users with more accurate career plans.

[1665] Example 1

[1666] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1667] In today's career development process, it is not easy for individual users to draw up specific career plans based on their own characteristics. Conventional systems have difficulty providing users with adequately customized suggestions, and do not adequately support users in choosing a career based on their own strengths, weaknesses, and values. Furthermore, they lack a mechanism for effectively incorporating feedback and improving the accuracy of the system.

[1668] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1669] In this invention, the server includes a means for authenticating the user's login information, a means for displaying questions to the user in an interactive format and collecting answers, and a means for transmitting the collected answer data to the server, thereby enabling the generation of a highly accurate career plan based on the user's characteristics.

[1670] "Means for authenticating login information" refers to a technology that collates the authentication information entered by the user into the terminal with information in a database and establishes a user session.

[1671] "Means for displaying questions in an interactive format and collecting answers" refers to a technique for displaying questions sent from a server to a user on a terminal and collecting answers from the user as input.

[1672] The "means for transmitting collected response data to a server" refers to a technique for transmitting user responses from the terminal to a server and storing them safely.

[1673] "Means of analyzing response data and extracting user characteristics" refers to techniques that analyze collected data using machine learning models and natural language processing techniques to reveal characteristics such as users' strengths, weaknesses, areas of expertise, areas of weakness, and values.

[1674] The "means for generating a career plan" is a technology that creates a career plan that includes the most suitable job type, required skills, experience, etc. for the user based on the analysis results.

[1675] The "means for proposing a career plan and a list of required skills and experiences to a user" is a technology for displaying a generated career plan and its detailed requirements to a user.

[1676] "Means for collecting feedback from users and reflecting it in system improvements" refers to a technique for collecting opinions and additional questions from users and incorporating them into system analysis to improve functionality.

[1677] "Means for vectorizing input text and analyzing it with a classification algorithm" refers to a technology that converts text data such as user responses into numerical vectors and analyzes them using a classification algorithm.

[1678] "Means for creating a list of occupations using a recommendation engine" refers to a technology that uses machine learning technology to list appropriate occupations based on the characteristics of the user.

[1679] The present invention is a system that allows users to specifically draw up their own career plans, and that interactively collects information such as the user's past experiences, areas of strength and weakness, and values, and then analyzes the information to propose an appropriate career plan. An embodiment of this system will be described in detail below.

[1680] System configuration

[1681] The system mainly includes the following components:

[1682] 1. Terminal

[1683] 2. Server

[1684] 3. Database

[1685] 4. Machine Learning Models

[1686] 5. Communication Protocol

[1687] Software and hardware used

[1688] Terminal

[1689] Users access the system using devices such as PCs, tablets, and smartphones, and the devices communicate with the server via a web browser or native application.

[1690] server

[1691] The server is the main component for running the program and uses web frameworks such as Django and Spring. The server handles major processes such as user authentication, data analysis, and career plan generation.

[1692] Database

[1693] MySQL and PostgreSQL are used as databases to store various data such as user information, response data, and analysis results.

[1694] Machine learning models

[1695] The machine learning model uses TensorFlow and PyTorch to analyze user data and generate career plans. Specifically, natural language processing technology is used to analyze user responses and extract characteristics.

[1696] communication protocol

[1697] The device and server send and receive data using a secure communication protocol such as HTTPS.

[1698] System action

[1699] User Login

[1700] The user enters their login information into the terminal, and the server authenticates them against the database. If authentication is successful, a session ID is generated and the user's session begins.

[1701] Asking questions and collecting answers

[1702] The server generates a series of questions for the user and sends them to the device, which then displays the questions on the user's screen and the user enters answers to these questions.The answers are then sent from the device to the server and stored in a database.

[1703] Data analysis

[1704] The server receives the collected response data and analyzes it using machine learning models and natural language processing technology to extract information such as the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that they hold dear.

[1705] Creating a career plan

[1706] The server generates an optimal career plan for the user based on the analysis results. This career plan includes recommended occupations, required skills and experience, etc. A recommendation engine is used to create a list of occupations that match the user's characteristics.

[1707] Career plan proposals

[1708] The server sends the generated career plan and a list of required skills and experience to the terminal, which then displays it to the user, who can then check the specific career plan based on their own characteristics.

[1709] User Feedback

[1710] Users input feedback and follow-up questions about their career plans into the device. Specific feedback includes, for example, "I'd like to know more about mastering presentation skills." The device collects the feedback and sends it to the server, which analyzes it and uses it to improve the system.

[1711] Examples of concrete examples and prompts

[1712] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server analyzes this and identifies "software engineer" as a suitable career path. It then generates a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggests them to the student via their device.

[1713] Prompt Sentence Examples

[1714] "Tell me about your past work experience"

[1715] "What events or people influenced you?"

[1716] "Please tell me your strengths and weaknesses."

[1717] "What are your strengths and weaknesses?"

[1718] "What values ​​do you hold dear?"

[1719] By using the above prompt sentences, detailed information can be collected from the user, and a more accurate career plan can be proposed.

