system

Abstract

We provide the system. [Solution] A natural language processing means that receives the consultation content entered by the user and analyzes the consultation content, A dialogue generation means that outputs a response generated based on the analysis, Career assessment methods for evaluating user characteristics and skills, A means of providing information that collects job information from the market and recommends suitable jobs to users, 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

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional career consultations rely on specialized knowledge, making it difficult for the consultee to freely express opinions, and the quality of the consultation is also likely to be influenced by the experience and ability of the consultant. Therefore, there are cases where individual consultees cannot be appropriately addressed, and efficient career support is required. In addition, there is also a lack of an environment where career advice optimized for individuals can be easily provided.

Means for Solving the Problems

[0005] This invention provides an interactive system that analyzes user-inputted consultation content using natural language processing and generates responses based on that analysis. Furthermore, by incorporating means to perform career assessments that evaluate the user's characteristics and skills, and to collect market job information and recommend suitable job openings to the user, it realizes efficient and personalized career support for each individual consultant.

[0006] "Natural language processing means" refers to technologies that analyze text data entered by users and understand its meaning.

[0007] A "dialogue generation method" is a means for creating an appropriate response to the user based on information obtained through natural language processing.

[0008] A "career assessment tool" is a means of evaluating a user's characteristics and skills, and analyzing their career path and vocational aptitude.

[0009] "Information provision means" refers to a means of aggregating job information available in the market and providing the most suitable job information based on the user's needs and characteristics.

[0010] An "evaluation generation method" is a means of generating career advice based on the user's characteristics and skills.

[0011] "Career theory" is a theoretical framework used to understand occupational choices and career development, and to optimize individual career paths. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]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.

Embodiments for Carrying Out the Invention

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

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

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

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

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

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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).

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] This invention relates to a system that supports career counseling, providing a terminal with an interface that allows users to freely input their career-related concerns and questions. The user inputs the content of their consultation, and the terminal sends that data to a server. The server analyzes the received text using natural language processing means, and based on the results, a dialogue generation means generates an appropriate response.

[0034] The responses are structured as advice based on career theories and past success stories, thereby providing effective career support to the user. The server also provides a process to evaluate the user's characteristics and skills as needed using career assessment tools. The assessment results are then fed back to the user as more precise advice and information regarding occupational suitability.

[0035] Furthermore, the server uses information provision methods to collect relevant market job information from job databases and recommends jobs that are suitable for the user's characteristics and desired conditions. This allows users to efficiently find career options that meet their needs.

[0036] For example, if a user enters "I'm interested in a new career, but I'd like to know which field is right for me," the server analyzes the inquiry and generates advice by applying relevant career theories. For instance, it might provide a response such as, "I recommend taking an assessment in your area of ​​interest and comparing it to your specific skill set." Links to assessment tools and relevant job postings are also provided at the same time.

[0037] This system aims to efficiently support career counseling tailored to the individual needs of each user and to provide users with a free and open consultation environment.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users enter questions and concerns about their careers using the interface on their device. Users are provided with an interactive environment where they can freely input text, thereby accessing the system.

[0041] Step 2:

[0042] The device receives the data entered by the user and sends it to the server in an encrypted format. This ensures data security and protects privacy.

[0043] Step 3:

[0044] The server passes the text data received from the user to a natural language processing (NLP) engine. The NLP engine analyzes the text and extracts important keywords and concepts.

[0045] Step 4:

[0046] The server's dialogue generation mechanism generates appropriate responses to user questions based on the analyzed information. These responses may include career theories and general career advice.

[0047] Step 5:

[0048] The server activates a career assessment tool and proposes a specific assessment to the user. This includes questions designed to evaluate the user's characteristics and skills in detail.

[0049] Step 6:

[0050] The user completes the proposed assessment and returns the results to the server via their device. The assessment results provide data that more specifically reflects the user's characteristics and interests.

[0051] Step 7:

[0052] The server integrates assessment results and generates more accurate advice for the user, including suggestions regarding specific career steps and directions.

[0053] Step 8:

[0054] The server uses information provision methods to search the job information database. It filters and selects job information that matches the user's assessment results and desired conditions.

[0055] Step 9:

[0056] The terminal displays the user the response received from the server and recommended job postings. Based on the information provided, the user can make choices to consider for their next career step.

[0057] (Example 1)

[0058] 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."

[0059] Conventional career counseling systems have struggled to provide effective advice and job information tailored to users' characteristics and preferences, and have lacked the accuracy to adequately meet diverse user needs. In particular, there was a need to generate quick and appropriate responses based on the specific content of users' consultations and to provide detailed support for diverse career paths.

[0060] 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.

[0061] In this invention, the server includes natural language processing means, dialogue generation means, characteristic evaluation means, information provision means, and filtering means. This makes it possible to accurately analyze the user's consultation content and provide advice and job information that is suitable for the user's characteristics. Furthermore, by filtering information according to characteristics, only job information that matches the user's desired conditions can be carefully examined and provided.

[0062] "Natural language processing means" refers to technologies that analyze text data entered by a user and semantically understand its content.

[0063] "Dialogue generation means" refers to technology that automatically generates appropriate responses to the user based on analysis results obtained by natural language processing means.

[0064] "Characteristic evaluation means" refers to technology that evaluates a user's abilities and personality and provides advice and support tailored to the user based on that evaluation.

[0065] "Information provision means" refers to technologies for collecting market job information and other related information and providing it to users.

[0066] "Filtering means" refers to technology that effectively selects and filters information collected through information provision means based on the conditions requested by the user, and presents the most suitable information to the user.

[0067] This invention provides an automated system to support users in their career consultations. Users can input their career concerns and questions using a terminal. The terminal receives this input and sends it to a server as text data.

[0068] The server analyzes the received text data using natural language processing tools (e.g., spaCy or BERT). The analyzed data is used to understand the user's intentions and emotions, and based on this information, a dialogue generation tool (e.g., GPT-4®) generates an appropriate response. This response aims to provide specific and useful advice regarding the user's career concerns.

[0069] Furthermore, the server utilizes characteristic evaluation tools to provide more accurate advice by thoroughly assessing the user's characteristics and skills. The evaluation results are used to improve the accuracy of solutions to the user's inquiries.

[0070] In addition, the server accesses the market's job database via information provision means and retrieves job information that matches the user. This information is filtered based on the user's desired conditions using filtering means, and only the most suitable job information is presented to the user.

[0071] As a concrete example, consider a scenario where a user inputs a question into the system: "I'm interested in a new career, but I'd like to know which field is right for me." Based on this question, the server applies relevant career theories and generates advice such as, "We recommend that you take an assessment in your area of ​​interest and compare it with your specific skill set." Links to assessment tools and relevant job postings are also provided at this time.

[0072] An example of a prompt message would be, "I'm interested in a new career and would like to know which fields and job types are suitable for me. What kind of career advice can you give me?" By inputting this, users can receive specific support.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user uses the terminal interface to input text about their concerns or questions regarding their carrier. This entered text is the first data processed within the system.

[0076] Step 2:

[0077] The terminal sends user input to the server as text data. This is done securely using the HTTPS communication protocol. The text input data is transmitted to the server.

[0078] Step 3:

[0079] The server uses natural language processing (NLP) to analyze the received text data. Specifically, it uses language models (e.g., BERT or spaCy) to perform semantic analysis, keyword extraction, and intent understanding of the text. This process generates structured data that allows for a detailed understanding of what the user is looking for.

[0080] Step 4:

[0081] Based on the analysis results, the server generates an appropriate response using a dialogue generation mechanism. Utilizing a generation AI model (e.g., GPT-4), it outputs specific advice and suggestions to the user in written form based on the input information.

[0082] Step 5:

[0083] If necessary, the server uses trait assessment tools to evaluate the user's traits and skills. This evaluation is based on information the user has previously provided and general survey data, and yields analysis results regarding the traits.

[0084] Step 6:

[0085] The server uses information provision methods to access market job databases and collect job information that matches the user's profile and preferences. This collected data is then used in a subsequent filtering process.

[0086] Step 7:

[0087] The server uses filtering mechanisms to filter the retrieved job postings based on user criteria. For example, it filters by geographical location, job type, salary range, etc., to select appropriate job postings.

[0088] Step 8:

[0089] The server sends the generated responses and filtered job postings to the terminal. The terminal displays this to the user, who can then take specific actions related to the question based on that information. This allows the user to see specific career advice and options.

[0090] (Application Example 1)

[0091] 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."

[0092] In today's world, it is extremely important for individuals to find the career path that is best suited to them. In particular, amidst the vast amount of information available, there is a need for accurate advice based on individual interests and preferences, as well as the ability to quickly and efficiently obtain suitable job information. However, existing systems lack sufficient mechanisms to comprehensively support this, creating a challenge where users struggle to receive career counseling that is best suited to their needs.

[0093] 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.

[0094] In this invention, the server includes a natural language processing means for receiving and analyzing consultation content input from a user, a dialogue generation means for outputting a response generated based on the analysis, and a career assessment means for evaluating the user's characteristics and skills. This makes it possible to receive consultations based on the user's interests and preferences and support them in making the optimal career choice.

[0095] "User-submitted consultation information" refers to information that individuals freely write and send to the system regarding their career-related questions and concerns.

[0096] "Natural language processing" refers to technologies that analyze text data entered by users and extract their intentions and keywords.

[0097] A "dialogue generation means" is a function that automatically generates an appropriate response to the user based on the analyzed text data.

[0098] A "career assessment method" is a process of evaluating an individual's characteristics and skills and diagnosing their suitability for a career.

[0099] An "information provision method" is a system that collects and recommends suitable job postings and career-related information, taking into account the user's characteristics and desired conditions.

[0100] "Information analysis methods for providing consultation based on user interests and preferences" refers to data analysis methods for providing optimal career counseling in line with the user's interests and preferences.

[0101] The system for implementing this invention consists of a terminal that includes an interface for users to consult about their careers, and a server that analyzes the consultation content and generates appropriate advice. The system is operated using terminals such as smartphones and smart glasses, through which users input their consultations.

[0102] The server analyzes the user's input using natural language processing. This process, for example, utilizes the Google Cloud Natural Language API to extract key information from the input. Based on this analysis, a dialogue generation system automatically creates corresponding advice and sends it back to the user. The generated dialogue provides the user with career assessment information and insights into specific skills they are seeking.

[0103] Furthermore, the server is equipped with a career assessment tool that evaluates the user's characteristics and skills. This assessment provides the user with the information necessary to discover the optimal career path and supports a more detailed assessment of their vocational aptitude. In addition, the server has a function to retrieve and present job postings from the job information database that match the user's interests and desired conditions through an information provision tool. In this way, users can efficiently obtain appropriate career information and job postings.

[0104] For example, a user might input a question via smart glasses, such as, "I want to explore new career possibilities in digital marketing." The system then analyzes the input and suggests career path options based on the user's interests. These suggestions include a list of relevant professional skills and current job postings.

[0105] Examples of prompt statements include the following:

[0106] User question: What are some suitable job roles in digital marketing?

[0107] Answer generation process: Based on the user's question, analyze the most relevant job categories and suggest them as appropriate.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] Users input their inquiries about their carrier through their device. The entered data is sent from the device to the server in text format. This input serves as the system's initial data and forms the basis for subsequent processing.

[0111] Step 2:

[0112] The server uses natural language processing to analyze the user's submitted inquiry. This process utilizes the Google Cloud Natural Language API to extract intent and keywords from the text. The results of this analysis become the input data for dialogue generation.

[0113] Step 3:

[0114] Based on the analysis results, the server generates appropriate advice using a dialogue generation mechanism. This process utilizes a generative AI model to form a response to the user's input intent. The generated advice is then provided to the user in the next step.

[0115] Step 4:

[0116] The server uses a career assessment tool to evaluate the user's characteristics and skills. It uses pre-provided skill information and past work history data from the user as input. Based on this data, assessment results are output, providing information to support the user's career choices.

