system

Abstract

We provide the system. [Solution] The response data obtained from users is analyzed using natural language processing techniques. A means of generating a set of information that characterizes the user's interests and skills, A method for selecting a suitable occupation for a user using a machine learning model based on the generated information set, A means of formulating a career achievement plan to present to the user based on the selected occupation, A means of generating and displaying information to visualize the formulated career achievement plan to the user, A means of providing individual job suggestions based on the labor market trends in the area where one lives, 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, 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

[0006] "Response data" refers to the collection of information that users enter in response to a question.

[0007] "Natural language processing techniques" are technologies that enable computers to understand and analyze human language.

[0008] A "characterizing dataset" is a collection of feature information extracted to indicate a user's interests and skills.

[0009] A "machine learning model" is an algorithm that learns patterns based on past data and uses them to make future predictions and classifications.

[0010] "Job selection" is the process of suggesting suitable occupations based on the user's characteristics.

[0011] A "career plan" is a plan that includes specific steps and goals that the user should achieve based on their occupational selection results.

[0012] "Data for visualization" refers to graphical data used to present information in an easily understandable format.

[0013] A "visual note tool" is a software tool used to display information visually. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

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

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

[0017] In the following embodiments, 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.

[0018] 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.

[0019] 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, etc.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system designed to help users more effectively design and promote their career growth. The system consists of multiple components that process user input data and present optimal occupations and career plans.

[0036] First, the user accesses the system using a device and enters answers to the questions presented. These questions concern the user's interests, skills, values, and career goals. The device collects this response data and sends it to the server.

[0037] The server analyzes the received response data using natural language processing techniques. Specifically, it extracts data to characterize the user's interests and skills. This generates a dataset based on the user's characteristics. This dataset serves as the foundation for suggesting the most suitable occupation for the user.

[0038] Next, the server uses a machine learning model based on the generated dataset to select suitable occupations for the user. This model predicts occupations that match the user's characteristics from an existing occupation database and proposes them to the user.

[0039] Furthermore, based on the occupation selection results, the server develops a detailed career plan for the user. This plan includes steps for acquiring specific skills and qualifications, as well as plans for career advancement. The server generates visualized data to provide this information to the user in an easy-to-understand format.

[0040] Ultimately, the device displays job suggestions and a visualized career plan received from the server to the user. Based on this information, the user can set their own career goals and decide on specific actions to achieve them.

[0041] For example, if a user responds with "I'm interested in creative work" or "I'm good at programming," the system uses natural language processing and machine learning to analyze the user's responses and suggest professions such as "software developer" or "UX designer," visualizing and presenting a career plan. In this way, users can clearly identify the most suitable career path based on their interests and skills and proceed systematically.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user answers questions provided on the device. These questions are designed to explore the user's interests, skills, and values.

[0045] Step 2:

[0046] The terminal converts the responses obtained from the user into a data format and sends them to the server using a secure communication protocol.

[0047] Step 3:

[0048] The server stores the received data in the database and prepares it for analysis.

[0049] Step 4:

[0050] The server uses natural language processing techniques to analyze the user's response data. This generates a dataset that characterizes the user's interests and skills.

[0051] Step 5:

[0052] The server uses the generated dataset to apply a machine learning model and select the most suitable occupation for the user. In this process, the model predicts suitable jobs from the occupation database.

[0053] Step 6:

[0054] Based on the results of the occupation selection, the server develops a personalized career plan for the user. The plan includes specific skill acquisition paths and suggestions for obtaining qualifications.

[0055] Step 7:

[0056] The server generates data for visualization and prepares it to be presented in an easy-to-understand format for career planning.

[0057] Step 8:

[0058] The terminal displays job suggestions and a visualized career plan received from the server to the user. The user can then decide on their next action based on this information.

[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] In modern society, it is difficult for individuals to find a job and career path that is optimized for their own characteristics. Many people search for the right path, but often face difficulties in the process of finding the optimal job based on their interests and skills and building an effective career plan. There is a need for an efficient and automated system to solve these problems.

[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] This invention includes a server that receives information about the user's interests, abilities, and goals entered into an information processing device, analyzes this information using natural language processing technology, and generates a data structure that represents the user's characteristics; a server that recommends suitable occupations to the user using a learning model based on the generated data structure; and a server that constructs an individual career plan based on the recommended occupations. This enables the user to quickly and efficiently find the optimal occupation and career plan based on their interests and skills, and to plan their actions based on the visualized plan.

[0064] An "information processing device" is an integrated computing system for inputting, processing, analyzing, and outputting data.

[0065] "Natural language processing technology" refers to a set of algorithms and techniques that enable computers to understand, analyze, and generate human language.

[0066] A "data structure" is a framework for organizing and storing data in a specific format, and refers to a format suitable for those operations.

[0067] A "learning model" is a mathematical or statistical process that extracts knowledge from input data and uses that knowledge to make predictions or decisions.

[0068] "Recommending occupations" means identifying occupations that match the user's characteristics and suggesting those options.

[0069] A "career plan" is a set of schedules or strategies that include the steps and actions necessary for a user to achieve their professional goals.

[0070] A "visualized plan" is a plan created to make information easily understandable to users by representing it in a graphical format.

[0071] This invention provides a method for efficiently designing a career plan through an information processing system accessed by the user using their own terminal. The user uses the terminal to input information about their interests, skills, values, and career goals into the system. The terminal collects this information and transmits it to a server via a network.

[0072] The server uses natural language processing libraries such as Python's NLTK or spaCy to analyze the information sent by the user. This analysis extracts keywords related to the user's interests and skills. Based on the extracted information, the server generates a data structure that represents the user's characteristics.

[0073] Next, the server uses machine learning libraries such as Scikit-learn and TENSORFLOW® to evaluate this data structure and select occupations suitable for the user. The machine learning model predicts the most suitable occupation for the user by comparing it with an existing occupation database.

[0074] Furthermore, the server creates a detailed career plan for the user based on their selected occupation. This plan includes recommended steps for acquiring necessary skills and qualifications. It also generates data to visualize the career plan in a format suitable for visual display, and the terminal provides this information to the user.

[0075] For example, a user might input information into the system such as "I'm interested in creative work" or "I'm good at programming." The system then recommends professions such as "software developer" or "UX designer," and visualizes and displays a plan for acquiring the necessary skills and qualifications for these professions.

[0076] An example of a prompt to use when inputting data into a generative AI model would be, "Please suggest the most suitable occupation based on my interests and skills. My interests are creative work, and my skills are programming." In this way, users can find the most suitable occupation based on their input information and establish a relevant career plan.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users access the system through their devices and input information about their interests, skills, values, and career goals. Specifically, users answer on-screen questions by typing text, such as "I am interested in creative work" or "I am good at programming." The input data is organized in JSON format and sent to the server.

[0080] Step 2:

[0081] The terminal collects input data from the user and sends it to the server via the internet. The input data is in text format and is formatted for analysis on the server side. The specific operation here is to securely transmit the data using a communication protocol.

[0082] Step 3:

[0083] The server analyzes the received data. Specifically, it uses natural language processing libraries such as Python's NLTK or spaCy to extract keywords from the user's input. The input data represents the user's interests and skills, and the server generates a list of characteristic keywords corresponding to those interests and skills as output.

[0084] Step 4:

[0085] The server generates a data structure using the extracted characteristic keywords. This involves organizing the extracted keywords and saving the user's characteristics as a dataset. The output is this data structure, which is used in subsequent processing.

[0086] Step 5:

[0087] The server uses a machine learning model based on the generated data structure to recommend the most suitable occupation for the user. Specifically, it utilizes Scikit-learn and TensorFlow, and the model selects suitable occupations using the database. The input is a characteristics data structure, and the output is a list of recommended occupations.

[0088] Step 6:

[0089] The server builds a detailed career plan based on the recommended occupation. This plan includes steps for acquiring skills and qualifications related to the target occupation. Its operation involves organizing the plan in text and graphical formats and generating visualized data. The output is in the form of a visualized career plan.

[0090] Step 7:

[0091] The terminal receives a visualized career plan from the server and displays it to the user. Its specific function is to present the career plan in an easy-to-understand manner through the user interface. Through this visualized information, users can make confident career decisions.

[0092] (Application Example 1)

[0093] 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."

[0094] The modern labor market is undergoing rapid changes, making it increasingly difficult for individuals to maximize their interests and skills and find the right career path. Therefore, there is a need for a system that allows users to select the most suitable occupation based on their characteristics, develop effective career achievement plans, and receive personalized suggestions that take into account local labor market trends.

[0095] 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.

[0096] In this invention, the server includes means for analyzing response data obtained from the user using natural language processing techniques to generate a set of information characterizing the user's interests and skills; means for selecting a suitable occupation for the user using a machine learning model based on the generated set of information; and means for making individual occupational suggestions based on the trends of the labor market in the area where the user lives. As a result, the user can find the optimal occupation based on their own characteristics and realize a career plan that is tailored to the local labor market.

[0097] A "user" is an individual who uses the system to develop career selection and achievement plans based on their own interests, skills, and career goals.

[0098] "Response data" refers to information about the user's interests, skills, values, and career goals that they input into the system.

[0099] "Natural language processing techniques" are technologies and methods that enable computers to understand, analyze, and manipulate human language, and include keyword extraction and sentiment analysis.

[0100] An "information set" is a dataset generated to characterize a user's interests and skills, and is used to select a suitable occupation for that user.

[0101] A "machine learning model" is an artificial intelligence technique that learns patterns based on past data and uses that knowledge to make predictions and classifications about new data.

[0102] A "career achievement plan" is a plan that outlines the steps a user needs to take to acquire the skills and qualifications necessary to achieve their chosen career goals.

[0103] "Information for visualization" refers to data illustrating career achievement plans, provided in a format that is easy for users to understand.

[0104] "Regional labor market" refers to the employment environment in a particular region, including the supply and demand for jobs and trends in growth sectors.

[0105] This invention is a system that enables users to effectively design their careers and promote their growth. First, users access the system using a device such as a smartphone or smart glasses and answer questions about their interests, skills, values, and career goals.

[0106] The terminal collects this response data and sends it to the server. The server uses natural language processing techniques with Python and the NLTK library to analyze the response data and generate a set of information that characterizes the user's interests and skills. This set of information forms the basis for career suggestions within the system.

[0107] Furthermore, the server uses machine learning models such as TensorFlow to select suitable occupations for the user based on the generated information. In this process, individual occupation suggestions are made, taking into account local labor market trends. Specifically, it analyzes the supply and demand in the labor market in the user's area and presents occupations accordingly.

[0108] Based on the selected occupation, a career achievement plan is developed for the user. This plan includes steps for acquiring specific skills and qualifications, as well as activities for career advancement. The server visualizes this information using the Python matplotlib library and presents it to the user in an easy-to-understand format.

[0109] For example, if a user living in Tokyo responds that they are "interested in AI" and "have data science skills," the server will consider Tokyo's labor market trends and suggest occupations such as "AI engineer" or "data scientist." It will also introduce online courses for acquiring those skills and present plans for participating in actual projects.

[0110] An example of a prompt message could be, "I am interested in AI and have data science skills. What kind of career should I pursue in Tokyo?" This invention provides practical support for users to find the optimal occupation based on their own characteristics and to develop a localized career plan.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] Users access the system using devices such as smartphones or smart glasses and answer questions about their interests, skills, values, and career goals. The user's response data is obtained as input. This response data will form the basis for future analysis.

[0114] Step 2:

[0115] The terminal sends the user's response data to the server. The transmitted data is stored in the server's database. The input is the user's response data, and the output is the data recorded on the server. This data is used for the next analysis.

[0116] Step 3:

[0117] The server analyzes the received response data using natural language processing techniques with Python and the NLTK library. Stored user response data is used as input, and the analysis outputs a set of information characterizing interests and skills. In this step, keyword extraction and emotional analysis are performed to construct a user profile.

[0118] Step 4:

[0119] The server uses TensorFlow to perform occupation selection using a machine learning model based on the generated information set. The input is the information set obtained in the previous step, and the output is a list of occupations suitable for the user. This list also includes individual occupation suggestions that take into account local labor market trends.

[0120] Step 5:

[0121] The server uses a list of suitable occupations for the user to create a specific career achievement plan. The input is selected occupation information, and the output generates data for corresponding skill acquisition plans and career plans, including scheduled activities and goal achievement schedules.

[0122] Step 6:

[0123] The server uses the Python matplotlib library to visualize the career achievement plan. The visualized data displays the planned schedule and achievement goals in an easy-to-understand format. For the user, it is formatted to allow them to understand the key action steps and overall progress at a glance.

[0124] Step 7:

[0125] The server sends visualized data to the terminal and displays it to the user. This allows the user to take action based on job suggestions tailored to their characteristics and the accompanying concrete career plan.