[1720] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1721] Step 1: User Login

[1722] The user enters login information (username and password) into the terminal. The terminal sends this information to the server, which then authenticates it by checking it against information in a database. If authentication is successful, the server generates a session ID and the user's session begins. The server returns the authentication result and session ID to the terminal.

[1723] Input: Username, Password

[1724] Data processing: Verification of input information (database search)

[1725] Output: Authentication result (success / failure), session ID

[1726] Specific behavior:

[1727] The device obtains the user's authentication information from the input fields and sends it to the server in JSON format.

[1728] The server checks the received authentication information against the information in its database, and if it matches, the authentication is successful and a session ID is generated.

[1729] The server returns the authentication result and session ID to the terminal in JSON format.

[1730] The device receives the authentication result, and if successful, transitions to the home screen.

[1731] Step 2: Posing the Question

[1732] The server generates a series of questions for the user who has been successfully authenticated and sends them to the device. The device receives the questions and displays them on the user's screen. The questions can be a pre-defined set of fixed questions or dynamically generated questions using a generative AI model.

[1733] Input: Session ID

[1734] Data processing: Question list generation (fixed or dynamic generation)

[1735] Output: Question list

[1736] Specific behavior:

[1737] The server checks the user information based on the session ID and selects or generates an appropriate questionnaire.

[1738] A series of questions (e.g., "Tell me about your past work experience") is sent to the device in JSON format.

[1739] The terminal displays the received list of questions on the screen.

[1740] Step 3: Collect and submit responses

[1741] The user answers the questions displayed through the terminal in detail, and the terminal collects the user's answers and sends them to the server, where they are stored.

[1742] Input: User's answer

[1743] Data processing: Collection and transmission of response data (JSON format)

[1744] Output: Answer data saved on the server

[1745] Specific behavior:

[1746] The user inputs answers to questions displayed on the screen using text boxes and options.

[1747] The device sends the user's answers in JSON format to the server.

[1748] The server stores the received response data in a database.

[1749] Step 4: Analyze the data

[1750] The server analyzes the user response data stored in the database, using machine learning models (e.g., TensorFlow or PyTorch) and natural language processing techniques to extract user characteristics.

[1751] Input: Answer data

[1752] Data processing: Vectorization of text data, analysis using machine learning models

[1753] Output: User characteristics (strengths, weaknesses, areas of expertise, areas of weakness, values)

[1754] Specific behavior:

[1755] The server retrieves the user's response data from the database and performs preprocessing (such as text cleaning).

[1756] Load the machine learning model and input the preprocessed data into the model.

[1757] The model output is analyzed to extract user characteristics.

[1758] Step 5: Create a career plan

[1759] The server generates a career plan suitable for the user based on the analysis results, which includes job types, required skills, recommended experience, etc. A recommendation engine is used to create a list of occupations that match the user's characteristics.

[1760] Input: User characteristics

[1761] Data processing: Generating career plans (recommended by algorithms)

[1762] Output: Career plan (job type, skills, experience list)

[1763] Specific behavior:

[1764] The server generates a list of occupations using a recommendation engine based on the user's characteristic data.

[1765] The occupational list includes the required skills and recommended experience for each job.

[1766] The generated carrier plan is sent to the terminal in JSON format.

[1767] Step 6: Propose a career plan

[1768] The server sends the generated carrier plan to the terminal, which displays it to the user, allowing the user to check the specific carrier plan.

[1769] Input: Career plan

[1770] Data processing: Sending career plan (JSON format)

[1771] Output: Career plan displayed to the user

[1772] Specific behavior:

[1773] The server sends the generated carrier plan in JSON format to the terminal.

[1774] The terminal displays the received carrier plan on the screen so that the user can check the contents.

[1775] Step 7: User feedback

[1776] Users input feedback and follow-up questions about their career plans into the device, which collects the feedback and sends it to the server, which analyzes it and uses it to improve the system.

[1777] Input: Feedback

[1778] Data processing: collecting and sending feedback (JSON format)

[1779] Output: Feedback stored on the server

[1780] Specific behavior:

[1781] Users can enter feedback or follow-up questions about the career plan in the text box.

[1782] The device sends feedback in JSON format to the server.

[1783] The server stores the received feedback in a database and uses it for subsequent analysis and system improvement.

[1784] (Application example 1)

[1785] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1786] Currently, many people find it difficult to map out their own career plans. In particular, in the food delivery industry, short-term employment and limited opportunities for skill development make it difficult to create long-term career plans. As a result, declining worker satisfaction and declining labor productivity have become problems. For example, there is a need for a system that can clarify how to advance one's career by utilizing one's experience and abilities.

[1787] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1788] In this invention, the server includes means for authenticating the user's login information, means for interactively displaying questions to the user and collecting answers, means for transmitting the collected answer data to the server, means for analyzing the answer data and extracting characteristics of the user, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, means for collecting feedback from the user and reflecting it in improving the system, and means for taking into account specific skills and experience in delivery work when generating a career plan by identifying needs and suggesting suitable jobs.

[1789] This allows users to draw up concrete and realistic career plans, and in the food delivery industry in particular, it makes it possible to clarify suitable jobs and ways to improve skills, which is expected to result in improved worker satisfaction and labor productivity.