[0117] Step 5:

[0118] The server uses information provision methods to extract relevant information from the job posting database. This process considers the user's interests and preferences to select the most relevant job postings. It then executes database queries and immediately recommends the information to the user based on those queries.

[0119] Step 6:

[0120] The terminal displays responses and job postings sent from the server to the user. Here, the user can view suggested career advice and job descriptions and select the next step. Through the terminal's interface, the user can also request more detailed information.

[0121] 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.

[0122] This invention is a system that effectively supports career counseling, combining natural language processing and an emotion engine to deeply understand the user's consultation content and provide accurate advice. Users can input questions and concerns about their careers via a terminal. The terminal sends this data to the server.

[0123] The server uses natural language processing to analyze the input text and extract important keywords and themes. Simultaneously, it uses an emotion engine to recognize the emotions contained in the user's inquiry. For example, it detects emotions such as stress, anxiety, and expectations from the word choices and sentence structure in the user's text.

[0124] Based on these analysis results, the server generates a response using a dialogue generation mechanism. This response is refined using emotional data obtained from the emotion engine, making it more attuned to the user's feelings. The response includes advice based on career theory, offering specific and actionable suggestions tailored to the user's situation.

[0125] Furthermore, the server utilizes career assessment tools to evaluate the user's characteristics and skills, taking emotional data into consideration. Based on the assessment results, it recommends suitable job postings to the user. The information provision tool searches market job databases and collects and presents appropriate job candidates to the user.

[0126] For example, if a user enters a message saying, "I've lost confidence in a recent project, but I want to take on a new challenge," the server will interpret the mixed emotions of anxiety and anticipation from the message and generate a corresponding response. For instance, it might say, "When taking on a new challenge, it's important to reflect on past successes and regain your confidence. Here are some suitable job options for you." Relevant job postings will also be provided.

[0127] In this way, the system takes user emotions into consideration, allowing for more personalized career counseling and advice, and enabling more effective support.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user uses their device to enter their career-related questions. Specifically, they might write something like, "I'm interested in a new job, but I've lost confidence after a recent failure," into a text box and then press the submit button.

[0131] Step 2:

[0132] The terminal receives the entered text data and securely transmits it to the server. This data is protected by encryption technology.

[0133] Step 3:

[0134] The server activates natural language processing (NLP) tools to analyze the received text data. Specifically, it extracts important keywords from the text (e.g., "new job," "losing confidence").

[0135] Step 4:

[0136] The server analyzes the emotions in the text via an emotion engine. This identifies hidden emotions such as anxiety and excitement within the user. The techniques used include tone analysis of the text and referencing an emotion dictionary.

[0137] Step 5:

[0138] The server's dialogue generation mechanism constructs an appropriate response for the user based on the results of natural language processing and sentiment analysis. For example, it might generate a response such as, "When considering new challenges, it's important to re-evaluate your existing strengths and focus on the positive aspects."

[0139] Step 6:

[0140] The server uses career assessment tools to evaluate the user's characteristics and skills. Emotional data is also taken into consideration, and advice based on interests and preferences is ready to be provided.

[0141] Step 7:

[0142] The server searches the job information database using information provision methods. It selects job postings that match the user's assessment results and desired conditions, and collects the most relevant information for the user.

[0143] Step 8:

[0144] The terminal displays the user the response it received from the server. This includes emotionally sensitive response messages and a list of job postings that match the user's profile. Based on this, the user can gain guidance for considering their next career step.

[0145] (Example 2)

[0146] 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".

[0147] In modern society, there is a lack of systems that support effective career counseling and job selection while taking into account individual characteristics and emotions. Conventional systems often provide only uniform advice without adequately considering the user's feelings, making it difficult for users to obtain the best possible job selection and career advice.

[0148] 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.

[0149] In this invention, the server includes language analysis means, emotion recognition means, response generation means, occupation evaluation means, and information recommendation means. This enables more accurate and user-friendly career counseling and occupational selection support by analyzing emotions based on user input information and generating personalized responses that are attuned to those emotions.

[0150] "Language analysis means" refers to a technology that uses natural language processing techniques to analyze information input by users and extract important keywords and context.

[0151] "Emotion recognition means" refers to technology for detecting emotions from analyzed text and generating data based on those emotions.

[0152] "Response generation means" refers to a technology that uses a generation AI model to generate appropriate dialogue based on user input information and detected emotions.

[0153] "Occupational assessment tools" are technologies used to evaluate a user's occupational aptitude and skills, and to conduct aptitude assessments based on their characteristics.

[0154] "Information recommendation methods" are technologies that analyze market job information and present users with the most suitable job postings and career options.

[0155] This invention is a system that effectively supports users in seeking advice regarding their careers. Users can input questions and concerns about their careers via a terminal. The terminal sends the input data to a server, which analyzes the data and provides appropriate advice.

[0156] The server analyzes the text received from the user using natural language processing (NLTK) tools. This process utilizes Python's natural language processing libraries, spaCy and NLTK, for grammatical analysis and keyword extraction. After extracting important information through analysis, the server uses the Hugging Face Transformer model to recognize the emotions contained in the user's text. This emotion data is then used to generate subsequent responses.

[0157] The response generation method utilizes the GPT-3® (registered trademark) generation AI model. This enables the generation of advice and dialogue that is empathetic to the user's emotions. The generated responses are adjusted based on career theory and occupation-related information, resulting in concrete and actionable suggestions for the user.

[0158] As a means of career assessment, the server evaluates the user's work history and abilities, referencing the user's profile information using the LinkedIn® API. Based on this evaluation, the information recommendation system searches market job databases and recommends suitable occupations to the user. The Indeed API is used to collect job postings, providing users with real-time information.

[0159] For example, if a user inputs "I've lost confidence in my recent project, but I want to take on a new challenge," the server analyzes this input and detects feelings of anxiety and anticipation. It then generates a response accordingly, providing specific advice such as, "When taking on a new challenge, it's important to reflect on past successes and regain your confidence. Here are some suitable job options for you." An example of a prompt to input into the generating AI model might be, "The user is currently experiencing stress and a loss of confidence in their recent career. Please consider the emotional data and provide career advice and relevant job information."

[0160] Thus, the present invention takes into account the user's emotions to personalize career counseling and achieve more effective and accurate support.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] Users use their devices to input questions and concerns about their carrier in text format. For example, they might input a question like, "I lost confidence in my recent project, but I want to take on a new challenge," and then click the "Send" button. The input is received as text data.

[0164] Step 2:

[0165] The terminal encrypts the text data entered by the user and sends it to the server. The HTTPS protocol is used for this transmission to maintain security. The input is the user's text data, and the output is the encrypted data sent to the server.

[0166] Step 3:

[0167] The server analyzes the received text data using natural language processing techniques. It uses the Python spaCy library for grammatical analysis and keyword extraction. The input is text data submitted by the user, and the output consists of extracted keywords and contextual information.

[0168] Step 4:

[0169] The server analyzes the emotions contained in the text using emotion recognition tools. Here, the Hugging Face Transformer model is used to detect emotions such as stress, anxiety, and anticipation. The input is contextual information of the text obtained through natural language processing, and the output is the detected emotion data.

[0170] Step 5:

[0171] The server uses a generative AI model to generate appropriate responses to user inquiries. The generative AI model utilizes GPT-3 to construct prompts and generate responses based on sentiment data. Input consists of sentiment data and textual context, while output is specific, emotionally resonant advice and dialogue.

[0172] Step 6:

[0173] The server uses occupational assessment tools to evaluate the user's occupational aptitude. This process references the user's history and skill data via the LinkedIn API. The input is the user's profile information, and the output is the user's occupational aptitude and skill set.

[0174] Step 7:

[0175] The server uses information recommendation tools to search for job information in the market and suggest suitable jobs to the user. It leverages the Indeed API to collect current job postings and match them with user needs. The input is job aptitude and skill set, and the output is a list of recommended job postings.

[0176] Step 8:

[0177] The terminal displays advice and job information received from the server to the user. The user interface presents specific career advice and a list of job postings. Input is data from the server, and the displayed information is the output.

[0178] (Application Example 2)

[0179] 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".

[0180] In career counseling, there is a need for appropriate and individualized advice and information based on each user's emotional state, characteristics, and skills. However, conventional systems have the challenge of not being able to adequately consider the user's emotions and delivering personalized content. As a result, the quality of career support that users desire is reduced, and they end up not being satisfied.

[0181] 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.

[0182] In this invention, the server includes a natural language processing means for analyzing the content of the consultation input by the user, a career assessment means for evaluating the user's characteristics and skills, an information provision means for collecting and recommending market employment information, and a content distribution means for recognizing the user's emotional state and delivering personalized information. This makes it possible to provide users with individualized career advice and information that takes their emotions into consideration.

[0183] A "user" refers to an individual who uses the system to receive career counseling or information.

[0184] "Consultation content" refers to information about career-related questions and concerns that users enter into the system.

[0185] "Natural language processing means" refers to technologies that analyze text input by users and extract important keywords and themes.

[0186] "Dialogue generation means" refers to a function that generates an appropriate response based on the results of natural language processing and presents it to the user.

[0187] A "career assessment tool" is a technology that evaluates a user's characteristics and skills and provides career advice based on those evaluations.

[0188] "Information provision means" refers to a function that uses collected market employment information to recommend suitable employment to users.

[0189] "Content delivery methods" refer to technologies that recognize a user's emotional state and provide personalized information and content.

[0190] The embodiment of the invention begins with a user entering their career consultation details using a terminal such as a smartphone or personal computer. The terminal then transmits this information to a server. The server employs Hugging Face's Transformers as a natural language processing tool to analyze the entered consultation details. As a result of the analysis, important keywords and themes are extracted.

[0191] Furthermore, the server uses the Google Cloud Natural Language API to perform sentiment analysis. By recognizing emotions such as expectation, anxiety, and stress from the user's input text, it understands the user's emotional state. This provides data for generating appropriate responses based on those emotions.

[0192] Based on the acquired data, the server uses dialogue generation mechanisms to generate a response appropriate to the user. At this stage, personalized advice reflecting the user's emotional state is formed. The generated response includes measures based on career theory and presents specific actions that the user can take.

[0193] In addition, the career assessment tool evaluates the user's characteristics and skills and uses that information to make individualized job recommendations. The information provision tool collects the latest employment information from a database and presents it to the user. Here too, the user's emotional state is taken into consideration, and the most suitable job information is provided to the user.

[0194] For example, if a user inputs "I feel anxious about my future career," the server recognizes the user's anxiety and provides articles or videos that can boost their motivation. A concrete example of an input prompt for the generating AI model might be: "User inquiry: 'I feel anxious about my future career.' Generated response: 'To alleviate this anxiety, let's learn from the career stories of those who came before you. Also, please watch the following relaxing video.'"

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user uses their device to input their inquiries about their carrier. The entered text data is sent to the server. At this stage, user input is the primary data.

[0198] Step 2:

[0199] The server analyzes the received text data using natural language processing techniques. Hugging Face Transformers are used to syntactically analyze the input text and extract important keywords and themes. The input is user text data, and the extracted keywords become the output.

[0200] Step 3:

[0201] The server performs sentiment analysis. Using the Google Cloud Natural Language API, it recognizes emotions from the user's text data. Emotions such as anxiety, anticipation, and stress are identified. The input is the analyzed text data, and the output is the recognized sentiment data.

[0202] Step 4:

[0203] The server generates a response using a dialogue generation mechanism based on the extracted keywords and sentiment data. An AI model generates a response that takes emotions into consideration, creating an appropriate prompt. Here, keywords and sentiment data are the input, and the generated response is the output.

[0204] Step 5:

[0205] The server uses career assessment tools to evaluate the user's characteristics and skills. This evaluation utilizes past user data and entered consultation content. Based on the evaluation, appropriate career selection advice is generated. The input is the user's profile information, and the output is advice based on the evaluation.