[0126] 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.

[0127] This invention relates to a career consultant system that incorporates an emotion engine. This system performs natural language processing and emotion analysis on user input data and provides personalized occupational and career plans based on these results.

[0128] First, the user accesses the system through a device and answers a series of questions. These questions are designed to understand the user's interests, skills, values, and emotional state. The answers entered by the user are stored on the device and securely and quickly transmitted to the server.

[0129] Upon receiving response data, the server uses natural language processing techniques to analyze the text data and generate a dataset that characterizes the user's interests and skills. This process includes keyword extraction and contextual analysis. Simultaneously, an emotion engine analyzes the emotions contained in the user's responses and recognizes specific emotional trends.

[0130] Based on the recognized emotions, the server adjusts the analysis results and selects the most suitable occupation using a machine learning model. The emotional state analyzed by the emotion engine is additionally considered as a factor in the occupation selection decision. This makes it possible to suggest the optimal occupation for the user's intrinsic motivation and stress level.

[0131] Furthermore, keeping the selected occupation in mind, the server develops a career plan for the user. This plan includes feedback based on sentiment analysis results and incorporates elements that enhance the plan's applicability and feasibility. The developed career plan is visualized in a way that is easy for the user to understand.

[0132] Finally, the terminal presents the user with job suggestions and a visualized career plan provided by the server. The user can then use this information to design their own career and provide further feedback to the system as needed.

[0133] For example, if a user inputs responses such as "I enjoy creative challenges" or "I'm somewhat dissatisfied with my current job," the system analyzes these responses along with the user's emotional state and suggests professions such as "Creative Director" or "Product Manager." Thus, a key feature of this invention is its ability to present the optimal career path while taking the user's emotional state into consideration.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user accesses a question form using their device and answers several questions presented by the system. The questions concern the user's interests, skills, and current emotional state.

[0137] Step 2:

[0138] The terminal converts the responses collected from the user into a standard data format and prepares them for transmission to the server. The converted data is then sent to the server using a secure protocol.

[0139] Step 3:

[0140] The server receives data sent from the terminal and begins analysis using a natural language processing engine. Through the analysis, keyword extraction and contextual understanding within the text generate a dataset that characterizes the user's interests and skills.

[0141] Step 4:

[0142] The server uses an emotion engine to extract and recognize emotions from user responses. This process identifies positive or negative emotions contained within the responses and adds them to the dataset.

[0143] Step 5:

[0144] The server uses the generated dataset and recognized sentiment data to apply a machine learning model and select the most suitable occupation for the user. Here, logic is used to determine job suitability from the occupation database.

[0145] Step 6:

[0146] Based on the selected occupation, the server develops a career plan that takes into account the user's emotional state. This plan includes various skill acquisition methods and emotional feedback.

[0147] Step 7:

[0148] The server converts the career plan it has developed into a data format for visualization, and then integrates it with a visual note tool to prepare the information in a way that is easier for users to understand.

[0149] Step 8:

[0150] The terminal displays visualization data and career suggestions received from the server to the user. The user views the presented information and uses it as a reference to design their own career plan.

[0151] (Example 2)

[0152] 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".

[0153] Current career counseling systems make it difficult to select a job that adequately considers the user's emotional state. Furthermore, they lack visually clear career planning tools, making it challenging for users to develop concrete action plans for their careers.

[0154] 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.

[0155] In this invention, the server includes means for analyzing response information obtained from the user using character processing technology to generate an information set that characterizes the user's interests and skills; means for analyzing the user's emotional tendencies using an emotion analysis device and making adjustments to the selected job to take emotional information into account; and means for formulating and visualizing a career plan to be presented to the user based on the selected job. This makes it possible to suggest the optimal occupation that takes the user's emotional state into account and to present a career plan in an easy-to-understand format.

[0156] "Response information" refers to information including answers, opinions, and emotions obtained from users.

[0157] "Text processing technology" refers to all methods used to extract and analyze useful information from text data.

[0158] "Interest" refers to the things or areas that a user is particularly interested in.

[0159] "Skills" refers to the abilities and abilities that a user demonstrates in a particular activity or job.

[0160] An "information set" is a series of pieces of information that represent the characteristics of an object, generated based on data.

[0161] An "educational machine structure" is a system that uses machine learning algorithms to learn patterns and relationships from given data.

[0162] "Job" refers to a specific occupation or task that is suitable for the user.

[0163] A "career plan" refers to a set of actionable guidelines for users regarding their occupational choices and career development.

[0164] "Visualization" is a method of aiding understanding by representing information and data in visual forms such as graphs and diagrams.

[0165] An "emotion analysis device" is a system that identifies and analyzes a user's emotions from text and psychological state.

[0166] "Correction" refers to the process of adjusting the proposed content based on the analysis results to provide a more suitable outcome.

[0167] The present invention is a career consultant system that combines an emotion engine to perform natural language processing and emotion analysis on user input data, and based on this, provides an individualized occupation and career plan. The following describes embodiments for carrying out the present invention.

[0168] Users access the system via a computer terminal and answer questions designed to understand their interests, skills, values, and emotional state. This information is collected at the terminal and transmitted to the server via a secure communication protocol. The server analyzes the received data using character processing techniques to generate a set of information characterizing the user's interests and skills. Python's NLTK and SpaCy are used as character processing libraries in this process.

[0169] Furthermore, the server utilizes an emotion analysis device to extract emotions from the user's responses. By using a generative AI model, it is possible to understand the emotional trends observed in the user's responses and incorporate them into the analysis results.

[0170] The analysis results are processed by an educational machine structure, which automatically selects a suitable job for the user. Based on the selected job, the server develops a career plan and incorporates feedback that takes emotional information into account. This career plan is visualized and displayed to the user, making it easy for the user to understand the specific next steps.

[0171] For example, if a user enters "I like creative challenges" and "I'm somewhat dissatisfied with my current job," the system will analyze this along with their emotional state and suggest jobs such as "Creative Director" or "Product Manager."

[0172] Examples of prompts include: "I enjoy creative challenges and am somewhat dissatisfied with my current job. Based on this, what career path would you recommend?"

[0173] This system aims to help users find the optimal career path based on their own emotional state.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The user accesses the system using a terminal and answers questions designed to understand their interests, skills, values, and emotional state. These questions are entered into the terminal in text format. The entered data is temporarily stored on the terminal and securely transmitted to the server. This step involves collecting subjective information about the user.

[0177] Step 2:

[0178] The server receives user response information sent from the terminal. This response information is taken in as raw data. The server uses character processing techniques to preprocess this text data, performing cleansing and tokenization. This prepares the data for analysis. The preprocessed data becomes the output of this step.

[0179] Step 3:

[0180] The server uses natural language processing techniques to extract keywords from preprocessed data and determine context. At this stage, libraries such as NLTK and SpaCy are used to identify important features. The server generates a dataset that identifies user interests and skills, which becomes the input for the next process. This step involves data analysis.

[0181] Step 4:

[0182] The server further processes the analysis results of the dataset using a generative AI model. Here, the sentiment analyzer plays a role in identifying the user's emotional tendencies. The generative AI model calculates an emotion score and uses it to adjust job selection. This analysis result forms the basis for appropriate job selection. The emotion score becomes the output of the step.

[0183] Step 5:

[0184] The server uses an educational machine structure, based on the analyzed information set and sentiment data, to select the most suitable job for the user. The selected job is presented as the one that best matches the user's interests and emotions. This result is used to formulate the next step. The proposed job is the output of this step.

[0185] Step 6:

[0186] The server develops a user-specific career plan based on the selected job. This plan incorporates feedback obtained from sentiment analysis. Specifically, it uses a visualized planning tool to structure the plan in an easy-to-understand format. This step involves actions that guide the user's action plan. The visualized career plan is the output of this step.

[0187] Step 7:

[0188] The terminal presents the user with job suggestions and a visualized career plan received from the server. The user can then use this information to consider their future career path. The terminal displays the output information in an intuitive and easy-to-understand manner and accepts user feedback. This step involves shaping the interaction with the user.

[0189] (Application Example 2)

[0190] 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".

[0191] In the career selection process, there is a need to develop methods for providing more accurate career suggestions in real time, while taking into account the emotional state, individual interests, and abilities of each user. Furthermore, a challenge is ensuring that users receive quick and easily understandable feedback when actually utilizing their career plans.

[0192] 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.

[0193] This invention includes a server that analyzes response data obtained from a user using natural language processing techniques to generate a dataset characterizing the user's interests and abilities; a server that uses a machine learning model based on the generated dataset to select suitable occupations for the user; and a server that analyzes the user's emotional state through voice input and adjusts occupational suggestions based on the tone of voice. This enables real-time occupational suggestions and feedback that reflect the user's individual requirements and emotions.

[0194] A "user" is an entity that utilizes the system to receive career selection and job suggestions.

[0195] "Natural language processing techniques" are technologies that analyze text data entered by users and extract their interests and abilities from its content.

[0196] A "dataset" is a collection of analyzed information that characterizes a user's interests and abilities.

[0197] A "machine learning model" is a data processing technique used to select the most suitable occupation for a user, and it has the ability to make predictions based on past data.

[0198] A "career plan" is a plan created based on a selected occupation, which includes specific steps for the user to build their career.

[0199] "Visualization" is the process of displaying a formulated career plan using diagrams and graphs so that users can easily understand it.

[0200] "Voice input" is a method by which users provide information to a system through their voice.

[0201] "Emotional state" refers to the psychological state that a user expresses through voice or other means, and is the subject of analysis by the system.

[0202] "Feedback" refers to information such as responses and evaluations regarding suggestions and career plans that the system provides to the user.

[0203] The system for carrying out this invention includes a device that performs voice input and analysis in order to interact with the user. The user can access the system through a terminal and answer questions about their carrier by voice. The terminal is responsible for acquiring the voice input and transmitting that data to the server.

[0204] The server uses speech recognition software to convert voice input into text. For example, solutions such as Google® Cloud Speech-to-Text are available. The converted text data is then analyzed using natural language processing techniques. This analysis includes keyword extraction and contextual analysis to identify interests and abilities. Natural language processing libraries such as NLTK and SpaCy are available.

[0205] Emotional state analysis utilizes sentiment analysis engines such as VADER and Affectiva. This identifies emotional trends derived from the user's voice. The server then uses this data to leverage machine learning models for selecting appropriate occupations. These models are designed based on scikit-learn and TensorFlow.

[0206] Based on the selected occupation, the server develops a career plan for the user and generates data to visualize that plan. User interface development tools such as Unity or Qt are used for visualization, allowing the user to receive information in an easily understandable format.

[0207] For example, if a user voice-inputs "I want to reduce stress while doing creative work," the server will analyze their emotional state and suggest professions such as "graphic designer" or "UX / UI designer."

[0208] An example of a prompt message would be: "Perform natural language processing and sentiment analysis based on user input to understand sentiment trends and generate optimal career suggestions."

[0209] This allows users to receive career plans in real time that best match their emotions, interests, and abilities.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The user accesses the system via a terminal and provides voice input. The terminal captures the user's voice with a high-precision microphone and sends the audio data to the server. The input is audio data, and the output is transmission to the server.

[0213] Step 2:

[0214] The server converts audio data into text data using speech recognition software. Specifically, Google Cloud Speech-to-Text analyzes the audio waveform and outputs text data from the input audio data.

[0215] Step 3:

[0216] The server performs natural language processing on the converted text data. Using tools such as NLTK, it extracts keywords and context from the input text and generates a dataset related to the user's interests and abilities. The output is a characterized dataset.

[0217] Step 4:

[0218] The server analyzes the user's text data using an emotion analysis engine. For example, it uses the VADER engine to identify emotion trends in the input text and obtains them as output.

[0219] Step 5:

[0220] The server uses a machine learning model to select appropriate occupations based on the collected dataset and sentiment analysis results. Using scikit-learn, this selection process is executed, outputting a list of appropriate occupations based on the input feature and sentiment data.

[0221] Step 6:

[0222] The server develops a career plan for the user based on the selected occupation and generates data for visualization. Using Unity or Qt, it outputs diagrams and graphs from the input list of occupations, creating data that presents information in a visually appealing format.

[0223] Step 7:

[0224] The terminal presents the user with visual data and audio feedback obtained from the server. The input is visual and audio data from the server, and the output is designed to provide information in an easily understandable way for the user.

[0225] 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.

[0226] Data generation model 58 is a type of 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention is a system designed to help users more effectively design and promote their career growth. The system consists of multiple components that process user input data and present optimal occupations and career plans.

[0242] First, the user accesses the system using a device and enters answers to the questions presented. These questions concern the user's interests, skills, values, and career goals. The device collects this response data and sends it to the server.