[1790] "Login information" means authentication information for a user to access a system, and typically includes a user ID and password.

[1791] "Dialogue" refers to a method by which a user interacts with a system, often in the form of questions and answers.

[1792] "Response data" is a collection of information entered interactively by the user, and includes specific responses to questions.

[1793] A "server" is a computer system that stores and processes data and is responsible for managing and analyzing data from users.

[1794] "User characteristics" refers to the individual characteristics of a user, such as the user's strengths, weaknesses, areas of expertise, areas of weakness, and values.

[1795] A "career plan" refers to a plan that clarifies the career goals, skills, and experience a user wants to achieve in the future.

[1796] A "skills and experience list" is a list of the skills and achievements required to advance in a particular career.

[1797] "Feedback" refers to opinions and requests for improvement that users provide to the system, and is used to improve the system's performance.

[1798] "System improvement" is the process of improving the functionality and performance of software based on collected feedback.

[1799] "Needs" refer to specific wants and desires that users currently have, and in the career context, this includes suitable jobs and opportunities for skill development.

[1800] "Job" refers to a specific occupation or job content, including the tasks and roles a user performs.

[1801] "Delivery work" refers to the occupation of delivering goods or services to customers, including the specific tasks and duties that occur along the way.

[1802] "Skills and experience" refers to the techniques required to perform a particular job and the knowledge and skills acquired through actual work.

[1803] A "machine learning model" is an algorithm that analyzes large amounts of data, learns patterns and trends, and makes future predictions.

[1804] The system for implementing this invention authenticates the user's login information, collects and analyzes the user's characteristics by interactively asking and answering questions, and generates and proposes an appropriate career plan. This system mainly depends on the interaction between the server, the terminal, and the user.

[1805] System configuration

[1806] The system includes the following main components:

[1807] 1. Login authentication module:

[1808] The server receives the user's login information (user ID, password, etc.) and performs authentication. If this authentication is successful, the user's session begins.

[1809] 2. Interactive question display module:

[1810] After authentication, the server prompts the device with a series of questions, including past experiences, strengths and weaknesses, areas of expertise, areas of weakness, and values ​​that are important to the user.

[1811] 3. Response collection and submission module:

[1812] Users answer questions through their terminals, and the answer data is sent to a server where it is managed centrally.

[1813] 4. Data Analysis Module:

[1814] The server analyzes the collected response data and extracts user characteristics, using a machine learning model for highly accurate analysis.

[1815] 5. Career plan generation module:

[1816] Based on the extracted user characteristics, the server generates a unique career plan, taking into account the user's skills and experience in delivery work in particular, and proposes a plan that includes suitable jobs and ways to improve skills.

[1817] 6. Suggestion and Feedback Collection Module:

[1818] The generated career plan and list of required skills and experience are sent to the terminal and presented to the user, who can then input feedback on the plan, which is used to improve the system.

[1819] Hardware and software used

[1820] Server: a computer system that stores and processes data

[1821] Terminal: A device (such as a smartphone or computer) through which a user interacts with the system.

[1822] Programming language: Python

[1823] Framework:Flask

[1824] Process example

[1825] For example, suppose a user with 10 years of experience working in the food delivery industry answers, "I'm mainly good at deliveries within a city. I also have some experience managing a team." Based on that data, the server proposes a career plan for the user as a "Delivery Operations Manager" and lists the required skills (e.g., Team Leadership, Logistics Management, Fleet Management).

[1826] Prompt Sentence Examples

[1827] The user can provide specific information to the system through prompts such as:

[1828] "I have been working in delivery jobs for 10 years. I am good at mainly intra-city deliveries and have some experience in team management. Please suggest the best career plan for me."

[1829] Such prompts enable the system to accurately understand the user's characteristics and propose an appropriate career plan.

[1830] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1831] Step 1:

[1832] A user accesses the system using a terminal and enters login information (user ID and password). The terminal sends this information to the server, which authenticates the login information. If authentication is successful, the user's session begins.

[1833] Input: User ID, Password

[1834] Data processing: The server checks the authentication information

[1835] Output: Session ID upon successful login

[1836] Step 2:

[1837] The server recognizes the user's login and prompts the terminal with a series of questions, including past experience, strengths and weaknesses, areas of expertise, areas of weakness, and values.

[1838] Input: None (after login authentication)

[1839] Data processing: The server selects the questions

[1840] Output: List of questions

[1841] Step 3:

[1842] The user answers the questions displayed on the terminal in detail, and the user's answer data is collected on the terminal and sent to the server.

[1843] Input: User's answer

[1844] Data processing: The device collects the answers and converts them into a data structure.

[1845] Output: Response data

[1846] Step 4:

[1847] The server receives the collected response data and stores it centrally. The server then analyzes the response data and extracts user characteristics. This analysis is performed with high accuracy using machine learning models.

[1848] Input: Answer data

[1849] Data processing: Data analysis using machine learning models

[1850] Output: User characteristics data

[1851] Step 5:

[1852] Based on the analysis results, the server generates a career plan suited to the user's characteristics. The plan takes into account the skills and experience of delivery work. A list of required skills and experience is also generated.

