system

Abstract

We provide the system. [Solution] A means for collecting and pre-processing user input information, A method using generative AI to analyze user interests and skills based on pre-processed data, A means of visualizing the analysis results and providing them to the user as visual information, A means of receiving user feedback and using it to improve the service, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern diverse occupational environments, it is a difficult task for individual users to select an optimal occupation or career plan based on their interests and skills. Conventional career consulting often remains at general advice and is often not individualized, and has not been able to meet the specific needs of users. Therefore, there is a need for a system that effectively supports users in making appropriate career choices and specific career planning.

Means for Solving the Problems

[0005] This invention provides a system that offers personalized career diagnosis and career planning by utilizing a generative AI model based on user input information. Specifically, it collects user input information and analyzes it using natural language processing technology to perform appropriate tagging. Next, it uses generative AI to analyze the user's interests and skills in detail and select suitable occupations and career plans. By visualizing these selection results and presenting them to the user as intuitive and easy-to-understand visual information, the system helps the user think concretely about their own career. Furthermore, by incorporating user feedback into the system and utilizing it for service improvement, it is possible to consistently provide highly accurate suggestions that meet the user's needs.

[0006] "User input information" refers to data provided by users of the system in the form of text or multiple-choice options, including their interests, skills, work experience, and career goals.

[0007] "Preprocessing" is the process of formatting input information obtained from the user into a format that is easy to analyze, and includes processes such as text normalization and missing value imputation.

[0008] "Generative AI" is a form of artificial intelligence that analyzes data based on user input and proposes appropriate occupations and career plans, operating based on machine learning technology.

[0009] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language, and includes capabilities such as text extraction, translation, and generation.

[0010] "Visualization" is the process of representing analysis results in the form of infographics, graphs, flowcharts, etc., and presenting them in a format that is easy for users to understand intuitively.

[0011] "Feedback" refers to user reactions and opinions on suggested content, and is useful information for improving the system and enhancing the accuracy of the service. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention uses generative AI to propose personalized occupational diagnoses and career plans for users, and specific embodiments thereof are described below.

[0034] The user first logs into the system using their device. After logging in, a dedicated questionnaire form is displayed on the device, and the user enters information about their interests, skill set, work experience, and career goals. This data is then sent from the device to the server.

[0035] The server receives data from the user and performs preprocessing to format it into the appropriate format. This preprocessing includes text normalization, information structuring, and necessary tagging using natural language processing techniques.

[0036] Next, the server uses a generative AI model to analyze the pre-processed data. This analysis extracts keywords based on the user's interests and skills and compares them with past data from similar profiles. Through this process, the most suitable occupation and career plan are formulated.

[0037] Subsequently, the server visualizes the analysis results using a visual note tool. The visualized results are converted into flowcharts and infographics, making them intuitively understandable to the user. This visualization allows users to easily grasp suggested career paths and suitable job information.

[0038] Finally, along with the generated visualizations, a detailed career diagnosis and career plan are sent to the user's device. This helps users consider their career choices and develop concrete action plans. Furthermore, users can provide feedback to improve the service and further enhance the accuracy of the analysis.

[0039] For example, if a user states that they "have technical skills and want to demonstrate leadership," the generative AI model analyzes the data and proposes a career plan as a project manager. It also suggests recommended skills and certification programs, allowing the user to take concrete steps to advance their career.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user accesses the terminal and logs in. During this process, the system authenticates the user and verifies their login status. Only if the user has access rights can they proceed to the next step.

[0043] Step 2:

[0044] The terminal provides authenticated users with a questionnaire form to input information necessary for career assessment and career planning. This form includes questions about interests, skills, work experience, and career goals. Users then answer these questions.

[0045] Step 3:

[0046] The terminal structures the data entered by the user and sends it to the server as a JSON-formatted request. This request contains the user's input information.

[0047] Step 4:

[0048] The server analyzes the data received from the terminal. First, it performs text normalization, unifying case sensitivity and removing unnecessary spaces. Furthermore, it maintains data integrity by checking for missing values ​​and detecting outliers.

[0049] Step 5:

[0050] The server uses natural language processing technology to tag user input. This highlights important keywords and attributes, creating a foundation for subsequent generative AI models to use for analysis.

[0051] Step 6:

[0052] The server uses a generative AI model to analyze pre-processed data. First, it extracts key keywords from the user's interests and skills. Next, it compares the extracted keywords with historical data and evaluates similarity to select the most suitable occupation and career plan.

[0053] Step 7:

[0054] The server visualizes the generated analysis results using a visual note tool. It creates career path flowcharts and graphs of interest and skill distributions, transforming them into a user-friendly format.

[0055] Step 8:

[0056] The server sends the visualization and analysis results to the terminal. The terminal displays this information to the user, allowing the user to review the data.

[0057] Step 9:

[0058] Users consider their own career paths based on the information provided. If necessary, they send feedback to the server via their device. The server analyzes this feedback to improve the service and enhance the accuracy of the generated AI models.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Conventional career assessment systems have struggled to provide occupational assessments and career plan suggestions that fully consider the individuality of each user. As a result, they often only offer generic suggestions that fail to meet the specific needs of each user. Furthermore, there were limitations in how to effectively utilize user feedback, which hindered service improvement.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for collecting input information from the user and pre-processing it using an information processing device, means for using a generative AI to analyze the user's interests and abilities based on the pre-processed information, and means for providing the analysis results as visual information using an information presentation device. This makes it possible to provide occupational diagnoses and career plans optimized for the individual characteristics of the user, and also facilitates continuous improvement of the service through the use of feedback.

[0064] "Input information" refers to information provided by the user, including data such as the user's interests, abilities, experience, and goals.

[0065] An "information processing device" is a device that appropriately formats input information obtained from a user and converts it into a format suitable for analysis.

[0066] "Generative AI" is a type of artificial intelligence that is modeled based on large amounts of data and used to analyze input information to present users with the most suitable occupations and career plans.

[0067] An "information presentation device" is a device that visually displays analysis results and provides information in an easy-to-understand manner for the user.

[0068] "Visual information" refers to a format in which analysis results are displayed clearly as diagrams or charts through a user interface.

[0069] "Feedback" refers to opinions and evaluations provided by users, and is information used to improve the performance of a service.

[0070] This invention is a system that provides personalized career assessments and career plans for users. Users begin by accessing the system using a terminal and logging in. After logging in, users enter information about their interests, skill sets, work experience, and career goals into a dedicated question form displayed on the terminal. This entered information is then transmitted from the terminal to the server.

[0071] The server uses an information processing device to process information received from the user. This device normalizes and structures the text and performs tagging through natural language processing techniques. This preprocessing prepares the data for analysis.

[0072] Next, the server uses a generative AI model to analyze the pre-processed data. In this analysis process, keywords are extracted based on the user's interests and skills, and compared with similar past data to formulate the most suitable occupation and career plan for the user.

[0073] Subsequently, the server visualizes the analysis results using an information display device. The visualized results are then converted into flowcharts and infographics to aid user understanding. This allows users to intuitively grasp suggested career paths and suitable job information.

[0074] For example, if a user states that they "possess technical skills and want to demonstrate leadership," the generative AI model analyzes this and proposes a career plan as a project manager. Furthermore, it also suggests programs for acquiring necessary skills and qualifications, allowing the user to advance their career based on this information.

[0075] An example of a prompt message might be, "I have technical skills and want to demonstrate leadership. Please recommend suitable occupations and the necessary skills and qualifications." This allows for personalized suggestions.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user logs into the system using a terminal. The terminal enters user authentication information and sends this information to the server. The server verifies the authentication information and initiates a session. This grants the user permission to begin using the system.

[0079] Step 2:

[0080] Users enter information about their interests, skill sets, work experience, and career goals into a question form displayed on their device. The input data is sent from the device to the server. The server receives this data and uses an information processing device to normalize and structure the text. Furthermore, it uses natural language processing techniques to add necessary tags and outputs the data formatted for analysis.

[0081] Step 3:

[0082] The server invokes a generative AI model and performs analysis using pre-processed data as input. Specifically, it extracts keywords based on the user's interests and skills and performs data calculations by comparing these keywords with similar past data. Through this analysis, the generative AI model evaluates the most suitable occupation and career plan for the user and outputs the recommended results.

[0083] Step 4:

[0084] The server uses an information display device to visualize the analysis results generated by the AI ​​model. The input includes the analysis results, and the output is data visualized as flowcharts or infographics. This visualization allows users to intuitively understand the results.

[0085] Step 5:

[0086] The server sends visualized information, detailed career assessments, and career plans to the user's device. The user receives this information and considers their career choices based on the suggestions. Users can also send feedback from their device to the server, which uses this feedback to improve the system. This enables continuous service improvement.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] There is a need for means to facilitate in-home career assessments and career planning suggestions, and to enable users to easily design their own careers and careers by providing personalized information to each user through audio and visual means. In such an environment, the challenge is to develop technologies that provide the analysis results of the information in an easy-to-understand format and make them easy to use.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for collecting and preprocessing information from the user, means for using generative AI to analyze the user's interests and abilities based on the preprocessed data, means for providing the analysis results as visual information, means for outputting the analysis results to a home device using voice and display, and means for receiving feedback from the user and utilizing it to improve the service. This makes it possible to easily receive career assessments and career plan suggestions at home.

[0092] "Methods for collecting information" refer to a series of steps taken to capture data and statements provided by users and prepare them as a database necessary for later analysis.

[0093] "Preprocessing methods" refer to techniques for preparing collected data into an analyzable format, including text normalization and data structure organization.

[0094] "A method using generative AI to analyze user interests and abilities" refers to a process that utilizes artificial intelligence to determine user interests and skills based on data provided by the user.

[0095] "Providing information visually" refers to methods of presenting analysis results in visual formats such as diagrams, graphs, and flowcharts in order to allow users to intuitively understand the results.

[0096] "A method for outputting analysis results using audio and a display on home devices" refers to a technology that communicates analyzed information to the user visually and audibly through devices used within the home.

[0097] "Methods for receiving user feedback and using it to improve services" refer to methods for obtaining feedback provided by users after they have used the service and using that feedback to improve the accuracy and functionality of the system.

[0098] To implement this invention, it is necessary to build a system that utilizes consumer robots and home terminals to propose personalized occupational diagnoses and career plans to users. The details are described below.

[0099] Program Overview

[0100] The server receives data from the user, analyzes it, and outputs the results using the following procedure.

[0101] 1. Hardware configuration:

[0102] Home appliances (devices that interact with the user)

[0103] Voice input devices (microphone, etc.)

[0104] Audio and visual output devices (speakers, displays, etc.)

[0105] 2. Software configuration:

[0106] By using a speech recognition system (e.g., Google® Speech-to-Text), user voice input is converted into text.

[0107] We will use natural language processing libraries (e.g., spaCy, NLTK) to structure and tag text information from users.

[0108] By utilizing a generative AI model (e.g., GPT-4®), text data is analyzed to generate career assessment results and career plans based on the user's interests and skills.

[0109] 3. Data processing / calculation:

[0110] The server converts the audio to text, and then normalizes and tags the text.

[0111] Using a generative AI model, the system selects the occupation and career plan that best matches the user profile.

[0112] The analysis results are visualized and provided to the user through the display and speakers of home appliances.

[0113] Operation and specific examples

[0114] The user speaks to the home robot, saying, "I want to become an engineer, what should I do?" The server converts the speech into text and begins analysis using natural language processing technology. The generative AI model formulates the necessary skills and qualifications for the user's desired profession and displays the results visually in a flowchart format on the screen. Detailed assistance is also provided via voice through the speaker.

[0115] Example prompts for generative AI models:

[0116] "User language input: Information for aspiring engineers. Suggest necessary skills and qualifications."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The user voice-inputs questions about their occupation into a home device. The device receives this voice input and converts it into text data using a speech recognition system. The converted text is then prepared for interpretation regarding the user's interests and career. In this step, the input is the user's voice data, and the output is text data.