[0243] The server analyzes the received response data using natural language processing techniques. Specifically, it extracts data to characterize the user's interests and skills. This generates a dataset based on the user's characteristics. This dataset serves as the foundation for suggesting the most suitable occupation for the user.

[0244] Next, the server uses a machine learning model based on the generated dataset to select suitable occupations for the user. This model predicts occupations that match the user's characteristics from an existing occupation database and proposes them to the user.

[0245] Furthermore, based on the occupation selection results, the server develops a detailed career plan for the user. This plan includes steps for acquiring specific skills and qualifications, as well as plans for career advancement. The server generates visualized data to provide this information to the user in an easy-to-understand format.

[0246] Ultimately, the device displays job suggestions and a visualized career plan received from the server to the user. Based on this information, the user can set their own career goals and decide on specific actions to achieve them.

[0247] For example, if a user responds with "I'm interested in creative work" or "I'm good at programming," the system uses natural language processing and machine learning to analyze the user's responses and suggest professions such as "software developer" or "UX designer," visualizing and presenting a career plan. In this way, users can clearly identify the most suitable career path based on their interests and skills and proceed systematically.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user answers questions provided on the device. These questions are designed to explore the user's interests, skills, and values.

[0251] Step 2:

[0252] The terminal converts the responses obtained from the user into a data format and sends them to the server using a secure communication protocol.

[0253] Step 3:

[0254] The server stores the received data in the database and prepares it for analysis.

[0255] Step 4:

[0256] The server uses natural language processing techniques to analyze the user's response data. This generates a dataset that characterizes the user's interests and skills.

[0257] Step 5:

[0258] The server uses the generated dataset to apply a machine learning model and select the most suitable occupation for the user. In this process, the model predicts suitable jobs from the occupation database.

[0259] Step 6:

[0260] Based on the results of the occupation selection, the server develops a personalized career plan for the user. The plan includes specific skill acquisition paths and suggestions for obtaining qualifications.

[0261] Step 7:

[0262] The server generates data for visualization and prepares it to be presented in an easy-to-understand format for career planning.

[0263] Step 8:

[0264] The terminal displays job suggestions and a visualized career plan received from the server to the user. The user can then decide on their next action based on this information.

[0265] (Example 1)

[0266] 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."

[0267] In modern society, it is difficult for individuals to find a job and career path that is optimized for their own characteristics. Many people search for the right path, but often face difficulties in the process of finding the optimal job based on their interests and skills and building an effective career plan. There is a need for an efficient and automated system to solve these problems.

[0268] 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.

[0269] This invention includes a server that receives information about the user's interests, abilities, and goals entered into an information processing device, analyzes this information using natural language processing technology, and generates a data structure that represents the user's characteristics; a server that recommends suitable occupations to the user using a learning model based on the generated data structure; and a server that constructs an individual career plan based on the recommended occupations. This enables the user to quickly and efficiently find the optimal occupation and career plan based on their interests and skills, and to plan their actions based on the visualized plan.

[0270] An "information processing device" is an integrated computing system for inputting, processing, analyzing, and outputting data.

[0271] "Natural language processing technology" refers to a set of algorithms and techniques that enable computers to understand, analyze, and generate human language.

[0272] A "data structure" is a framework for organizing and storing data in a specific format, and refers to a format suitable for those operations.

[0273] A "learning model" is a mathematical or statistical process that extracts knowledge from input data and uses that knowledge to make predictions or decisions.

[0274] "Recommending occupations" means identifying occupations that match the user's characteristics and suggesting those options.

[0275] A "career plan" is a set of schedules or strategies that include the steps and actions necessary for a user to achieve their professional goals.

[0276] A "visualized plan" is a plan created to make information easily understandable to users by representing it in a graphical format.

[0277] This invention provides a method for efficiently designing a career plan through an information processing system accessed by the user using their own terminal. The user uses the terminal to input information about their interests, skills, values, and career goals into the system. The terminal collects this information and transmits it to a server via a network.

[0278] The server uses natural language processing libraries such as Python's NLTK or spaCy to analyze the information sent by the user. This analysis extracts keywords related to the user's interests and skills. Based on the extracted information, the server generates a data structure that represents the user's characteristics.

[0279] Next, the server utilizes machine learning libraries such as Scikit-learn and TensorFlow to evaluate this data structure and select a suitable occupation for the user. The machine learning model predicts the optimal occupation for the user while comparing with an existing occupation database.

[0280] Furthermore, based on the selected occupation, the server constructs a detailed career plan for the user. This plan includes recommended steps for acquiring necessary skills and qualifications. Also, data for visualizing the career plan in a format suitable for visual display is generated, and the terminal provides this information to the user.

[0281] As a specific example, the user inputs information such as "interested in creative jobs" and "good at programming" into the system. Then, the system recommends occupations such as "software developer" and "UX designer", and visualizes and displays the plan for acquiring the necessary skills and qualifications for these occupations.

[0282] Examples of prompt texts when inputting into the generation AI model include content such as "Please propose the optimal occupation based on my interests and skills. My interests are in creative jobs, and my skills are in programming." In this way, the user can find the most suitable occupation based on their input information and establish a related career plan.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user accesses the system through the terminal and inputs information regarding interests, skills, values, and career goals. Specifically, the user inputs text in response to the questions on the screen, such as "interested in creative jobs" and "good at programming". The input data is organized in JSON format and sent to the server.

[0286] Step 2:

[0287] The terminal collects input data from the user and sends it to the server via the Internet. The input data is in text format and is formatted for analysis on the server side. The specific operation here is to securely send the data using a communication protocol.

[0288] Step 3:

[0289] The server analyzes the received data. Specifically, keywords are extracted from the user's input using a natural language processing library such as Python's NLTK or spaCy. The input data represents the user's interests and skills, and as output, a list of characteristic keywords corresponding to the interests and skills is generated.

[0290] Step 4:

[0291] The server uses the extracted characteristic keywords to generate a data structure. This includes the process of organizing the extracted keywords and storing the user's characteristics as a dataset. The output is this data structure, which is used in subsequent processing.

[0292] Step 5:

[0293] Based on the generated data structure, the server uses a machine learning model to recommend the most suitable occupations for the user. Specifically, by leveraging Scikit-learn or TensorFlow, the model selects occupations that fit using a database. The input is the characteristic data structure, and the output is a list of recommended occupations.

[0294] Step 6:

[0295] The server builds a detailed career plan based on the recommended occupation. This plan includes steps for acquiring skills and qualifications related to the target occupation. Its operation involves organizing the plan in text and graphical formats and generating visualized data. The output is in the form of a visualized career plan.

[0296] Step 7:

[0297] The terminal receives a visualized career plan from the server and displays it to the user. Its specific function is to present the career plan in an easy-to-understand manner through the user interface. Through this visualized information, users can make confident career decisions.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] The modern labor market is undergoing rapid changes, making it increasingly difficult for individuals to maximize their interests and skills and find the right career path. Therefore, there is a need for a system that allows users to select the most suitable occupation based on their characteristics, develop effective career achievement plans, and receive personalized suggestions that take into account local labor market trends.

[0301] 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.

[0302] In this invention, the server includes means for analyzing response data obtained from a user by natural language processing techniques to generate a set of information characterizing the user's interests and skills, means for selecting a suitable occupation for the user using a machine learning model based on the generated set of information, and means for making individual occupation proposals based on the trends in the local labor market. As a result, the user can find an optimal occupation based on their own characteristics and can realize a career plan tailored to the local labor market.

[0303] A "user" is an individual who attempts to formulate an occupation selection and achievement plan based on their interests, skills, and career goals using the system.

[0304] "Response data" refers to information regarding the user's interests, skills, values, and career goals that the user inputs into the system.

[0305] "Natural language processing techniques" are technical methods for a computer to understand, analyze, and manipulate human language, including keyword extraction and sentiment analysis.

[0306] A "set of information" is a dataset generated to characterize the user's interests and skills and is information used for selecting an occupation suitable for the user.

[0307] A "machine learning model" is an artificial intelligence method that learns patterns based on past data and uses that knowledge to make predictions and classifications for new data.

[0308] A "career achievement plan" is a plan that specifies the steps for skill acquisition and qualification attainment necessary for the user to achieve their goals based on the selected occupation.

[0309] "Information for visualization" refers to data that illustrates the career achievement plan provided in a format that is easy for the user to understand.

[0310] "Regional labor market" refers to the employment environment in a particular region, including the supply and demand for jobs and trends in growth sectors.

[0311] This invention is a system that enables users to effectively design their careers and promote their growth. First, users access the system using a device such as a smartphone or smart glasses and answer questions about their interests, skills, values, and career goals.

[0312] The terminal collects this response data and sends it to the server. The server uses natural language processing techniques with Python and the NLTK library to analyze the response data and generate a set of information that characterizes the user's interests and skills. This set of information forms the basis for career suggestions within the system.

[0313] Furthermore, the server uses machine learning models such as TensorFlow to select suitable occupations for the user based on the generated information. In this process, individual occupation suggestions are made, taking into account local labor market trends. Specifically, it analyzes the supply and demand in the labor market in the user's area and presents occupations accordingly.

[0314] Based on the selected occupation, a career achievement plan is developed for the user. This plan includes steps for acquiring specific skills and qualifications, as well as activities for career advancement. The server visualizes this information using the Python matplotlib library and presents it to the user in an easy-to-understand format.

[0315] For example, if a user living in Tokyo responds that they are "interested in AI" and "have data science skills," the server will consider Tokyo's labor market trends and suggest occupations such as "AI engineer" or "data scientist." It will also introduce online courses for acquiring those skills and present plans for participating in actual projects.

[0316] An example of a prompt message could be, "I am interested in AI and have data science skills. What kind of career should I pursue in Tokyo?" This invention provides practical support for users to find the optimal occupation based on their own characteristics and to develop a localized career plan.

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

[0318] Step 1:

[0319] Users access the system using devices such as smartphones or smart glasses and answer questions about their interests, skills, values, and career goals. The user's response data is obtained as input. This response data will form the basis for future analysis.

[0320] Step 2:

[0321] The terminal sends the user's response data to the server. The transmitted data is stored in the server's database. The input is the user's response data, and the output is the data recorded on the server. This data is used for the next analysis.

[0322] Step 3:

[0323] The server analyzes the received response data using natural language processing techniques with Python and the NLTK library. Stored user response data is used as input, and the analysis outputs a set of information characterizing interests and skills. In this step, keyword extraction and emotional analysis are performed to construct a user profile.

[0324] Step 4:

[0325] The server uses TensorFlow to perform occupation selection using a machine learning model based on the generated information set. The input is the information set obtained in the previous step, and the output is a list of occupations suitable for the user. This list also includes individual occupation suggestions that take into account local labor market trends.

[0326] Step 5:

[0327] The server uses a list of suitable occupations for the user to create a specific career achievement plan. The input is selected occupation information, and the output generates data for corresponding skill acquisition plans and career plans, including scheduled activities and goal achievement schedules.

[0328] Step 6:

[0329] The server uses the Python matplotlib library to visualize the career achievement plan. The visualized data displays the planned schedule and achievement goals in an easy-to-understand format. For the user, it is formatted to allow them to understand the key action steps and overall progress at a glance.

[0330] Step 7:

[0331] The server sends visualized data to the terminal and displays it to the user. This allows the user to take action based on job suggestions tailored to their characteristics and the accompanying concrete career plan.

[0332] 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.

[0333] This invention relates to a career consultant system that incorporates an emotion engine. This system performs natural language processing and emotion analysis on user input data and provides personalized occupational and career plans based on these results.

[0334] First, the user accesses the system through a device and answers a series of questions. These questions are designed to understand the user's interests, skills, values, and emotional state. The answers entered by the user are stored on the device and securely and quickly transmitted to the server.

[0335] Upon receiving response data, the server uses natural language processing techniques to analyze the text data and generate a dataset that characterizes the user's interests and skills. This process includes keyword extraction and contextual analysis. Simultaneously, an emotion engine analyzes the emotions contained in the user's responses and recognizes specific emotional trends.

[0336] Based on the recognized emotions, the server adjusts the analysis results and selects the most suitable occupation using a machine learning model. The emotional state analyzed by the emotion engine is additionally considered as a factor in the occupation selection decision. This makes it possible to suggest the optimal occupation for the user's intrinsic motivation and stress level.

[0337] Furthermore, keeping the selected occupation in mind, the server develops a career plan for the user. This plan includes feedback based on sentiment analysis results and incorporates elements that enhance the plan's applicability and feasibility. The developed career plan is visualized in a way that is easy for the user to understand.

[0338] Finally, the terminal presents the user with job suggestions and a visualized career plan provided by the server. The user can then use this information to design their own career and provide further feedback to the system as needed.