[1853] Input: User characteristics data

[1854] Data processing: Career planning

[1855] Output: Career plan, skills and experience list

[1856] Step 6:

[1857] The server sends the generated career plan and skill / experience list to the terminal and presents it to the user, who then checks the proposal and enters feedback.

[1858] Input: Career plan, skills and experience list

[1859] Data processing: None (Suggestion display)

[1860] Output: Feedback data

[1861] Step 7:

[1862] The device collects feedback from users and sends it to the server, which stores the feedback and reflects it in subsequent plan generation to improve the system.

[1863] Input: Feedback data

[1864] Data processing: Analysis and storage of feedback

[1865] Output: Improved system data

[1866] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1867] This invention is a system that allows users to draw up specific career plans based on their own characteristic data, including their own emotions. This system features a mechanism in which users interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values ​​they hold dear, and then combines this information with an emotion engine that recognizes the user's emotions to analyze the information and propose an appropriate career plan.

[1868] In this system, the following processing is performed.

[1869] Log in and ask a question

[1870] A user logs in to a terminal

[1871] A user accesses the system and enters their login information. The terminal sends this information to the server, which performs authentication. If authentication is successful, the user's session begins.

[1872] The device displays a question to the user.

[1873] Once the server recognizes the user's login, the system prompts the user with a series of questions, including about past experiences, influences, strengths and weaknesses, values, and so on.

[1874] Emotion recognition by emotion engine

[1875] The device recognizes the user's emotions

[1876] While answering questions, the emotion engine recognizes emotions from the user's facial expressions and voice in real time, and this emotional data is used to adjust the content and order of questions.

[1877] Collecting and submitting responses

[1878] The user answers the question

[1879] The user answers the questions displayed on the screen in detail. Once the answer is complete, the device collects the answer and sends it to the server. At the same time, emotional data about the user's response is also collected.

[1880] The server receives the data

[1881] The server receives the user's response data and emotion data sent from the device and stores them centrally.

[1882] Data analysis

[1883] The server analyzes the data

[1884] The server analyzes the collected data and extracts the user's characteristics, such as their strengths, weaknesses, areas of expertise, areas of weakness, and values, and also incorporates emotional data.

[1885] The server generates a carrier plan.

[1886] Based on the analysis results, the server generates a career plan suited to the user's characteristics and emotions, including job type, required skills, recommended experience, etc. Furthermore, machine learning models are used to improve the accuracy of the career plan generation.

[1887] Career plan proposals

[1888] The server sends the proposal to the device

[1889] The server sends the generated career plan and a list of required skills and experience to the terminal.

[1890] The device displays suggestions to the user

[1891] The terminal displays the proposed career plan and a list of skills and experiences to the user, allowing the user to confirm a specific career plan based on their own characteristics and feelings.

[1892] User Feedback

[1893] Users provide feedback

[1894] The user can then input feedback or additional questions about the proposed career plan into the device, and the emotion engine will recognize the user's emotions and collect the data.

[1895] The device collects feedback and sends it to the server

[1896] The device collects feedback and sends it to the server, which uses it to improve the system and reflect it in future interactions.

[1897] Specific examples

[1898] For example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won a prize at a hackathon," the server analyzes this and identifies "software engineer" as a suitable career path. Furthermore, the emotion engine collects emotional data from the student's facial expressions, such as excitement and interest, and incorporates this into the plan generation process, providing a more accurate career plan. Next, it creates a list of required skills, such as advanced programming skills, database management, team collaboration, and presentation skills, and suggests them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics and emotional data.

[1899] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[1900] The processing flow will be explained below.

[1901] Step 1:

[1902] The user logs in to the terminal. The user enters their user ID and password on the system login screen and presses the login button.

[1903] Step 2:

[1904] The terminal sends the user's login information to the server. The terminal encrypts the entered user ID and password and sends them to the server.

[1905] Step 3:

[1906] The server authenticates the user. The server checks the user ID and password in the database and starts a session if they match. If they don't match, it returns an authentication error.

[1907] Step 4:

[1908] The terminal displays the authentication result to the user. If authentication is successful, the user can proceed to the next screen. If authentication is unsuccessful, a login error screen is displayed.

[1909] Step 5:

[1910] The server prepares a series of questions for the user and sends them to the device, including questions about past experiences, influences, strengths and weaknesses, values, etc.

[1911] Step 6:

[1912] The device displays a question to the user, and the emotion engine is activated to recognize emotions from the user's facial expressions and voice in real time.

[1913] Step 7:

[1914] The user answers each question and enters it into the terminal. The emotion engine recognizes and simultaneously collects the emotional data of the user when answering.

[1915] Step 8:

[1916] The device sends the collected response data and emotion data to the server. Once all questions have been answered, the data is sent to the server in one batch.

[1917] Step 9:

[1918] The server receives the response data and emotion data and begins analysis. The server analyzes the response data and extracts the user's characteristics. Specifically, it analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc. At the same time, it takes emotion data into consideration.

[1919] Step 10:

[1920] The server generates a career plan suited to the user. Based on the analysis results, a machine learning model is used to propose the optimal career plan. This plan includes information such as job type, required skills, and recommended experience. The user's emotional data is also reflected in the plan generation.

