System

Abstract

A system is provided.SOLUTION: This system is provided with a means for receiving skills, experiences and interests inputted by a job change applicant, a means for analyzing the AI of the job change applicant by using generated date, and for listing the optimal job category or enterprise, and a means for presenting the listed job category or enterprise to the job change applicant.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Job seekers struggle with how to effectively showcase their skills and experience and find appropriate job information. Many job seekers also face challenges due to a lack of confidence in interview preparation. Traditional job change support services and job information sites lack support tailored to individual needs, creating a need for job change support tailored to the needs of each individual user. Therefore, the purpose of this invention is to use generative AI to help job seekers make more appropriate career choices and facilitate a smooth job search. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving skills, experience, and interests input by a job seeker, a means for analyzing the job seeker's data using a generation AI to list the most suitable jobs and companies, and a means for presenting the listed jobs and companies to the job seeker. Furthermore, the present invention solves the problems faced by job seekers by providing a system including a means for collecting the job seeker's input data and generating attractive resume text, a means for presenting the generated resume text to the job seeker, a means for generating interview questions for the job seeker using a generation AI, a means for receiving the job seeker's answers and generating feedback using a generation AI, and a means for presenting the generated feedback to the job seeker.

[0006] A "job seeker" refers to an individual who wishes to leave their current job and start working at a new job.

[0007] "Skills" refer to the knowledge and abilities required to perform a specific job or function.

[0008] "Experience" refers to the accumulation of knowledge and skills gained through previous work or projects.

[0009] "Interests" refers to the work or fields that job seekers are interested in.

[0010] "Generative AI" refers to artificial intelligence that processes large amounts of data to automatically generate new information and suggestions.

[0011] "Occupation" refers to a type of occupation with specific job duties or work content.

[0012] "Enterprise" refers to a legal entity or organization that provides goods or services.

[0013] A "resume" refers to a document that lists an individual's work history, skills, and educational background.

[0014] A "question" is something you pose to someone in order to elicit information.

[0015] An "answer" refers to a response to a question.

[0016] "Feedback" refers to the response or evaluation given to a particular action or response.

[0017] A "system" refers to an integrated mechanism or device in which multiple components work together. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a customized system that supports job changes by suggesting the most suitable jobs and companies based on the skills, experience, and interests of job seekers. The system is mainly composed of a device used by job seekers, a server that processes data, and multiple modules that use generation AI to automatically generate resumes, generate interview questions and feedback, and suggest job information.

[0040] Career Design Assistant

[0041] 1. The user enters their skills, experience, and interests through the terminal. For example, they enter information such as "Software development, 5 years of experience, interested in AI technology."

[0042] 2. The device sends the entered data to the server in JSON format.

[0043] 3. The server uses generative AI to analyze the user's data and create a list of suitable jobs and companies.

[0044] 4. The server sends the result to the terminal again in JSON format.

[0045] 5. The device analyzes the results received from the server and displays them to the user. For example, it displays a list of job titles and companies, such as "AI project manager" or "data scientist."

[0046] Resume creation support

[0047] 1. The user enters their past work experience into the terminal. For example, they enter "Server-side development, Java, Python, project management."

[0048] 2. The device sends the entered data to the server in JSON format.

[0049] 3. The server uses AI to generate compelling resume sentences. Based on the input data, the AI ​​automatically generates sentences such as, "I have five years of server-side development experience and excellent project management skills using Java and Python."

[0050] 4. The server sends the generated results to the terminal in JSON format.

[0051] 5. The device displays a preview of the received resume to the user.

[0052] Interview support

[0053] 1. The user selects the interview practice they wish to participate in on their device. For example, they can select "Interview practice for an AI engineer position."

[0054] 2. The device sends the request to the server in JSON format.

[0055] 3. The server uses generative AI to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[0056] 4. The server sends the generated question to the device in JSON format.

[0057] 5. The user answers the questions through the terminal and sends the answers to the server.

[0058] 6. The server analyzes the answer and generates feedback using generative AI, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[0059] 7. The server sends the generated feedback in JSON format to the device.

[0060] 8. The device displays the received feedback to the user.