[0120] Step 2:

[0121] The server analyzes the text data using a natural language processing library to extract important keywords related to interests and abilities. This process includes tagging and text normalization. The server generates structured information from the raw text data; the input is the text data obtained in step 1, and the output is structured data.

[0122] Step 3:

[0123] The server uses a generative AI model to select the most suitable occupation and career plan for the user based on structured data. This process utilizes analyzed keywords to generate occupational information that best fits the user profile. The input here is the structured data from step 2, and the output is a list of optional occupations and career plans.

[0124] Step 4:

[0125] The server visualizes the analysis results and presents them to the user using the display of a home appliance. This visualization uses flowcharts and infographics and is provided in a way that is easy for the user to understand. In this step, the output data from step 3 is converted into a visual format.

[0126] Step 5:

[0127] The home device notifies the user of the analysis results via a speaker, providing a more detailed explanation. This audio output is designed to be easy for the user to hear and adjusts the content of the speech to suit the user's environment. The input for this step is the analysis results from step 3, and the output is audio information.

[0128] Step 6:

[0129] Users provide feedback via voice or touch interface based on information provided through home devices. The server receives this feedback and uses it to improve the accuracy of the service and enhance the user experience. The input in this step is user feedback information, and the output is insights for service improvement.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system for optimizing personalized career diagnosis and career plan suggestions for users, taking into account their emotions. By combining a generative AI with an emotion engine, this system analyzes the user's emotions from their text input and provides more precise suggestions based on that analysis.

[0132] Users access the system using a terminal and log in. After authentication, the terminal provides the user with a questionnaire form. This form includes questions about interests, skills, work experience, and career goals, which the user answers. The user's input may also include emotional elements such as their feelings about their career and their hopes and anxieties regarding their occupation.

[0133] The terminal structures this input information and sends it to the server. The server first preprocesses the data as in conventional technology, and then activates the emotion engine. The emotion engine extracts and quantifies emotions from the user's input using natural language processing and machine learning models. As a result, emotion labels such as positive and negative are generated, and a generating AI uses these labels to formulate career diagnoses and career plans that take emotions into account.

[0134] The generated plans are visualized using a visual note-taking tool and provided to the user via their device. This allows users to consider their career choices while understanding their emotional expectations and potential issues.

[0135] As a concrete example, suppose a user responds, "I feel anxious about working in a team, but I want to develop my leadership skills." In this case, the emotion engine extracts emotions such as "anxiety" and "hope" as labels. The generating AI takes this into consideration and proposes a career plan that emphasizes individual skill development while gradually building experience as a team leader in small projects. In this way, this system can provide more empathetic and practical career support that takes into account the user's emotional aspects, rather than just performing data-based analysis.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user logs into the system using their device. Upon successful login, a question form appears on the device. This form includes questions about the user's interests, skills, work experience, and career goals. The user answers these questions.

[0139] Step 2:

[0140] The terminal collects data entered by the user, structures it in JSON format, and sends it to the server. This data includes responses in text format.

[0141] Step 3:

[0142] The server analyzes the data received from the terminal, first normalizing the text by unifying case sensitivity and removing unnecessary spaces. Next, it detects data anomalies and corrects them as needed.

[0143] Step 4:

[0144] The server uses an emotion engine to extract emotions from the text entered by the user. Natural language processing techniques are used to perform sentiment analysis and generate emotion labels such as positive, negative, and neutral. These emotion labels are processed as numerical data and used for subsequent analysis.

[0145] Step 5:

[0146] The server inputs pre-processed data and emotion labels into a generative AI model. The generative AI considers the user's interests, skills, and emotions to develop optimal occupations and career plans. This enables more personalized suggestions that include emotional aspects.

[0147] Step 6:

[0148] The server visualizes the generated career plan using a visual note tool. It generates career path flowcharts and graphs including sentiment analysis results, and converts them into a format that the user can intuitively understand.

[0149] Step 7:

[0150] The server sends visualized information to the terminal. The terminal then presents these results to the user, clearly indicating detailed information and specific career steps.

[0151] Step 8:

[0152] Users review the provided career plans and send feedback based on their emotions and needs to the server via their device. The server receives this feedback and uses it to improve its emotion engine and generative AI models.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] Traditional career assessment systems propose career plans based on a user's experience and skills, but they often fail to adequately address user needs because they do not take user emotions into consideration. This problem is particularly pronounced in situations where anxiety and aspirations related to career changes play a significant role. Therefore, there is a need for career plan proposals that take user emotions into account.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes means for collecting and pre-processing user input information, means for analyzing the user's emotions and using that information to personalize the proposed content, and means for visualizing the analysis results and providing them to the user as visual information. This makes it possible to provide a personalized career plan that takes the user's emotions into consideration.

[0158] "User input information" refers to data about the user's interests, skills, work experience, career goals, and emotions, which the user provides through their device.

[0159] "Preprocessing" refers to the process of normalizing and denoising data in order to convert input information collected from users into an analyzable format.

[0160] "Generative AI" refers to artificial intelligence that uses machine learning models to analyze user input and automatically generate personalized career diagnoses and career plans.

[0161] "Visualization" refers to the process of displaying analysis results in a form that is easy for users to understand, such as graphs and charts.

[0162] "Feedback" refers to opinions and impressions from users regarding the services provided, and is used to improve future services.

[0163] An "emotion engine" is a system that uses natural language processing technology to extract and quantify emotions from user input.

[0164] "Personalization" refers to the process of adjusting and providing suggestions in a way that is specifically tailored to the user, taking into account their characteristics and emotions.

[0165] This invention is a system that provides personalized career diagnosis and career plan suggestions for users. This system uses a generative AI model and an emotion engine to analyze the user's emotions from text input, and then provides more precise suggestions based on that analysis.

[0166] Users access the system and log in using a terminal. After authentication, the terminal presents the user with a questionnaire form. This questionnaire includes information about the user's interests, skills, work experience, and career goals, which the user answers. Emotional elements such as feelings, hopes, and anxieties about their career can also be entered.

[0167] The terminal structures the user's input information and sends it to the server. The server first preprocesses the received data, removing noise and ensuring format consistency. Then, it activates an emotion engine and uses natural language processing techniques to analyze the user's emotions and generate emotion labels such as "anxiety" and "hope."

[0168] Next, a generative AI model implemented on the server generates personalized career diagnoses and career plans based on the user's emotion labels. This generation process takes into account the user's specific needs and emotions, enabling the delivery of more personalized results.

[0169] The generated career plan is visualized using a visual note tool and provided to the user via their device in an easy-to-understand format. This allows users to make career choices while considering their own feelings and understanding their expectations and potential problems.

[0170] For example, if a user inputs "I feel anxious about working in a team, but I want to develop my leadership skills," the emotion engine will extract the emotions "anxiety" and "hope." Taking this information into consideration, the generating AI will propose a career plan that includes a stage where the user gains experience as a team leader in small projects.

[0171] An example of a prompt for a generative AI model would be an instruction such as, "Generate career advice considering the user's sentiment label."

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] A user accesses the system using a terminal and logs in. The input is the user's authentication information, and the output is a message indicating successful or unsuccessful login. The terminal sends the authentication information to the server, which then authenticates it to initiate a session.

[0175] Step 2:

[0176] After receiving a notification of successful authentication from the server, the terminal displays a question form to the user. The input includes information about the user's interests, skills, work experience, career goals, and feelings regarding their occupational choices. The user answers these questions, and the input data is structured and output to the terminal.

[0177] Step 3:

[0178] The terminal sends structured data to the server. The input data includes information on the user's interests, skills, experiences, and emotions entered into a form. The server preprocesses the received information, performing tasks such as noise reduction and formatting consistency, and outputs an analyzable dataset.

[0179] Step 4:

[0180] The server runs the emotion engine using pre-processed data. Inputs include user text information and pre-processed data, and the emotion engine uses natural language processing techniques to extract and quantify emotion labels. The output generates emotion labels such as "anxiety" and "hope."

[0181] Step 5:

[0182] The server uses a generative AI model to generate a career plan using emotion labels and the user's occupation-related information as input. The generative AI takes these labels into consideration and outputs an occupation diagnosis and career plan optimized for the user.

[0183] Step 6:

[0184] The career plan generated on the server is visualized using a visual note tool. The input contains information about the generated career plan, and the output is data in a visualized format. Graphs and charts are generated to enhance user understanding.

[0185] Step 7:

[0186] The device provides users with a visualized career plan. Through the display connected to the device, users can evaluate the career plan, taking their own emotional labels into account, and use this information to make decisions that include emotional considerations.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] Conventional career assessment and career plan generation systems make suggestions based on the user's skills and experience, but they have the problem of not adequately meeting the needs of individual users because they do not take into account the user's emotional aspects. The present invention aims to provide a system that can make suggestions that take such emotions into consideration, and to provide more appropriate support that incorporates emotional elements, including the user's expectations and anxieties in career choices.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for collecting and pre-processing user input information, means for analyzing the user's interests and skills using a generated AI based on the collected data, and means for emotional analysis for extracting and quantifying emotional information included in the user's input. This makes it possible to propose personalized occupations and career plans that also take emotional aspects into consideration.

[0192] "User input information" refers to information about the user's interests, skills, work experience, career goals, and emotions.

[0193] "Preprocessing" refers to data processing that converts user input information into a format suitable for analysis.

[0194] "Generative AI" is an artificial intelligence technology that analyzes a user's interests and skills based on their input information and generates the most suitable occupation and career plan.

[0195] "Visualization" refers to a method of displaying analysis results and proposed career plans in a way that is easy for users to understand.

[0196] "Emotional analysis" is the process of extracting emotional information from user input, quantifying it, and analyzing it.

[0197] "Quantification" is a process that expresses extracted emotional information as numerical data, making analysis and comparison possible.

[0198] "Feedback" is the process of collecting and analyzing responses and opinions from users to improve the service.

[0199] This invention is a system for optimizing a user's occupational diagnosis and career plan generation while taking emotions into consideration. This system consists of a terminal used by the user, a server responsible for data processing, a generation AI, and an emotion engine for emotion analysis.

[0200] The terminal collects input information from the user regarding their interests, skills, work experience, career goals, and emotions. This information is structured and sent to the server. The server preprocesses the input information, extracts emotional information using an emotion engine, and quantifies it. During this process, the emotional information is labeled using natural language processing techniques and expressed as elements such as positive / negative emotions, anxiety, and hope.

[0201] The server uses a generative AI to generate a career diagnosis and career plan that takes into account the user's input information and emotional information. The generated plan is visualized using a visualization tool and provided to the user via the terminal. The visualized information enables the user to make more appropriate decisions regarding their career choices, taking into account the emotional factors involved.

[0202] For example, if a user inputs "I feel anxious about changes in my job, but I want to learn new skills," the emotion engine will generate labels such as "anxiety" and "want to learn." Based on this, the generating AI will consider the user's emotions and propose a step-by-step skill improvement plan. This might include recommendations for online courses to learn new skills or mindfulness practices to reduce anxiety.

[0203] An example of a prompt is: "Generate the following sentiment analysis and career plan: I feel anxious about job changes, but I want to learn new technologies."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] Users use their devices to input information about their career interests, skills, work experience, and feelings, and send it to the system. The input information is sent to the server in text data format.

[0207] Step 2:

[0208] When the server receives input from a user, it first performs data preprocessing. Specifically, it converts text data into a format that is easy to parse and removes unnecessary information. The preprocessed data is then stored as structured data.

[0209] Step 3:

[0210] The server extracts and quantifies emotional information from data preprocessed using natural language processing technology. An emotion engine is used to generate emotional labels such as positive, negative, anxiety, and hope from the text data. The extracted emotional labels are output and passed to the generation AI.

[0211] Step 4:

[0212] The server uses generative AI to generate optimal occupations and career plans based on the user's interests, skills, and emotional information. The generative AI considers emotional labels and makes suggestions that take the user's emotional aspects into account. As a result of this process, a personalized career plan is output.

[0213] Step 5:

[0214] The generated career plan is visualized using a visual note tool and sent to the device. The visualized information is displayed in a way that makes it easier for the user to understand their career choices.