[1921] Step 11:

[1922] The server transmits the generated career plan and a list of required skills and experiences to the terminal.

[1923] Step 12:

[1924] The device displays the career plan and skills / experience list to the user, who can then review the proposed content.

[1925] Step 13:

[1926] The user provides feedback by inputting their opinions or questions about the proposal into the device. At this time, the emotion engine recognizes the user's emotions in real time and collects the data.

[1927] Step 14:

[1928] The device collects feedback and emotion data and sends it to a server.

[1929] Step 15:

[1930] The server receives and analyzes the feedback and emotional data, and uses it to improve the system's algorithms and question flow.

[1931] Step 16:

[1932] The server then incorporates feedback and sentiment data into the next session to improve the system, allowing it to continuously learn and adjust to provide users with more accurate career plans.

[1933] Example 2

[1934] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1935] Previous career plan proposal systems were based solely on user response data and did not take into account the user's emotions or immediate reactions. As a result, it was difficult to generate a career plan that fully reflected the user's characteristics and intentions. Furthermore, the system lacked a mechanism for incorporating feedback into system improvements, making continuous system optimization difficult.

[1936] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for authenticating the user's login information, means for displaying questions to the user in an interactive format and collecting answers, means for transmitting the collected answer data to the server, means for recognizing emotion data accompanying the collected answer data, means for analyzing the answers and emotion data and extracting user characteristics, means for generating a career plan based on the user's characteristics, means for proposing the generated career plan and a list of required skills and experience to the user, and means for collecting feedback from the user and reflecting the feedback in improving the system. This enables the generation of a highly accurate career plan that incorporates the user's emotions and immediate reactions. Furthermore, the system can be continuously improved based on the feedback.

[1937] "Means for authenticating login information" refers to the functionality that verifies the authentication information entered by the user and confirms the user's access privileges.

[1938] "Means for interactively displaying questions and collecting answers" refers to the functionality of displaying a series of questions to a user and receiving the user's answers through an interface.

[1939] The "means for transmitting collected response data to a server" refers to a function for transmitting the response data collected from users to a server via a network.

[1940] "Means for recognizing emotional data accompanying collected response data" refers to a function that analyzes the user's facial expressions and voice while answering questions and recognizes their emotional state in real time.

[1941] "Means of analyzing response and emotional data and extracting user characteristics" refers to the function of analyzing and extracting a user's characteristics, strengths, weaknesses, areas of expertise and weaknesses, values, etc. based on the acquired response data and emotional data.

[1942] "Means for generating a career plan based on the characteristics of the user" refers to a function for generating an optimal career plan based on analyzed characteristic information of the user.

[1943] "Means for proposing the generated career plan and a list of required skills and experience to the user" refers to a function for presenting the generated career plan and the required skills and experience to the user.

[1944] "Means for collecting feedback from users and reflecting it in improving the system" refers to the function of collecting feedback provided by users and improving and optimizing the system based on that feedback.

[1945] This invention is a system that allows users to draw up specific career plans based on their own characteristic data, including their own emotions. This system features a mechanism in which users interactively input information such as their past experiences, influences, areas of expertise and weaknesses, strengths and weaknesses, and values ​​they hold dear, and then combines this information with an emotion engine that recognizes the user's emotions to analyze the information and propose an appropriate career plan.

[1946] The system includes a means for a user to authenticate by logging in using a terminal and transmitting login information to a server, which then displays a series of questions to the user on the terminal, to which the user enters answers.

[1947] The device is equipped with a built-in camera and microphone, and while the user is answering questions, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. This data is collected along with the answers and sent from the device to the server.

[1948] The server analyzes the collected response data and sentiment data to extract user characteristics. This can be done using Python data analysis tools such as Pandas and NumPy. A career plan is then generated based on the analysis results. Machine learning libraries such as Scikit-learn and TensorFlow are used for generation.

[1949] The generated career plan and a list of the skills and experience required for it are sent from the server to the terminal and displayed to the user. The user can review the displayed career plan and enter feedback or additional questions. The terminal collects this feedback and sends it back to the server. The server can analyze this feedback and reflect it in improving the system.

[1950] As a concrete example, if a student logs in and answers the question "What is your past experience?" with "I took a programming class and won an award at a hackathon," the server will analyze this and identify "software engineer" as a suitable career path. Furthermore, the emotion engine collects emotional data from the student's facial expressions, such as excitement and interest at the time of the answer, and incorporates this into the plan generation process, providing a more accurate career plan. Next, the system creates a list of required skills, such as advanced programming skills, database management, team collaboration skills, and presentation skills, and suggests them to the student via their device. In this way, the system can provide a detailed career plan based on the user's characteristics and emotional data.

[1951] Prompt Sentence Examples

[1952] "What about past experiences?"

[1953] "What influenced you?"

[1954] "Please tell me your strengths and weaknesses."

[1955] "What are your strengths and weaknesses?"

[1956] "What values ​​do you hold dear?"

[1957] This system provides a career plan tailored to each individual user, helping them to make concrete and realistic plans for their future, which is expected to lead to appropriate career choices and improve user satisfaction and labor productivity.