[0061] Job Changer Network

[0062] 1. The server periodically collects introductions and interview articles of successful job-changers. It collects and organizes successful job-change cases from various data sources and stores them in a database.

[0063] 2. When a user opens the job seeker network page on their device, the device retrieves the relevant list from the server.

[0064] 3. The server sends a list of successful job changes in JSON format to the device.

[0065] 4. The device displays the received success stories and interview articles to the user. For example, a specific success story such as "A former engineer becomes a project manager and is successful in remote work" is presented.

[0066] In this way, the present invention is a system that uses various generative AIs to provide comprehensive support to job seekers. Each module provides information tailored to the needs of job seekers, allowing users to make appropriate career choices.

[0067] The processing flow will be explained below.

[0068] Career Design Assistant

[0069] Step 1:

[0070] The user uses the device to input their skills, experience, and interests. For example, the user inputs "Software development, 5 years, interested in AI technology."

[0071] Step 2:

[0072] The terminal converts the input data into JSON format and sends it to the server.

[0073] Step 3:

[0074] The server parses the received JSON data.

[0075] Step 4:

[0076] The server passes the user's data to the generation AI module, which analyzes it and creates a list of suitable jobs and companies.

[0077] Step 5:

[0078] The server converts the generated results into JSON format and sends them to the terminal.

[0079] Step 6:

[0080] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[0081] Resume creation support

[0082] Step 1:

[0083] A user uses a terminal to enter their work history, for example, "Server-side development, Java, Python, project management."

[0084] Step 2:

[0085] The terminal converts the input data into JSON format and sends it to the server.

[0086] Step 3:

[0087] The server parses the received JSON data.

[0088] Step 4:

[0089] The server passes the user's data to the generation AI module, which analyzes it and automatically generates an attractive resume.

[0090] Step 5:

[0091] The server converts the generated resume text into JSON format and sends it to the terminal.

[0092] Step 6:

[0093] The device analyzes the resume text received and displays it to the user. For example, a preview such as "Has five years of server-side development experience and has excellent project management skills using Java and Python" is displayed.

[0094] Interview support

[0095] Step 1:

[0096] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[0097] Step 2:

[0098] The device converts the request into JSON format and sends it to the server.

[0099] Step 3:

[0100] The server analyzes the received request.

[0101] Step 4:

[0102] The server passes the data to a generation AI module to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[0103] Step 5:

[0104] The server converts the generated question into JSON format and sends it to the terminal.

[0105] Step 6:

[0106] The terminal displays the received question to the user.

[0107] Step 7:

[0108] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[0109] Step 8:

[0110] The server analyzes the received response.

[0111] Step 9:

[0112] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more concrete examples."

[0113] Step 10:

[0114] The server converts the generated feedback into JSON format and sends it to the device.

[0115] Step 11:

[0116] The terminal displays the received feedback to the user.

[0117] Job Changer Network

[0118] Step 1:

[0119] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[0120] Step 2:

[0121] Based on the data accumulated by the server, a list of success stories is generated for job seekers.

[0122] Step 3:

[0123] The user opens a job seeker network page using a device.

[0124] Step 4:

[0125] The device sends a request to the server.

[0126] Step 5:

[0127] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[0128] Step 6:

[0129] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[0130] In this way, each function is realized through specific processing steps.

[0131] Example 1

[0132] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0133] Currently, job seekers must expend a great deal of time and effort to find the job and company that best suits them. They also have to create resumes and prepare for interviews on their own, making it difficult to conduct a job search efficiently.

[0134] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0135] In this invention, the server includes a means for analyzing data of job seekers and listing the most suitable jobs and companies, a means for generating attractive resumes, and a means for generating interview questions and generating feedback on the answers, thereby enabling job seekers to effectively find the most suitable jobs and companies and efficiently conduct their job search.

[0136] A "job seeker" is an individual who wants to leave their current occupation and take up a new one.

[0137] "Skills" refers to the specialized techniques and knowledge possessed by job seekers.

[0138] "Experience" refers to the achievements and experiences that a job seeker has gained in the job or work they have previously performed.

[0139] "Interest" refers to the job seeker's interest in the job or industry.

[0140] "Data terminal" refers to the electronic device used by job seekers to enter information.