[0215] Step 6:

[0216] Users review the provided career plan and, if necessary, send feedback to the server via their device. The server uses the received feedback as data to improve the accuracy of future suggestions.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention uses generative AI to propose personalized occupational diagnoses and career plans for users, and specific embodiments thereof are described below.

[0234] The user first logs into the system using their device. After logging in, a dedicated questionnaire form is displayed on the device, and the user enters information about their interests, skill set, work experience, and career goals. This data is then sent from the device to the server.

[0235] The server receives data from the user and performs preprocessing to format it into the appropriate format. This preprocessing includes text normalization, information structuring, and necessary tagging using natural language processing techniques.

[0236] Next, the server uses a generative AI model to analyze the pre-processed data. This analysis extracts keywords based on the user's interests and skills and compares them with past data from similar profiles. Through this process, the most suitable occupation and career plan are formulated.

[0237] Subsequently, the server visualizes the analysis results using a visual note tool. The visualized results are converted into flowcharts and infographics, making them intuitively understandable to the user. This visualization allows users to easily grasp suggested career paths and suitable job information.

[0238] Finally, along with the generated visualizations, a detailed career diagnosis and career plan are sent to the user's device. This helps users consider their career choices and develop concrete action plans. Furthermore, users can provide feedback to improve the service and further enhance the accuracy of the analysis.

[0239] For example, if a user states that they "have technical skills and want to demonstrate leadership," the generative AI model analyzes the data and proposes a career plan as a project manager. It also suggests recommended skills and certification programs, allowing the user to take concrete steps to advance their career.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user accesses the terminal and logs in. During this process, the system authenticates the user and verifies their login status. Only if the user has access rights can they proceed to the next step.

[0243] Step 2:

[0244] The terminal provides authenticated users with a questionnaire form to input information necessary for career assessment and career planning. This form includes questions about interests, skills, work experience, and career goals. Users then answer these questions.

[0245] Step 3:

[0246] The terminal structures the data entered by the user and sends it to the server as a JSON-formatted request. This request contains the user's input information.

[0247] Step 4:

[0248] The server analyzes the data received from the terminal. First, it performs text normalization, unifying case sensitivity and removing unnecessary spaces. Furthermore, it maintains data integrity by checking for missing values ​​and detecting outliers.

[0249] Step 5:

[0250] The server uses natural language processing technology to tag user input. This highlights important keywords and attributes, creating a foundation for subsequent generative AI models to use for analysis.

[0251] Step 6:

[0252] The server uses a generative AI model to analyze pre-processed data. First, it extracts key keywords from the user's interests and skills. Next, it compares the extracted keywords with historical data and evaluates similarity to select the most suitable occupation and career plan.

[0253] Step 7:

[0254] The server visualizes the generated analysis results using a visual note tool. It creates career path flowcharts and graphs of interest and skill distributions, transforming them into a user-friendly format.

[0255] Step 8:

[0256] The server sends the visualization and analysis results to the terminal. The terminal displays this information to the user, allowing the user to review the data.

[0257] Step 9:

[0258] Users consider their own career paths based on the information provided. If necessary, they send feedback to the server via their device. The server analyzes this feedback to improve the service and enhance the accuracy of the generated AI models.

[0259] (Example 1)

[0260] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] Conventional career assessment systems have struggled to provide occupational assessments and career plan suggestions that fully consider the individuality of each user. As a result, they often only offer generic suggestions that fail to meet the specific needs of each user. Furthermore, there were limitations in how to effectively utilize user feedback, which hindered service improvement.

[0262] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0263] In this invention, the server includes means for collecting input information from the user and pre-processing it using an information processing device, means for using a generative AI to analyze the user's interests and abilities based on the pre-processed information, and means for providing the analysis results as visual information using an information presentation device. This makes it possible to provide occupational diagnoses and career plans optimized for the individual characteristics of the user, and also facilitates continuous improvement of the service through the use of feedback.

[0264] "Input information" refers to information provided by the user, including data such as the user's interests, abilities, experience, and goals.

[0265] An "information processing device" is a device that appropriately formats input information obtained from a user and converts it into a format suitable for analysis.

[0266] "Generative AI" is a type of artificial intelligence that is modeled based on large amounts of data and used to analyze input information to present users with the most suitable occupations and career plans.

[0267] An "information presentation device" is a device that visually displays analysis results and provides information in an easy-to-understand manner for the user.

[0268] "Visual information" refers to a format in which analysis results are displayed clearly as diagrams or charts through a user interface.

[0269] "Feedback" refers to opinions and evaluations provided by users, and is information used to improve the performance of a service.

[0270] This invention is a system that provides personalized career assessments and career plans for users. Users begin by accessing the system using a terminal and logging in. After logging in, users enter information about their interests, skill sets, work experience, and career goals into a dedicated question form displayed on the terminal. This entered information is then transmitted from the terminal to the server.

[0271] The server uses an information processing device to process information received from the user. This device normalizes and structures the text and performs tagging through natural language processing techniques. This preprocessing prepares the data for analysis.

[0272] Next, the server uses a generative AI model to analyze the pre-processed data. In this analysis process, keywords are extracted based on the user's interests and skills, and compared with similar past data to formulate the most suitable occupation and career plan for the user.

[0273] Subsequently, the server visualizes the analysis results using an information display device. The visualized results are then converted into flowcharts and infographics to aid user understanding. This allows users to intuitively grasp suggested career paths and suitable job information.

[0274] For example, if a user states that they "possess technical skills and want to demonstrate leadership," the generative AI model analyzes this and proposes a career plan as a project manager. Furthermore, it also suggests programs for acquiring necessary skills and qualifications, allowing the user to advance their career based on this information.

[0275] An example of a prompt message might be, "I have technical skills and want to demonstrate leadership. Please recommend suitable occupations and the necessary skills and qualifications." This allows for personalized suggestions.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The user logs in to the system using a terminal. The input is the user's authentication information, and the terminal sends this information to the server. The server verifies the authentication information and starts a session. Thereby, the user obtains the right to start using the system.

[0279] Step 2:

[0280] The user inputs information about their interests, skill set, work experience, and career goals into the question form displayed on the terminal. The input data is sent from the terminal to the server. The server receives this data and uses an information processing device to normalize and structure the text. Furthermore, necessary tags are added using natural language processing technology, and data formatted in a form suitable for analysis is output.

[0281] Step 3:

[0282] The server calls the generative AI model and performs analysis using the preprocessed data as input. Specifically, keywords are extracted based on the user's interests and skills, and data operations are performed to compare this with past similar data. Through this analysis, the generative AI model evaluates the optimal occupation and career plan for the user and outputs the recommendation result.

[0283] Step 4:

[0284] The server uses an information presentation device to visualize the analysis results by the generative AI model. The analysis results are included as input, and data visualized as a flowchart or infographic is output. Through this visualization, the user can intuitively understand the results.

[0285] Step 5:

[0286] The server sends the visualized information and detailed career diagnosis and career plans to the terminal. The user receives this and considers their career choices based on the proposed content. Also, the user can send feedback from the terminal to the server, and the server utilizes this feedback for system improvement. This enables continuous improvement of the service.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] Means are required to facilitate career diagnosis and career plan proposals within the home and to provide individualized information to individual users both aurally and visually, enabling users to easily conduct their own career and career design. In such an environment, the technology of providing the analysis results of information in an easily understandable form and making it easily available is an issue.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0291] In this invention, the server includes means for collecting and preprocessing information from the user, means using generative AI for analyzing the user's interests and abilities based on the preprocessed data, means for providing the analysis results as visual information, means for outputting the analysis results to household devices using voice and display, and means for receiving opinions from the user and utilizing them for service improvement. This enables users to easily receive career diagnosis and career plan proposals at home.

[0292] The "method of collecting information" is a series of procedures for capturing the data and statements provided by the user and preparing them as a database required for subsequent analysis.

[0293] "Preprocessing methods" refer to techniques for preparing collected data into an analyzable format, including text normalization and data structure organization.

[0294] "A method using generative AI to analyze user interests and abilities" refers to a process that utilizes artificial intelligence to determine user interests and skills based on data provided by the user.

[0295] "Providing information visually" refers to methods of presenting analysis results in visual formats such as diagrams, graphs, and flowcharts in order to allow users to intuitively understand the results.

[0296] "A method for outputting analysis results using audio and a display on home devices" refers to a technology that communicates analyzed information to the user visually and audibly through devices used within the home.

[0297] "Methods for receiving user feedback and using it to improve services" refer to methods for obtaining feedback provided by users after they have used the service and using that feedback to improve the accuracy and functionality of the system.

[0298] To implement this invention, it is necessary to build a system that utilizes consumer robots and home terminals to propose personalized occupational diagnoses and career plans to users. The details are described below.

[0299] Program Overview

[0300] The server receives data from the user, analyzes it, and outputs the results using the following procedure.

[0301] 1. Hardware configuration:

[0302] Home appliances (devices that interact with the user)

[0303] Voice input devices (microphone, etc.)

[0304] Audio and visual output devices (such as speakers, displays, etc.)

[0305] 2. Software configuration:

[0306] By using a speech recognition system (e.g., Google Speech-to-Text), convert the user's voice input into text.

[0307] Using a natural language processing library (e.g., spaCy, NLTK), structure and tag the text information from the user.

[0308] Utilize a generative AI model (e.g., GPT-4) to analyze the text data and generate career diagnosis results and career plans based on the user's interests and skills.

[0309] 3. Data processing / operation:

[0310] The server converts the voice into text and performs normalization and tagging of the text.

[0311] Using the generative AI model, select the occupation and career plan that best match the user profile.

[0312] Visualize the analysis results and provide them to the user through the display and speaker of the household device.

[0313] Operations and specific examples

[0314] The user talks to the household robot, "I'm aiming to be an engineer. What should I do?" The server converts the voice into text and starts analysis using natural language processing technology. The generative AI model formulates the skills and qualifications required for the occupation the user is aiming for and displays the results visually in the form of a flowchart on the display. Also, detailed assistance is provided audibly from the speaker.

[0315] Example of prompt text for the generative AI model:

[0316] "User language input: Information for aspiring engineers. Suggest necessary skills and qualifications."

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The user voice-inputs questions about their occupation into a home device. The device receives this voice input and converts it into text data using a speech recognition system. The converted text is then prepared for interpretation regarding the user's interests and career. In this step, the input is the user's voice data, and the output is text data.

[0320] Step 2:

[0321] The server analyzes the text data using a natural language processing library to extract important keywords related to interests and abilities. This process includes tagging and text normalization. The server generates structured information from the raw text data; the input is the text data obtained in step 1, and the output is structured data.

[0322] Step 3:

[0323] The server uses a generative AI model to select the most suitable occupation and career plan for the user based on structured data. This process utilizes analyzed keywords to generate occupational information that best fits the user profile. The input here is the structured data from step 2, and the output is a list of optional occupations and career plans.

[0324] Step 4:

[0325] The server visualizes the analysis results and presents them to the user using the display of a home appliance. This visualization uses flowcharts and infographics and is provided in a way that is easy for the user to understand. In this step, the output data from step 3 is converted into a visual format.

[0326] Step 5:

[0327] The home device notifies the user of the analysis results via a speaker, providing a more detailed explanation. This audio output is designed to be easy for the user to hear and adjusts the content of the speech to suit the user's environment. The input for this step is the analysis results from step 3, and the output is audio information.

[0328] Step 6:

[0329] Users provide feedback via voice or touch interface based on information provided through home devices. The server receives this feedback and uses it to improve the accuracy of the service and enhance the user experience. The input in this step is user feedback information, and the output is insights for service improvement.

[0330] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0331] This invention is a system for optimizing personalized career diagnosis and career plan suggestions for users, taking into account their emotions. By combining a generative AI with an emotion engine, this system analyzes the user's emotions from their text input and provides more precise suggestions based on that analysis.

[0332] Users access the system using a terminal and log in. After authentication, the terminal provides the user with a questionnaire form. This form includes questions about interests, skills, work experience, and career goals, which the user answers. The user's input may also include emotional elements such as their feelings about their career and their hopes and anxieties regarding their occupation.