[0339] For example, if a user inputs responses such as "I enjoy creative challenges" or "I'm somewhat dissatisfied with my current job," the system analyzes these responses along with the user's emotional state and suggests professions such as "Creative Director" or "Product Manager." Thus, a key feature of this invention is its ability to present the optimal career path while taking the user's emotional state into consideration.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The user accesses a question form using their device and answers several questions presented by the system. The questions concern the user's interests, skills, and current emotional state.

[0343] Step 2:

[0344] The terminal converts the responses collected from the user into a standard data format and prepares them for transmission to the server. The converted data is then sent to the server using a secure protocol.

[0345] Step 3:

[0346] The server receives data sent from the terminal and begins analysis using a natural language processing engine. Through the analysis, keyword extraction and contextual understanding within the text generate a dataset that characterizes the user's interests and skills.

[0347] Step 4:

[0348] The server uses an emotion engine to extract and recognize emotions from user responses. This process identifies positive or negative emotions contained within the responses and adds them to the dataset.

[0349] Step 5:

[0350] The server uses the generated dataset and recognized sentiment data to apply a machine learning model and select the most suitable occupation for the user. Here, logic is used to determine job suitability from the occupation database.

[0351] Step 6:

[0352] Based on the selected occupation, the server develops a career plan that takes into account the user's emotional state. This plan includes various skill acquisition methods and emotional feedback.

[0353] Step 7:

[0354] The server converts the career plan it has developed into a data format for visualization, and then integrates it with a visual note tool to prepare the information in a way that is easier for users to understand.

[0355] Step 8:

[0356] The terminal displays visualization data and career suggestions received from the server to the user. The user views the presented information and uses it as a reference to design their own career plan.

[0357] (Example 2)

[0358] 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".

[0359] Current career counseling systems make it difficult to select a job that adequately considers the user's emotional state. Furthermore, they lack visually clear career planning tools, making it challenging for users to develop concrete action plans for their careers.

[0360] 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.

[0361] In this invention, the server includes means for analyzing response information obtained from the user using character processing technology to generate an information set that characterizes the user's interests and skills; means for analyzing the user's emotional tendencies using an emotion analysis device and making adjustments to the selected job to take emotional information into account; and means for formulating and visualizing a career plan to be presented to the user based on the selected job. This makes it possible to suggest the optimal occupation that takes the user's emotional state into account and to present a career plan in an easy-to-understand format.

[0362] "Response information" refers to information including answers, opinions, and emotions obtained from users.

[0363] "Text processing technology" refers to all methods used to extract and analyze useful information from text data.

[0364] "Interest" refers to the things or areas that a user is particularly interested in.

[0365] "Skills" refers to the abilities and abilities that a user demonstrates in a particular activity or job.

[0366] An "information set" is a series of pieces of information that represent the characteristics of an object, generated based on data.

[0367] An "educational machine structure" is a system that uses machine learning algorithms to learn patterns and relationships from given data.

[0368] "Job" refers to a specific occupation or task that is suitable for the user.

[0369] A "career plan" refers to a set of actionable guidelines for users regarding their occupational choices and career development.

[0370] "Visualization" is a method of aiding understanding by representing information and data in visual forms such as graphs and diagrams.

[0371] An "emotion analysis device" is a system that identifies and analyzes a user's emotions from text and psychological state.

[0372] "Correction" refers to the process of adjusting the proposed content based on the analysis results to provide a more suitable outcome.

[0373] The present invention is a career consultant system that combines an emotion engine to perform natural language processing and emotion analysis on user input data, and based on this, provides an individualized occupation and career plan. The following describes embodiments for carrying out the present invention.

[0374] Users access the system via a computer terminal and answer questions designed to understand their interests, skills, values, and emotional state. This information is collected at the terminal and transmitted to the server via a secure communication protocol. The server analyzes the received data using character processing techniques to generate a set of information characterizing the user's interests and skills. Python's NLTK and SpaCy are used as character processing libraries in this process.

[0375] Furthermore, the server utilizes an emotion analysis device to extract emotions from the user's responses. By using a generative AI model, it is possible to understand the emotional trends observed in the user's responses and incorporate them into the analysis results.

[0376] The analysis results are processed by an educational machine structure, which automatically selects a suitable job for the user. Based on the selected job, the server develops a career plan and incorporates feedback that takes emotional information into account. This career plan is visualized and displayed to the user, making it easy for the user to understand the specific next steps.

[0377] For example, if a user enters "I like creative challenges" and "I'm somewhat dissatisfied with my current job," the system will analyze this along with their emotional state and suggest jobs such as "Creative Director" or "Product Manager."

[0378] Examples of prompts include: "I enjoy creative challenges and am somewhat dissatisfied with my current job. Based on this, what career path would you recommend?"

[0379] This system aims to help users find the optimal career path based on their own emotional state.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The user accesses the system using a terminal and answers questions designed to understand their interests, skills, values, and emotional state. These questions are entered into the terminal in text format. The entered data is temporarily stored on the terminal and securely transmitted to the server. This step involves collecting subjective information about the user.

[0383] Step 2:

[0384] The server receives user response information sent from the terminal. This response information is taken in as raw data. The server uses character processing techniques to preprocess this text data, performing cleansing and tokenization. This prepares the data for analysis. The preprocessed data becomes the output of this step.

[0385] Step 3:

[0386] The server uses natural language processing techniques to extract keywords from preprocessed data and determine context. At this stage, libraries such as NLTK and SpaCy are used to identify important features. The server generates a dataset that identifies user interests and skills, which becomes the input for the next process. This step involves data analysis.

[0387] Step 4:

[0388] The server further processes the analysis results of the dataset using a generative AI model. Here, the sentiment analyzer plays a role in identifying the user's emotional tendencies. The generative AI model calculates an emotion score and uses it to adjust job selection. This analysis result forms the basis for appropriate job selection. The emotion score becomes the output of the step.

[0389] Step 5:

[0390] The server uses an educational machine structure, based on the analyzed information set and sentiment data, to select the most suitable job for the user. The selected job is presented as the one that best matches the user's interests and emotions. This result is used to formulate the next step. The proposed job is the output of this step.

[0391] Step 6:

[0392] The server develops a user-specific career plan based on the selected job. This plan incorporates feedback obtained from sentiment analysis. Specifically, it uses a visualized planning tool to structure the plan in an easy-to-understand format. This step involves actions that guide the user's action plan. The visualized career plan is the output of this step.

[0393] Step 7:

[0394] The terminal presents the user with job suggestions and a visualized career plan received from the server. The user can then use this information to consider their future career path. The terminal displays the output information in an intuitive and easy-to-understand manner and accepts user feedback. This step involves shaping the interaction with the user.

[0395] (Application Example 2)

[0396] 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."

[0397] In the career selection process, there is a need to develop methods for providing more accurate career suggestions in real time, while taking into account the emotional state, individual interests, and abilities of each user. Furthermore, a challenge is ensuring that users receive quick and easily understandable feedback when actually utilizing their career plans.

[0398] 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.

[0399] This invention includes a server that analyzes response data obtained from a user using natural language processing techniques to generate a dataset characterizing the user's interests and abilities; a server that uses a machine learning model based on the generated dataset to select suitable occupations for the user; and a server that analyzes the user's emotional state through voice input and adjusts occupational suggestions based on the tone of voice. This enables real-time occupational suggestions and feedback that reflect the user's individual requirements and emotions.

[0400] A "user" is an entity that utilizes the system to receive career selection and job suggestions.

[0401] "Natural language processing techniques" are technologies that analyze text data entered by users and extract their interests and abilities from its content.

[0402] A "dataset" is a collection of analyzed information that characterizes a user's interests and abilities.

[0403] A "machine learning model" is a data processing technique used to select the most suitable occupation for a user, and it has the ability to make predictions based on past data.

[0404] A "career plan" is a plan created based on a selected occupation, which includes specific steps for the user to build their career.

[0405] "Visualization" is the process of displaying a formulated career plan using diagrams and graphs so that users can easily understand it.

[0406] "Voice input" is a method by which users provide information to a system through their voice.

[0407] "Emotional state" refers to the psychological state that a user expresses through voice or other means, and is the subject of analysis by the system.

[0408] "Feedback" refers to information such as responses and evaluations regarding suggestions and career plans that the system provides to the user.

[0409] The system for carrying out this invention includes a device that performs voice input and analysis in order to interact with the user. The user can access the system through a terminal and answer questions about their carrier by voice. The terminal is responsible for acquiring the voice input and transmitting that data to the server.

[0410] The server uses speech recognition software to convert speech input into text. For example, solutions such as Google Cloud Speech-to-Text are available. The converted text data is then analyzed using natural language processing techniques. This analysis includes keyword extraction and contextual analysis to identify interests and abilities. Natural language processing libraries such as NLTK and SpaCy are available.

[0411] Emotional state analysis utilizes sentiment analysis engines such as VADER and Affectiva. This identifies emotional trends derived from the user's voice. The server then uses this data to leverage machine learning models for selecting appropriate occupations. These models are designed based on scikit-learn and TensorFlow.

[0412] Based on the selected occupation, the server develops a career plan for the user and generates data to visualize that plan. User interface development tools such as Unity or Qt are used for visualization, allowing the user to receive information in an easily understandable format.

[0413] For example, if a user voice-inputs "I want to reduce stress while doing creative work," the server will analyze their emotional state and suggest professions such as "graphic designer" or "UX / UI designer."

[0414] An example of a prompt message would be: "Perform natural language processing and sentiment analysis based on user input to understand sentiment trends and generate optimal career suggestions."

[0415] This allows users to receive career plans in real time that best match their emotions, interests, and abilities.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The user accesses the system via a terminal and provides voice input. The terminal captures the user's voice with a high-precision microphone and sends the audio data to the server. The input is audio data, and the output is transmission to the server.

[0419] Step 2:

[0420] The server converts audio data into text data using speech recognition software. Specifically, Google Cloud Speech-to-Text analyzes the audio waveform and outputs text data from the input audio data.

[0421] Step 3:

[0422] The server performs natural language processing on the converted text data. Using tools such as NLTK, it extracts keywords and context from the input text and generates a dataset related to the user's interests and abilities. The output is a characterized dataset.

[0423] Step 4:

[0424] The server analyzes the user's text data using an emotion analysis engine. For example, it uses the VADER engine to identify emotion trends in the input text and obtains them as output.

[0425] Step 5:

[0426] The server uses a machine learning model to select appropriate occupations based on the collected dataset and sentiment analysis results. Using scikit-learn, this selection process is executed, outputting a list of appropriate occupations based on the input feature and sentiment data.

[0427] Step 6:

[0428] The server develops a career plan for the user based on the selected occupation and generates data for visualization. Using Unity or Qt, it outputs diagrams and graphs from the input list of occupations, creating data that presents information in a visually appealing format.

[0429] Step 7:

[0430] The terminal presents the user with visual data and audio feedback obtained from the server. The input is visual and audio data from the server, and the output is designed to provide information in an easily understandable way for the user.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Third Embodiment]

[0435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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".

[0447] This invention is a system designed to help users more effectively design and promote their career growth. The system consists of multiple components that process user input data and present optimal occupations and career plans.

[0448] First, the user accesses the system using a device and enters answers to the questions presented. These questions concern the user's interests, skills, values, and career goals. The device collects this response data and sends it to the server.

[0449] The server analyzes the received response data using natural language processing techniques. Specifically, it extracts data to characterize the user's interests and skills. This generates a dataset based on the user's characteristics. This dataset serves as the foundation for suggesting the most suitable occupation for the user.

[0450] Next, the server uses a machine learning model based on the generated dataset to select suitable occupations for the user. This model predicts occupations that match the user's characteristics from an existing occupation database and proposes them to the user.

[0451] Furthermore, based on the occupation selection results, the server develops a detailed career plan for the user. This plan includes steps for acquiring specific skills and qualifications, as well as plans for career advancement. The server generates visualized data to provide this information to the user in an easy-to-understand format.

[0452] Ultimately, the device displays job suggestions and a visualized career plan received from the server to the user. Based on this information, the user can set their own career goals and decide on specific actions to achieve them.

[0453] For example, if a user responds with "I'm interested in creative work" or "I'm good at programming," the system uses natural language processing and machine learning to analyze the user's responses and suggest professions such as "software developer" or "UX designer," visualizing and presenting a career plan. In this way, users can clearly identify the most suitable career path based on their interests and skills and proceed systematically.

[0454] The following describes the processing flow.

[0455] Step 1:

[0456] The user answers questions provided on the device. These questions are designed to explore the user's interests, skills, and values.

[0457] Step 2:

[0458] The terminal converts the responses obtained from the user into a data format and sends them to the server using a secure communication protocol.

[0459] Step 3:

[0460] The server stores the received data in the database and prepares it for analysis.

[0461] Step 4:

[0462] The server uses natural language processing techniques to analyze the user's response data. This generates a dataset that characterizes the user's interests and skills.