[0141] "JSON format" refers to a JavaScript Object Notation data structure used for exchanging and storing data.

[0142] "Server" refers to a central processing unit that analyzes data, generates resumes, creates interview questions, etc.

[0143] "Generative AI" refers to an artificial intelligence model that performs natural language processing based on large amounts of data.

[0144] "Job type" refers to the type of work within a particular industry or field.

[0145] "Company" refers to the corporate organization where the job seeker seeks employment.

[0146] A resume is a document submitted by a job seeker listing their skills and experience.

[0147] "Interview questions" refer to questions that job seekers prepare in anticipation of being asked in an interview with a company.

[0148] "Feedback" refers to evaluation of the job seeker's answers and instructions, including areas for improvement.

[0149] This invention is a system that supports job-hunting activities by suggesting the most suitable jobs and companies for job seekers based on their skills, experience, and interests. The system provides various support functions using data terminals used by job seekers, a server that processes data, and a generative AI model.

[0150] The system's hardware configuration includes a data terminal (e.g., PC, smartphone, tablet, etc.) for job seekers to input data, and a server for analyzing the data and providing information. The software uses a generative AI model, such as OpenAI GPT-4.

[0151] The system provides the following main functions:

[0152] 1. Career design assistant function

[0153] Users input their skills, experience, and interests through a terminal. For example, they input information such as "Software development, 5 years of experience, interested in AI technology."

[0154] The terminal converts the input data into JSON format and sends it to the server.

[0155] The server uses a generative AI to analyze the user's data and create a list of suitable jobs and companies. For example, the generative AI uses the following prompt:

[0156] Generate the following prompt based on the user's skills, experience, and interests: 'Software development, 5 years of experience, interested in AI technology'

[0157] The server sends the listed results back to the terminal in JSON format.

[0158] The device analyzes the results received from the server and displays them to the user. For example, it displays a list of job titles and companies, such as "AI project manager" and "data scientist."

[0159] 2. Resume creation support function

[0160] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[0161] The terminal converts the input data into JSON format and sends it to the server.

[0162] The server uses generative AI to generate compelling resume prompts based on the input data. Examples of prompts used include:

[0163] Generate compelling resume copy based on your work experience: 'Server-side development, Java, Python, Project management'

[0164] The server sends the generated resume text in JSON format to the terminal.

[0165] The terminal displays a preview of the received resume to the user.

[0166] 3. Interview support function

[0167] The user selects the interview practice they wish to have on their device, such as "Interview practice for an AI engineer position."

[0168] The device sends the request to the server in JSON format.

[0169] The server generates interview questions using a generation AI. Examples of prompts include:

[0170] Generate AI engineer job questions for interview practice

[0171] The server sends the generated question in JSON format to the device.

[0172] The user answers the questions through the terminal and sends the answers to the server.

[0173] The server analyzes the answers and generates feedback using generative AI. Example prompts for generating feedback:

[0174] Generate feedback based on the user's answer. Example: The answer is vague, so it would be helpful to include more examples.

[0175] The server sends the generated feedback in JSON format to the device.

[0176] The terminal displays the received feedback to the user.

[0177] Each of these functions is designed to comprehensively support users' job-hunting activities and enable them to make efficient and effective career choices. The purpose of this invention is to provide customized support tailored to the individual circumstances of job seekers and improve the quality of the entire job-hunting process.

[0178] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0179] Career Design Assistant

[0180] Step 1: Data entry

[0181] Users input their skills, experience, and interests through a terminal. For example, they input information such as "Software development, 5 years of experience, interested in AI technology."

[0182] Input: User's skills, experience, and interests (in text format)

[0183] Output: User input data to the terminal (text format)

[0184] Step 2: Send data

[0185] The terminal converts the input data into JSON format and sends it to the server.

[0186] Specifically, the device extracts the information entered by the user and generates JSON data like this:

[0187] json

[0188] {

[0189] "skill": "software development",

[0190] "experience": "5 years",

[0191] "interest": "AI technology"

[0192] }

[0193] Input: User's skills, experience, and interests (in text format)

[0194] Output: JSON format data to the server

[0195] Step 3: Propose a job

[0196] The server uses generative AI to analyze the data it receives and create a list of the most suitable jobs and companies.