[0333] The terminal structures this input information and sends it to the server. The server first preprocesses the data as in conventional technology, and then activates the emotion engine. The emotion engine extracts and quantifies emotions from the user's input using natural language processing and machine learning models. As a result, emotion labels such as positive and negative are generated, and a generating AI uses these labels to formulate career diagnoses and career plans that take emotions into account.

[0334] The generated plans are visualized using a visual note-taking tool and provided to the user via their device. This allows users to consider their career choices while understanding their emotional expectations and potential issues.

[0335] As a concrete example, suppose a user responds, "I feel anxious about working in a team, but I want to develop my leadership skills." In this case, the emotion engine extracts emotions such as "anxiety" and "hope" as labels. The generating AI takes this into consideration and proposes a career plan that emphasizes individual skill development while gradually building experience as a team leader in small projects. In this way, this system can provide more empathetic and practical career support that takes into account the user's emotional aspects, rather than just performing data-based analysis.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user logs into the system using their device. Upon successful login, a question form appears on the device. This form includes questions about the user's interests, skills, work experience, and career goals. The user answers these questions.

[0339] Step 2:

[0340] The terminal collects data entered by the user, structures it in JSON format, and sends it to the server. This data includes responses in text format.

[0341] Step 3:

[0342] The server analyzes the data received from the terminal, first normalizing the text by unifying case sensitivity and removing unnecessary spaces. Next, it detects data anomalies and corrects them as needed.

[0343] Step 4:

[0344] The server uses an emotion engine to extract emotions from the text entered by the user. Natural language processing techniques are used to perform sentiment analysis and generate emotion labels such as positive, negative, and neutral. These emotion labels are processed as numerical data and used for subsequent analysis.

[0345] Step 5:

[0346] The server inputs pre-processed data and emotion labels into a generative AI model. The generative AI considers the user's interests, skills, and emotions to develop optimal occupations and career plans. This enables more personalized suggestions that include emotional aspects.

[0347] Step 6:

[0348] The server visualizes the generated career plan using a visual note tool. It generates career path flowcharts and graphs including sentiment analysis results, and converts them into a format that the user can intuitively understand.

[0349] Step 7:

[0350] The server sends visualized information to the terminal. The terminal then presents these results to the user, clearly indicating detailed information and specific career steps.

[0351] Step 8:

[0352] Users review the provided career plans and send feedback based on their emotions and needs to the server via their device. The server receives this feedback and uses it to improve its emotion engine and generative AI models.

[0353] (Example 2)

[0354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0355] Traditional career assessment systems propose career plans based on a user's experience and skills, but they often fail to adequately address user needs because they do not take user emotions into consideration. This problem is particularly pronounced in situations where anxiety and aspirations related to career changes play a significant role. Therefore, there is a need for career plan proposals that take user emotions into account.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes means for collecting and pre-processing user input information, means for analyzing the user's emotions and using that information to personalize the proposed content, and means for visualizing the analysis results and providing them to the user as visual information. This makes it possible to provide a personalized career plan that takes the user's emotions into consideration.

[0358] "User input information" refers to data about the user's interests, skills, work experience, career goals, and emotions, which the user provides through their device.

[0359] "Preprocessing" refers to the process of normalizing and denoising data in order to convert input information collected from users into an analyzable format.

[0360] "Generative AI" refers to artificial intelligence that uses machine learning models to analyze user input and automatically generate personalized career diagnoses and career plans.

[0361] "Visualization" refers to the process of displaying analysis results in a form that is easy for users to understand, such as graphs and charts.

[0362] "Feedback" refers to opinions and impressions from users regarding the services provided, and is used to improve future services.

[0363] An "emotion engine" is a system that uses natural language processing technology to extract and quantify emotions from user input.

[0364] "Personalization" refers to the process of adjusting and providing suggestions in a way that is specifically tailored to the user, taking into account their characteristics and emotions.

[0365] This invention is a system that provides personalized career diagnosis and career plan suggestions for users. This system uses a generative AI model and an emotion engine to analyze the user's emotions from text input, and then provides more precise suggestions based on that analysis.

[0366] Users access the system and log in using a terminal. After authentication, the terminal presents the user with a questionnaire form. This questionnaire includes information about the user's interests, skills, work experience, and career goals, which the user answers. Emotional elements such as feelings, hopes, and anxieties about their career can also be entered.

[0367] The terminal structures the user's input information and sends it to the server. The server first preprocesses the received data, removing noise and ensuring format consistency. Then, it activates an emotion engine and uses natural language processing techniques to analyze the user's emotions and generate emotion labels such as "anxiety" and "hope."

[0368] Next, a generative AI model implemented on the server generates personalized career diagnoses and career plans based on the user's emotion labels. This generation process takes into account the user's specific needs and emotions, enabling the delivery of more personalized results.

[0369] The generated career plan is visualized using a visual note tool and provided to the user via their device in an easy-to-understand format. This allows users to make career choices while considering their own feelings and understanding their expectations and potential problems.

[0370] For example, if a user inputs "I feel anxious about working in a team, but I want to develop my leadership skills," the emotion engine will extract the emotions "anxiety" and "hope." Taking this information into consideration, the generating AI will propose a career plan that includes a stage where the user gains experience as a team leader in small projects.

[0371] An example of a prompt for a generative AI model would be an instruction such as, "Generate career advice considering the user's sentiment label."

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] A user accesses the system using a terminal and logs in. The input is the user's authentication information, and the output is a message indicating successful or unsuccessful login. The terminal sends the authentication information to the server, which then authenticates it to initiate a session.

[0375] Step 2:

[0376] After receiving a notification of successful authentication from the server, the terminal displays a question form to the user. The input includes information about the user's interests, skills, work experience, career goals, and feelings regarding their occupational choices. The user answers these questions, and the input data is structured and output to the terminal.

[0377] Step 3:

[0378] The terminal sends structured data to the server. The input data includes information on the user's interests, skills, experiences, and emotions entered into a form. The server preprocesses the received information, performing tasks such as noise reduction and formatting consistency, and outputs an analyzable dataset.

[0379] Step 4:

[0380] The server runs the emotion engine using pre-processed data. Inputs include user text information and pre-processed data, and the emotion engine uses natural language processing techniques to extract and quantify emotion labels. The output generates emotion labels such as "anxiety" and "hope."

[0381] Step 5:

[0382] The server uses a generative AI model to generate a career plan using emotion labels and the user's occupation-related information as input. The generative AI takes these labels into consideration and outputs an occupation diagnosis and career plan optimized for the user.

[0383] Step 6:

[0384] The career plan generated on the server is visualized using a visual note tool. The input contains information about the generated career plan, and the output is data in a visualized format. Graphs and charts are generated to enhance user understanding.

[0385] Step 7:

[0386] The device provides users with a visualized career plan. Through the display connected to the device, users can evaluate the career plan, taking their own emotional labels into account, and use this information to make decisions that include emotional considerations.

[0387] (Application Example 2)

[0388] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0389] Conventional career assessment and career plan generation systems make suggestions based on the user's skills and experience, but they have the problem of not adequately meeting the needs of individual users because they do not take into account the user's emotional aspects. The present invention aims to provide a system that can make suggestions that take such emotions into consideration, and to provide more appropriate support that incorporates emotional elements, including the user's expectations and anxieties in career choices.

[0390] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0391] In this invention, the server includes means for collecting and pre-processing user input information, means for analyzing the user's interests and skills using a generated AI based on the collected data, and means for emotional analysis for extracting and quantifying emotional information included in the user's input. This makes it possible to propose personalized occupations and career plans that also take emotional aspects into consideration.

[0392] "User input information" refers to information about the user's interests, skills, work experience, career goals, and emotions.

[0393] "Preprocessing" refers to data processing that converts user input information into a format suitable for analysis.

[0394] "Generative AI" is an artificial intelligence technology that analyzes a user's interests and skills based on their input information and generates the most suitable occupation and career plan.

[0395] "Visualization" refers to a method of displaying analysis results and proposed career plans in a way that is easy for users to understand.

[0396] "Emotional analysis" is the process of extracting emotional information from user input, quantifying it, and analyzing it.

[0397] "Quantification" is a process that expresses extracted emotional information as numerical data, making analysis and comparison possible.

[0398] "Feedback" is the process of collecting and analyzing responses and opinions from users to improve the service.

[0399] This invention is a system for optimizing a user's occupational diagnosis and career plan generation while taking emotions into consideration. This system consists of a terminal used by the user, a server responsible for data processing, a generation AI, and an emotion engine for emotion analysis.

[0400] The terminal collects input information from the user regarding their interests, skills, work experience, career goals, and emotions. This information is structured and sent to the server. The server preprocesses the input information, extracts emotional information using an emotion engine, and quantifies it. During this process, the emotional information is labeled using natural language processing techniques and expressed as elements such as positive / negative emotions, anxiety, and hope.

[0401] The server uses a generative AI to generate a career diagnosis and career plan that takes into account the user's input information and emotional information. The generated plan is visualized using a visualization tool and provided to the user via the terminal. The visualized information enables the user to make more appropriate decisions regarding their career choices, taking into account the emotional factors involved.

[0402] For example, if a user inputs "I feel anxious about changes in my job, but I want to learn new skills," the emotion engine will generate labels such as "anxiety" and "want to learn." Based on this, the generating AI will consider the user's emotions and propose a step-by-step skill improvement plan. This might include recommendations for online courses to learn new skills or mindfulness practices to reduce anxiety.

[0403] An example of a prompt is: "Generate the following sentiment analysis and career plan: I feel anxious about job changes, but I want to learn new technologies."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] Users use their devices to input information about their career interests, skills, work experience, and feelings, and send it to the system. The input information is sent to the server in text data format.

[0407] Step 2:

[0408] When the server receives input from a user, it first performs data preprocessing. Specifically, it converts text data into a format that is easy to parse and removes unnecessary information. The preprocessed data is then stored as structured data.

[0409] Step 3:

[0410] The server extracts and quantifies emotional information from data preprocessed using natural language processing technology. An emotion engine is used to generate emotional labels such as positive, negative, anxiety, and hope from the text data. The extracted emotional labels are output and passed to the generation AI.

[0411] Step 4:

[0412] The server uses generative AI to generate optimal occupations and career plans based on the user's interests, skills, and emotional information. The generative AI considers emotional labels and makes suggestions that take the user's emotional aspects into account. As a result of this process, a personalized career plan is output.

[0413] Step 5:

[0414] The generated career plan is visualized using a visual note tool and sent to the device. The visualized information is displayed in a way that makes it easier for the user to understand their career choices.

[0415] Step 6:

[0416] Users review the provided career plan and, if necessary, send feedback to the server via their device. The server uses the received feedback as data to improve the accuracy of future suggestions.

[0417] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0418] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0419] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0423] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0424] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0425] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0426] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0427] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0428] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0429] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0430] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0431] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0432] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0433] This invention uses generative AI to propose personalized occupational diagnoses and career plans for users, and specific embodiments thereof are described below.

[0434] The user first logs into the system using their device. After logging in, a dedicated questionnaire form is displayed on the device, and the user enters information about their interests, skill set, work experience, and career goals. This data is then sent from the device to the server.

[0435] The server receives data from the user and performs preprocessing to format it into the appropriate format. This preprocessing includes text normalization, information structuring, and necessary tagging using natural language processing techniques.

[0436] Next, the server uses a generative AI model to analyze the pre-processed data. This analysis extracts keywords based on the user's interests and skills and compares them with past data from similar profiles. Through this process, the most suitable occupation and career plan are formulated.

[0437] Subsequently, the server visualizes the analysis results using a visual note tool. The visualized results are converted into flowcharts and infographics, making them intuitively understandable to the user. This visualization allows users to easily grasp suggested career paths and suitable job information.

[0438] Finally, along with the generated visualizations, a detailed career diagnosis and career plan are sent to the user's device. This helps users consider their career choices and develop concrete action plans. Furthermore, users can provide feedback to improve the service and further enhance the accuracy of the analysis.