[0463] Step 5:

[0464] The server uses the generated dataset to apply a machine learning model and select the most suitable occupation for the user. In this process, the model predicts suitable jobs from the occupation database.

[0465] Step 6:

[0466] Based on the results of the occupation selection, the server develops a personalized career plan for the user. The plan includes specific skill acquisition paths and suggestions for obtaining qualifications.

[0467] Step 7:

[0468] The server generates data for visualization and prepares it to be presented in an easy-to-understand format for career planning.

[0469] Step 8:

[0470] The terminal displays job suggestions and a visualized career plan received from the server to the user. The user can then decide on their next action based on this information.

[0471] (Example 1)

[0472] 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."

[0473] In modern society, it is difficult for individuals to find a job and career path that is optimized for their own characteristics. Many people search for the right path, but often face difficulties in the process of finding the optimal job based on their interests and skills and building an effective career plan. There is a need for an efficient and automated system to solve these problems.

[0474] 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.

[0475] This invention includes a server that receives information about the user's interests, abilities, and goals entered into an information processing device, analyzes this information using natural language processing technology, and generates a data structure that represents the user's characteristics; a server that recommends suitable occupations to the user using a learning model based on the generated data structure; and a server that constructs an individual career plan based on the recommended occupations. This enables the user to quickly and efficiently find the optimal occupation and career plan based on their interests and skills, and to plan their actions based on the visualized plan.

[0476] An "information processing device" is an integrated computing system for inputting, processing, analyzing, and outputting data.

[0477] "Natural language processing technology" refers to a set of algorithms and techniques that enable computers to understand, analyze, and generate human language.

[0478] A "data structure" is a framework for organizing and storing data in a specific format, and refers to a format suitable for those operations.

[0479] A "learning model" is a mathematical or statistical process that extracts knowledge from input data and uses that knowledge to make predictions or decisions.

[0480] "Recommending occupations" means identifying occupations that match the user's characteristics and suggesting those options.

[0481] A "career plan" is a set of schedules or strategies that include the steps and actions necessary for a user to achieve their professional goals.

[0482] A "visualized plan" is a plan created to make information easily understandable to users by representing it in a graphical format.

[0483] This invention provides a method for efficiently designing a career plan through an information processing system accessed by the user using their own terminal. The user uses the terminal to input information about their interests, skills, values, and career goals into the system. The terminal collects this information and transmits it to a server via a network.

[0484] The server uses natural language processing libraries such as Python's NLTK or spaCy to analyze the information sent by the user. This analysis extracts keywords related to the user's interests and skills. Based on the extracted information, the server generates a data structure that represents the user's characteristics.

[0485] Next, the server uses machine learning libraries such as Scikit-learn and TensorFlow to evaluate this data structure and select occupations suitable for the user. The machine learning model predicts the most suitable occupation for the user by comparing it with an existing occupation database.

[0486] Furthermore, the server creates a detailed career plan for the user based on their selected occupation. This plan includes recommended steps for acquiring necessary skills and qualifications. It also generates data to visualize the career plan in a format suitable for visual display, and the terminal provides this information to the user.

[0487] For example, a user might input information into the system such as "I'm interested in creative work" or "I'm good at programming." The system then recommends professions such as "software developer" or "UX designer," and visualizes and displays a plan for acquiring the necessary skills and qualifications for these professions.

[0488] An example of a prompt to use when inputting data into a generative AI model would be, "Please suggest the most suitable occupation based on my interests and skills. My interests are creative work, and my skills are programming." In this way, users can find the most suitable occupation based on their input information and establish a relevant career plan.

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] Users access the system through their devices and input information about their interests, skills, values, and career goals. Specifically, users answer on-screen questions by typing text, such as "I am interested in creative work" or "I am good at programming." The input data is organized in JSON format and sent to the server.

[0492] Step 2:

[0493] The terminal collects input data from the user and sends it to the server via the internet. The input data is in text format and is formatted for analysis on the server side. The specific operation here is to securely transmit the data using a communication protocol.

[0494] Step 3:

[0495] The server analyzes the received data. Specifically, it uses natural language processing libraries such as Python's NLTK or spaCy to extract keywords from the user's input. The input data represents the user's interests and skills, and the server generates a list of characteristic keywords corresponding to those interests and skills as output.

[0496] Step 4:

[0497] The server generates a data structure using the extracted characteristic keywords. This involves organizing the extracted keywords and saving the user's characteristics as a dataset. The output is this data structure, which is used in subsequent processing.

[0498] Step 5:

[0499] The server uses a machine learning model based on the generated data structure to recommend the most suitable occupation for the user. Specifically, it utilizes Scikit-learn and TensorFlow, and the model selects suitable occupations using the database. The input is a characteristics data structure, and the output is a list of recommended occupations.

[0500] Step 6:

[0501] The server builds a detailed career plan based on the recommended occupation. This plan includes steps for acquiring skills and qualifications related to the target occupation. Its operation involves organizing the plan in text and graphical formats and generating visualized data. The output is in the form of a visualized career plan.

[0502] Step 7:

[0503] The terminal receives a visualized career plan from the server and displays it to the user. Its specific function is to present the career plan in an easy-to-understand manner through the user interface. Through this visualized information, users can make confident career decisions.

[0504] (Application Example 1)

[0505] 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."

[0506] The modern labor market is undergoing rapid changes, making it increasingly difficult for individuals to maximize their interests and skills and find the right career path. Therefore, there is a need for a system that allows users to select the most suitable occupation based on their characteristics, develop effective career achievement plans, and receive personalized suggestions that take into account local labor market trends.

[0507] 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.

[0508] In this invention, the server includes means for analyzing response data obtained from the user using natural language processing techniques to generate a set of information characterizing the user's interests and skills; means for selecting a suitable occupation for the user using a machine learning model based on the generated set of information; and means for making individual occupational suggestions based on the trends of the labor market in the area where the user lives. As a result, the user can find the optimal occupation based on their own characteristics and realize a career plan that is tailored to the local labor market.

[0509] A "user" is an individual who uses the system to develop career selection and achievement plans based on their own interests, skills, and career goals.

[0510] "Response data" refers to information about the user's interests, skills, values, and career goals that they input into the system.

[0511] "Natural language processing techniques" are technologies and methods that enable computers to understand, analyze, and manipulate human language, and include keyword extraction and sentiment analysis.

[0512] An "information set" is a dataset generated to characterize a user's interests and skills, and is used to select a suitable occupation for that user.

[0513] A "machine learning model" is an artificial intelligence technique that learns patterns based on past data and uses that knowledge to make predictions and classifications about new data.

[0514] A "career achievement plan" is a plan that outlines the steps a user needs to take to acquire the skills and qualifications necessary to achieve their chosen career goals.

[0515] "Information for visualization" refers to data illustrating career achievement plans, provided in a format that is easy for users to understand.

[0516] "Regional labor market" refers to the employment environment in a particular region, including the supply and demand for jobs and trends in growth sectors.

[0517] This invention is a system that enables users to effectively design their careers and promote their growth. First, users access the system using a device such as a smartphone or smart glasses and answer questions about their interests, skills, values, and career goals.

[0518] The terminal collects this response data and sends it to the server. The server uses natural language processing techniques with Python and the NLTK library to analyze the response data and generate a set of information that characterizes the user's interests and skills. This set of information forms the basis for career suggestions within the system.

[0519] Furthermore, the server uses machine learning models such as TensorFlow to select suitable occupations for the user based on the generated information. In this process, individual occupation suggestions are made, taking into account local labor market trends. Specifically, it analyzes the supply and demand in the labor market in the user's area and presents occupations accordingly.

[0520] Based on the selected occupation, a career achievement plan is developed for the user. This plan includes steps for acquiring specific skills and qualifications, as well as activities for career advancement. The server visualizes this information using the Python matplotlib library and presents it to the user in an easy-to-understand format.

[0521] For example, if a user living in Tokyo responds that they are "interested in AI" and "have data science skills," the server will consider Tokyo's labor market trends and suggest occupations such as "AI engineer" or "data scientist." It will also introduce online courses for acquiring those skills and present plans for participating in actual projects.

[0522] An example of a prompt message could be, "I am interested in AI and have data science skills. What kind of career should I pursue in Tokyo?" This invention provides practical support for users to find the optimal occupation based on their own characteristics and to develop a localized career plan.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] Users access the system using devices such as smartphones or smart glasses and answer questions about their interests, skills, values, and career goals. The user's response data is obtained as input. This response data will form the basis for future analysis.

[0526] Step 2:

[0527] The terminal sends the user's response data to the server. The transmitted data is stored in the server's database. The input is the user's response data, and the output is the data recorded on the server. This data is used for the next analysis.

[0528] Step 3:

[0529] The server analyzes the received response data using natural language processing techniques with Python and the NLTK library. Stored user response data is used as input, and the analysis outputs a set of information characterizing interests and skills. In this step, keyword extraction and emotional analysis are performed to construct a user profile.

[0530] Step 4:

[0531] The server uses TensorFlow to perform occupation selection using a machine learning model based on the generated information set. The input is the information set obtained in the previous step, and the output is a list of occupations suitable for the user. This list also includes individual occupation suggestions that take into account local labor market trends.

[0532] Step 5:

[0533] The server uses a list of suitable occupations for the user to create a specific career achievement plan. The input is selected occupation information, and the output generates data for corresponding skill acquisition plans and career plans, including scheduled activities and goal achievement schedules.

[0534] Step 6:

[0535] The server uses the Python matplotlib library to visualize the career achievement plan. The visualized data displays the planned schedule and achievement goals in an easy-to-understand format. For the user, it is formatted to allow them to understand the key action steps and overall progress at a glance.

[0536] Step 7:

[0537] The server sends visualized data to the terminal and displays it to the user. This allows the user to take action based on job suggestions tailored to their characteristics and the accompanying concrete career plan.

[0538] 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.

[0539] This invention relates to a career consultant system that incorporates an emotion engine. This system performs natural language processing and emotion analysis on user input data and provides personalized occupational and career plans based on these results.

[0540] First, the user accesses the system through a device and answers a series of questions. These questions are designed to understand the user's interests, skills, values, and emotional state. The answers entered by the user are stored on the device and securely and quickly transmitted to the server.

[0541] Upon receiving response data, the server uses natural language processing techniques to analyze the text data and generate a dataset that characterizes the user's interests and skills. This process includes keyword extraction and contextual analysis. Simultaneously, an emotion engine analyzes the emotions contained in the user's responses and recognizes specific emotional trends.

[0542] Based on the recognized emotions, the server adjusts the analysis results and selects the most suitable occupation using a machine learning model. The emotional state analyzed by the emotion engine is additionally considered as a factor in the occupation selection decision. This makes it possible to suggest the optimal occupation for the user's intrinsic motivation and stress level.

[0543] Furthermore, keeping the selected occupation in mind, the server develops a career plan for the user. This plan includes feedback based on sentiment analysis results and incorporates elements that enhance the plan's applicability and feasibility. The developed career plan is visualized in a way that is easy for the user to understand.

[0544] Finally, the terminal presents the user with job suggestions and a visualized career plan provided by the server. The user can then use this information to design their own career and provide further feedback to the system as needed.

[0545] For example, if a user inputs responses such as "I enjoy creative challenges" or "I'm somewhat dissatisfied with my current job," the system analyzes these responses along with the user's emotional state and suggests professions such as "Creative Director" or "Product Manager." Thus, a key feature of this invention is its ability to present the optimal career path while taking the user's emotional state into consideration.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] The user accesses a question form using their device and answers several questions presented by the system. The questions concern the user's interests, skills, and current emotional state.

[0549] Step 2:

[0550] The terminal converts the responses collected from the user into a standard data format and prepares them for transmission to the server. The converted data is then sent to the server using a secure protocol.

[0551] Step 3:

[0552] The server receives data sent from the terminal and begins analysis using a natural language processing engine. Through the analysis, keyword extraction and contextual understanding within the text generate a dataset that characterizes the user's interests and skills.

[0553] Step 4:

[0554] The server uses an emotion engine to extract and recognize emotions from user responses. This process identifies positive or negative emotions contained within the responses and adds them to the dataset.

[0555] Step 5:

[0556] The server uses the generated dataset and recognized sentiment data to apply a machine learning model and select the most suitable occupation for the user. Here, logic is used to determine job suitability from the occupation database.

[0557] Step 6:

[0558] Based on the selected occupation, the server develops a career plan that takes into account the user's emotional state. This plan includes various skill acquisition methods and emotional feedback.

[0559] Step 7:

[0560] The server converts the career plan it has developed into a data format for visualization, and then integrates it with a visual note tool to prepare the information in a way that is easier for users to understand.