[0197] Input: JSON format data sent from the terminal

[0198] Specific operation: The server uses a generative AI (e.g., OpenAI GPT-4) and inputs the following prompt into the generative AI:

[0199] Generate the following prompt based on the user's skills, experience, and interests: 'Software development, 5 years of experience, interested in AI technology'

[0200] Data processing: Generative AI analyzes and creates a list of suitable jobs and companies

[0201] Output: Listing results in JSON format

[0202] Step 4: Send results

[0203] The server generates the results and sends them to the terminal in JSON format. The data from the server is sent in the following format:

[0204] json

[0205] {

[0206] "recommendations": [

[0207] {"position": "AI Project Manager", "company": "Technology Company"},

[0208] {"position": "Data Scientist", "company": "Data Solutions Inc."}

[0209] ]

[0210] }

[0211] Input: Generated list result (JSON format)

[0212] Output: JSON format data to the terminal

[0213] Step 5: View the results

[0214] The terminal analyzes the received results and displays them to the user.

[0215] Example: The terminal parses the received JSON data and displays it in the GUI:

[0216] AI Project Manager - Technology Company

[0217] Data Scientist - Data Solutions Inc.

[0218] Input: Result sent from the server (JSON format)

[0219] Output: What is displayed to the user (GUI format)

[0220] Resume creation support

[0221] Step 1: Enter your work history

[0222] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[0223] Input: User's work history (text format)

[0224] Output: User input data to the terminal (text format)

[0225] Step 2: Send data

[0226] The terminal converts the input data into JSON format and sends it to the server.

[0227] Specifically, the data is prepared as follows:

[0228] json

[0229] {

[0230] "employment_history": [

[0231] {"role": "Server-side development", "skills": ["Java", "Python", "Project management"]}

[0232] ]

[0233] }

[0234] Input: User's work history (text format)

[0235] Output: JSON format data to the server

[0236] Step 3: Generate resume text

[0237] The server uses generation AI to generate resume text.

[0238] Specific operation: The server inputs the following prompt to the generation AI:

[0239] Generate compelling resume copy based on your work experience: 'Server-side development, Java, Python, Project management'

[0240] Input: JSON format data sent from the terminal

[0241] Data processing: Generative AI creates text

[0242] Output: Generated resume text (JSON format)

[0243] Step 4: Sending the generated results

[0244] The server sends the generated resume text in JSON format to the terminal.

[0245] Input: Generated resume text (JSON format)

[0246] Output: JSON format data to the terminal

[0247] Step 5: Preview your resume

[0248] The terminal displays the contents of the received resume to the user.

[0249] Example: The device parses the received JSON data and displays it to the user as follows:

[0250] He has 5 years of server-side development experience and is skilled in project management using Java and Python.

[0251] Input: Result sent from the server (JSON format)

[0252] Output: What is displayed to the user (GUI format)

[0253] Interview support

[0254] Step 1: Enter your interview practice preferences

[0255] The user selects the interview practice they wish to have on their device, such as "Interview practice for an AI engineer position."

[0256] Input: Interview practice request (text format)

[0257] Output: User input data to the terminal (text format)

[0258] Step 2: Submitting a request

[0259] The device sends the request to the server in JSON format.

[0260] Specifically, the data is prepared as follows:

[0261] json

[0262] {

[0263] "interview_practice": "AI engineer position"

[0264] }

[0265] Input: Interview practice request (text format)

[0266] Output: JSON format data to the server

[0267] Step 3: Generate interview questions

[0268] The server generates interview questions using a generation AI.

[0269] Specific operation: The server inputs the following prompt to the generation AI:

[0270] Generate AI engineer job questions for interview practice

[0271] Input: JSON format data sent from the terminal

[0272] Data manipulation: Generative AI creates interview questions

[0273] Output: Generated questions (JSON format)

[0274] Step 4: Submit your question

[0275] The server sends the generated question in JSON format to the device.

[0276] Input: Generated question (JSON format)

[0277] Output: JSON format data to the terminal

[0278] Step 5: Answer the questions

[0279] The user answers the questions through the terminal and sends the answers to the server.