[0206] Step 6:

[0207] The server uses information provision methods to search for market employment information collected from job information databases and presents job postings suitable for the user. The input is the result of a career assessment, and the output is filtered job information.

[0208] Step 7:

[0209] The server creates personalized content based on the user's emotions through a content delivery method. It generates and delivers articles and videos that boost motivation. Here, emotion data and generated response sentences are the inputs, and personalized content is the output.

[0210] 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.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention relates to a system that supports career counseling, providing a terminal with an interface that allows users to freely input their career-related concerns and questions. The user inputs the content of their consultation, and the terminal sends that data to a server. The server analyzes the received text using natural language processing means, and based on the results, a dialogue generation means generates an appropriate response.

[0227] The responses are structured as advice based on career theories and past success stories, thereby providing effective career support to the user. The server also provides a process to evaluate the user's characteristics and skills as needed using career assessment tools. The assessment results are then fed back to the user as more precise advice and information regarding occupational suitability.

[0228] Furthermore, the server uses information provision methods to collect relevant market job information from job databases and recommends jobs that are suitable for the user's characteristics and desired conditions. This allows users to efficiently find career options that meet their needs.

[0229] For example, if a user enters "I'm interested in a new career, but I'd like to know which field is right for me," the server analyzes the inquiry and generates advice by applying relevant career theories. For instance, it might provide a response such as, "I recommend taking an assessment in your area of ​​interest and comparing it to your specific skill set." Links to assessment tools and relevant job postings are also provided at the same time.

[0230] This system aims to efficiently support career counseling tailored to the individual needs of each user and to provide users with a free and open consultation environment.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] Users enter questions and concerns about their careers using the interface on their device. Users are provided with an interactive environment where they can freely input text, thereby accessing the system.

[0234] Step 2:

[0235] The device receives the data entered by the user and sends it to the server in an encrypted format. This ensures data security and protects privacy.

[0236] Step 3:

[0237] The server passes the text data received from the user to a natural language processing (NLP) engine. The NLP engine analyzes the text and extracts important keywords and concepts.

[0238] Step 4:

[0239] The server's dialogue generation mechanism generates appropriate responses to user questions based on the analyzed information. These responses may include career theories and general career advice.

[0240] Step 5:

[0241] The server activates a career assessment tool and proposes a specific assessment to the user. This includes questions designed to evaluate the user's characteristics and skills in detail.

[0242] Step 6:

[0243] The user completes the proposed assessment and returns the results to the server via their device. The assessment results provide data that more specifically reflects the user's characteristics and interests.

[0244] Step 7:

[0245] The server integrates assessment results and generates more accurate advice for the user, including suggestions regarding specific career steps and directions.

[0246] Step 8:

[0247] The server uses information provision methods to search the job information database. It filters and selects job information that matches the user's assessment results and desired conditions.

[0248] Step 9:

[0249] The terminal displays the user the response received from the server and recommended job postings. Based on the information provided, the user can make choices to consider for their next career step.

[0250] (Example 1)

[0251] 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."

[0252] Conventional career counseling systems have struggled to provide effective advice and job information tailored to users' characteristics and preferences, and have lacked the accuracy to adequately meet diverse user needs. In particular, there was a need to generate quick and appropriate responses based on the specific content of users' consultations and to provide detailed support for diverse career paths.

[0253] 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.

[0254] In this invention, the server includes natural language processing means, dialogue generation means, characteristic evaluation means, information provision means, and filtering means. This makes it possible to accurately analyze the user's consultation content and provide advice and job information that is suitable for the user's characteristics. Furthermore, by filtering information according to characteristics, only job information that matches the user's desired conditions can be carefully examined and provided.

[0255] "Natural language processing means" refers to technologies that analyze text data entered by a user and semantically understand its content.

[0256] "Dialogue generation means" refers to technology that automatically generates appropriate responses to the user based on analysis results obtained by natural language processing means.

[0257] "Characteristic evaluation means" refers to technology that evaluates a user's abilities and personality and provides advice and support tailored to the user based on that evaluation.

[0258] "Information provision means" refers to technologies for collecting market job information and other related information and providing it to users.

[0259] "Filtering means" refers to technology that effectively selects and filters information collected through information provision means based on the conditions requested by the user, and presents the most suitable information to the user.

[0260] This invention provides an automated system to support users in their career consultations. Users can input their career concerns and questions using a terminal. The terminal receives this input and sends it to a server as text data.

[0261] The server analyzes the received text data using natural language processing tools (e.g., spaCy or BERT). The analyzed data is used to understand the user's intentions and emotions, and based on this information, a dialogue generation tool (e.g., GPT-4) generates an appropriate response. This response aims to provide specific and useful advice regarding the user's career concerns.

[0262] Furthermore, the server utilizes characteristic evaluation tools to provide more accurate advice by thoroughly assessing the user's characteristics and skills. The evaluation results are used to improve the accuracy of solutions to the user's inquiries.

[0263] In addition, the server accesses the market's job database via information provision means and retrieves job information that matches the user. This information is filtered based on the user's desired conditions using filtering means, and only the most suitable job information is presented to the user.

[0264] As a concrete example, consider a scenario where a user inputs a question into the system: "I'm interested in a new career, but I'd like to know which field is right for me." Based on this question, the server applies relevant career theories and generates advice such as, "We recommend that you take an assessment in your area of ​​interest and compare it with your specific skill set." Links to assessment tools and relevant job postings are also provided at this time.

[0265] An example of a prompt message would be, "I'm interested in a new career and would like to know which fields and job types are suitable for me. What kind of career advice can you give me?" By inputting this, users can receive specific support.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The user uses the terminal interface to input text about their concerns or questions regarding their carrier. This entered text is the first data processed within the system.

[0269] Step 2:

[0270] The terminal sends user input to the server as text data. This is done securely using the HTTPS communication protocol. The text input data is transmitted to the server.

[0271] Step 3:

[0272] The server uses natural language processing (NLP) to analyze the received text data. Specifically, it uses language models (e.g., BERT or spaCy) to perform semantic analysis, keyword extraction, and intent understanding of the text. This process generates structured data that allows for a detailed understanding of what the user is looking for.

[0273] Step 4:

[0274] Based on the analysis results, the server generates an appropriate response using a dialogue generation mechanism. Utilizing a generation AI model (e.g., GPT-4), it outputs specific advice and suggestions to the user in written form based on the input information.

[0275] Step 5:

[0276] If necessary, the server uses trait assessment tools to evaluate the user's traits and skills. This evaluation is based on information the user has previously provided and general survey data, and yields analysis results regarding the traits.

[0277] Step 6:

[0278] The server uses information provision methods to access market job databases and collect job information that matches the user's profile and preferences. This collected data is then used in a subsequent filtering process.

[0279] Step 7:

[0280] The server uses filtering mechanisms to filter the retrieved job postings based on user criteria. For example, it filters by geographical location, job type, salary range, etc., to select appropriate job postings.

[0281] Step 8:

[0282] The server sends the generated response and the filtered job information to the terminal. The terminal displays this to the user, and the user can take specific actions associated with the question based on that information. As a result, the user can check specific advice and options regarding the carrier.

[0283] (Application Example 1)

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

[0285] In modern times, it is extremely important for an individual to find the optimal career path for oneself. In particular, among the vast amount of information, it is required to provide accurate advice based on individual interests and orientations, and to obtain more suitable job information quickly and efficiently. However, existing systems lack a mechanism to comprehensively support this, and there is a problem that it is difficult for users to receive optimal career consultations for themselves.

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

[0287] In this invention, the server includes natural language processing means for receiving the consultation content input from the user and analyzing the consultation content, dialogue generation means for outputting a response generated based on the analysis, and career assessment means for evaluating the characteristics and skills of the user. As a result, it becomes possible to receive consultations based on the user's interests and orientations and support optimal career selection.

[0288] The "consultation content input from the user" is information that an individual freely describes questions and concerns regarding their own career and sends to the system.

[0289] The "natural language processing means" is a technology for analyzing the text data input by the user and extracting intentions and keywords.

[0290] A "dialogue generation means" is a function that automatically generates an appropriate response to the user based on the analyzed text data.

[0291] A "career assessment method" is a process of evaluating an individual's characteristics and skills and diagnosing their suitability for a career.

[0292] An "information provision method" is a system that collects and recommends suitable job postings and career-related information, taking into account the user's characteristics and desired conditions.

[0293] "Information analysis methods for providing consultation based on user interests and preferences" refers to data analysis methods for providing optimal career counseling in line with the user's interests and preferences.

[0294] The system for implementing this invention consists of a terminal that includes an interface for users to consult about their careers, and a server that analyzes the consultation content and generates appropriate advice. The system is operated using terminals such as smartphones and smart glasses, through which users input their consultations.

[0295] The server analyzes the user's input using natural language processing. This process, for example, utilizes the Google Cloud Natural Language API to extract key information from the inquiry. Based on this analysis, a dialogue generation system automatically creates corresponding advice and sends it back to the user. The generated dialogue provides the user with career assessment information and insights into specific skills they are seeking.

[0296] Furthermore, the server is equipped with a career assessment tool that evaluates the user's characteristics and skills. This assessment provides the user with the information necessary to discover the optimal career path and supports a more detailed assessment of their vocational aptitude. In addition, the server has a function to retrieve and present job postings from the job information database that match the user's interests and desired conditions through an information provision tool. In this way, users can efficiently obtain appropriate career information and job postings.

[0297] For example, a user might input a question via smart glasses, such as, "I want to explore new career possibilities in digital marketing." The system then analyzes the input and suggests career path options based on the user's interests. These suggestions include a list of relevant professional skills and current job postings.

[0298] Examples of prompt statements include the following:

[0299] User question: What are some suitable job roles in digital marketing?

[0300] Answer generation process: Based on the user's question, analyze the most relevant job categories and suggest them as appropriate.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] Users input their inquiries about their carrier through their device. The entered data is sent from the device to the server in text format. This input serves as the system's initial data and forms the basis for subsequent processing.

[0304] Step 2:

[0305] The server uses natural language processing means to analyze the consultation content sent by the user. In this process, the Google Cloud Natural Language API is utilized to extract the intent and keywords from the text. This analysis result serves as the input data for dialogue generation.

[0306] Step 3:

[0307] Based on the analysis result, the server uses dialogue generation means to generate appropriate advice. In this process, a generation AI model is utilized to form an answer to the user's input intent. The generated advice is provided to the user in the next step.

[0308] Step 4:

[0309] The server uses carrier assessment means to evaluate the user's characteristics and skills. As input, the skill information and past work history data previously provided by the user are used. Based on these data, an assessment result is output, which becomes information to assist the user's career choice.

[0310] Step 5:

[0311] The server uses information provision means to extract relevant information from the job offer database. In this process, the user's interests and desired conditions are considered to select the most relevant job offers. A database query is executed, and the information based on it is immediately recommended to the user.

[0312] Step 6:

[0313] The terminal displays the response and job offer information sent from the server to the user. Here, the user can view the proposed career advice and job offer details and select the next step. Through the terminal interface, the user can also request more detailed information.

[0314] 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.

[0315] This invention is a system that effectively supports career counseling, combining natural language processing and an emotion engine to deeply understand the user's consultation content and provide accurate advice. Users can input questions and concerns about their careers via a terminal. The terminal sends this data to the server.

[0316] The server uses natural language processing to analyze the input text and extract important keywords and themes. Simultaneously, it uses an emotion engine to recognize the emotions contained in the user's inquiry. For example, it detects emotions such as stress, anxiety, and expectations from the word choices and sentence structure in the user's text.

[0317] Based on these analysis results, the server generates a response using a dialogue generation mechanism. This response is refined using emotional data obtained from the emotion engine, making it more attuned to the user's feelings. The response includes advice based on career theory, offering specific and actionable suggestions tailored to the user's situation.