[0561] Step 8:

[0562] The terminal displays visualization data and career suggestions received from the server to the user. The user views the presented information and uses it as a reference to design their own career plan.

[0563] (Example 2)

[0564] 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."

[0565] Current career counseling systems make it difficult to select a job that adequately considers the user's emotional state. Furthermore, they lack visually clear career planning tools, making it challenging for users to develop concrete action plans for their careers.

[0566] 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.

[0567] In this invention, the server includes means for analyzing response information obtained from the user using character processing technology to generate an information set that characterizes the user's interests and skills; means for analyzing the user's emotional tendencies using an emotion analysis device and making adjustments to the selected job to take emotional information into account; and means for formulating and visualizing a career plan to be presented to the user based on the selected job. This makes it possible to suggest the optimal occupation that takes the user's emotional state into account and to present a career plan in an easy-to-understand format.

[0568] "Response information" refers to information including answers, opinions, and emotions obtained from users.

[0569] "Text processing technology" refers to all methods used to extract and analyze useful information from text data.

[0570] "Interest" refers to the things or areas that a user is particularly interested in.

[0571] "Skills" refers to the abilities and abilities that a user demonstrates in a particular activity or job.

[0572] An "information set" is a series of pieces of information that represent the characteristics of an object, generated based on data.

[0573] An "educational machine structure" is a system that uses machine learning algorithms to learn patterns and relationships from given data.

[0574] "Job" refers to a specific occupation or task that is suitable for the user.

[0575] A "career plan" refers to a set of actionable guidelines for users regarding their occupational choices and career development.

[0576] "Visualization" is a method of aiding understanding by representing information and data in visual forms such as graphs and diagrams.

[0577] An "emotion analysis device" is a system that identifies and analyzes a user's emotions from text and psychological state.

[0578] "Correction" refers to the process of adjusting the proposed content based on the analysis results to provide a more suitable outcome.

[0579] The present invention is a career consultant system that combines an emotion engine to perform natural language processing and emotion analysis on user input data, and based on this, provides an individualized occupation and career plan. The following describes embodiments for carrying out the present invention.

[0580] Users access the system via a computer terminal and answer questions designed to understand their interests, skills, values, and emotional state. This information is collected at the terminal and transmitted to the server via a secure communication protocol. The server analyzes the received data using character processing techniques to generate a set of information characterizing the user's interests and skills. Python's NLTK and SpaCy are used as character processing libraries in this process.

[0581] Furthermore, the server utilizes an emotion analysis device to extract emotions from the user's responses. By using a generative AI model, it is possible to understand the emotional trends observed in the user's responses and incorporate them into the analysis results.

[0582] The analysis results are processed by an educational machine structure, which automatically selects a suitable job for the user. Based on the selected job, the server develops a career plan and incorporates feedback that takes emotional information into account. This career plan is visualized and displayed to the user, making it easy for the user to understand the specific next steps.

[0583] For example, if a user enters "I like creative challenges" and "I'm somewhat dissatisfied with my current job," the system will analyze this along with their emotional state and suggest jobs such as "Creative Director" or "Product Manager."

[0584] Examples of prompts include: "I enjoy creative challenges and am somewhat dissatisfied with my current job. Based on this, what career path would you recommend?"

[0585] This system aims to help users find the optimal career path based on their own emotional state.

[0586] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0587] Step 1:

[0588] The user accesses the system using a terminal and answers questions designed to understand their interests, skills, values, and emotional state. These questions are entered into the terminal in text format. The entered data is temporarily stored on the terminal and securely transmitted to the server. This step involves collecting subjective information about the user.

[0589] Step 2:

[0590] The server receives user response information sent from the terminal. This response information is taken in as raw data. The server uses character processing techniques to preprocess this text data, performing cleansing and tokenization. This prepares the data for analysis. The preprocessed data becomes the output of this step.

[0591] Step 3:

[0592] The server uses natural language processing techniques to extract keywords from preprocessed data and determine context. At this stage, libraries such as NLTK and SpaCy are used to identify important features. The server generates a dataset that identifies user interests and skills, which becomes the input for the next process. This step involves data analysis.

[0593] Step 4:

[0594] The server further processes the analysis results of the dataset using a generative AI model. Here, the sentiment analyzer plays a role in identifying the user's emotional tendencies. The generative AI model calculates an emotion score and uses it to adjust job selection. This analysis result forms the basis for appropriate job selection. The emotion score becomes the output of the step.

[0595] Step 5:

[0596] The server uses an educational machine structure, based on the analyzed information set and sentiment data, to select the most suitable job for the user. The selected job is presented as the one that best matches the user's interests and emotions. This result is used to formulate the next step. The proposed job is the output of this step.

[0597] Step 6:

[0598] The server develops a user-specific career plan based on the selected job. This plan incorporates feedback obtained from sentiment analysis. Specifically, it uses a visualized planning tool to structure the plan in an easy-to-understand format. This step involves actions that guide the user's action plan. The visualized career plan is the output of this step.

[0599] Step 7:

[0600] The terminal presents the user with job suggestions and a visualized career plan received from the server. The user can then use this information to consider their future career path. The terminal displays the output information in an intuitive and easy-to-understand manner and accepts user feedback. This step involves shaping the interaction with the user.

[0601] (Application Example 2)

[0602] 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."

[0603] In the career selection process, there is a need to develop methods for providing more accurate career suggestions in real time, while taking into account the emotional state, individual interests, and abilities of each user. Furthermore, a challenge is ensuring that users receive quick and easily understandable feedback when actually utilizing their career plans.

[0604] 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.

[0605] This invention includes a server that analyzes response data obtained from a user using natural language processing techniques to generate a dataset characterizing the user's interests and abilities; a server that uses a machine learning model based on the generated dataset to select suitable occupations for the user; and a server that analyzes the user's emotional state through voice input and adjusts occupational suggestions based on the tone of voice. This enables real-time occupational suggestions and feedback that reflect the user's individual requirements and emotions.

[0606] A "user" is an entity that utilizes the system to receive career selection and job suggestions.

[0607] "Natural language processing techniques" are technologies that analyze text data entered by users and extract their interests and abilities from its content.

[0608] A "dataset" is a collection of analyzed information that characterizes a user's interests and abilities.

[0609] A "machine learning model" is a data processing technique used to select the most suitable occupation for a user, and it has the ability to make predictions based on past data.

[0610] A "career plan" is a plan created based on a selected occupation, which includes specific steps for the user to build their career.

[0611] "Visualization" is the process of displaying a formulated career plan using diagrams and graphs so that users can easily understand it.

[0612] "Voice input" is a method by which users provide information to a system through their voice.

[0613] "Emotional state" refers to the psychological state that a user expresses through voice or other means, and is the subject of analysis by the system.

[0614] "Feedback" refers to information such as responses and evaluations regarding suggestions and career plans that the system provides to the user.

[0615] The system for carrying out this invention includes a device that performs voice input and analysis in order to interact with the user. The user can access the system through a terminal and answer questions about their carrier by voice. The terminal is responsible for acquiring the voice input and transmitting that data to the server.

[0616] The server uses speech recognition software to convert speech input into text. For example, solutions such as Google Cloud Speech-to-Text are available. The converted text data is then analyzed using natural language processing techniques. This analysis includes keyword extraction and contextual analysis to identify interests and abilities. Natural language processing libraries such as NLTK and SpaCy are available.

[0617] Emotional state analysis utilizes sentiment analysis engines such as VADER and Affectiva. This identifies emotional trends derived from the user's voice. The server then uses this data to leverage machine learning models for selecting appropriate occupations. These models are designed based on scikit-learn and TensorFlow.

[0618] Based on the selected occupation, the server develops a career plan for the user and generates data to visualize that plan. User interface development tools such as Unity or Qt are used for visualization, allowing the user to receive information in an easily understandable format.

[0619] For example, if a user voice-inputs "I want to reduce stress while doing creative work," the server will analyze their emotional state and suggest professions such as "graphic designer" or "UX / UI designer."

[0620] An example of a prompt message would be: "Perform natural language processing and sentiment analysis based on user input to understand sentiment trends and generate optimal career suggestions."

[0621] This allows users to receive career plans in real time that best match their emotions, interests, and abilities.

[0622] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0623] Step 1:

[0624] The user accesses the system via a terminal and provides voice input. The terminal captures the user's voice with a high-precision microphone and sends the audio data to the server. The input is audio data, and the output is transmission to the server.

[0625] Step 2:

[0626] The server converts audio data into text data using speech recognition software. Specifically, Google Cloud Speech-to-Text analyzes the audio waveform and outputs text data from the input audio data.

[0627] Step 3:

[0628] The server performs natural language processing on the converted text data. Using tools such as NLTK, it extracts keywords and context from the input text and generates a dataset related to the user's interests and abilities. The output is a characterized dataset.

[0629] Step 4:

[0630] The server analyzes the user's text data using an emotion analysis engine. For example, it uses the VADER engine to identify emotion trends in the input text and obtains them as output.

[0631] Step 5:

[0632] The server uses a machine learning model to select appropriate occupations based on the collected dataset and sentiment analysis results. Using scikit-learn, this selection process is executed, outputting a list of appropriate occupations based on the input feature and sentiment data.

[0633] Step 6:

[0634] The server develops a career plan for the user based on the selected occupation and generates data for visualization. Using Unity or Qt, it outputs diagrams and graphs from the input list of occupations, creating data that presents information in a visually appealing format.

[0635] Step 7:

[0636] The terminal presents the user with visual data and audio feedback obtained from the server. The input is visual and audio data from the server, and the output is designed to provide information in an easily understandable way for the user.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] [Fourth Embodiment]

[0641] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0642] 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.

[0643] 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).

[0644] 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.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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".

[0654] This invention is a system designed to help users more effectively design and promote their career growth. The system consists of multiple components that process user input data and present optimal occupations and career plans.

[0655] First, the user accesses the system using a device and enters answers to the questions presented. These questions concern the user's interests, skills, values, and career goals. The device collects this response data and sends it to the server.

[0656] The server analyzes the received response data using natural language processing techniques. Specifically, it extracts data to characterize the user's interests and skills. This generates a dataset based on the user's characteristics. This dataset serves as the foundation for suggesting the most suitable occupation for the user.

[0657] Next, the server uses a machine learning model based on the generated dataset to select suitable occupations for the user. This model predicts occupations that match the user's characteristics from an existing occupation database and proposes them to the user.

[0658] Furthermore, based on the occupation selection results, the server develops a detailed career plan for the user. This plan includes steps for acquiring specific skills and qualifications, as well as plans for career advancement. The server generates visualized data to provide this information to the user in an easy-to-understand format.

[0659] Ultimately, the device displays job suggestions and a visualized career plan received from the server to the user. Based on this information, the user can set their own career goals and decide on specific actions to achieve them.

[0660] For example, if a user responds with "I'm interested in creative work" or "I'm good at programming," the system uses natural language processing and machine learning to analyze the user's responses and suggest professions such as "software developer" or "UX designer," visualizing and presenting a career plan. In this way, users can clearly identify the most suitable career path based on their interests and skills and proceed systematically.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The user answers questions provided on the device. These questions are designed to explore the user's interests, skills, and values.

[0664] Step 2:

[0665] The terminal converts the responses obtained from the user into a data format and sends them to the server using a secure communication protocol.

[0666] Step 3:

[0667] The server stores the received data in the database and prepares it for analysis.

[0668] Step 4:

[0669] The server uses natural language processing techniques to analyze the user's response data. This generates a dataset that characterizes the user's interests and skills.

[0670] Step 5:

[0671] The server uses the generated dataset to apply a machine learning model and select the most suitable occupation for the user. In this process, the model predicts suitable jobs from the occupation database.

[0672] Step 6:

[0673] Based on the results of the occupation selection, the server develops a personalized career plan for the user. The plan includes specific skill acquisition paths and suggestions for obtaining qualifications.

[0674] Step 7:

[0675] The server generates data for visualization and prepares it to be presented in an easy-to-understand format for career planning.

[0676] Step 8:

[0677] The terminal displays job suggestions and a visualized career plan received from the server to the user. The user can then decide on their next action based on this information.

[0678] (Example 1)

[0679] 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".

[0680] In modern society, it is difficult for individuals to find a job and career path that is optimized for their own characteristics. Many people search for the right path, but often face difficulties in the process of finding the optimal job based on their interests and skills and building an effective career plan. There is a need for an efficient and automated system to solve these problems.

[0681] 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.

[0682] This invention includes a server that receives information about the user's interests, abilities, and goals entered into an information processing device, analyzes this information using natural language processing technology, and generates a data structure that represents the user's characteristics; a server that recommends suitable occupations to the user using a learning model based on the generated data structure; and a server that constructs an individual career plan based on the recommended occupations. This enables the user to quickly and efficiently find the optimal occupation and career plan based on their interests and skills, and to plan their actions based on the visualized plan.