[0280] Input: User's answers to interview questions (in text format)

[0281] Output: JSON format data to the server

[0282] Step 6: Feedback generation

[0283] The server analyzes the answers and generates feedback using generative AI.

[0284] Specific action: The server inputs the following prompt to the generation AI:

[0285] Generate feedback based on the user's answer. Example: The answer is vague, so it would be helpful to include more examples.

[0286] Input: User's answer (in text format)

[0287] Data manipulation: Generative AI creates feedback

[0288] Output: Generated feedback (JSON format)

[0289] Step 7: Submit your feedback

[0290] The server sends the generated feedback in JSON format to the device.

[0291] Input: Generated feedback (JSON format)

[0292] Output: JSON format data to the terminal

[0293] Step 8: Feedback display

[0294] The terminal displays the received feedback to the user.

[0295] Example: The device parses the received JSON data and displays it to the user as follows:

[0296] This answer is a bit vague, so it would be helpful to include some more examples.

[0297] Input: Result sent from the server (JSON format)

[0298] Output: What is displayed to the user (GUI format)

[0299] Job Changer Network

[0300] Step 1: Collecting success stories

[0301] The server regularly collects profiles and interviews of successful job seekers.

[0302] What it does: Collects data using web scraping or APIs and stores it in a database.

[0303] Input: Successful career change cases from various data sources (text format)

[0304] Output: Accumulation in the database on the server

[0305] Step 2: Page display request

[0306] When a user opens a job-changer network page on their device, the device retrieves the relevant list from the server.

[0307] Input: User request (text format)

[0308] Output: Request to server (text format)

[0309] Step 3: Submit your success story list

[0310] The server sends a list of successful job change cases in JSON format to the terminal.

[0311] Input: List of success stories in the database

[0312] Output: JSON format data to the terminal

[0313] Step 4: View success stories

[0314] The terminal displays the received success stories to the user.

[0315] Specific example: Specific success stories such as "A former engineer transitioned to project manager and is now thriving in remote work" are displayed.

[0316] Input: Result sent from the server (JSON format)

[0317] Output: What is displayed to the user (GUI format)

[0318] The above is the specific processing flow of this system.

[0319] (Application example 1)

[0320] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0321] This invention relates to a system for providing advanced job change support and customer service in brick-and-mortar stores. Conventional job change support systems can suggest optimal job types and companies for individual job seekers, but they have the problem that the subsequent interview preparation, resume preparation support, and customer service in brick-and-mortar stores take time and effort. In particular, it has been difficult to grasp the interests and preferences of customers in brick-and-mortar stores in real time and quickly make suggestions based on them.

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

[0323] In this invention, the server includes a means for receiving skills, experience, and interests entered by job seekers, a means for analyzing data on job seekers using a generation AI and listing optimal jobs and companies, a means for presenting the listed jobs and companies to job seekers, and a means related to a device for store staff to use the generation AI to analyze interests and preferences from customer comments and actions and propose optimal products and services. This not only enables comprehensive job change support for job seekers, but also makes it possible to grasp customer needs in real time and make appropriate proposals in physical stores.

[0324] A "job seeker" is an individual who wishes to change jobs from their current position to another position.

[0325] "Skills" refer to the techniques and abilities required for a particular job or task.

[0326] "Experience" refers to the track record and history of past work and tasks.

[0327] "Interest" refers to a concern or desire for a particular field or activity.

[0328] "Generative AI" refers to a system that uses artificial intelligence technology to generate and analyze data.

[0329] "Listing" refers to the act of compiling multiple candidates into a list.

[0330] "Job type" refers to specific job content or work category.

[0331] An "enterprise" is an organization that provides goods and services.

[0332] An "apparatus" is a machine or device configured to perform a particular function.

[0333] "Customer" means an individual or entity that purchases or uses goods or services.

[0334] "Statement" refers to verbal expressions of intent or opinions.

[0335] "Behavior" refers to human movements or activities that are carried out with a specific purpose.

[0336] "Interests and preferences" refer to the areas or products in which a customer has particular preferences or interests.

[0337] A "suggestion" is the act of presenting a particular idea or option.