[0318] Furthermore, the server utilizes career assessment tools to evaluate the user's characteristics and skills, taking emotional data into consideration. Based on the assessment results, it recommends suitable job postings to the user. The information provision tool searches market job databases and collects and presents appropriate job candidates to the user.

[0319] For example, if a user enters a message saying, "I've lost confidence in a recent project, but I want to take on a new challenge," the server will interpret the mixed emotions of anxiety and anticipation from the message and generate a corresponding response. For instance, it might say, "When taking on a new challenge, it's important to reflect on past successes and regain your confidence. Here are some suitable job options for you." Relevant job postings will also be provided.

[0320] In this way, the system takes user emotions into consideration, allowing for more personalized career counseling and advice, and enabling more effective support.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The user uses their device to enter their career-related questions. Specifically, they might write something like, "I'm interested in a new job, but I've lost confidence after a recent failure," into a text box and then press the submit button.

[0324] Step 2:

[0325] The terminal receives the entered text data and securely transmits it to the server. This data is protected by encryption technology.

[0326] Step 3:

[0327] The server activates natural language processing (NLP) tools to analyze the received text data. Specifically, it extracts important keywords from the text (e.g., "new job," "losing confidence").

[0328] Step 4:

[0329] The server analyzes the emotions in the text via an emotion engine. This identifies hidden emotions such as anxiety and excitement within the user. The techniques used include tone analysis of the text and referencing an emotion dictionary.

[0330] Step 5:

[0331] The server's dialogue generation mechanism constructs an appropriate response for the user based on the results of natural language processing and sentiment analysis. For example, it might generate a response such as, "When considering new challenges, it's important to re-evaluate your existing strengths and focus on the positive aspects."

[0332] Step 6:

[0333] The server uses career assessment tools to evaluate the user's characteristics and skills. Emotional data is also taken into consideration, and advice based on interests and preferences is ready to be provided.

[0334] Step 7:

[0335] The server searches the job information database using information provision methods. It selects job postings that match the user's assessment results and desired conditions, and collects the most relevant information for the user.

[0336] Step 8:

[0337] The terminal displays the user the response it received from the server. This includes emotionally sensitive response messages and a list of job postings that match the user's profile. Based on this, the user can gain guidance for considering their next career step.

[0338] (Example 2)

[0339] 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".

[0340] In modern society, there is a lack of systems that support effective career counseling and job selection while taking into account individual characteristics and emotions. Conventional systems often provide only uniform advice without adequately considering the user's feelings, making it difficult for users to obtain the best possible job selection and career advice.

[0341] 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.

[0342] In this invention, the server includes language analysis means, emotion recognition means, response generation means, occupation evaluation means, and information recommendation means. This enables more accurate and user-friendly career counseling and occupational selection support by analyzing emotions based on user input information and generating personalized responses that are attuned to those emotions.

[0343] "Language analysis means" refers to a technology that uses natural language processing techniques to analyze information input by users and extract important keywords and context.

[0344] "Emotion recognition means" refers to technology for detecting emotions from analyzed text and generating data based on those emotions.

[0345] "Response generation means" refers to a technology that uses a generation AI model to generate appropriate dialogue based on user input information and detected emotions.

[0346] "Occupational assessment tools" are technologies used to evaluate a user's occupational aptitude and skills, and to conduct aptitude assessments based on their characteristics.

[0347] "Information recommendation methods" are technologies that analyze market job information and present users with the most suitable job postings and career options.

[0348] This invention is a system that effectively supports users in seeking advice regarding their careers. Users can input questions and concerns about their careers via a terminal. The terminal sends the input data to a server, which analyzes the data and provides appropriate advice.

[0349] The server analyzes the text received from the user using natural language processing (NLTK) tools. This process utilizes Python's natural language processing libraries, spaCy and NLTK, for grammatical analysis and keyword extraction. After extracting important information through analysis, the server uses the Hugging Face Transformer model to recognize the emotions contained in the user's text. This emotion data is then used to generate subsequent responses.

[0350] The response generation mechanism uses the GPT-3 generation AI model. This allows for the generation of advice and dialogue that is empathetic to the user's emotions. The generated responses are adjusted based on career theory and occupation-related information, resulting in concrete and actionable suggestions for the user.

[0351] As a means of career assessment, the server evaluates the user's work history and skills, referencing the user's profile information using the LinkedIn API. Based on this evaluation, the information recommendation system searches market job databases and recommends suitable occupations to the user. The Indeed API is used to collect job postings, providing users with real-time information.

[0352] For example, if a user inputs "I've lost confidence in my recent project, but I want to take on a new challenge," the server analyzes this input and detects feelings of anxiety and anticipation. It then generates a response accordingly, providing specific advice such as, "When taking on a new challenge, it's important to reflect on past successes and regain your confidence. Here are some suitable job options for you." An example of a prompt to input into the generating AI model might be, "The user is currently experiencing stress and a loss of confidence in their recent career. Please consider the emotional data and provide career advice and relevant job information."

[0353] Thus, the present invention takes into account the user's emotions to personalize career counseling and achieve more effective and accurate support.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] Users use their devices to input questions and concerns about their carrier in text format. For example, they might input a question like, "I lost confidence in my recent project, but I want to take on a new challenge," and then click the "Send" button. The input is received as text data.

[0357] Step 2:

[0358] The terminal encrypts the text data entered by the user and sends it to the server. The HTTPS protocol is used for this transmission to maintain security. The input is the user's text data, and the output is the encrypted data sent to the server.

[0359] Step 3:

[0360] The server analyzes the received text data using natural language processing techniques. It uses the Python spaCy library for grammatical analysis and keyword extraction. The input is text data submitted by the user, and the output consists of extracted keywords and contextual information.

[0361] Step 4:

[0362] The server analyzes the emotions contained in the text using emotion recognition tools. Here, the Hugging Face Transformer model is used to detect emotions such as stress, anxiety, and anticipation. The input is contextual information of the text obtained through natural language processing, and the output is the detected emotion data.

[0363] Step 5:

[0364] The server uses a generative AI model to generate appropriate responses to user inquiries. The generative AI model utilizes GPT-3 to construct prompts and generate responses based on sentiment data. Input consists of sentiment data and textual context, while output is specific, emotionally resonant advice and dialogue.

[0365] Step 6:

[0366] The server uses occupational assessment tools to evaluate the user's occupational aptitude. This process references the user's history and skill data via the LinkedIn API. The input is the user's profile information, and the output is the user's occupational aptitude and skill set.

[0367] Step 7:

[0368] The server uses information recommendation tools to search for job information in the market and suggest suitable jobs to the user. It leverages the Indeed API to collect current job postings and match them with user needs. The input is job aptitude and skill set, and the output is a list of recommended job postings.

[0369] Step 8:

[0370] The terminal displays advice and job information received from the server to the user. The user interface presents specific career advice and a list of job postings. Input is data from the server, and the displayed information is the output.

[0371] (Application Example 2)

[0372] 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."

[0373] In career counseling, there is a need for appropriate and individualized advice and information based on each user's emotional state, characteristics, and skills. However, conventional systems have the challenge of not being able to adequately consider the user's emotions and delivering personalized content. As a result, the quality of career support that users desire is reduced, and they end up not being satisfied.

[0374] 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.

[0375] In this invention, the server includes a natural language processing means for analyzing the content of the consultation input by the user, a career assessment means for evaluating the user's characteristics and skills, an information provision means for collecting and recommending market employment information, and a content distribution means for recognizing the user's emotional state and delivering personalized information. This makes it possible to provide users with individualized career advice and information that takes their emotions into consideration.

[0376] A "user" refers to an individual who uses the system to receive career counseling or information.

[0377] "Consultation content" refers to information about career-related questions and concerns that users enter into the system.

[0378] "Natural language processing means" refers to technologies that analyze text input by users and extract important keywords and themes.

[0379] "Dialogue generation means" refers to a function that generates an appropriate response based on the results of natural language processing and presents it to the user.

[0380] A "career assessment tool" is a technology that evaluates a user's characteristics and skills and provides career advice based on those evaluations.

[0381] "Information provision means" refers to a function that uses collected market employment information to recommend suitable employment to users.

[0382] "Content delivery methods" refer to technologies that recognize a user's emotional state and provide personalized information and content.

[0383] The embodiment of the invention begins with a user entering their career consultation details using a terminal such as a smartphone or personal computer. The terminal then transmits this information to a server. The server employs Hugging Face's Transformers as a natural language processing tool to analyze the entered consultation details. As a result of the analysis, important keywords and themes are extracted.

[0384] Furthermore, the server uses the Google Cloud Natural Language API to perform sentiment analysis. By recognizing emotions such as expectation, anxiety, and stress from the user's input text, it understands the user's emotional state. This provides data for generating appropriate responses based on those emotions.

[0385] Based on the acquired data, the server uses dialogue generation mechanisms to generate a response appropriate to the user. At this stage, personalized advice reflecting the user's emotional state is formed. The generated response includes measures based on career theory and presents specific actions that the user can take.

[0386] In addition, the career assessment tool evaluates the user's characteristics and skills and uses that information to make individualized job recommendations. The information provision tool collects the latest employment information from a database and presents it to the user. Here too, the user's emotional state is taken into consideration, and the most suitable job information is provided to the user.

[0387] For example, if a user inputs "I feel anxious about my future career," the server recognizes the user's anxiety and provides articles or videos that can boost their motivation. A concrete example of an input prompt for the generating AI model might be: "User inquiry: 'I feel anxious about my future career.' Generated response: 'To alleviate this anxiety, let's learn from the career stories of those who came before you. Also, please watch the following relaxing video.'"

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The user uses their device to input their inquiries about their carrier. The entered text data is sent to the server. At this stage, user input is the primary data.

[0391] Step 2:

[0392] The server analyzes the received text data using natural language processing techniques. Hugging Face Transformers are used to syntactically analyze the input text and extract important keywords and themes. The input is user text data, and the extracted keywords become the output.

[0393] Step 3:

[0394] The server performs sentiment analysis. Using the Google Cloud Natural Language API, it recognizes emotions from the user's text data. Emotions such as anxiety, anticipation, and stress are identified. The input is the analyzed text data, and the output is the recognized sentiment data.

[0395] Step 4:

[0396] The server generates a response using a dialogue generation mechanism based on the extracted keywords and sentiment data. An AI model generates a response that takes emotions into consideration, creating an appropriate prompt. Here, keywords and sentiment data are the input, and the generated response is the output.

[0397] Step 5:

[0398] The server uses career assessment tools to evaluate the user's characteristics and skills. This evaluation utilizes past user data and entered consultation content. Based on the evaluation, appropriate career selection advice is generated. The input is the user's profile information, and the output is advice based on the evaluation.

[0399] Step 6:

[0400] The server uses information provision methods to search for market employment information collected from job information databases and presents job postings suitable for the user. The input is the result of a career assessment, and the output is filtered job information.

[0401] Step 7:

[0402] The server creates personalized content based on the user's emotions through a content delivery method. It generates and delivers articles and videos that boost motivation. Here, emotion data and generated response sentences are the inputs, and personalized content is the output.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] This invention relates to a system that supports career counseling, providing a terminal with an interface that allows users to freely input their career-related concerns and questions. The user inputs the content of their consultation, and the terminal sends that data to a server. The server analyzes the received text using natural language processing means, and based on the results, a dialogue generation means generates an appropriate response.

[0420] The responses are structured as advice based on career theories and past success stories, thereby providing effective career support to the user. The server also provides a process to evaluate the user's characteristics and skills as needed using career assessment tools. The assessment results are then fed back to the user as more precise advice and information regarding occupational suitability.

[0421] Furthermore, the server uses information provision methods to collect relevant market job information from job databases and recommends jobs that are suitable for the user's characteristics and desired conditions. This allows users to efficiently find career options that meet their needs.

[0422] For example, if a user enters "I'm interested in a new career, but I'd like to know which field is right for me," the server analyzes the inquiry and generates advice by applying relevant career theories. For instance, it might provide a response such as, "I recommend taking an assessment in your area of ​​interest and comparing it to your specific skill set." Links to assessment tools and relevant job postings are also provided at the same time.