[1958] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1959] Step 1:

[1960] The user enters login information. The user enters a username and password on the terminal's login screen. The terminal sends the entered information to the server. The server compares the information with the user information stored in the database and performs authentication. If authentication is successful, the user's session begins.

[1961] Input: Username and Password

[1962] Output: Session started

[1963] Step 2:

[1964] The server sends a list of questions to the terminal, which then displays the questions to the user in an interactive format. The server generates a series of questions and sends them to the terminal, which receives them and displays them to the user in sequence.

[1965] Input: Question List

[1966] Output: Question printed to terminal

[1967] Step 3:

[1968] The user answers the questions. The user types in an answer to each question displayed on the device. For example, in response to the question "What is your past experience?", the user types in "I took a programming class and won a prize at a hackathon."

[1969] Input: The user's answer to the question displayed

[1970] Output: User response data

[1971] Step 4:

[1972] The device recognizes the user's emotions. While the user is entering their answers, the device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time, and recognizes the emotional data using an emotion engine.

[1973] Input: User's facial expressions and voice

[1974] Output: Emotion data

[1975] Step 5:

[1976] The device sends the answers and emotion data to the server. Once the user has answered all the questions, the device sends the answer data and emotion data to the server. The data is encrypted before being sent.

[1977] Input: User response data and emotion data

[1978] Output: Data sent to the server

[1979] Step 6:

[1980] The server analyzes the response data and emotion data. Using Python's Pandas and NumPy, the server analyzes the received data and extracts the user's characteristics. Specifically, it analyzes the user's strengths, weaknesses, areas of expertise, areas of weakness, values, etc.

[1981] Input: User response data and emotion data

[1982] Output: User characteristics data

[1983] Step 7:

[1984] The server generates a career plan based on the user's characteristics. Based on the analysis results, the server uses a machine learning model (such as Scikit-learn or TensorFlow) to generate an optimal career plan. The career plan includes recommended job types, required skills, and experience.

[1985] Input: User characteristics data

[1986] Output: Career plan

[1987] Step 8:

[1988] The server sends the career plan to the terminal. The generated career plan and the accompanying skill and experience list are then sent from the server to the terminal.

[1989] Input: Career plan

[1990] Output: Data sent to the terminal

[1991] Step 9:

[1992] The terminal displays the career plan to the user. The terminal displays the received career plan and skill list to the user.

[1993] Input: Data sent from the server

[1994] Output: Career plan displayed to the user

[1995] Step 10:

[1996] The user provides feedback on the career plan. The user checks the displayed career plan and enters feedback or additional questions into the terminal. For example, the user enters a comment such as, "I'm interested in this job, but are there any other options?"

[1997] Input: User feedback on career plans

[1998] Output: User feedback data

[1999] Step 11:

[2000] The device sends the feedback to the server. The device collects the feedback data entered by the user and sends it to the server. The data is encrypted before being sent.

[2001] Input: User feedback data

[2002] Output: Feedback data sent to the server

[2003] Step 12:

[2004] The server analyzes the feedback and reflects it in improvements to the system. The server analyzes the received feedback and analyzes its content. The system is improved based on the analysis results and reflected in future interactions.

[2005] Input: Feedback data

[2006] Output: System improvement data

[2007] (Application example 2)

[2008] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2009] Conventional career plan generation systems make suggestions based on the user's characteristics and experience, but because they do not take emotional data into account, they are unable to reflect the user's psychological state or emotional satisfaction. Furthermore, because they do not consider optimizing specific work schedules based on the proposed career plan, it is difficult to provide an efficient work schedule based on the user's aptitude and emotions. This has limited the improvement of employee satisfaction and productivity.

[2010] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2011] In this invention, the server includes means for authenticating user login information, means for displaying questions to the user in an interactive format and collecting answers, means for transmitting the collected answer data and emotion data to the server, means for analyzing the answer data and emotion data and extracting user characteristics, means for generating a career plan based on the user's characteristics and emotion data, means for proposing the generated career plan and a list of required skills and experience to the user, means for optimizing employee work schedules based on the proposed career plan, and means for collecting feedback from users and reflecting it in improving the system. This makes it possible to provide highly accurate career plans based on the user's emotions and characteristics and to optimize employee work schedules.

[2012] "Login Information" means authentication data for a user to access a system.

[2013] "Dialogue" refers to a format in which the user and the system communicate with each other through questions and answers.

[2014] "Answer data" refers to the response information provided by a user in response to a question presented by the system.

[2015] "Emotion data" is information indicating the emotional state of the user obtained by analyzing the user's facial expressions and voice.

[2016] A "server" is a computer system for receiving and processing data sent by users.

[2017] "Analysis" is the process of extracting and analyzing specific information from collected data.

[2018] "Characteristics" refers to individual characteristics of a user, such as strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that are important to the user.

[2019] A "career plan" is a plan for career choices and career activities that is proposed based on the user's characteristics and emotional data.

[2020] "Skills" are the techniques and abilities required to perform a specific job or task.

[2021] A "work schedule" is a plan that specifies what tasks an employee will perform at what time.

[2022] "Feedback" refers to users providing their opinions and impressions about the proposed career plan and use of the system.