[0683] An "information processing device" is an integrated computing system for inputting, processing, analyzing, and outputting data.

[0684] "Natural language processing technology" refers to a set of algorithms and techniques that enable computers to understand, analyze, and generate human language.

[0685] A "data structure" is a framework for organizing and storing data in a specific format, and refers to a format suitable for those operations.

[0686] A "learning model" is a mathematical or statistical process that extracts knowledge from input data and uses that knowledge to make predictions or decisions.

[0687] "Recommending occupations" means identifying occupations that match the user's characteristics and suggesting those options.

[0688] A "career plan" is a set of schedules or strategies that include the steps and actions necessary for a user to achieve their professional goals.

[0689] A "visualized plan" is a plan created to make information easily understandable to users by representing it in a graphical format.

[0690] This invention provides a method for efficiently designing a career plan through an information processing system accessed by the user using their own terminal. The user uses the terminal to input information about their interests, skills, values, and career goals into the system. The terminal collects this information and transmits it to a server via a network.

[0691] The server uses natural language processing libraries such as Python's NLTK or spaCy to analyze the information sent by the user. This analysis extracts keywords related to the user's interests and skills. Based on the extracted information, the server generates a data structure that represents the user's characteristics.

[0692] Next, the server uses machine learning libraries such as Scikit-learn and TensorFlow to evaluate this data structure and select occupations suitable for the user. The machine learning model predicts the most suitable occupation for the user by comparing it with an existing occupation database.

[0693] Furthermore, the server creates a detailed career plan for the user based on their selected occupation. This plan includes recommended steps for acquiring necessary skills and qualifications. It also generates data to visualize the career plan in a format suitable for visual display, and the terminal provides this information to the user.

[0694] For example, a user might input information into the system such as "I'm interested in creative work" or "I'm good at programming." The system then recommends professions such as "software developer" or "UX designer," and visualizes and displays a plan for acquiring the necessary skills and qualifications for these professions.

[0695] An example of a prompt to use when inputting data into a generative AI model would be, "Please suggest the most suitable occupation based on my interests and skills. My interests are creative work, and my skills are programming." In this way, users can find the most suitable occupation based on their input information and establish a relevant career plan.

[0696] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0697] Step 1:

[0698] Users access the system through their devices and input information about their interests, skills, values, and career goals. Specifically, users answer on-screen questions by typing text, such as "I am interested in creative work" or "I am good at programming." The input data is organized in JSON format and sent to the server.

[0699] Step 2:

[0700] The terminal collects input data from the user and sends it to the server via the internet. The input data is in text format and is formatted for analysis on the server side. The specific operation here is to securely transmit the data using a communication protocol.

[0701] Step 3:

[0702] The server analyzes the received data. Specifically, it uses natural language processing libraries such as Python's NLTK or spaCy to extract keywords from the user's input. The input data represents the user's interests and skills, and the server generates a list of characteristic keywords corresponding to those interests and skills as output.

[0703] Step 4:

[0704] The server generates a data structure using the extracted characteristic keywords. This involves organizing the extracted keywords and saving the user's characteristics as a dataset. The output is this data structure, which is used in subsequent processing.

[0705] Step 5:

[0706] The server uses a machine learning model based on the generated data structure to recommend the most suitable occupation for the user. Specifically, it utilizes Scikit-learn and TensorFlow, and the model selects suitable occupations using the database. The input is a characteristics data structure, and the output is a list of recommended occupations.

[0707] Step 6:

[0708] The server builds a detailed career plan based on the recommended occupation. This plan includes steps for acquiring skills and qualifications related to the target occupation. Its operation involves organizing the plan in text and graphical formats and generating visualized data. The output is in the form of a visualized career plan.

[0709] Step 7:

[0710] The terminal receives a visualized career plan from the server and displays it to the user. Its specific function is to present the career plan in an easy-to-understand manner through the user interface. Through this visualized information, users can make confident career decisions.

[0711] (Application Example 1)

[0712] 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".

[0713] The modern labor market is undergoing rapid changes, making it increasingly difficult for individuals to maximize their interests and skills and find the right career path. Therefore, there is a need for a system that allows users to select the most suitable occupation based on their characteristics, develop effective career achievement plans, and receive personalized suggestions that take into account local labor market trends.

[0714] 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.

[0715] In this invention, the server includes means for analyzing response data obtained from the user using natural language processing techniques to generate a set of information characterizing the user's interests and skills; means for selecting a suitable occupation for the user using a machine learning model based on the generated set of information; and means for making individual occupational suggestions based on the trends of the labor market in the area where the user lives. As a result, the user can find the optimal occupation based on their own characteristics and realize a career plan that is tailored to the local labor market.

[0716] A "user" is an individual who uses the system to develop career selection and achievement plans based on their own interests, skills, and career goals.

[0717] "Response data" refers to information about the user's interests, skills, values, and career goals that they input into the system.

[0718] "Natural language processing techniques" are technologies and methods that enable computers to understand, analyze, and manipulate human language, and include keyword extraction and sentiment analysis.

[0719] An "information set" is a dataset generated to characterize a user's interests and skills, and is used to select a suitable occupation for that user.

[0720] A "machine learning model" is an artificial intelligence technique that learns patterns based on past data and uses that knowledge to make predictions and classifications about new data.

[0721] A "career achievement plan" is a plan that outlines the steps a user needs to take to acquire the skills and qualifications necessary to achieve their chosen career goals.

[0722] "Information for visualization" refers to data illustrating career achievement plans, provided in a format that is easy for users to understand.

[0723] "Regional labor market" refers to the employment environment in a particular region, including the supply and demand for jobs and trends in growth sectors.

[0724] This invention is a system that enables users to effectively design their careers and promote their growth. First, users access the system using a device such as a smartphone or smart glasses and answer questions about their interests, skills, values, and career goals.

[0725] The terminal collects this response data and sends it to the server. The server uses natural language processing techniques with Python and the NLTK library to analyze the response data and generate a set of information that characterizes the user's interests and skills. This set of information forms the basis for career suggestions within the system.

[0726] Furthermore, the server uses machine learning models such as TensorFlow to select suitable occupations for the user based on the generated information. In this process, individual occupation suggestions are made, taking into account local labor market trends. Specifically, it analyzes the supply and demand in the labor market in the user's area and presents occupations accordingly.

[0727] Based on the selected occupation, a career achievement plan is developed for the user. This plan includes steps for acquiring specific skills and qualifications, as well as activities for career advancement. The server visualizes this information using the Python matplotlib library and presents it to the user in an easy-to-understand format.

[0728] For example, if a user living in Tokyo responds that they are "interested in AI" and "have data science skills," the server will consider Tokyo's labor market trends and suggest occupations such as "AI engineer" or "data scientist." It will also introduce online courses for acquiring those skills and present plans for participating in actual projects.

[0729] An example of a prompt message could be, "I am interested in AI and have data science skills. What kind of career should I pursue in Tokyo?" This invention provides practical support for users to find the optimal occupation based on their own characteristics and to develop a localized career plan.

[0730] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0731] Step 1:

[0732] Users access the system using devices such as smartphones or smart glasses and answer questions about their interests, skills, values, and career goals. The user's response data is obtained as input. This response data will form the basis for future analysis.

[0733] Step 2:

[0734] The terminal sends the user's response data to the server. The transmitted data is stored in the server's database. The input is the user's response data, and the output is the data recorded on the server. This data is used for the next analysis.

[0735] Step 3:

[0736] The server analyzes the received response data using natural language processing techniques with Python and the NLTK library. Stored user response data is used as input, and the analysis outputs a set of information characterizing interests and skills. In this step, keyword extraction and emotional analysis are performed to construct a user profile.

[0737] Step 4:

[0738] The server uses TensorFlow to perform occupation selection using a machine learning model based on the generated information set. The input is the information set obtained in the previous step, and the output is a list of occupations suitable for the user. This list also includes individual occupation suggestions that take into account local labor market trends.

[0739] Step 5:

[0740] The server uses a list of suitable occupations for the user to create a specific career achievement plan. The input is selected occupation information, and the output generates data for corresponding skill acquisition plans and career plans, including scheduled activities and goal achievement schedules.

[0741] Step 6:

[0742] The server uses the Python matplotlib library to visualize the career achievement plan. The visualized data displays the planned schedule and achievement goals in an easy-to-understand format. For the user, it is formatted to allow them to understand the key action steps and overall progress at a glance.

[0743] Step 7:

[0744] The server sends visualized data to the terminal and displays it to the user. This allows the user to take action based on job suggestions tailored to their characteristics and the accompanying concrete career plan.

[0745] 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.

[0746] This invention relates to a career consultant system that incorporates an emotion engine. This system performs natural language processing and emotion analysis on user input data and provides personalized occupational and career plans based on these results.

[0747] First, the user accesses the system through a device and answers a series of questions. These questions are designed to understand the user's interests, skills, values, and emotional state. The answers entered by the user are stored on the device and securely and quickly transmitted to the server.

[0748] Upon receiving response data, the server uses natural language processing techniques to analyze the text data and generate a dataset that characterizes the user's interests and skills. This process includes keyword extraction and contextual analysis. Simultaneously, an emotion engine analyzes the emotions contained in the user's responses and recognizes specific emotional trends.

[0749] Based on the recognized emotions, the server adjusts the analysis results and selects the most suitable occupation using a machine learning model. The emotional state analyzed by the emotion engine is additionally considered as a factor in the occupation selection decision. This makes it possible to suggest the optimal occupation for the user's intrinsic motivation and stress level.

[0750] Furthermore, keeping the selected occupation in mind, the server develops a career plan for the user. This plan includes feedback based on sentiment analysis results and incorporates elements that enhance the plan's applicability and feasibility. The developed career plan is visualized in a way that is easy for the user to understand.

[0751] Finally, the terminal presents the user with job suggestions and a visualized career plan provided by the server. The user can then use this information to design their own career and provide further feedback to the system as needed.

[0752] For example, if a user inputs responses such as "I enjoy creative challenges" or "I'm somewhat dissatisfied with my current job," the system analyzes these responses along with the user's emotional state and suggests professions such as "Creative Director" or "Product Manager." Thus, a key feature of this invention is its ability to present the optimal career path while taking the user's emotional state into consideration.

[0753] The following describes the processing flow.

[0754] Step 1:

[0755] The user accesses a question form using their device and answers several questions presented by the system. The questions concern the user's interests, skills, and current emotional state.

[0756] Step 2:

[0757] The terminal converts the responses collected from the user into a standard data format and prepares them for transmission to the server. The converted data is then sent to the server using a secure protocol.

[0758] Step 3:

[0759] The server receives data sent from the terminal and begins analysis using a natural language processing engine. Through the analysis, keyword extraction and contextual understanding within the text generate a dataset that characterizes the user's interests and skills.

[0760] Step 4:

[0761] The server uses an emotion engine to extract and recognize emotions from user responses. This process identifies positive or negative emotions contained within the responses and adds them to the dataset.

[0762] Step 5:

[0763] The server uses the generated dataset and recognized sentiment data to apply a machine learning model and select the most suitable occupation for the user. Here, logic is used to determine job suitability from the occupation database.

[0764] Step 6:

[0765] Based on the selected occupation, the server develops a career plan that takes into account the user's emotional state. This plan includes various skill acquisition methods and emotional feedback.

[0766] Step 7:

[0767] The server converts the career plan it has developed into a data format for visualization, and then integrates it with a visual note tool to prepare the information in a way that is easier for users to understand.

[0768] Step 8:

[0769] The terminal displays visualization data and career suggestions received from the server to the user. The user views the presented information and uses it as a reference to design their own career plan.

[0770] (Example 2)

[0771] 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".

[0772] Current career counseling systems make it difficult to select a job that adequately considers the user's emotional state. Furthermore, they lack visually clear career planning tools, making it challenging for users to develop concrete action plans for their careers.

[0773] 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.

[0774] In this invention, the server includes means for analyzing response information obtained from the user using character processing technology to generate an information set that characterizes the user's interests and skills; means for analyzing the user's emotional tendencies using an emotion analysis device and making adjustments to the selected job to take emotional information into account; and means for formulating and visualizing a career plan to be presented to the user based on the selected job. This makes it possible to suggest the optimal occupation that takes the user's emotional state into account and to present a career plan in an easy-to-understand format.

[0775] "Response information" refers to information including answers, opinions, and emotions obtained from users.

[0776] "Text processing technology" refers to all methods used to extract and analyze useful information from text data.

[0777] "Interest" refers to the things or areas that a user is particularly interested in.

[0778] "Skills" refers to the abilities and abilities that a user demonstrates in a particular activity or job.

[0779] An "information set" is a series of pieces of information that represent the characteristics of an object, generated based on data.