[0423] This system aims to efficiently support career counseling tailored to the individual needs of each user and to provide users with a free and open consultation environment.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] Users enter questions and concerns about their careers using the interface on their device. Users are provided with an interactive environment where they can freely input text, thereby accessing the system.

[0427] Step 2:

[0428] The device receives the data entered by the user and sends it to the server in an encrypted format. This ensures data security and protects privacy.

[0429] Step 3:

[0430] The server passes the text data received from the user to a natural language processing (NLP) engine. The NLP engine analyzes the text and extracts important keywords and concepts.

[0431] Step 4:

[0432] The server's dialogue generation mechanism generates appropriate responses to user questions based on the analyzed information. These responses may include career theories and general career advice.

[0433] Step 5:

[0434] The server activates a career assessment tool and proposes a specific assessment to the user. This includes questions designed to evaluate the user's characteristics and skills in detail.

[0435] Step 6:

[0436] The user completes the proposed assessment and returns the results to the server via their device. The assessment results provide data that more specifically reflects the user's characteristics and interests.

[0437] Step 7:

[0438] The server integrates assessment results and generates more accurate advice for the user, including suggestions regarding specific career steps and directions.

[0439] Step 8:

[0440] The server uses information provision methods to search the job information database. It filters and selects job information that matches the user's assessment results and desired conditions.

[0441] Step 9:

[0442] The terminal displays the user the response received from the server and recommended job postings. Based on the information provided, the user can make choices to consider for their next career step.

[0443] (Example 1)

[0444] 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."

[0445] Conventional career counseling systems have struggled to provide effective advice and job information tailored to users' characteristics and preferences, and have lacked the accuracy to adequately meet diverse user needs. In particular, there was a need to generate quick and appropriate responses based on the specific content of users' consultations and to provide detailed support for diverse career paths.

[0446] 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.

[0447] In this invention, the server includes natural language processing means, dialogue generation means, characteristic evaluation means, information provision means, and filtering means. This makes it possible to accurately analyze the user's consultation content and provide advice and job information that is suitable for the user's characteristics. Furthermore, by filtering information according to characteristics, only job information that matches the user's desired conditions can be carefully examined and provided.

[0448] "Natural language processing means" refers to technologies that analyze text data entered by a user and semantically understand its content.

[0449] "Dialogue generation means" refers to technology that automatically generates appropriate responses to the user based on analysis results obtained by natural language processing means.

[0450] "Characteristic evaluation means" refers to technology that evaluates a user's abilities and personality and provides advice and support tailored to the user based on that evaluation.

[0451] "Information provision means" refers to technologies for collecting market job information and other related information and providing it to users.

[0452] "Filtering means" refers to technology that effectively selects and filters information collected through information provision means based on the conditions requested by the user, and presents the most suitable information to the user.

[0453] This invention provides an automated system to support users in their career consultations. Users can input their career concerns and questions using a terminal. The terminal receives this input and sends it to a server as text data.

[0454] The server analyzes the received text data using natural language processing tools (e.g., spaCy or BERT). The analyzed data is used to understand the user's intentions and emotions, and based on this information, a dialogue generation tool (e.g., GPT-4) generates an appropriate response. This response aims to provide specific and useful advice regarding the user's career concerns.

[0455] Furthermore, the server utilizes characteristic evaluation tools to provide more accurate advice by thoroughly assessing the user's characteristics and skills. The evaluation results are used to improve the accuracy of solutions to the user's inquiries.

[0456] In addition, the server accesses the market's job database via information provision means and retrieves job information that matches the user. This information is filtered based on the user's desired conditions using filtering means, and only the most suitable job information is presented to the user.

[0457] As a concrete example, consider a scenario where a user inputs a question into the system: "I'm interested in a new career, but I'd like to know which field is right for me." Based on this question, the server applies relevant career theories and generates advice such as, "We recommend that you take an assessment in your area of ​​interest and compare it with your specific skill set." Links to assessment tools and relevant job postings are also provided at this time.

[0458] An example of a prompt message would be, "I'm interested in a new career and would like to know which fields and job types are suitable for me. What kind of career advice can you give me?" By inputting this, users can receive specific support.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The user uses the terminal interface to input text about their concerns or questions regarding their carrier. This entered text is the first data processed within the system.

[0462] Step 2:

[0463] The terminal sends user input to the server as text data. This is done securely using the HTTPS communication protocol. The text input data is transmitted to the server.

[0464] Step 3:

[0465] The server uses natural language processing (NLP) to analyze the received text data. Specifically, it uses language models (e.g., BERT or spaCy) to perform semantic analysis, keyword extraction, and intent understanding of the text. This process generates structured data that allows for a detailed understanding of what the user is looking for.

[0466] Step 4:

[0467] Based on the analysis results, the server generates an appropriate response using a dialogue generation mechanism. Utilizing a generation AI model (e.g., GPT-4), it outputs specific advice and suggestions to the user in written form based on the input information.

[0468] Step 5:

[0469] If necessary, the server uses trait assessment tools to evaluate the user's traits and skills. This evaluation is based on information the user has previously provided and general survey data, and yields analysis results regarding the traits.

[0470] Step 6:

[0471] The server uses information provision methods to access market job databases and collect job information that matches the user's profile and preferences. This collected data is then used in a subsequent filtering process.

[0472] Step 7:

[0473] The server uses filtering mechanisms to filter the retrieved job postings based on user criteria. For example, it filters by geographical location, job type, salary range, etc., to select appropriate job postings.

[0474] Step 8:

[0475] The server sends the generated responses and filtered job postings to the terminal. The terminal displays this to the user, who can then take specific actions related to the question based on that information. This allows the user to see specific career advice and options.

[0476] (Application Example 1)

[0477] 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."

[0478] In today's world, it is extremely important for individuals to find the career path that is best suited to them. In particular, amidst the vast amount of information available, there is a need for accurate advice based on individual interests and preferences, as well as the ability to quickly and efficiently obtain suitable job information. However, existing systems lack sufficient mechanisms to comprehensively support this, creating a challenge where users struggle to receive career counseling that is best suited to their needs.

[0479] 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.

[0480] In this invention, the server includes a natural language processing means for receiving and analyzing consultation content input from a user, a dialogue generation means for outputting a response generated based on the analysis, and a career assessment means for evaluating the user's characteristics and skills. This makes it possible to receive consultations based on the user's interests and preferences and support them in making the optimal career choice.

[0481] "User-submitted consultation information" refers to information that individuals freely write and send to the system regarding their career-related questions and concerns.

[0482] "Natural language processing" refers to technologies that analyze text data entered by users and extract their intentions and keywords.

[0483] A "dialogue generation means" is a function that automatically generates an appropriate response to the user based on the analyzed text data.

[0484] A "career assessment method" is a process of evaluating an individual's characteristics and skills and diagnosing their suitability for a career.

[0485] An "information provision method" is a system that collects and recommends suitable job postings and career-related information, taking into account the user's characteristics and desired conditions.

[0486] "Information analysis methods for providing consultation based on user interests and preferences" refers to data analysis methods for providing optimal career counseling in line with the user's interests and preferences.

[0487] The system for implementing this invention consists of a terminal that includes an interface for users to consult about their careers, and a server that analyzes the consultation content and generates appropriate advice. The system is operated using terminals such as smartphones and smart glasses, through which users input their consultations.

[0488] The server analyzes the user's input using natural language processing. This process, for example, utilizes the Google Cloud Natural Language API to extract key information from the inquiry. Based on this analysis, a dialogue generation system automatically creates corresponding advice and sends it back to the user. The generated dialogue provides the user with career assessment information and insights into specific skills they are seeking.

[0489] Furthermore, the server is equipped with a career assessment tool that evaluates the user's characteristics and skills. This assessment provides the user with the information necessary to discover the optimal career path and supports a more detailed assessment of their vocational aptitude. In addition, the server has a function to retrieve and present job postings from the job information database that match the user's interests and desired conditions through an information provision tool. In this way, users can efficiently obtain appropriate career information and job postings.

[0490] For example, a user might input a question via smart glasses, such as, "I want to explore new career possibilities in digital marketing." The system then analyzes the input and suggests career path options based on the user's interests. These suggestions include a list of relevant professional skills and current job postings.

[0491] Examples of prompt statements include the following:

[0492] User question: What are some suitable job roles in digital marketing?

[0493] Answer generation process: Based on the user's question, analyze the most relevant job categories and suggest them as appropriate.

[0494] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0495] Step 1:

[0496] Users input their inquiries about their carrier through their device. The entered data is sent from the device to the server in text format. This input serves as the system's initial data and forms the basis for subsequent processing.

[0497] Step 2:

[0498] The server uses natural language processing to analyze the user's submitted inquiry. This process utilizes the Google Cloud Natural Language API to extract intent and keywords from the text. The results of this analysis become the input data for dialogue generation.

[0499] Step 3:

[0500] Based on the analysis results, the server generates appropriate advice using a dialogue generation mechanism. This process utilizes a generative AI model to form a response to the user's input intent. The generated advice is then provided to the user in the next step.

[0501] Step 4:

[0502] The server uses a career assessment tool to evaluate the user's characteristics and skills. It uses pre-provided skill information and past work history data from the user as input. Based on this data, assessment results are output, providing information to support the user's career choices.

[0503] Step 5:

[0504] The server uses information provision methods to extract relevant information from the job posting database. This process considers the user's interests and preferences to select the most relevant job postings. It then executes database queries and immediately recommends the information to the user based on those queries.

[0505] Step 6:

[0506] The terminal displays responses and job postings sent from the server to the user. Here, the user can view suggested career advice and job descriptions and select the next step. Through the terminal's interface, the user can also request more detailed information.

[0507] 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.

[0508] This invention is a system that effectively supports career counseling, combining natural language processing and an emotion engine to deeply understand the user's consultation content and provide accurate advice. Users can input questions and concerns about their careers via a terminal. The terminal sends this data to the server.

[0509] The server uses natural language processing to analyze the input text and extract important keywords and themes. Simultaneously, it uses an emotion engine to recognize the emotions contained in the user's inquiry. For example, it detects emotions such as stress, anxiety, and expectations from the word choices and sentence structure in the user's text.

[0510] Based on these analysis results, the server generates a response using a dialogue generation mechanism. This response is refined using emotional data obtained from the emotion engine, making it more attuned to the user's feelings. The response includes advice based on career theory, offering specific and actionable suggestions tailored to the user's situation.

[0511] Furthermore, the server utilizes career assessment tools to evaluate the user's characteristics and skills, taking emotional data into consideration. Based on the assessment results, it recommends suitable job postings to the user. The information provision tool searches market job databases and collects and presents appropriate job candidates to the user.

[0512] For example, if a user enters a message saying, "I've lost confidence in a recent project, but I want to take on a new challenge," the server will interpret the mixed emotions of anxiety and anticipation from the message and generate a corresponding response. For instance, it might say, "When taking on a new challenge, it's important to reflect on past successes and regain your confidence. Here are some suitable job options for you." Relevant job postings will also be provided.

[0513] In this way, the system takes user emotions into consideration, allowing for more personalized career counseling and advice, and enabling more effective support.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The user uses their device to enter their career-related questions. Specifically, they might write something like, "I'm interested in a new job, but I've lost confidence after a recent failure," into a text box and then press the submit button.

[0517] Step 2:

[0518] The terminal receives the entered text data and securely transmits it to the server. This data is protected by encryption technology.

[0519] Step 3:

[0520] The server activates natural language processing (NLP) tools to analyze the received text data. Specifically, it extracts important keywords from the text (e.g., "new job," "losing confidence").

[0521] Step 4:

[0522] The server analyzes the emotions in the text via an emotion engine. This identifies hidden emotions such as anxiety and excitement within the user. The techniques used include tone analysis of the text and referencing an emotion dictionary.