[2023] "Optimization" is the process of improving plans and operations to be efficient and effective according to a purpose.

[2024] The system for realizing this application example is implemented as a smartphone application for optimizing employees' career plans and work schedules. Specific embodiments for carrying out the invention are described in detail below.

[2025] The system consists of the following main components:

[2026] 1. User authentication:

[2027] When a user logs in to a smartphone application, the server authenticates the user's login information, using standard ID and password or biometric authentication such as fingerprint or face recognition.

[2028] 2. Interactive Q&A:

[2029] After successful authentication, the user is presented with a series of interactive questions, including past experiences, strengths and weaknesses, values, etc. The user then enters answers to these questions.

[2030] 3. Collecting Emotional Data:

[2031] While the user answers questions, the smartphone's camera and microphone are used to collect real-time emotional data, which is then analyzed using an emotion recognition engine such as the Microsoft Azure Face API.

[2032] 4. Data transmission and analysis:

[2033] The collected response data and emotion data are sent to a server, which analyzes the data and extracts user characteristics. A machine learning model is applied to the analysis using Python and Scikit-learn.

[2034] 5. Career Plan and Schedule Generation:

[2035] The server generates an appropriate career plan based on the user's characteristics and emotional data. This career plan includes recommended job types, required skills, and experience. Furthermore, using tools such as Google's OR-Tools, the server optimizes the work schedule based on the proposed career plan.

[2036] 6. User Suggestions and Feedback:

[2037] The generated career plan and optimized work schedule are presented to the user via a smartphone application, and the user can provide feedback, which is then sent to the server, which then uses this feedback to improve the system.

[2038] Specific examples include the following cases:

[2039] An employee logs in and answers questions about their past strengths, values, and past accomplishments. For example, they might provide an answer like, "I took a programming class and won a hackathon." Emotional data, such as excitement and interest, is collected from their facial expressions while answering.

[2040] The server analyzes this data and suggests "software engineer" as the best career plan for the employee, listing necessary skills such as advanced programming skills and teamwork ability.

[2041] Based on the proposal, employees' daily schedules will be optimized to consist of one hour of welding work and two hours of quality control training.

[2042] Additionally, specific prompts are generated using a generative AI model, such as:

[2043] Example prompt: "Analyze user Chris's past experience (welding), and suggest a schedule for today based on his strengths (welding) and interests (quality control). For example, suggest one hour of welding and two hours of quality control training. Optimize the schedule so that Chris shows positive emotions while working."

[2044] This will enable the provision of highly accurate career plans based on users' emotions and characteristics, as well as the optimization of employee work schedules.

[2045] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2046] Step 1:

[2047] A user logs in to an application on their smartphone.

[2048] Specific operation: The user enters login information using ID and password, or fingerprint or facial recognition.

[2049] Input: User authentication information (ID, password, biometric information)

[2050] Output: Authentication result (success or failure)

[2051] Data processing: The app device sends the entered authentication information to the server, which then performs the authentication process. The server compares the information with the authentication information in its database and generates an authentication result.

[2052] Step 2:

[2053] After successful login, the terminal interactively displays questions to the user and collects answers.

[2054] Specific actions: Questions should be about past experiences, strengths, weaknesses, values, and strengths.

[2055] Input: User response data

[2056] Output: User response data collection results

[2057] Data processing: The device collects the user's answers as text data according to a format. The collected data is temporarily stored on the device.

[2058] Step 3:

[2059] The device collects the user's emotional data.

[2060] Specific operation: While answering questions, the user's facial expressions and voice are recorded in real time through the device's camera and microphone, and analyzed using an emotion recognition engine (such as Microsoft Azure Face API).

[2061] Input: User's facial expression data, voice data

[2062] Output: Emotion data (anger, joy, sadness, surprise, etc.)

[2063] Data processing: The acquired image and audio data is input into an emotion recognition engine, and emotion data is generated as an analysis result.

[2064] Step 4:

[2065] The terminal transmits the collected response data and emotion data to the server.

[2066] Specific operation: The device sends the temporarily stored data to the server using HTTPS communication.

[2067] Input: User response data, emotion data

[2068] Output: Data sent to the server

[2069] Data processing: Data is compressed, encrypted, and securely sent to the server.

[2070] Step 5:

[2071] The server analyzes the collected data.

[2072] Specific operation: The server analyzes the response data and sentiment data and extracts user characteristics using a machine learning model (Python, Scikit-learn).

[2073] Input: User response data, emotion data

[2074] Output: User characteristics data

[2075] Data processing: The server integrates the response data and emotion data stored in the database and runs an algorithm to extract features to analyze the user's characteristics.

[2076] Step 6:

[2077] The server generates a career plan based on the user's characteristics and optimizes the work schedule.

[2078] Specific operation: Using machine learning models (Python, Scikit-learn) and schedule optimization algorithms (Google OR-Tools), we generate career plans and work schedules that are suitable for users.

[2079] Input: User characteristic data

[2080] Output: Career plan, optimized work schedule

[2081] Data processing: The server inputs the characteristic data into a machine learning model and applies a schedule optimization algorithm based on the generated carrier plan.