[0780] An "educational machine structure" is a system that uses machine learning algorithms to learn patterns and relationships from given data.

[0781] "Job" refers to a specific occupation or task that is suitable for the user.

[0782] A "career plan" refers to a set of actionable guidelines for users regarding their occupational choices and career development.

[0783] "Visualization" is a method of aiding understanding by representing information and data in visual forms such as graphs and diagrams.

[0784] An "emotion analysis device" is a system that identifies and analyzes a user's emotions from text and psychological state.

[0785] "Correction" refers to the process of adjusting the proposed content based on the analysis results to provide a more suitable outcome.

[0786] The present invention is a career consultant system that combines an emotion engine to perform natural language processing and emotion analysis on user input data, and based on this, provides an individualized occupation and career plan. The following describes embodiments for carrying out the present invention.

[0787] Users access the system via a computer terminal and answer questions designed to understand their interests, skills, values, and emotional state. This information is collected at the terminal and transmitted to the server via a secure communication protocol. The server analyzes the received data using character processing techniques to generate a set of information characterizing the user's interests and skills. Python's NLTK and SpaCy are used as character processing libraries in this process.

[0788] Furthermore, the server utilizes an emotion analysis device to extract emotions from the user's responses. By using a generative AI model, it is possible to understand the emotional trends observed in the user's responses and incorporate them into the analysis results.

[0789] The analysis results are processed by an educational machine structure, which automatically selects a suitable job for the user. Based on the selected job, the server develops a career plan and incorporates feedback that takes emotional information into account. This career plan is visualized and displayed to the user, making it easy for the user to understand the specific next steps.

[0790] For example, if a user enters "I like creative challenges" and "I'm somewhat dissatisfied with my current job," the system will analyze this along with their emotional state and suggest jobs such as "Creative Director" or "Product Manager."

[0791] Examples of prompts include: "I enjoy creative challenges and am somewhat dissatisfied with my current job. Based on this, what career path would you recommend?"

[0792] This system aims to help users find the optimal career path based on their own emotional state.

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] The user accesses the system using a terminal and answers questions designed to understand their interests, skills, values, and emotional state. These questions are entered into the terminal in text format. The entered data is temporarily stored on the terminal and securely transmitted to the server. This step involves collecting subjective information about the user.

[0796] Step 2:

[0797] The server receives user response information sent from the terminal. This response information is taken in as raw data. The server uses character processing techniques to preprocess this text data, performing cleansing and tokenization. This prepares the data for analysis. The preprocessed data becomes the output of this step.

[0798] Step 3:

[0799] The server uses natural language processing techniques to extract keywords from preprocessed data and determine context. At this stage, libraries such as NLTK and SpaCy are used to identify important features. The server generates a dataset that identifies user interests and skills, which becomes the input for the next process. This step involves data analysis.

[0800] Step 4:

[0801] The server further processes the analysis results of the dataset using a generative AI model. Here, the sentiment analyzer plays a role in identifying the user's emotional tendencies. The generative AI model calculates an emotion score and uses it to adjust job selection. This analysis result forms the basis for appropriate job selection. The emotion score becomes the output of the step.

[0802] Step 5:

[0803] The server uses an educational machine structure, based on the analyzed information set and sentiment data, to select the most suitable job for the user. The selected job is presented as the one that best matches the user's interests and emotions. This result is used to formulate the next step. The proposed job is the output of this step.

[0804] Step 6:

[0805] The server develops a user-specific career plan based on the selected job. This plan incorporates feedback obtained from sentiment analysis. Specifically, it uses a visualized planning tool to structure the plan in an easy-to-understand format. This step involves actions that guide the user's action plan. The visualized career plan is the output of this step.

[0806] Step 7:

[0807] The terminal presents the user with job suggestions and a visualized career plan received from the server. The user can then use this information to consider their future career path. The terminal displays the output information in an intuitive and easy-to-understand manner and accepts user feedback. This step involves shaping the interaction with the user.

[0808] (Application Example 2)

[0809] 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".

[0810] In the career selection process, there is a need to develop methods for providing more accurate career suggestions in real time, while taking into account the emotional state, individual interests, and abilities of each user. Furthermore, a challenge is ensuring that users receive quick and easily understandable feedback when actually utilizing their career plans.

[0811] 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.

[0812] This invention includes a server that analyzes response data obtained from a user using natural language processing techniques to generate a dataset characterizing the user's interests and abilities; a server that uses a machine learning model based on the generated dataset to select suitable occupations for the user; and a server that analyzes the user's emotional state through voice input and adjusts occupational suggestions based on the tone of voice. This enables real-time occupational suggestions and feedback that reflect the user's individual requirements and emotions.

[0813] A "user" is an entity that utilizes the system to receive career selection and job suggestions.

[0814] "Natural language processing techniques" are technologies that analyze text data entered by users and extract their interests and abilities from its content.

[0815] A "dataset" is a collection of analyzed information that characterizes a user's interests and abilities.

[0816] A "machine learning model" is a data processing technique used to select the most suitable occupation for a user, and it has the ability to make predictions based on past data.

[0817] A "career plan" is a plan created based on a selected occupation, which includes specific steps for the user to build their career.

[0818] "Visualization" is the process of displaying a formulated career plan using diagrams and graphs so that users can easily understand it.

[0819] "Voice input" is a method by which users provide information to a system through their voice.

[0820] "Emotional state" refers to the psychological state that a user expresses through voice or other means, and is the subject of analysis by the system.

[0821] "Feedback" refers to information such as responses and evaluations regarding suggestions and career plans that the system provides to the user.

[0822] The system for carrying out this invention includes a device that performs voice input and analysis in order to interact with the user. The user can access the system through a terminal and answer questions about their carrier by voice. The terminal is responsible for acquiring the voice input and transmitting that data to the server.

[0823] The server uses speech recognition software to convert speech input into text. For example, solutions such as Google Cloud Speech-to-Text are available. The converted text data is then analyzed using natural language processing techniques. This analysis includes keyword extraction and contextual analysis to identify interests and abilities. Natural language processing libraries such as NLTK and SpaCy are available.

[0824] Emotional state analysis utilizes sentiment analysis engines such as VADER and Affectiva. This identifies emotional trends derived from the user's voice. The server then uses this data to leverage machine learning models for selecting appropriate occupations. These models are designed based on scikit-learn and TensorFlow.

[0825] Based on the selected occupation, the server develops a career plan for the user and generates data to visualize that plan. User interface development tools such as Unity or Qt are used for visualization, allowing the user to receive information in an easily understandable format.

[0826] For example, if a user voice-inputs "I want to reduce stress while doing creative work," the server will analyze their emotional state and suggest professions such as "graphic designer" or "UX / UI designer."

[0827] An example of a prompt message would be: "Perform natural language processing and sentiment analysis based on user input to understand sentiment trends and generate optimal career suggestions."

[0828] This allows users to receive career plans in real time that best match their emotions, interests, and abilities.

[0829] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0830] Step 1:

[0831] The user accesses the system via a terminal and provides voice input. The terminal captures the user's voice with a high-precision microphone and sends the audio data to the server. The input is audio data, and the output is transmission to the server.

[0832] Step 2:

[0833] The server converts audio data into text data using speech recognition software. Specifically, Google Cloud Speech-to-Text analyzes the audio waveform and outputs text data from the input audio data.

[0834] Step 3:

[0835] The server performs natural language processing on the converted text data. Using tools such as NLTK, it extracts keywords and context from the input text and generates a dataset related to the user's interests and abilities. The output is a characterized dataset.

[0836] Step 4:

[0837] The server analyzes the user's text data using an emotion analysis engine. For example, it uses the VADER engine to identify emotion trends in the input text and obtains them as output.

[0838] Step 5:

[0839] The server uses a machine learning model to select appropriate occupations based on the collected dataset and sentiment analysis results. Using scikit-learn, this selection process is executed, outputting a list of appropriate occupations based on the input feature and sentiment data.

[0840] Step 6:

[0841] The server develops a career plan for the user based on the selected occupation and generates data for visualization. Using Unity or Qt, it outputs diagrams and graphs from the input list of occupations, creating data that presents information in a visually appealing format.

[0842] Step 7:

[0843] The terminal presents the user with visual data and audio feedback obtained from the server. The input is visual and audio data from the server, and the output is designed to provide information in an easily understandable way for the user.

[0844] 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.

[0845] 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.

[0846] 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 robot 414.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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."

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] The following is further disclosed regarding the embodiments described above.

[0866] (Claim 1)

[0867] The response data obtained from users is analyzed using natural language processing techniques.

[0868] A means of generating a dataset that characterizes users' interests and skills,

[0869] A method for selecting suitable occupations for users using a machine learning model based on the generated dataset,

[0870] A means of formulating a career plan to present to the user based on the selected occupation,

[0871] A means of generating and displaying data to visualize the formulated career plan to the user,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] In the system described in claim 1,

[0875] It includes a means to integrate with visual note-taking tools when generating data for visualization.

[0876] The system according to claim 1.

[0877] (Claim 3)

[0878] In the system described in claim 1,

[0879] Natural language processing techniques include keyword extraction and sentiment analysis.

[0880] The system according to claim 1.

[0881] "Example 1"

[0882] (Claim 1)

[0883] The information processing device receives information about the user's interests, abilities, and goals.

[0884] A means for analyzing this information using natural language processing technology and generating a data structure that represents the characteristics of the above-mentioned user,

[0885] A method for recommending suitable occupations to users using a learning model based on the generated data structure,

[0886] Means for developing individual career plans based on recommended occupations,

[0887] A means of generating and displaying data to visualize and present a constructed career plan to the user,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, wherein data for visualization is generated in cooperation with an external display support tool.

[0891] (Claim 3)

[0892] The system according to claim 1, wherein the natural language processing technique includes summarizing information and analyzing sentiment.

[0893] "Application Example 1"

[0894] (Claim 1)

[0895] The response data obtained from users is analyzed using natural language processing techniques.

[0896] A means of generating a set of information that characterizes the user's interests and skills,

[0897] A method for selecting a suitable occupation for a user using a machine learning model based on the generated information set,

[0898] A means of formulating a career achievement plan to present to the user based on the selected occupation,

[0899] A means of generating and displaying information to visualize the formulated career achievement plan to the user,

[0900] A means of providing individual job suggestions based on the labor market trends in the area where one lives,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] It has a means of coordinating with a drawing application when generating information for visualization.

[0904] The system according to claim 1.

[0905] (Claim 3)

[0906] Natural language processing techniques include keyword extraction and sentiment analysis.

[0907] The system according to claim 1.

[0908] "Example 2 of combining an emotion engine"

[0909] (Claim 1)

[0910] The response information obtained from the user is analyzed using character processing technology.

[0911] A means for generating a set of information that characterizes the user's interests and skills,

[0912] A means for selecting a job suitable for the user using an educational machine structure based on the generated information set,

[0913] A means of formulating a career plan to present to the user based on the selected job,

[0914] A means of generating and displaying information to visualize the formulated career plan to the user,

[0915] A means for analyzing the user's emotional tendencies using an emotion analysis device and making adjustments to the selected job that take emotional information into account,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, comprising means for coordinating with a visual annotation tool when generating information for visualization.

[0919] (Claim 3)

[0920] The system according to claim 1, wherein the text processing technology includes keyword extraction and sentiment analysis.

[0921] "Application example 2 when combining with an emotional engine"

[0922] (Claim 1)

[0923] The response data obtained from users is analyzed using natural language processing techniques.

[0924] A means of generating a dataset that characterizes users' interests and abilities,

[0925] A method for selecting suitable occupations for users using a machine learning model based on the generated dataset,

[0926] A means of formulating a career plan to present to the user based on the selected occupation,

[0927] A means of generating and displaying information to visualize the formulated career plan to the user,

[0928] A means of analyzing the user's emotional state through voice input and refining career suggestions based on the tone of voice,

[0929] A means of providing real-time audio and visual feedback on the proposed content to the user,

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, comprising means for coordinating with a visualization tool when generating information for visualization.

[0933] (Claim 3)

[0934] The system according to claim 1, wherein the natural language processing method includes keyword extraction and sentiment analysis. [Explanation of Symbols]

[0935] 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. The response data obtained from users is analyzed using natural language processing techniques. A means of generating a set of information that characterizes the user's interests and skills, A method for selecting a suitable occupation for a user using a machine learning model based on the generated information set, A means of formulating a career achievement plan to present to the user based on the selected occupation, A means of generating and displaying information to visualize the formulated career achievement plan to the user, A means of providing individual job suggestions based on the labor market trends in the area where one lives, A system that includes this.

2. It has a means of coordinating with a drawing application when generating information for visualization. The system according to claim 1.

3. Natural language processing techniques include keyword extraction and sentiment analysis. The system according to claim 1.

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