[0523] Step 5:

[0524] The server's dialogue generation mechanism constructs an appropriate response for the user based on the results of natural language processing and sentiment analysis. For example, it might generate a response such as, "When considering new challenges, it's important to re-evaluate your existing strengths and focus on the positive aspects."

[0525] Step 6:

[0526] The server uses career assessment tools to evaluate the user's characteristics and skills. Emotional data is also taken into consideration, and advice based on interests and preferences is ready to be provided.

[0527] Step 7:

[0528] The server searches the job information database using information provision methods. It selects job postings that match the user's assessment results and desired conditions, and collects the most relevant information for the user.

[0529] Step 8:

[0530] The terminal displays the user the response it received from the server. This includes emotionally sensitive response messages and a list of job postings that match the user's profile. Based on this, the user can gain guidance for considering their next career step.

[0531] (Example 2)

[0532] 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."

[0533] In modern society, there is a lack of systems that support effective career counseling and job selection while taking into account individual characteristics and emotions. Conventional systems often provide only uniform advice without adequately considering the user's feelings, making it difficult for users to obtain the best possible job selection and career advice.

[0534] 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.

[0535] In this invention, the server includes language analysis means, emotion recognition means, response generation means, occupation evaluation means, and information recommendation means. This enables more accurate and user-friendly career counseling and occupational selection support by analyzing emotions based on user input information and generating personalized responses that are attuned to those emotions.

[0536] "Language analysis means" refers to a technology that uses natural language processing techniques to analyze information input by users and extract important keywords and context.

[0537] "Emotion recognition means" refers to technology for detecting emotions from analyzed text and generating data based on those emotions.

[0538] "Response generation means" refers to a technology that uses a generation AI model to generate appropriate dialogue based on user input information and detected emotions.

[0539] "Occupational assessment tools" are technologies used to evaluate a user's occupational aptitude and skills, and to conduct aptitude assessments based on their characteristics.

[0540] "Information recommendation methods" are technologies that analyze market job information and present users with the most suitable job postings and career options.

[0541] This invention is a system that effectively supports users in seeking advice regarding their careers. Users can input questions and concerns about their careers via a terminal. The terminal sends the input data to a server, which analyzes the data and provides appropriate advice.

[0542] The server analyzes the text received from the user using natural language processing (NLTK) tools. This process utilizes Python's natural language processing libraries, spaCy and NLTK, for grammatical analysis and keyword extraction. After extracting important information through analysis, the server uses the Hugging Face Transformer model to recognize the emotions contained in the user's text. This emotion data is then used to generate subsequent responses.

[0543] The response generation mechanism uses the GPT-3 generation AI model. This allows for the generation of advice and dialogue that is empathetic to the user's emotions. The generated responses are adjusted based on career theory and occupation-related information, resulting in concrete and actionable suggestions for the user.

[0544] As a means of career assessment, the server evaluates the user's work history and skills, referencing the user's profile information using the LinkedIn API. Based on this evaluation, the information recommendation system searches market job databases and recommends suitable occupations to the user. The Indeed API is used to collect job postings, providing users with real-time information.

[0545] For example, if a user inputs "I've lost confidence in my recent project, but I want to take on a new challenge," the server analyzes this input and detects feelings of anxiety and anticipation. It then generates a response accordingly, providing specific advice such as, "When taking on a new challenge, it's important to reflect on past successes and regain your confidence. Here are some suitable job options for you." An example of a prompt to input into the generating AI model might be, "The user is currently experiencing stress and a loss of confidence in their recent career. Please consider the emotional data and provide career advice and relevant job information."

[0546] Thus, the present invention takes into account the user's emotions to personalize career counseling and achieve more effective and accurate support.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] Users use their devices to input questions and concerns about their carrier in text format. For example, they might input a question like, "I lost confidence in my recent project, but I want to take on a new challenge," and then click the "Send" button. The input is received as text data.

[0550] Step 2:

[0551] The terminal encrypts the text data entered by the user and sends it to the server. The HTTPS protocol is used for this transmission to maintain security. The input is the user's text data, and the output is the encrypted data sent to the server.

[0552] Step 3:

[0553] The server analyzes the received text data using natural language processing techniques. It uses the Python spaCy library for grammatical analysis and keyword extraction. The input is text data submitted by the user, and the output consists of extracted keywords and contextual information.

[0554] Step 4:

[0555] The server analyzes the emotions contained in the text using emotion recognition tools. Here, the Hugging Face Transformer model is used to detect emotions such as stress, anxiety, and anticipation. The input is contextual information of the text obtained through natural language processing, and the output is the detected emotion data.

[0556] Step 5:

[0557] The server uses a generative AI model to generate appropriate responses to user inquiries. The generative AI model utilizes GPT-3 to construct prompts and generate responses based on sentiment data. Input consists of sentiment data and textual context, while output is specific, emotionally resonant advice and dialogue.

[0558] Step 6:

[0559] The server uses occupational assessment tools to evaluate the user's occupational aptitude. This process references the user's history and skill data via the LinkedIn API. The input is the user's profile information, and the output is the user's occupational aptitude and skill set.

[0560] Step 7:

[0561] The server uses information recommendation tools to search for job information in the market and suggest suitable jobs to the user. It leverages the Indeed API to collect current job postings and match them with user needs. The input is job aptitude and skill set, and the output is a list of recommended job postings.

[0562] Step 8:

[0563] The terminal displays advice and job information received from the server to the user. The user interface presents specific career advice and a list of job postings. Input is data from the server, and the displayed information is the output.

[0564] (Application Example 2)

[0565] 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."

[0566] In career counseling, there is a need for appropriate and individualized advice and information based on each user's emotional state, characteristics, and skills. However, conventional systems have the challenge of not being able to adequately consider the user's emotions and delivering personalized content. As a result, the quality of career support that users desire is reduced, and they end up not being satisfied.

[0567] 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.

[0568] In this invention, the server includes a natural language processing means for analyzing the content of the consultation input by the user, a career assessment means for evaluating the user's characteristics and skills, an information provision means for collecting and recommending market employment information, and a content distribution means for recognizing the user's emotional state and delivering personalized information. This makes it possible to provide users with individualized career advice and information that takes their emotions into consideration.

[0569] A "user" refers to an individual who uses the system to receive career counseling or information.

[0570] "Consultation content" refers to information about career-related questions and concerns that users enter into the system.

[0571] "Natural language processing means" refers to technologies that analyze text input by users and extract important keywords and themes.

[0572] "Dialogue generation means" refers to a function that generates an appropriate response based on the results of natural language processing and presents it to the user.

[0573] A "career assessment tool" is a technology that evaluates a user's characteristics and skills and provides career advice based on those evaluations.

[0574] "Information provision means" refers to a function that uses collected market employment information to recommend suitable employment to users.

[0575] "Content delivery methods" refer to technologies that recognize a user's emotional state and provide personalized information and content.

[0576] The embodiment of the invention begins with a user entering their career consultation details using a terminal such as a smartphone or personal computer. The terminal then transmits this information to a server. The server employs Hugging Face's Transformers as a natural language processing tool to analyze the entered consultation details. As a result of the analysis, important keywords and themes are extracted.

[0577] Furthermore, the server uses the Google Cloud Natural Language API to perform sentiment analysis. By recognizing emotions such as expectation, anxiety, and stress from the user's input text, it understands the user's emotional state. This provides data for generating appropriate responses based on those emotions.

[0578] Based on the acquired data, the server uses dialogue generation mechanisms to generate a response appropriate to the user. At this stage, personalized advice reflecting the user's emotional state is formed. The generated response includes measures based on career theory and presents specific actions that the user can take.

[0579] In addition, the career assessment tool evaluates the user's characteristics and skills and uses that information to make individualized job recommendations. The information provision tool collects the latest employment information from a database and presents it to the user. Here too, the user's emotional state is taken into consideration, and the most suitable job information is provided to the user.

[0580] For example, if a user inputs "I feel anxious about my future career," the server recognizes the user's anxiety and provides articles or videos that can boost their motivation. A concrete example of an input prompt for the generating AI model might be: "User inquiry: 'I feel anxious about my future career.' Generated response: 'To alleviate this anxiety, let's learn from the career stories of those who came before you. Also, please watch the following relaxing video.'"

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The user uses their device to input their inquiries about their carrier. The entered text data is sent to the server. At this stage, user input is the primary data.

[0584] Step 2:

[0585] The server analyzes the received text data using natural language processing techniques. Hugging Face Transformers are used to syntactically analyze the input text and extract important keywords and themes. The input is user text data, and the extracted keywords become the output.

[0586] Step 3:

[0587] The server performs sentiment analysis. Using the Google Cloud Natural Language API, it recognizes emotions from the user's text data. Emotions such as anxiety, anticipation, and stress are identified. The input is the analyzed text data, and the output is the recognized sentiment data.

[0588] Step 4:

[0589] The server generates a response using a dialogue generation mechanism based on the extracted keywords and sentiment data. An AI model generates a response that takes emotions into consideration, creating an appropriate prompt. Here, keywords and sentiment data are the input, and the generated response is the output.

[0590] Step 5:

[0591] The server uses career assessment tools to evaluate the user's characteristics and skills. This evaluation utilizes past user data and entered consultation content. Based on the evaluation, appropriate career selection advice is generated. The input is the user's profile information, and the output is advice based on the evaluation.

[0592] Step 6:

[0593] The server uses information provision methods to search for market employment information collected from job information databases and presents job postings suitable for the user. The input is the result of a career assessment, and the output is filtered job information.

[0594] Step 7:

[0595] The server creates personalized content based on the user's emotions through a content delivery method. It generates and delivers articles and videos that boost motivation. Here, emotion data and generated response sentences are the inputs, and personalized content is the output.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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".

[0613] This invention relates to a system that supports career counseling, providing a terminal with an interface that allows users to freely input their career-related concerns and questions. The user inputs the content of their consultation, and the terminal sends that data to a server. The server analyzes the received text using natural language processing means, and based on the results, a dialogue generation means generates an appropriate response.

[0614] The responses are structured as advice based on career theories and past success stories, thereby providing effective career support to the user. The server also provides a process to evaluate the user's characteristics and skills as needed using career assessment tools. The assessment results are then fed back to the user as more precise advice and information regarding occupational suitability.

[0615] Furthermore, the server uses information provision methods to collect relevant market job information from job databases and recommends jobs that are suitable for the user's characteristics and desired conditions. This allows users to efficiently find career options that meet their needs.

[0616] For example, if a user enters "I'm interested in a new career, but I'd like to know which field is right for me," the server analyzes the inquiry and generates advice by applying relevant career theories. For instance, it might provide a response such as, "I recommend taking an assessment in your area of ​​interest and comparing it to your specific skill set." Links to assessment tools and relevant job postings are also provided at the same time.

[0617] This system aims to efficiently support career counseling tailored to the individual needs of each user and to provide users with a free and open consultation environment.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] Users enter questions and concerns about their careers using the interface on their device. Users are provided with an interactive environment where they can freely input text, thereby accessing the system.

[0621] Step 2:

[0622] The device receives the data entered by the user and sends it to the server in an encrypted format. This ensures data security and protects privacy.

[0623] Step 3:

[0624] The server passes the text data received from the user to a natural language processing (NLP) engine. The NLP engine analyzes the text and extracts important keywords and concepts.

[0625] Step 4:

[0626] The server's dialogue generation mechanism generates appropriate responses to user questions based on the analyzed information. These responses may include career theories and general career advice.

[0627] Step 5:

[0628] The server activates a career assessment tool and proposes a specific assessment to the user. This includes questions designed to evaluate the user's characteristics and skills in detail.

[0629] Step 6:

[0630] The user completes the proposed assessment and returns the results to the server via their device. The assessment results provide data that more specifically reflects the user's characteristics and interests.

[0631] Step 7:

[0632] The server integrates assessment results and generates more accurate advice for the user, including suggestions regarding specific career steps and directions.