[0439] For example, if a user states that they "have technical skills and want to demonstrate leadership," the generative AI model analyzes the data and proposes a career plan as a project manager. It also suggests recommended skills and certification programs, allowing the user to take concrete steps to advance their career.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The user accesses the terminal and logs in. During this process, the system authenticates the user and verifies their login status. Only if the user has access rights can they proceed to the next step.

[0443] Step 2:

[0444] The terminal provides authenticated users with a questionnaire form to input information necessary for career assessment and career planning. This form includes questions about interests, skills, work experience, and career goals. Users then answer these questions.

[0445] Step 3:

[0446] The terminal structures the data entered by the user and sends it to the server as a JSON-formatted request. This request contains the user's input information.

[0447] Step 4:

[0448] The server analyzes the data received from the terminal. First, it performs text normalization, unifying case sensitivity and removing unnecessary spaces. Furthermore, it maintains data integrity by checking for missing values ​​and detecting outliers.

[0449] Step 5:

[0450] The server uses natural language processing technology to tag user input. This highlights important keywords and attributes, creating a foundation for subsequent generative AI models to use for analysis.

[0451] Step 6:

[0452] The server uses a generative AI model to analyze pre-processed data. First, it extracts key keywords from the user's interests and skills. Next, it compares the extracted keywords with historical data and evaluates similarity to select the most suitable occupation and career plan.

[0453] Step 7:

[0454] The server visualizes the generated analysis results using a visual note tool. It creates career path flowcharts and graphs of interest and skill distributions, transforming them into a user-friendly format.

[0455] Step 8:

[0456] The server sends the visualization and analysis results to the terminal. The terminal displays this information to the user, allowing the user to review the data.

[0457] Step 9:

[0458] Users consider their own career paths based on the information provided. If necessary, they send feedback to the server via their device. The server analyzes this feedback to improve the service and enhance the accuracy of the generated AI models.

[0459] (Example 1)

[0460] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0461] Conventional career assessment systems have struggled to provide occupational assessments and career plan suggestions that fully consider the individuality of each user. As a result, they often only offer generic suggestions that fail to meet the specific needs of each user. Furthermore, there were limitations in how to effectively utilize user feedback, which hindered service improvement.

[0462] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0463] In this invention, the server includes means for collecting input information from the user and pre-processing it using an information processing device, means for using a generative AI to analyze the user's interests and abilities based on the pre-processed information, and means for providing the analysis results as visual information using an information presentation device. This makes it possible to provide occupational diagnoses and career plans optimized for the individual characteristics of the user, and also facilitates continuous improvement of the service through the use of feedback.

[0464] "Input information" refers to information provided by the user, including data such as the user's interests, abilities, experience, and goals.

[0465] An "information processing device" is a device that appropriately formats input information obtained from a user and converts it into a format suitable for analysis.

[0466] "Generative AI" is a type of artificial intelligence that is modeled based on large amounts of data and used to analyze input information to present users with the most suitable occupations and career plans.

[0467] An "information presentation device" is a device that visually displays analysis results and provides information in an easy-to-understand manner for the user.

[0468] "Visual information" refers to a format in which analysis results are displayed clearly as diagrams or charts through a user interface.

[0469] "Feedback" refers to opinions and evaluations provided by users, and is information used to improve the performance of a service.

[0470] This invention is a system that provides personalized career assessments and career plans for users. Users begin by accessing the system using a terminal and logging in. After logging in, users enter information about their interests, skill sets, work experience, and career goals into a dedicated question form displayed on the terminal. This entered information is then transmitted from the terminal to the server.

[0471] The server uses an information processing device to process information received from the user. This device normalizes and structures the text and performs tagging through natural language processing techniques. This preprocessing prepares the data for analysis.

[0472] Next, the server uses a generative AI model to analyze the pre-processed data. In this analysis process, keywords are extracted based on the user's interests and skills, and compared with similar past data to formulate the most suitable occupation and career plan for the user.

[0473] Subsequently, the server visualizes the analysis results using an information display device. The visualized results are then converted into flowcharts and infographics to aid user understanding. This allows users to intuitively grasp suggested career paths and suitable job information.

[0474] For example, if a user states that they "possess technical skills and want to demonstrate leadership," the generative AI model analyzes this and proposes a career plan as a project manager. Furthermore, it also suggests programs for acquiring necessary skills and qualifications, allowing the user to advance their career based on this information.

[0475] An example of a prompt message might be, "I have technical skills and want to demonstrate leadership. Please recommend suitable occupations and the necessary skills and qualifications." This allows for personalized suggestions.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The user logs into the system using a terminal. The terminal enters user authentication information and sends this information to the server. The server verifies the authentication information and initiates a session. This grants the user permission to begin using the system.

[0479] Step 2:

[0480] Users enter information about their interests, skill sets, work experience, and career goals into a question form displayed on their device. The input data is sent from the device to the server. The server receives this data and uses an information processing device to normalize and structure the text. Furthermore, it uses natural language processing techniques to add necessary tags and outputs the data formatted for analysis.

[0481] Step 3:

[0482] The server invokes a generative AI model and performs analysis using pre-processed data as input. Specifically, it extracts keywords based on the user's interests and skills and performs data calculations by comparing these keywords with similar past data. Through this analysis, the generative AI model evaluates the most suitable occupation and career plan for the user and outputs the recommended results.

[0483] Step 4:

[0484] The server uses an information display device to visualize the analysis results generated by the AI ​​model. The input includes the analysis results, and the output is data visualized as flowcharts or infographics. This visualization allows users to intuitively understand the results.

[0485] Step 5:

[0486] The server sends visualized information, detailed career assessments, and career plans to the user's device. The user receives this information and considers their career choices based on the suggestions. Users can also send feedback from their device to the server, which uses this feedback to improve the system. This enables continuous service improvement.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] There is a need for means to facilitate in-home career assessments and career planning suggestions, and to enable users to easily design their own careers and careers by providing personalized information to each user through audio and visual means. In such an environment, the challenge is to develop technologies that provide the analysis results of the information in an easy-to-understand format and make them easy to use.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for collecting and preprocessing information from the user, means for using generative AI to analyze the user's interests and abilities based on the preprocessed data, means for providing the analysis results as visual information, means for outputting the analysis results to a home device using voice and display, and means for receiving feedback from the user and utilizing it to improve the service. This makes it possible to easily receive career assessments and career plan suggestions at home.

[0492] "Methods for collecting information" refer to a series of steps taken to capture data and statements provided by users and prepare them as a database necessary for later analysis.

[0493] "Preprocessing methods" refer to techniques for preparing collected data into an analyzable format, including text normalization and data structure organization.

[0494] "A method using generative AI to analyze user interests and abilities" refers to a process that utilizes artificial intelligence to determine user interests and skills based on data provided by the user.

[0495] "Providing information visually" refers to methods of presenting analysis results in visual formats such as diagrams, graphs, and flowcharts in order to allow users to intuitively understand the results.

[0496] "A method for outputting analysis results using audio and a display on home devices" refers to a technology that communicates analyzed information to the user visually and audibly through devices used within the home.

[0497] "Methods for receiving user feedback and using it to improve services" refer to methods for obtaining feedback provided by users after they have used the service and using that feedback to improve the accuracy and functionality of the system.

[0498] To implement this invention, it is necessary to build a system that utilizes consumer robots and home terminals to propose personalized occupational diagnoses and career plans to users. The details are described below.

[0499] Program Overview

[0500] The server receives data from the user, analyzes it, and outputs the results using the following procedure.

[0501] 1. Hardware configuration:

[0502] Home appliances (devices that interact with the user)

[0503] Voice input devices (microphone, etc.)

[0504] Audio and visual output devices (speakers, displays, etc.)

[0505] 2. Software configuration:

[0506] By using a speech recognition system (e.g., Google Speech-to-Text), user input in voice is converted into text.

[0507] We will use natural language processing libraries (e.g., spaCy, NLTK) to structure and tag text information from users.

[0508] By utilizing generative AI models (e.g., GPT-4), text data is analyzed to generate career assessment results and career plans based on the user's interests and skills.

[0509] 3. Data processing / calculation:

[0510] The server converts the audio to text, and then normalizes and tags the text.

[0511] Using a generative AI model, the system selects the occupation and career plan that best matches the user profile.

[0512] The analysis results are visualized and provided to the user through the display and speakers of home appliances.

[0513] Operation and specific examples

[0514] The user speaks to the home robot, saying, "I want to become an engineer, what should I do?" The server converts the speech into text and begins analysis using natural language processing technology. The generative AI model formulates the necessary skills and qualifications for the user's desired profession and displays the results visually in a flowchart format on the screen. Detailed assistance is also provided via voice through the speaker.

[0515] Example prompts for generative AI models:

[0516] "User language input: Information for aspiring engineers. Suggest necessary skills and qualifications."

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] The user voice-inputs questions about their occupation into a home device. The device receives this voice input and converts it into text data using a speech recognition system. The converted text is then prepared for interpretation regarding the user's interests and career. In this step, the input is the user's voice data, and the output is text data.

[0520] Step 2:

[0521] The server analyzes the text data using a natural language processing library to extract important keywords related to interests and abilities. This process includes tagging and text normalization. The server generates structured information from the raw text data; the input is the text data obtained in step 1, and the output is structured data.

[0522] Step 3:

[0523] The server uses a generative AI model to select the most suitable occupation and career plan for the user based on structured data. This process utilizes analyzed keywords to generate occupational information that best fits the user profile. The input here is the structured data from step 2, and the output is a list of optional occupations and career plans.

[0524] Step 4:

[0525] The server visualizes the analysis results and presents them to the user using the display of a home appliance. This visualization uses flowcharts and infographics and is provided in a way that is easy for the user to understand. In this step, the output data from step 3 is converted into a visual format.

[0526] Step 5:

[0527] The home device notifies the user of the analysis results via a speaker, providing a more detailed explanation. This audio output is designed to be easy for the user to hear and adjusts the content of the speech to suit the user's environment. The input for this step is the analysis results from step 3, and the output is audio information.

[0528] Step 6:

[0529] Users provide feedback via voice or touch interface based on information provided through home devices. The server receives this feedback and uses it to improve the accuracy of the service and enhance the user experience. The input in this step is user feedback information, and the output is insights for service improvement.

[0530] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0531] This invention is a system for optimizing personalized career diagnosis and career plan suggestions for users, taking into account their emotions. By combining a generative AI with an emotion engine, this system analyzes the user's emotions from their text input and provides more precise suggestions based on that analysis.

[0532] Users access the system using a terminal and log in. After authentication, the terminal provides the user with a questionnaire form. This form includes questions about interests, skills, work experience, and career goals, which the user answers. The user's input may also include emotional elements such as their feelings about their career and their hopes and anxieties regarding their occupation.

[0533] The terminal structures this input information and sends it to the server. The server first preprocesses the data as in conventional technology, and then activates the emotion engine. The emotion engine extracts and quantifies emotions from the user's input using natural language processing and machine learning models. As a result, emotion labels such as positive and negative are generated, and a generating AI uses these labels to formulate career diagnoses and career plans that take emotions into account.

[0534] The generated plans are visualized using a visual note-taking tool and provided to the user via their device. This allows users to consider their career choices while understanding their emotional expectations and potential issues.

[0535] As a concrete example, suppose a user responds, "I feel anxious about working in a team, but I want to develop my leadership skills." In this case, the emotion engine extracts emotions such as "anxiety" and "hope" as labels. The generating AI takes this into consideration and proposes a career plan that emphasizes individual skill development while gradually building experience as a team leader in small projects. In this way, this system can provide more empathetic and practical career support that takes into account the user's emotional aspects, rather than just performing data-based analysis.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user logs into the system using their device. Upon successful login, a question form appears on the device. This form includes questions about the user's interests, skills, work experience, and career goals. The user answers these questions.

[0539] Step 2:

[0540] The terminal collects data entered by the user, structures it in JSON format, and sends it to the server. This data includes responses in text format.