[0338] "Store staff" refers to employees who deal with customers and provide services in physical stores.

[0339] "Real-time" means that events or actions occur almost immediately, corresponding to actual time.

[0340] The present invention is a system that proposes the most suitable job types and companies based on data on job seekers, and supports resume creation, interview practice, and customer service in brick-and-mortar stores. Specific embodiments of the system are described below.

[0341] 1. Implementation of the job change support system

[0342] The system of this invention is composed of a terminal used by job seekers, a server that processes data, and a generation AI. When a user enters their skills, experience, and interests through the terminal, the terminal sends the entered data in JSON format to the server. The server uses the generation AI to analyze the user data, lists the most suitable jobs and companies, and sends the results back to the terminal in JSON format. The user can then check the listed jobs and companies on the terminal.

[0343] 2. Resume creation support

[0344] When a user enters their work history into the device, the device sends the data in JSON format to the server. The server uses a generative AI to generate an attractive resume and sends the generated results in JSON format to the device. The device then displays a preview of the received resume to the user.

[0345] 3. Interview practice support

[0346] When a user selects their preference for interview practice on their device, the device sends a request to the server in JSON format. The server uses a generation AI to generate interview questions and sends the generated questions to the device in JSON format. The user answers the questions through their device and sends the answers to the server. The server analyzes the answers, generates feedback using the generation AI, and sends the generated feedback to the device in JSON format. The user then checks the received feedback on their device.

[0347] 4. Physical store applications

[0348] Store staff wear smart glasses to gather information about customers' interests and preferences from their comments and behavior. The smart glasses capture what customers say and send the data to a server in real time. The server then uses generative AI to analyze the customer data and generate information to recommend optimal products and services. The generated recommendation information is then sent back to the smart glasses, where staff can view the suggestions on the glasses' display and make them to the customer.

[0349] Hardware and software used

[0350] Device: The device on which the user enters information (smartphone, tablet, PC, etc.)

[0351] Server: Receives and transmits data, and executes AI generation (e.g., cloud server)

[0352] Generative AI: Data analysis, resume generation, interview question generation, feedback generation (e.g., OpenAI's GPT-3)

[0353] Smart glasses: Used for customer service in physical stores (smart glasses from specific manufacturers)

[0354] Examples and prompts

[0355] As a specific example, if a store staff member wearing smart glasses captures a customer saying, "I've been interested in the outdoors lately," the program will respond as follows:

[0356] Example prompt sentence:

[0357] text

[0358] A customer might say: 'I've been interested in the outdoors lately'. Suggest the best products based on that.

[0359] The generated suggestions are displayed on the smart glasses in the form of, for example, "If you're interested in the outdoors, we recommend the latest compact tent and waterproof jacket. In particular, outdoor gear on sale this week is very popular."

[0360] In this way, a system can be realized that can provide advanced information and support in real time to a variety of users, including not only job seekers but also store staff.

[0361] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0362] Step 1:

[0363] The device receives input data from the user, such as skills, experience, and interests. The user enters specific information, such as "software development, 5 years of experience, interested in AI technology." The device converts this data into JSON format and sends it to the server.

[0364] Step 2:

[0365] The server receives the JSON data sent from the device and analyzes the data using a generative AI model (e.g., GPT-3). The generative AI model analyzes the user data and lists the most suitable jobs and companies. The analysis results are structured in JSON format and sent back from the server to the device.

[0366] Step 3:

[0367] The device analyzes the JSON data of the analysis results received from the server and displays a list of jobs and companies that are most suitable for the user. For example, it may list jobs such as "data scientist" or "AI project manager."

[0368] Step 4:

[0369] The user enters their work experience, such as "server-side development, Java, Python, project management," into the terminal. The terminal then sends this data in JSON format to the server.

[0370] Step 5:

[0371] The server receives the JSON data sent from the device and generates resume text using a generative AI model. Based on the input data, the generative AI model automatically generates an appealing resume text such as "Has five years of server-side development experience and has excellent project management skills using Java and Python," and sends it in JSON format to the device.

[0372] Step 6:

[0373] The device parses the JSON data of the resume text received from the server and displays a preview to the user. The user can then check the generated resume and make any necessary corrections.