[0633] Step 8:

[0634] The server uses information provision methods to search the job information database. It filters and selects job information that matches the user's assessment results and desired conditions.

[0635] Step 9:

[0636] The terminal displays the user the response received from the server and recommended job postings. Based on the information provided, the user can make choices to consider for their next career step.

[0637] (Example 1)

[0638] 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".

[0639] Conventional career counseling systems have struggled to provide effective advice and job information tailored to users' characteristics and preferences, and have lacked the accuracy to adequately meet diverse user needs. In particular, there was a need to generate quick and appropriate responses based on the specific content of users' consultations and to provide detailed support for diverse career paths.

[0640] 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.

[0641] In this invention, the server includes natural language processing means, dialogue generation means, characteristic evaluation means, information provision means, and filtering means. This makes it possible to accurately analyze the user's consultation content and provide advice and job information that is suitable for the user's characteristics. Furthermore, by filtering information according to characteristics, only job information that matches the user's desired conditions can be carefully examined and provided.

[0642] "Natural language processing means" refers to technologies that analyze text data entered by a user and semantically understand its content.

[0643] "Dialogue generation means" refers to technology that automatically generates appropriate responses to the user based on analysis results obtained by natural language processing means.

[0644] "Characteristic evaluation means" refers to technology that evaluates a user's abilities and personality and provides advice and support tailored to the user based on that evaluation.

[0645] "Information provision means" refers to technologies for collecting market job information and other related information and providing it to users.

[0646] "Filtering means" refers to technology that effectively selects and filters information collected through information provision means based on the conditions requested by the user, and presents the most suitable information to the user.

[0647] This invention provides an automated system to support users in their career consultations. Users can input their career concerns and questions using a terminal. The terminal receives this input and sends it to a server as text data.

[0648] The server analyzes the received text data using natural language processing tools (e.g., spaCy or BERT). The analyzed data is used to understand the user's intentions and emotions, and based on this information, a dialogue generation tool (e.g., GPT-4) generates an appropriate response. This response aims to provide specific and useful advice regarding the user's career concerns.

[0649] Furthermore, the server utilizes characteristic evaluation tools to provide more accurate advice by thoroughly assessing the user's characteristics and skills. The evaluation results are used to improve the accuracy of solutions to the user's inquiries.

[0650] In addition, the server accesses the market's job database via information provision means and retrieves job information that matches the user. This information is filtered based on the user's desired conditions using filtering means, and only the most suitable job information is presented to the user.

[0651] As a concrete example, consider a scenario where a user inputs a question into the system: "I'm interested in a new career, but I'd like to know which field is right for me." Based on this question, the server applies relevant career theories and generates advice such as, "We recommend that you take an assessment in your area of ​​interest and compare it with your specific skill set." Links to assessment tools and relevant job postings are also provided at this time.

[0652] An example of a prompt message would be, "I'm interested in a new career and would like to know which fields and job types are suitable for me. What kind of career advice can you give me?" By inputting this, users can receive specific support.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] The user uses the terminal interface to input text about their concerns or questions regarding their carrier. This entered text is the first data processed within the system.

[0656] Step 2:

[0657] The terminal sends user input to the server as text data. This is done securely using the HTTPS communication protocol. The text input data is transmitted to the server.

[0658] Step 3:

[0659] The server uses natural language processing (NLP) to analyze the received text data. Specifically, it uses language models (e.g., BERT or spaCy) to perform semantic analysis, keyword extraction, and intent understanding of the text. This process generates structured data that allows for a detailed understanding of what the user is looking for.

[0660] Step 4:

[0661] Based on the analysis results, the server generates an appropriate response using a dialogue generation mechanism. Utilizing a generation AI model (e.g., GPT-4), it outputs specific advice and suggestions to the user in written form based on the input information.

[0662] Step 5:

[0663] If necessary, the server uses trait assessment tools to evaluate the user's traits and skills. This evaluation is based on information the user has previously provided and general survey data, and yields analysis results regarding the traits.

[0664] Step 6:

[0665] The server uses information provision methods to access market job databases and collect job information that matches the user's profile and preferences. This collected data is then used in a subsequent filtering process.

[0666] Step 7:

[0667] The server uses filtering mechanisms to filter the retrieved job postings based on user criteria. For example, it filters by geographical location, job type, salary range, etc., to select appropriate job postings.

[0668] Step 8:

[0669] The server sends the generated responses and filtered job postings to the terminal. The terminal displays this to the user, who can then take specific actions related to the question based on that information. This allows the user to see specific career advice and options.

[0670] (Application Example 1)

[0671] 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".

[0672] In today's world, it is extremely important for individuals to find the career path that is best suited to them. In particular, amidst the vast amount of information available, there is a need for accurate advice based on individual interests and preferences, as well as the ability to quickly and efficiently obtain suitable job information. However, existing systems lack sufficient mechanisms to comprehensively support this, creating a challenge where users struggle to receive career counseling that is best suited to their needs.

[0673] 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.

[0674] In this invention, the server includes a natural language processing means for receiving and analyzing consultation content input from a user, a dialogue generation means for outputting a response generated based on the analysis, and a career assessment means for evaluating the user's characteristics and skills. This makes it possible to receive consultations based on the user's interests and preferences and support them in making the optimal career choice.

[0675] "User-submitted consultation information" refers to information that individuals freely write and send to the system regarding their career-related questions and concerns.

[0676] "Natural language processing" refers to technologies that analyze text data entered by users and extract their intentions and keywords.

[0677] A "dialogue generation means" is a function that automatically generates an appropriate response to the user based on the analyzed text data.

[0678] A "career assessment method" is a process of evaluating an individual's characteristics and skills and diagnosing their suitability for a career.

[0679] An "information provision method" is a system that collects and recommends suitable job postings and career-related information, taking into account the user's characteristics and desired conditions.

[0680] "Information analysis methods for providing consultation based on user interests and preferences" refers to data analysis methods for providing optimal career counseling in line with the user's interests and preferences.

[0681] The system for implementing this invention consists of a terminal that includes an interface for users to consult about their careers, and a server that analyzes the consultation content and generates appropriate advice. The system is operated using terminals such as smartphones and smart glasses, through which users input their consultations.

[0682] The server analyzes the user's input using natural language processing. This process, for example, utilizes the Google Cloud Natural Language API to extract key information from the inquiry. Based on this analysis, a dialogue generation system automatically creates corresponding advice and sends it back to the user. The generated dialogue provides the user with career assessment information and insights into specific skills they are seeking.

[0683] Furthermore, the server is equipped with a career assessment tool that evaluates the user's characteristics and skills. This assessment provides the user with the information necessary to discover the optimal career path and supports a more detailed assessment of their vocational aptitude. In addition, the server has a function to retrieve and present job postings from the job information database that match the user's interests and desired conditions through an information provision tool. In this way, users can efficiently obtain appropriate career information and job postings.

[0684] For example, a user might input a question via smart glasses, such as, "I want to explore new career possibilities in digital marketing." The system then analyzes the input and suggests career path options based on the user's interests. These suggestions include a list of relevant professional skills and current job postings.

[0685] Examples of prompt statements include the following:

[0686] User question: What are some suitable job roles in digital marketing?

[0687] Answer generation process: Based on the user's question, analyze the most relevant job categories and suggest them as appropriate.

[0688] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0689] Step 1:

[0690] Users input their inquiries about their carrier through their device. The entered data is sent from the device to the server in text format. This input serves as the system's initial data and forms the basis for subsequent processing.

[0691] Step 2:

[0692] The server uses natural language processing to analyze the user's submitted inquiry. This process utilizes the Google Cloud Natural Language API to extract intent and keywords from the text. The results of this analysis become the input data for dialogue generation.

[0693] Step 3:

[0694] Based on the analysis results, the server generates appropriate advice using a dialogue generation mechanism. This process utilizes a generative AI model to form a response to the user's input intent. The generated advice is then provided to the user in the next step.

[0695] Step 4:

[0696] The server uses a career assessment tool to evaluate the user's characteristics and skills. It uses pre-provided skill information and past work history data from the user as input. Based on this data, assessment results are output, providing information to support the user's career choices.

[0697] Step 5:

[0698] The server uses information provision methods to extract relevant information from the job posting database. This process considers the user's interests and preferences to select the most relevant job postings. It then executes database queries and immediately recommends the information to the user based on those queries.

[0699] Step 6:

[0700] The terminal displays responses and job postings sent from the server to the user. Here, the user can view suggested career advice and job descriptions and select the next step. Through the terminal's interface, the user can also request more detailed information.

[0701] 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.

[0702] This invention is a system that effectively supports career counseling, combining natural language processing and an emotion engine to deeply understand the user's consultation content and provide accurate advice. Users can input questions and concerns about their careers via a terminal. The terminal sends this data to the server.

[0703] The server uses natural language processing to analyze the input text and extract important keywords and themes. Simultaneously, it uses an emotion engine to recognize the emotions contained in the user's inquiry. For example, it detects emotions such as stress, anxiety, and expectations from the word choices and sentence structure in the user's text.

[0704] Based on these analysis results, the server generates a response using a dialogue generation mechanism. This response is refined using emotional data obtained from the emotion engine, making it more attuned to the user's feelings. The response includes advice based on career theory, offering specific and actionable suggestions tailored to the user's situation.

[0705] Furthermore, the server utilizes career assessment tools to evaluate the user's characteristics and skills, taking emotional data into consideration. Based on the assessment results, it recommends suitable job postings to the user. The information provision tool searches market job databases and collects and presents appropriate job candidates to the user.

[0706] For example, if a user enters a message saying, "I've lost confidence in a recent project, but I want to take on a new challenge," the server will interpret the mixed emotions of anxiety and anticipation from the message and generate a corresponding response. For instance, it might say, "When taking on a new challenge, it's important to reflect on past successes and regain your confidence. Here are some suitable job options for you." Relevant job postings will also be provided.

[0707] In this way, the system takes user emotions into consideration, allowing for more personalized career counseling and advice, and enabling more effective support.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The user uses their device to enter their career-related questions. Specifically, they might write something like, "I'm interested in a new job, but I've lost confidence after a recent failure," into a text box and then press the submit button.

[0711] Step 2:

[0712] The terminal receives the entered text data and securely transmits it to the server. This data is protected by encryption technology.

[0713] Step 3:

[0714] The server activates natural language processing (NLP) tools to analyze the received text data. Specifically, it extracts important keywords from the text (e.g., "new job," "losing confidence").

[0715] Step 4:

[0716] The server analyzes the emotions in the text via an emotion engine. This identifies hidden emotions such as anxiety and excitement within the user. The techniques used include tone analysis of the text and referencing an emotion dictionary.

[0717] Step 5:

[0718] The server's dialogue generation mechanism constructs an appropriate response for the user based on the results of natural language processing and sentiment analysis. For example, it might generate a response such as, "When considering new challenges, it's important to re-evaluate your existing strengths and focus on the positive aspects."

[0719] Step 6:

[0720] The server uses career assessment tools to evaluate the user's characteristics and skills. Emotional data is also taken into consideration, and advice based on interests and preferences is ready to be provided.

[0721] Step 7:

[0722] The server searches the job information database using information provision methods. It selects job postings that match the user's assessment results and desired conditions, and collects the most relevant information for the user.

[0723] Step 8:

[0724] The terminal displays the user the response it received from the server. This includes emotionally sensitive response messages and a list of job postings that match the user's profile. Based on this, the user can gain guidance for considering their next career step.

[0725] (Example 2)

[0726] 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".

[0727] In modern society, there is a lack of systems that support effective career counseling and job selection while taking into account individual characteristics and emotions. Conventional systems often provide only uniform advice without adequately considering the user's feelings, making it difficult for users to obtain the best possible job selection and career advice.

[0728] 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.