[0541] Step 3:

[0542] The server analyzes the data received from the terminal, first normalizing the text by unifying case sensitivity and removing unnecessary spaces. Next, it detects data anomalies and corrects them as needed.

[0543] Step 4:

[0544] The server uses an emotion engine to extract emotions from the text entered by the user. Natural language processing techniques are used to perform sentiment analysis and generate emotion labels such as positive, negative, and neutral. These emotion labels are processed as numerical data and used for subsequent analysis.

[0545] Step 5:

[0546] The server inputs pre-processed data and emotion labels into a generative AI model. The generative AI considers the user's interests, skills, and emotions to develop optimal occupations and career plans. This enables more personalized suggestions that include emotional aspects.

[0547] Step 6:

[0548] The server visualizes the generated career plan using a visual note tool. It generates career path flowcharts and graphs including sentiment analysis results, and converts them into a format that the user can intuitively understand.

[0549] Step 7:

[0550] The server sends visualized information to the terminal. The terminal then presents these results to the user, clearly indicating detailed information and specific career steps.

[0551] Step 8:

[0552] Users review the provided career plans and send feedback based on their emotions and needs to the server via their device. The server receives this feedback and uses it to improve its emotion engine and generative AI models.

[0553] (Example 2)

[0554] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] Traditional career assessment systems propose career plans based on a user's experience and skills, but they often fail to adequately address user needs because they do not take user emotions into consideration. This problem is particularly pronounced in situations where anxiety and aspirations related to career changes play a significant role. Therefore, there is a need for career plan proposals that take user emotions into account.

[0556] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0557] In this invention, the server includes means for collecting and pre-processing user input information, means for analyzing the user's emotions and using that information to personalize the proposed content, and means for visualizing the analysis results and providing them to the user as visual information. This makes it possible to provide a personalized career plan that takes the user's emotions into consideration.

[0558] "User input information" refers to data about the user's interests, skills, work experience, career goals, and emotions, which the user provides through their device.

[0559] "Preprocessing" refers to the process of normalizing and denoising data in order to convert input information collected from users into an analyzable format.

[0560] "Generative AI" refers to artificial intelligence that uses machine learning models to analyze user input and automatically generate personalized career diagnoses and career plans.

[0561] "Visualization" refers to the process of displaying analysis results in a form that is easy for users to understand, such as graphs and charts.

[0562] "Feedback" refers to opinions and impressions from users regarding the services provided, and is used to improve future services.

[0563] An "emotion engine" is a system that uses natural language processing technology to extract and quantify emotions from user input.

[0564] "Personalization" refers to the process of adjusting and providing suggestions in a way that is specifically tailored to the user, taking into account their characteristics and emotions.

[0565] This invention is a system that provides personalized career diagnosis and career plan suggestions for users. This system uses a generative AI model and an emotion engine to analyze the user's emotions from text input, and then provides more precise suggestions based on that analysis.

[0566] Users access the system and log in using a terminal. After authentication, the terminal presents the user with a questionnaire form. This questionnaire includes information about the user's interests, skills, work experience, and career goals, which the user answers. Emotional elements such as feelings, hopes, and anxieties about their career can also be entered.

[0567] The terminal structures the user's input information and sends it to the server. The server first preprocesses the received data, removing noise and ensuring format consistency. Then, it activates an emotion engine and uses natural language processing techniques to analyze the user's emotions and generate emotion labels such as "anxiety" and "hope."

[0568] Next, a generative AI model implemented on the server generates personalized career diagnoses and career plans based on the user's emotion labels. This generation process takes into account the user's specific needs and emotions, enabling the delivery of more personalized results.

[0569] The generated career plan is visualized using a visual note tool and provided to the user via their device in an easy-to-understand format. This allows users to make career choices while considering their own feelings and understanding their expectations and potential problems.

[0570] For example, if a user inputs "I feel anxious about working in a team, but I want to develop my leadership skills," the emotion engine will extract the emotions "anxiety" and "hope." Taking this information into consideration, the generating AI will propose a career plan that includes a stage where the user gains experience as a team leader in small projects.

[0571] An example of a prompt for a generative AI model would be an instruction such as, "Generate career advice considering the user's sentiment label."

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] A user accesses the system using a terminal and logs in. The input is the user's authentication information, and the output is a message indicating successful or unsuccessful login. The terminal sends the authentication information to the server, which then authenticates it to initiate a session.

[0575] Step 2:

[0576] After receiving a notification of successful authentication from the server, the terminal displays a question form to the user. The input includes information about the user's interests, skills, work experience, career goals, and feelings regarding their occupational choices. The user answers these questions, and the input data is structured and output to the terminal.

[0577] Step 3:

[0578] The terminal sends structured data to the server. The input data includes information on the user's interests, skills, experiences, and emotions entered into a form. The server preprocesses the received information, performing tasks such as noise reduction and formatting consistency, and outputs an analyzable dataset.

[0579] Step 4:

[0580] The server runs the emotion engine using pre-processed data. Inputs include user text information and pre-processed data, and the emotion engine uses natural language processing techniques to extract and quantify emotion labels. The output generates emotion labels such as "anxiety" and "hope."

[0581] Step 5:

[0582] The server uses a generative AI model to generate a career plan using emotion labels and the user's occupation-related information as input. The generative AI takes these labels into consideration and outputs an occupation diagnosis and career plan optimized for the user.

[0583] Step 6:

[0584] The career plan generated on the server is visualized using a visual note tool. The input contains information about the generated career plan, and the output is data in a visualized format. Graphs and charts are generated to enhance user understanding.

[0585] Step 7:

[0586] The device provides users with a visualized career plan. Through the display connected to the device, users can evaluate the career plan, taking their own emotional labels into account, and use this information to make decisions that include emotional considerations.

[0587] (Application Example 2)

[0588] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0589] Conventional career assessment and career plan generation systems make suggestions based on the user's skills and experience, but they have the problem of not adequately meeting the needs of individual users because they do not take into account the user's emotional aspects. The present invention aims to provide a system that can make suggestions that take such emotions into consideration, and to provide more appropriate support that incorporates emotional elements, including the user's expectations and anxieties in career choices.

[0590] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0591] In this invention, the server includes means for collecting and pre-processing user input information, means for analyzing the user's interests and skills using a generated AI based on the collected data, and means for emotional analysis for extracting and quantifying emotional information included in the user's input. This makes it possible to propose personalized occupations and career plans that also take emotional aspects into consideration.

[0592] "User input information" refers to information about the user's interests, skills, work experience, career goals, and emotions.

[0593] "Preprocessing" refers to data processing that converts user input information into a format suitable for analysis.

[0594] "Generative AI" is an artificial intelligence technology that analyzes a user's interests and skills based on their input information and generates the most suitable occupation and career plan.

[0595] "Visualization" refers to a method of displaying analysis results and proposed career plans in a way that is easy for users to understand.

[0596] "Emotional analysis" is the process of extracting emotional information from user input, quantifying it, and analyzing it.

[0597] "Quantification" is a process that expresses extracted emotional information as numerical data, making analysis and comparison possible.

[0598] "Feedback" is the process of collecting and analyzing responses and opinions from users to improve the service.

[0599] This invention is a system for optimizing a user's occupational diagnosis and career plan generation while taking emotions into consideration. This system consists of a terminal used by the user, a server responsible for data processing, a generation AI, and an emotion engine for emotion analysis.

[0600] The terminal collects input information from the user regarding their interests, skills, work experience, career goals, and emotions. This information is structured and sent to the server. The server preprocesses the input information, extracts emotional information using an emotion engine, and quantifies it. During this process, the emotional information is labeled using natural language processing techniques and expressed as elements such as positive / negative emotions, anxiety, and hope.

[0601] The server uses a generative AI to generate a career diagnosis and career plan that takes into account the user's input information and emotional information. The generated plan is visualized using a visualization tool and provided to the user via the terminal. The visualized information enables the user to make more appropriate decisions regarding their career choices, taking into account the emotional factors involved.

[0602] For example, if a user inputs "I feel anxious about changes in my job, but I want to learn new skills," the emotion engine will generate labels such as "anxiety" and "want to learn." Based on this, the generating AI will consider the user's emotions and propose a step-by-step skill improvement plan. This might include recommendations for online courses to learn new skills or mindfulness practices to reduce anxiety.

[0603] An example of a prompt is: "Generate the following sentiment analysis and career plan: I feel anxious about job changes, but I want to learn new technologies."

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] Users use their devices to input information about their career interests, skills, work experience, and feelings, and send it to the system. The input information is sent to the server in text data format.

[0607] Step 2:

[0608] When the server receives input from a user, it first performs data preprocessing. Specifically, it converts text data into a format that is easy to parse and removes unnecessary information. The preprocessed data is then stored as structured data.

[0609] Step 3:

[0610] The server extracts and quantifies emotional information from data preprocessed using natural language processing technology. An emotion engine is used to generate emotional labels such as positive, negative, anxiety, and hope from the text data. The extracted emotional labels are output and passed to the generation AI.

[0611] Step 4:

[0612] The server uses generative AI to generate optimal occupations and career plans based on the user's interests, skills, and emotional information. The generative AI considers emotional labels and makes suggestions that take the user's emotional aspects into account. As a result of this process, a personalized career plan is output.

[0613] Step 5:

[0614] The generated career plan is visualized using a visual note tool and sent to the device. The visualized information is displayed in a way that makes it easier for the user to understand their career choices.

[0615] Step 6:

[0616] Users review the provided career plan and, if necessary, send feedback to the server via their device. The server uses the received feedback as data to improve the accuracy of future suggestions.

[0617] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0618] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0619] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0623] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0624] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0625] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0626] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0627] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0628] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0629] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0630] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0631] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0632] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0633] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] This invention uses generative AI to propose personalized occupational diagnoses and career plans for users, and specific embodiments thereof are described below.

[0635] The user first logs into the system using their device. After logging in, a dedicated questionnaire form is displayed on the device, and the user enters information about their interests, skill set, work experience, and career goals. This data is then sent from the device to the server.

[0636] The server receives data from the user and performs preprocessing to format it into the appropriate format. This preprocessing includes text normalization, information structuring, and necessary tagging using natural language processing techniques.

[0637] Next, the server uses a generative AI model to analyze the pre-processed data. This analysis extracts keywords based on the user's interests and skills and compares them with past data from similar profiles. Through this process, the most suitable occupation and career plan are formulated.

[0638] Subsequently, the server visualizes the analysis results using a visual note tool. The visualized results are converted into flowcharts and infographics, making them intuitively understandable to the user. This visualization allows users to easily grasp suggested career paths and suitable job information.

[0639] Finally, along with the generated visualizations, a detailed career diagnosis and career plan are sent to the user's device. This helps users consider their career choices and develop concrete action plans. Furthermore, users can provide feedback to improve the service and further enhance the accuracy of the analysis.

[0640] For example, if a user states that they "have technical skills and want to demonstrate leadership," the generative AI model analyzes the data and proposes a career plan as a project manager. It also suggests recommended skills and certification programs, allowing the user to take concrete steps to advance their career.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] The user accesses the terminal and logs in. During this process, the system authenticates the user and verifies their login status. Only if the user has access rights can they proceed to the next step.

[0644] Step 2:

[0645] The terminal provides authenticated users with a questionnaire form to input information necessary for career assessment and career planning. This form includes questions about interests, skills, work experience, and career goals. Users then answer these questions.

[0646] Step 3:

[0647] The terminal structures the data entered by the user and sends it to the server as a JSON-formatted request. This request contains the user's input information.

[0648] Step 4:

[0649] The server analyzes the data received from the terminal. First, it performs text normalization, unifying case sensitivity and removing unnecessary spaces. Furthermore, it maintains data integrity by checking for missing values ​​and detecting outliers.

[0650] Step 5:

[0651] The server uses natural language processing technology to tag user input. This highlights important keywords and attributes, creating a foundation for subsequent generative AI models to use for analysis.