[2082] Step 7:

[2083] The server transmits the generated career plan and work schedule to the terminal.

[2084] Specific operation: The server sends the proposed career plan, skill list, and optimized schedule to the terminal.

[2085] Input: Career plan, work schedule

[2086] Output: Result of sending the proposal data to the user

[2087] Data processing: The data is compressed and encrypted again and sent securely to the device.

[2088] Step 8:

[2089] The terminal displays the generated career plan and work schedule to the user.

[2090] Specific operation: A career plan, a list of required skills, and a work schedule are displayed on the terminal screen.

[2091] Input: Proposal data (career plan, skill list, work schedule)

[2092] Output: The result displayed to the user

[2093] Data processing: The received data is decompressed and decrypted, and presented visually to the user.

[2094] Step 9:

[2095] The user provides feedback.

[2096] Specific operation: The user enters feedback and questions about the proposed career plan and work schedule.

[2097] Input: User feedback data

[2098] Output: Feedback collection results

[2099] Data processing: The device collects feedback in text format and sends it to the server.

[2100] Step 10:

[2101] The server collects feedback and uses it to improve the system.

[2102] Specific operation: Analyze the feedback data and update the algorithm to reflect it in future career plan proposals.

[2103] Input: User feedback data

[2104] Output: System improvement information

[2105] Data processing: Feedback data is stored and analyzed to identify areas for improvement in the system.

[2106] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2108] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2109] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2110] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2111] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2112] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2113] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2114] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2115] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2116] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2117] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2118] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2119] 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.

[2120] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2121] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2122] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2123] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2124] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2125] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2126] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2127] The following is further disclosed regarding the above embodiment.

[2128] (Claim 1)

[2129] a means for authenticating a user's login information;

[2130] means for interactively displaying questions to a user and collecting answers;

[2131] A means for transmitting the collected response data to a server;

[2132] A means for analyzing the response data and extracting user characteristics;

[2133] means for generating a career plan based on the characteristics of a user;

[2134] A means for proposing the generated career plan and a list of required skills and experiences to the user;

[2135] A means of collecting user feedback and incorporating it into system improvements;

[2136] A system including:

[2137] (Claim 2)

[2138] 10. The system of claim 1, further comprising means for analyzing the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that he or she holds dear.

[2139] (Claim 3)

[2140] 10. The system of claim 1, further comprising means for using a machine learning model in generating the career plan.

[2141] "Example 1"

[2142] (Claim 1)

[2143] a means for authenticating a user's login information;

[2144] means for interactively displaying questions to a user and collecting answers;

[2145] A means for transmitting the collected response data to a server;

[2146] A means for analyzing the response data and extracting user characteristics;

[2147] means for generating a career plan based on the characteristics of a user;

[2148] A means for proposing the generated career plan and a list of required skills and experiences to the user;

[2149] A means of collecting user feedback and incorporating it into system improvements;

[2150] A means for vectorizing the user's input text and analyzing it with a classification algorithm;

[2151] A means for generating a list of occupations using a recommendation engine;

[2152] A system including:

[2153] (Claim 2)

[2154] 10. The system of claim 1, further comprising means for analyzing the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that he or she holds dear.

[2155] (Claim 3)

[2156] 10. The system of claim 1, further comprising means for using a machine learning model in generating the career plan.

[2157] "Application Example 1"

[2158] (Claim 1)

[2159] a means for authenticating a user's login information;

[2160] means for interactively displaying questions to a user and collecting answers;

[2161] A means for transmitting the collected response data to a server;

[2162] A means for analyzing the response data and extracting user characteristics;

[2163] means for generating a career plan based on the characteristics of a user;

[2164] A means for proposing the generated career plan and a list of required skills and experiences to the user;

[2165] A means of collecting user feedback and incorporating it into system improvements;

[2166] A means of taking into account specific skills and experience in delivery work when generating career plans by identifying needs and suggesting suitable jobs;

[2167] A system including:

[2168] (Claim 2)

[2169] 10. The system of claim 1, further comprising means for analyzing the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that he or she holds dear.

[2170] (Claim 3)

[2171] 10. The system of claim 1, further comprising means for using a machine learning model in generating the career plan.

[2172] "Example 2: Combining Emotion Engines"

[2173] (Claim 1)

[2174] a means for authenticating a user's login information;

[2175] means for interactively displaying questions to a user and collecting answers;

[2176] A means for transmitting the collected response data to a server;

[2177] means for recognizing emotion data accompanying the collected response data;

[2178] A means for analyzing the response and emotion data and extracting user characterist...

Claims

1. a means for authenticating a user's login information; means for interactively displaying questions to a user and collecting answers; A means for transmitting the collected response data to a server; A means for analyzing the response data and extracting user characteristics; means for generating a career plan based on the characteristics of a user; A means for proposing the generated career plan and a list of required skills and experiences to the user; A means of collecting user feedback and incorporating it into system improvements; A system including:

2. 10. The system of claim 1, further comprising means for analyzing the user's strengths, weaknesses, areas of expertise, areas of weakness, and values ​​that he or she holds dear.

3. The system of claim 1 , further comprising means for using a machine learning model in generating the career plan.

Images