[0729] In this invention, the server includes language analysis means, emotion recognition means, response generation means, occupation evaluation means, and information recommendation means. This enables more accurate and user-friendly career counseling and occupational selection support by analyzing emotions based on user input information and generating personalized responses that are attuned to those emotions.

[0730] "Language analysis means" refers to a technology that uses natural language processing techniques to analyze information input by users and extract important keywords and context.

[0731] "Emotion recognition means" refers to technology for detecting emotions from analyzed text and generating data based on those emotions.

[0732] "Response generation means" refers to a technology that uses a generation AI model to generate appropriate dialogue based on user input information and detected emotions.

[0733] "Occupational assessment tools" are technologies used to evaluate a user's occupational aptitude and skills, and to conduct aptitude assessments based on their characteristics.

[0734] "Information recommendation methods" are technologies that analyze market job information and present users with the most suitable job postings and career options.

[0735] This invention is a system that effectively supports users in seeking advice regarding their careers. Users can input questions and concerns about their careers via a terminal. The terminal sends the input data to a server, which analyzes the data and provides appropriate advice.

[0736] The server analyzes the text received from the user using natural language processing (NLTK) tools. This process utilizes Python's natural language processing libraries, spaCy and NLTK, for grammatical analysis and keyword extraction. After extracting important information through analysis, the server uses the Hugging Face Transformer model to recognize the emotions contained in the user's text. This emotion data is then used to generate subsequent responses.

[0737] The response generation mechanism uses the GPT-3 generation AI model. This allows for the generation of advice and dialogue that is empathetic to the user's emotions. The generated responses are adjusted based on career theory and occupation-related information, resulting in concrete and actionable suggestions for the user.

[0738] As a means of career assessment, the server evaluates the user's work history and skills, referencing the user's profile information using the LinkedIn API. Based on this evaluation, the information recommendation system searches market job databases and recommends suitable occupations to the user. The Indeed API is used to collect job postings, providing users with real-time information.

[0739] For example, if a user inputs "I've lost confidence in my recent project, but I want to take on a new challenge," the server analyzes this input and detects feelings of anxiety and anticipation. It then generates a response accordingly, providing specific advice such as, "When taking on a new challenge, it's important to reflect on past successes and regain your confidence. Here are some suitable job options for you." An example of a prompt to input into the generating AI model might be, "The user is currently experiencing stress and a loss of confidence in their recent career. Please consider the emotional data and provide career advice and relevant job information."

[0740] Thus, the present invention takes into account the user's emotions to personalize career counseling and achieve more effective and accurate support.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] Users use their devices to input questions and concerns about their carrier in text format. For example, they might input a question like, "I lost confidence in my recent project, but I want to take on a new challenge," and then click the "Send" button. The input is received as text data.

[0744] Step 2:

[0745] The terminal encrypts the text data entered by the user and sends it to the server. The HTTPS protocol is used for this transmission to maintain security. The input is the user's text data, and the output is the encrypted data sent to the server.

[0746] Step 3:

[0747] The server analyzes the received text data using natural language processing techniques. It uses the Python spaCy library for grammatical analysis and keyword extraction. The input is text data submitted by the user, and the output consists of extracted keywords and contextual information.

[0748] Step 4:

[0749] The server analyzes the emotions contained in the text using emotion recognition tools. Here, the Hugging Face Transformer model is used to detect emotions such as stress, anxiety, and anticipation. The input is contextual information of the text obtained through natural language processing, and the output is the detected emotion data.

[0750] Step 5:

[0751] The server uses a generative AI model to generate appropriate responses to user inquiries. The generative AI model utilizes GPT-3 to construct prompts and generate responses based on sentiment data. Input consists of sentiment data and textual context, while output is specific, emotionally resonant advice and dialogue.

[0752] Step 6:

[0753] The server uses occupational assessment tools to evaluate the user's occupational aptitude. This process references the user's history and skill data via the LinkedIn API. The input is the user's profile information, and the output is the user's occupational aptitude and skill set.

[0754] Step 7:

[0755] The server uses information recommendation tools to search for job information in the market and suggest suitable jobs to the user. It leverages the Indeed API to collect current job postings and match them with user needs. The input is job aptitude and skill set, and the output is a list of recommended job postings.

[0756] Step 8:

[0757] The terminal displays advice and job information received from the server to the user. The user interface presents specific career advice and a list of job postings. Input is data from the server, and the displayed information is the output.

[0758] (Application Example 2)

[0759] 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".

[0760] In career counseling, there is a need for appropriate and individualized advice and information based on each user's emotional state, characteristics, and skills. However, conventional systems have the challenge of not being able to adequately consider the user's emotions and delivering personalized content. As a result, the quality of career support that users desire is reduced, and they end up not being satisfied.

[0761] 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.

[0762] In this invention, the server includes a natural language processing means for analyzing the content of the consultation input by the user, a career assessment means for evaluating the user's characteristics and skills, an information provision means for collecting and recommending market employment information, and a content distribution means for recognizing the user's emotional state and delivering personalized information. This makes it possible to provide users with individualized career advice and information that takes their emotions into consideration.

[0763] A "user" refers to an individual who uses the system to receive career counseling or information.

[0764] "Consultation content" refers to information about career-related questions and concerns that users enter into the system.

[0765] "Natural language processing means" refers to technologies that analyze text input by users and extract important keywords and themes.

[0766] "Dialogue generation means" refers to a function that generates an appropriate response based on the results of natural language processing and presents it to the user.

[0767] A "career assessment tool" is a technology that evaluates a user's characteristics and skills and provides career advice based on those evaluations.

[0768] "Information provision means" refers to a function that uses collected market employment information to recommend suitable employment to users.

[0769] "Content delivery methods" refer to technologies that recognize a user's emotional state and provide personalized information and content.

[0770] The embodiment of the invention begins with a user entering their career consultation details using a terminal such as a smartphone or personal computer. The terminal then transmits this information to a server. The server employs Hugging Face's Transformers as a natural language processing tool to analyze the entered consultation details. As a result of the analysis, important keywords and themes are extracted.

[0771] Furthermore, the server uses the Google Cloud Natural Language API to perform sentiment analysis. By recognizing emotions such as expectation, anxiety, and stress from the user's input text, it understands the user's emotional state. This provides data for generating appropriate responses based on those emotions.

[0772] Based on the acquired data, the server uses dialogue generation mechanisms to generate a response appropriate to the user. At this stage, personalized advice reflecting the user's emotional state is formed. The generated response includes measures based on career theory and presents specific actions that the user can take.

[0773] In addition, the career assessment tool evaluates the user's characteristics and skills and uses that information to make individualized job recommendations. The information provision tool collects the latest employment information from a database and presents it to the user. Here too, the user's emotional state is taken into consideration, and the most suitable job information is provided to the user.

[0774] For example, if a user inputs "I feel anxious about my future career," the server recognizes the user's anxiety and provides articles or videos that can boost their motivation. A concrete example of an input prompt for the generating AI model might be: "User inquiry: 'I feel anxious about my future career.' Generated response: 'To alleviate this anxiety, let's learn from the career stories of those who came before you. Also, please watch the following relaxing video.'"

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The user uses their device to input their inquiries about their carrier. The entered text data is sent to the server. At this stage, user input is the primary data.

[0778] Step 2:

[0779] The server analyzes the received text data using natural language processing techniques. Hugging Face Transformers are used to syntactically analyze the input text and extract important keywords and themes. The input is user text data, and the extracted keywords become the output.

[0780] Step 3:

[0781] The server performs sentiment analysis. Using the Google Cloud Natural Language API, it recognizes emotions from the user's text data. Emotions such as anxiety, anticipation, and stress are identified. The input is the analyzed text data, and the output is the recognized sentiment data.

[0782] Step 4:

[0783] The server generates a response using a dialogue generation mechanism based on the extracted keywords and sentiment data. An AI model generates a response that takes emotions into consideration, creating an appropriate prompt. Here, keywords and sentiment data are the input, and the generated response is the output.

[0784] Step 5:

[0785] The server uses career assessment tools to evaluate the user's characteristics and skills. This evaluation utilizes past user data and entered consultation content. Based on the evaluation, appropriate career selection advice is generated. The input is the user's profile information, and the output is advice based on the evaluation.

[0786] Step 6:

[0787] The server uses information provision methods to search for market employment information collected from job information databases and presents job postings suitable for the user. The input is the result of a career assessment, and the output is filtered job information.

[0788] Step 7:

[0789] The server creates personalized content based on the user's emotions through a content delivery method. It generates and delivers articles and videos that boost motivation. Here, emotion data and generated response sentences are the inputs, and personalized content is the output.

[0790] 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.

[0791] 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.

[0792] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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 as being incorporated by reference.

[0811] The following is further disclosed regarding the embodiments described above.

[0812] (Claim 1)

[0813] A natural language processing means that receives the consultation content entered by the user and analyzes the consultation content,

[0814] A dialogue generation means that outputs a response generated based on the analysis,

[0815] Career assessment methods for evaluating user characteristics and skills,

[0816] A means of providing information that collects job information from the market and recommends suitable jobs to users,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising an evaluation generation means for providing advice based on the user's characteristics and skills.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising means for applying career theory to respond to the consultation content entered by the user.

[0822] "Example 1"

[0823] (Claim 1)

[0824] A natural language processing means that receives the consultation content entered by the user and analyzes the consultation content,

[0825] A dialogue generation means that provides a response generated based on the analysis,

[0826] A characteristic evaluation method for evaluating user characteristics and abilities,

[0827] A means of providing information that collects job information from the market and recommends suitable jobs to users,

[0828] A filtering method that filters job postings based on data from information provision methods and presents only job postings that match the user's desired conditions,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising an evaluation generation means for providing advice based on the user's characteristics and abilities and presenting the generated dialogue to the user.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising a method for applying career theory to respond to user-entered consultation content and generating advice by utilizing past success stories.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] A natural language processing means that receives the consultation content entered by the user and analyzes the consultation content,

[0837] A dialogue generation means that outputs a response generated based on the analysis,

[0838] Career assessment methods for evaluating user characteristics and skills,

[0839] A means of providing information that collects job information from the market and recommends suitable jobs to users,

[0840] Information analysis means for receiving consultations based on the user's interests and preferences,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising an evaluation generation means for providing advice based on the user's characteristics and skills.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for applying career theory to respond to the consultation content entered by the user.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A language analysis means that receives information input from a user and analyzes the content based on that information,

[0849] An emotion recognition means that recognizes the emotions extracted by the analysis and generates a response corresponding to those emotions,

[0850] A response generation means that outputs a dialogue using a response generated by artificial intelligence,

[0851] A vocational assessment tool for evaluating a user's vocational aptitude and abilities,

[0852] An information recommendation system that collects market occupational information and recommends suitable occupations to users,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising an evaluation generation means that provides emotionally supportive advice based on the user's occupational aptitude and abilities.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for applying occupational theory based on information provided by the user.

[0858] "Application example 2 when combining with an emotional engine"

[0859] (Claim 1)

[0860] A natural language processing means that receives the consultation content entered by the user and analyzes the consultation content,

[0861] A dialogue generation means that outputs a response generated based on the analysis,

[0862] Career assessment methods that evaluate user characteristics and skills,

[0863] A means of providing information that collects market employment information and recommends suitable employment to users,

[0864] A content delivery method that recognizes the user's emotional state and delivers personalized information,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, further comprising evaluation generation means for providing advice based on user characteristics and skills.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising means for applying occupational theory to respond to the consultation content entered by the user. [Explanation of symbols]

[0870] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A natural language processing means that receives the consultation content entered by the user and analyzes the consultation content, A dialogue generation means that outputs a response generated based on the analysis, Career assessment methods for evaluating user characteristics and skills, A means of providing information that collects job information from the market and recommends suitable jobs to users, A system that includes this.

2. The system according to claim 1, further comprising an evaluation generation means for providing advice based on the user's characteristics and skills.

3. The system according to claim 1, further comprising means for applying career theory to respond to the consultation content entered by the user.

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