[0652] Step 6:

[0653] The server uses a generative AI model to analyze pre-processed data. First, it extracts key keywords from the user's interests and skills. Next, it compares the extracted keywords with historical data and evaluates similarity to select the most suitable occupation and career plan.

[0654] Step 7:

[0655] The server visualizes the generated analysis results using a visual note tool. It creates career path flowcharts and graphs of interest and skill distributions, transforming them into a user-friendly format.

[0656] Step 8:

[0657] The server sends the visualization and analysis results to the terminal. The terminal displays this information to the user, allowing the user to review the data.

[0658] Step 9:

[0659] Users consider their own career paths based on the information provided. If necessary, they send feedback to the server via their device. The server analyzes this feedback to improve the service and enhance the accuracy of the generated AI models.

[0660] (Example 1)

[0661] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] Conventional career assessment systems have struggled to provide occupational assessments and career plan suggestions that fully consider the individuality of each user. As a result, they often only offer generic suggestions that fail to meet the specific needs of each user. Furthermore, there were limitations in how to effectively utilize user feedback, which hindered service improvement.

[0663] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0664] In this invention, the server includes means for collecting input information from the user and pre-processing it using an information processing device, means for using a generative AI to analyze the user's interests and abilities based on the pre-processed information, and means for providing the analysis results as visual information using an information presentation device. This makes it possible to provide occupational diagnoses and career plans optimized for the individual characteristics of the user, and also facilitates continuous improvement of the service through the use of feedback.

[0665] "Input information" refers to information provided by the user, including data such as the user's interests, abilities, experience, and goals.

[0666] An "information processing device" is a device that appropriately formats input information obtained from a user and converts it into a format suitable for analysis.

[0667] "Generative AI" is a type of artificial intelligence that is modeled based on large amounts of data and used to analyze input information to present users with the most suitable occupations and career plans.

[0668] An "information presentation device" is a device that visually displays analysis results and provides information in an easy-to-understand manner for the user.

[0669] "Visual information" refers to a format in which analysis results are displayed clearly as diagrams or charts through a user interface.

[0670] "Feedback" refers to opinions and evaluations provided by users, and is information used to improve the performance of a service.

[0671] This invention is a system that provides personalized career assessments and career plans for users. Users begin by accessing the system using a terminal and logging in. After logging in, users enter information about their interests, skill sets, work experience, and career goals into a dedicated question form displayed on the terminal. This entered information is then transmitted from the terminal to the server.

[0672] The server uses an information processing device to process information received from the user. This device normalizes and structures the text and performs tagging through natural language processing techniques. This preprocessing prepares the data for analysis.

[0673] Next, the server uses a generative AI model to analyze the pre-processed data. In this analysis process, keywords are extracted based on the user's interests and skills, and compared with similar past data to formulate the most suitable occupation and career plan for the user.

[0674] Subsequently, the server visualizes the analysis results using an information display device. The visualized results are then converted into flowcharts and infographics to aid user understanding. This allows users to intuitively grasp suggested career paths and suitable job information.

[0675] For example, if a user states that they "possess technical skills and want to demonstrate leadership," the generative AI model analyzes this and proposes a career plan as a project manager. Furthermore, it also suggests programs for acquiring necessary skills and qualifications, allowing the user to advance their career based on this information.

[0676] An example of a prompt message might be, "I have technical skills and want to demonstrate leadership. Please recommend suitable occupations and the necessary skills and qualifications." This allows for personalized suggestions.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The user logs into the system using a terminal. The terminal enters user authentication information and sends this information to the server. The server verifies the authentication information and initiates a session. This grants the user permission to begin using the system.

[0680] Step 2:

[0681] Users enter information about their interests, skill sets, work experience, and career goals into a question form displayed on their device. The input data is sent from the device to the server. The server receives this data and uses an information processing device to normalize and structure the text. Furthermore, it uses natural language processing techniques to add necessary tags and outputs the data formatted for analysis.

[0682] Step 3:

[0683] The server invokes a generative AI model and performs analysis using pre-processed data as input. Specifically, it extracts keywords based on the user's interests and skills and performs data calculations by comparing these keywords with similar past data. Through this analysis, the generative AI model evaluates the most suitable occupation and career plan for the user and outputs the recommended results.

[0684] Step 4:

[0685] The server uses an information display device to visualize the analysis results generated by the AI ​​model. The input includes the analysis results, and the output is data visualized as flowcharts or infographics. This visualization allows users to intuitively understand the results.

[0686] Step 5:

[0687] The server sends visualized information, detailed career assessments, and career plans to the user's device. The user receives this information and considers their career choices based on the suggestions. Users can also send feedback from their device to the server, which uses this feedback to improve the system. This enables continuous service improvement.

[0688] (Application Example 1)

[0689] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] There is a need for means to facilitate in-home career assessments and career planning suggestions, and to enable users to easily design their own careers and careers by providing personalized information to each user through audio and visual means. In such an environment, the challenge is to develop technologies that provide the analysis results of the information in an easy-to-understand format and make them easy to use.

[0691] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0692] In this invention, the server includes means for collecting and preprocessing information from the user, means for using generative AI to analyze the user's interests and abilities based on the preprocessed data, means for providing the analysis results as visual information, means for outputting the analysis results to a home device using voice and display, and means for receiving feedback from the user and utilizing it to improve the service. This makes it possible to easily receive career assessments and career plan suggestions at home.

[0693] "Methods for collecting information" refer to a series of steps taken to capture data and statements provided by users and prepare them as a database necessary for later analysis.

[0694] "Preprocessing methods" refer to techniques for preparing collected data into an analyzable format, including text normalization and data structure organization.

[0695] "A method using generative AI to analyze user interests and abilities" refers to a process that utilizes artificial intelligence to determine user interests and skills based on data provided by the user.

[0696] "Providing information visually" refers to methods of presenting analysis results in visual formats such as diagrams, graphs, and flowcharts in order to allow users to intuitively understand the results.

[0697] "A method for outputting analysis results using audio and a display on home devices" refers to a technology that communicates analyzed information to the user visually and audibly through devices used within the home.

[0698] "Methods for receiving user feedback and using it to improve services" refer to methods for obtaining feedback provided by users after they have used the service and using that feedback to improve the accuracy and functionality of the system.

[0699] To implement this invention, it is necessary to build a system that utilizes consumer robots and home terminals to propose personalized occupational diagnoses and career plans to users. The details are described below.

[0700] Program Overview

[0701] The server receives data from the user, analyzes it, and outputs the results using the following procedure.

[0702] 1. Hardware configuration:

[0703] Home appliances (devices that interact with the user)

[0704] Voice input devices (microphone, etc.)

[0705] Audio and visual output devices (speakers, displays, etc.)

[0706] 2. Software configuration:

[0707] By using a speech recognition system (e.g., Google Speech-to-Text), user input in voice is converted into text.

[0708] We will use natural language processing libraries (e.g., spaCy, NLTK) to structure and tag text information from users.

[0709] By utilizing generative AI models (e.g., GPT-4), text data is analyzed to generate career assessment results and career plans based on the user's interests and skills.

[0710] 3. Data processing / calculation:

[0711] The server converts the audio to text, and then normalizes and tags the text.

[0712] Using a generative AI model, the system selects the occupation and career plan that best matches the user profile.

[0713] The analysis results are visualized and provided to the user through the display and speakers of home appliances.

[0714] Operation and specific examples

[0715] The user speaks to the home robot, saying, "I want to become an engineer, what should I do?" The server converts the speech into text and begins analysis using natural language processing technology. The generative AI model formulates the necessary skills and qualifications for the user's desired profession and displays the results visually in a flowchart format on the screen. Detailed assistance is also provided via voice through the speaker.

[0716] Example prompts for generative AI models:

[0717] "User language input: Information for aspiring engineers. Suggest necessary skills and qualifications."

[0718] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0719] Step 1:

[0720] The user voice-inputs questions about their occupation into a home device. The device receives this voice input and converts it into text data using a speech recognition system. The converted text is then prepared for interpretation regarding the user's interests and career. In this step, the input is the user's voice data, and the output is text data.

[0721] Step 2:

[0722] The server analyzes the text data using a natural language processing library to extract important keywords related to interests and abilities. This process includes tagging and text normalization. The server generates structured information from the raw text data; the input is the text data obtained in step 1, and the output is structured data.

[0723] Step 3:

[0724] The server uses a generative AI model to select the most suitable occupation and career plan for the user based on structured data. This process utilizes analyzed keywords to generate occupational information that best fits the user profile. The input here is the structured data from step 2, and the output is a list of optional occupations and career plans.

[0725] Step 4:

[0726] The server visualizes the analysis results and presents them to the user using the display of a home appliance. This visualization uses flowcharts and infographics and is provided in a way that is easy for the user to understand. In this step, the output data from step 3 is converted into a visual format.

[0727] Step 5:

[0728] The home device notifies the user of the analysis results via a speaker, providing a more detailed explanation. This audio output is designed to be easy for the user to hear and adjusts the content of the speech to suit the user's environment. The input for this step is the analysis results from step 3, and the output is audio information.

[0729] Step 6:

[0730] Users provide feedback via voice or touch interface based on information provided through home devices. The server receives this feedback and uses it to improve the accuracy of the service and enhance the user experience. The input in this step is user feedback information, and the output is insights for service improvement.

[0731] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0732] This invention is a system for optimizing personalized career diagnosis and career plan suggestions for users, taking into account their emotions. By combining a generative AI with an emotion engine, this system analyzes the user's emotions from their text input and provides more precise suggestions based on that analysis.

[0733] Users access the system using a terminal and log in. After authentication, the terminal provides the user with a questionnaire form. This form includes questions about interests, skills, work experience, and career goals, which the user answers. The user's input may also include emotional elements such as their feelings about their career and their hopes and anxieties regarding their occupation.

[0734] The terminal structures this input information and sends it to the server. The server first preprocesses the data as in conventional technology, and then activates the emotion engine. The emotion engine extracts and quantifies emotions from the user's input using natural language processing and machine learning models. As a result, emotion labels such as positive and negative are generated, and a generating AI uses these labels to formulate career diagnoses and career plans that take emotions into account.

[0735] The generated plans are visualized using a visual note-taking tool and provided to the user via their device. This allows users to consider their career choices while understanding their emotional expectations and potential issues.

[0736] As a concrete example, suppose a user responds, "I feel anxious about working in a team, but I want to develop my leadership skills." In this case, the emotion engine extracts emotions such as "anxiety" and "hope" as labels. The generating AI takes this into consideration and proposes a career plan that emphasizes individual skill development while gradually building experience as a team leader in small projects. In this way, this system can provide more empathetic and practical career support that takes into account the user's emotional aspects, rather than just performing data-based analysis.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The user logs into the system using their device. Upon successful login, a question form appears on the device. This form includes questions about the user's interests, skills, work experience, and career goals. The user answers these questions.

[0740] Step 2:

[0741] The terminal collects data entered by the user, structures it in JSON format, and sends it to the server. This data includes responses in text format.

[0742] Step 3:

[0743] The server analyzes the data received from the terminal, first normalizing the text by unifying case sensitivity and removing unnecessary spaces. Next, it detects data anomalies and corrects them as needed.

[0744] Step 4:

[0745] The server uses an emotion engine to extract emotions from the text entered by the user. Natural language processing techniques are used to perform sentiment analysis and generate emotion labels such as positive, negative, and neutral. These emotion labels are processed as numerical data and used for subsequent analysis.

[0746] Step 5:

[0747] The server inputs pre-processed data and emotion labels into a generative AI model. The generative AI considers the user's interests, skills, and emotions to develop optimal occupations and career plans. This enables more personalized suggestions that include emotional aspects.

[0748] Step 6:

[0749] The server visualizes the generated career plan using a visual note tool. It generates career path flowcharts and graphs including sentiment analysis results, and converts them into a format that the user can intuitively understand.

[0750] Step 7:

[0751] The server sends visualized information to the terminal. The terminal then presents these results to the user, clearly indicating detailed information and specific career steps.

[0752] Step 8:

[0753] Users review the provided career plans and send feedback based on their emotions and needs to the server via their device. The server receives this feedback and uses it to improve its emotion engine and generative AI models.

[0754] (Example 2)

[0755] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] Traditional career assessment systems propose career plans based on a user's experience and skills, but they often fail to adequately address user needs because they do not take user emotions into consideration. This problem is particularly pronounced in situations where anxiety and aspirations related to career changes play a significant role. Therefore, there is a need for career plan proposals that take user emotions into account.

[0757] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0758] In this invention, the server includes means for collecting and pre-processing user input information, means for analyzing the user's emotions and using that information to personalize the proposed content, and means for visualizing the analysis results and providing them to the user as visual information. This makes it possible to provide a personalized career plan that takes the user's emotions into consideration.

[0759] "User input information" refers to data about the user's interests, skills, work experience, career goals, and emotions, which the user provides through their device.

[0760] "Preprocessing" refers to the process of normalizing and denoising data in order to convert input information collected from users into an analyzable format.

[0761] "Generative AI" refers to artificial intelligence that uses machine learning models to analyze user input and automatically generate personalized career diagnoses and career plans.

[0762] "Visualization" refers to the process of displaying analysis results in a form that is easy for users to understand, such as graphs and charts.

[0763] "Feedback" refers to opinions and impressions from users regarding the services provided, and is used to improve future services.

[0764] An "emotion engine" is a system that uses natural language processing technology to extract and quantify emotions from user input.

[0765] "Personalization" refers to the process of adjusting and providing suggestions in a way that is specifically tailored to the user, taking into account their characteristics and emotions.

[0766] This invention is a system that provides personalized career diagnosis and career plan suggestions for users. This system uses a generative AI model and an emotion engine to analyze the user's emotions from text input, and then provides more precise suggestions based on that analysis.

[0767] Users access the system and log in using a terminal. After authentication, the terminal presents the user with a questionnaire form. This questionnaire includes information about the user's interests, skills, work experience, and career goals, which the user answers. Emotional elements such as feelings, hopes, and anxieties about their career can also be entered.

[0768] The terminal structures the user's input information and sends it to the server. The server first preprocesses the received data, removing noise and ensuring format consistency. Then, it activates an emotion engine and uses natural language processing techniques to analyze the user's emotions and generate emotion labels such as "anxiety" and "hope."

[0769] Next, a generative AI model implemented on the server generates personalized career diagnoses and career plans based on the user's emotion labels. This generation process takes into account the user's specific needs and emotions, enabling the delivery of more personalized results.

[0770] The generated career plan is visualized using a visual note tool and provided to the user via their device in an easy-to-understand format. This allows users to make career choices while considering their own feelings and understanding their expectations and potential problems.

[0771] For example, if a user inputs "I feel anxious about working in a team, but I want to develop my leadership skills," the emotion engine will extract the emotions "anxiety" and "hope." Taking this information into consideration, the generating AI will propose a career plan that includes a stage where the user gains experience as a team leader in small projects.

[0772] An example of a prompt for a generative AI model would be an instruction such as, "Generate career advice considering the user's sentiment label."

[0773] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0774] Step 1:

[0775] A user accesses the system using a terminal and logs in. The input is the user's authentication information, and the output is a message indicating successful or unsuccessful login. The terminal sends the authentication information to the server, which then authenticates it to initiate a session.

[0776] Step 2:

[0777] After receiving a notification of successful authentication from the server, the terminal displays a question form to the user. The input includes information about the user's interests, skills, work experience, career goals, and feelings regarding their occupational choices. The user answers these questions, and the input data is structured and output to the terminal.

[0778] Step 3:

[0779] The terminal sends structured data to the server. The input data includes information on the user's interests, skills, experiences, and emotions entered into a form. The server preprocesses the received information, performing tasks such as noise reduction and formatting consistency, and outputs an analyzable dataset.

[0780] Step 4:

[0781] The server runs the emotion engine using pre-processed data. Inputs include user text information and pre-processed data, and the emotion engine uses natural language processing techniques to extract and quantify emotion labels. The output generates emotion labels such as "anxiety" and "hope."

[0782] Step 5:

[0783] The server uses a generative AI model to generate a career plan using emotion labels and the user's occupation-related information as input. The generative AI takes these labels into consideration and outputs an occupation diagnosis and career plan optimized for the user.

[0784] Step 6:

[0785] The career plan generated on the server is visualized using a visual note tool. The input contains information about the generated career plan, and the output is data in a visualized format. Graphs and charts are generated to enhance user understanding.

[0786] Step 7:

[0787] The device provides users with a visualized career plan. Through the display connected to the device, users can evaluate the career plan, taking their own emotional labels into account, and use this information to make decisions that include emotional considerations.

[0788] (Application Example 2)

[0789] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0790] Conventional career assessment and career plan generation systems make suggestions based on the user's skills and experience, but they have the problem of not adequately meeting the needs of individual users because they do not take into account the user's emotional aspects. The present invention aims to provide a system that can make suggestions that take such emotions into consideration, and to provide more appropriate support that incorporates emotional elements, including the user's expectations and anxieties in career choices.

[0791] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0792] In this invention, the server includes means for collecting and pre-processing user input information, means for analyzing the user's interests and skills using a generated AI based on the collected data, and means for emotional analysis for extracting and quantifying emotional information included in the user's input. This makes it possible to propose personalized occupations and career plans that also take emotional aspects into consideration.

[0793] "User input information" refers to information about the user's interests, skills, work experience, career goals, and emotions.

[0794] "Preprocessing" refers to data processing that converts user input information into a format suitable for analysis.

[0795] "Generative AI" is an artificial intelligence technology that analyzes a user's interests and skills based on their input information and generates the most suitable occupation and career plan.

[0796] "Visualization" refers to a method of displaying analysis results and proposed career plans in a way that is easy for users to understand.

[0797] "Emotional analysis" is the process of extracting emotional information from user input, quantifying it, and analyzing it.

[0798] "Quantification" is a process that expresses extracted emotional information as numerical data, making analysis and comparison possible.

[0799] "Feedback" is the process of collecting and analyzing responses and opinions from users to improve the service.

[0800] This invention is a system for optimizing a user's occupational diagnosis and career plan generation while taking emotions into consideration. This system consists of a terminal used by the user, a server responsible for data processing, a generation AI, and an emotion engine for emotion analysis.

[0801] The terminal collects input information from the user regarding their interests, skills, work experience, career goals, and emotions. This information is structured and sent to the server. The server preprocesses the input information, extracts emotional information using an emotion engine, and quantifies it. During this process, the emotional information is labeled using natural language processing techniques and expressed as elements such as positive / negative emotions, anxiety, and hope.

[0802] The server uses a generative AI to generate a career diagnosis and career plan that takes into account the user's input information and emotional information. The generated plan is visualized using a visualization tool and provided to the user via the terminal. The visualized information enables the user to make more appropriate decisions regarding their career choices, taking into account the emotional factors involved.

[0803] For example, if a user inputs "I feel anxious about changes in my job, but I want to learn new skills," the emotion engine will generate labels such as "anxiety" and "want to learn." Based on this, the generating AI will consider the user's emotions and propose a step-by-step skill improvement plan. This might include recommendations for online courses to learn new skills or mindfulness practices to reduce anxiety.

[0804] An example of a prompt is: "Generate the following sentiment analysis and career plan: I feel anxious about job changes, but I want to learn new technologies."

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] Users use their devices to input information about their career interests, skills, work experience, and feelings, and send it to the system. The input information is sent to the server in text data format.

[0808] Step 2:

[0809] When the server receives input from a user, it first performs data preprocessing. Specifically, it converts text data into a format that is easy to parse and removes unnecessary information. The preprocessed data is then stored as structured data.

[0810] Step 3:

[0811] The server extracts and quantifies emotional information from data preprocessed using natural language processing technology. An emotion engine is used to generate emotional labels such as positive, negative, anxiety, and hope from the text data. The extracted emotional labels are output and passed to the generation AI.

[0812] Step 4:

[0813] The server uses generative AI to generate optimal occupations and career plans based on the user's interests, skills, and emotional information. The generative AI considers emotional labels and makes suggestions that take the user's emotional aspects into account. As a result of this process, a personalized career plan is output.

[0814] Step 5:

[0815] The generated career plan is visualized using a visual note tool and sent to the device. The visualized information is displayed in a way that makes it easier for the user to understand their career choices.

[0816] Step 6:

[0817] Users review the provided career plan and, if necessary, send feedback to the server via their device. The server uses the received feedback as data to improve the accuracy of future suggestions.

[0818] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0819] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0820] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0821] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0822] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0823] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0824] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0825] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0826] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0827] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0828] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0829] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0830] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0831] 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.

[0832] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0833] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0834] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0835] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0836] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0837] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0838] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] A means for collecting and pre-processing user input information,

[0842] A method using generative AI to analyze user interests and skills based on pre-processed data,

[0843] A means of visualizing the analysis results and providing them to the user as visual information,

[0844] A means of receiving user feedback and using it to improve the service,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, comprising means for tagging user input information using natural language processing technology.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising a means for selecting the most suitable occupation and career plan for a user using generative AI.

[0850] "Example 1"

[0851] (Claim 1)

[0852] A means for collecting user input information and performing preprocessing using an information processing device,

[0853] A method using generative AI that analyzes the user's interests and abilities based on pre-processed information,

[0854] A means of providing the analysis results as visual information using an information display device,

[0855] A means of receiving user feedback and using it to improve the service,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, comprising means for tagging user input information using natural language processing technology.

[0859] (Claim 3)

[0860] The system according to claim 1, comprising means for selecting the most suitable occupation and future plans for a user using generative AI.

[0861] "Application Example 1"

[0862] (Claim 1)

[0863] Methods for collecting and preprocessing information from users,

[0864] A method using generative AI that analyzes user interests and abilities based on preprocessed data,

[0865] Methods for providing analysis results as visual information,

[0866] A method for outputting analysis results using audio and a display on a home device,

[0867] How to receive user feedback and use it to improve the service,

[0868] Technologies that include this.

[0869] (Claim 2)

[0870] The technology according to claim 1, comprising a method for tagging user information using natural language processing technology.

[0871] (Claim 3)

[0872] The technology according to claim 1, comprising a method for using generative AI to select the most suitable occupation and career plan for a user and providing it visually and audibly through a home device.

[0873] "Example 2 of combining an emotion engine"

[0874] (Claim 1)

[0875] A means for collecting and pre-processing user input information,

[0876] A method using generative AI that analyzes user interests and skills based on pre-processed data,

[0877] A means of visualizing the analysis results and providing them to the user as visual information,

[0878] A means of receiving user feedback and using it to improve the service,

[0879] A means of analyzing user emotions and using that information to personalize the suggested content,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, comprising means for tagging user input information using natural language processing technology and extracting and quantifying emotions.

[0883] (Claim 3)

[0884] The system according to claim 1, comprising a means for selecting the optimal occupation and career plan, taking into account the user's emotions, using generative AI.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] A means for collecting and pre-processing user input information,

[0888] A method using generative AI that analyzes user interests and skills based on pre-processed data,

[0889] A means of visualizing the analysis results and providing them to the user as visual information,

[0890] A sentiment analysis method that extracts and quantifies emotional information contained in user input,

[0891] A method using generative AI that considers emotional information to suggest the most suitable occupation and career plan for the user,

[0892] A means of receiving user feedback and using it to improve the service,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, comprising means for tagging user input information using natural language processing technology and performing sentiment analysis.

[0896] (Claim 3)

[0897] The system according to claim 1, comprising means for using generative AI to consider emotional information, select the most suitable occupation and career plan for the user, and provide it in a visualized form. [Explanation of symbols]

[0898] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting and pre-processing user input information, A method using generative AI to analyze user interests and skills based on pre-processed data, A means of visualizing the analysis results and providing them to the user as visual information, A means of receiving user feedback and using it to improve the service, A system that includes this.

2. The system according to claim 1, further comprising means for tagging user input information using natural language processing technology.

3. The system according to claim 1, comprising means for selecting the most suitable occupation and career plan for a user using generative AI.

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