[0374] Step 7:

[0375] The user selects the interview practice they wish to do on their device. For example, they select "Interview practice for an AI engineer position." The device then sends the selected information to the server in JSON format.

[0376] Step 8:

[0377] The server receives the JSON data sent from the device and generates interview questions using a generative AI model, such as "What was the biggest challenge in your previous projects?", and sends them to the device in JSON format.

[0378] Step 9:

[0379] The device parses the JSON data of the interview questions received from the server and displays the questions to the user. The user answers the questions and sends the answers back to the server via the device.

[0380] Step 10:

[0381] The server receives the user's response data and generates feedback using a generative AI model. The generative AI model analyzes the response data and generates feedback such as "This response is not specific enough, so it would be better to include more specific examples," and sends it to the device in JSON format.

[0382] Step 11:

[0383] The device parses the JSON data of the feedback received from the server and displays the feedback to the user, who can then improve their answer based on the feedback.

[0384] Step 12:

[0385] In a physical store, store staff wearing smart glasses capture customer speech. For example, if a customer says, "I've recently become interested in outdoor activities," the smart glasses receive the speech data and send it to a server in real time.

[0386] Step 13:

[0387] The server receives the voice data sent from the smart glasses, analyzes the customer's interests and preferences using a generative AI model, and generates information to recommend the most suitable products and services. For example, it generates information such as "If you're interested in outdoor activities, we recommend the latest compact tent and waterproof jacket," and sends it back to the smart glasses in JSON format.

[0388] Step 14:

[0389] The smart glasses display the received recommendation information and store staff make suggestions to customers, enabling them to provide optimal products and services to customers in real time.

[0390] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0391] This invention uses a system that combines generative AI and an emotion engine to provide customized career change support to job seekers. The system analyzes the skills, experience, and interests entered by job seekers, lists the most suitable jobs and companies, and also creates resumes, provides interview practice, and generates feedback.

[0392] Career Design Assistant

[0393] Users input their skills, experience, and interests through the terminal. For example, they could input "Software development, 5 years, interested in AI technology."

[0394] The terminal converts the input data into JSON format and sends it to the server.

[0395] The server parses the received JSON data and uses an emotion engine to recognize and evaluate the user's emotion based on the input data.

[0396] The server passes the user's data and emotional evaluation results to the generation AI module, which then uses this information to create a list of suitable jobs and companies.

[0397] The server converts the generated results into JSON format and sends them to the terminal.

[0398] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[0399] Resume creation support

[0400] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[0401] The terminal converts the input data into JSON format and sends it to the server.

[0402] The server parses the received JSON data and uses an emotion engine to evaluate the input data and the user's reaction in real time.

[0403] The server passes the user's data and sentiment assessment to the generation AI module, which then uses this data to automatically generate compelling resume sentences, such as "I have five years of server-side development experience and excellent project management skills using Java and Python."

[0404] The server converts the generated resume text into JSON format and sends it to the terminal.

[0405] The terminal analyzes the received resume text and displays it to the user, who can then check the generated resume text.

[0406] Interview support

[0407] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[0408] The device converts the request into JSON format and sends it to the server.

[0409] The server analyzes the received request.

[0410] The server uses an emotion engine to evaluate emotions based on the user's selection and passes the data to a generative AI module, which then automatically generates questions such as, "What was the biggest challenge in your previous project?"

[0411] The server converts the generated question into JSON format and sends it to the terminal.

[0412] The terminal displays the received question to the user.

[0413] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[0414] The server analyzes the received answers, and the emotion engine evaluates the emotion based on the user's answer data.

[0415] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[0416] The server converts the generated feedback into JSON format and sends it to the device.

[0417] The device will display the received feedback to the user, who can review it and learn from it to improve.

[0418] Job Changer Network

[0419] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[0420] Based on the accumulated data, the server generates a list of success stories for job seekers.

[0421] The user opens a job seeker network page using a device.

[0422] The device sends a request to the server.

[0423] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[0424] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[0425] In this way, each function is realized through a specific processing flow, and by making full use of generative AI and an emotion engine, comprehensive support is provided to job seekers.

[0426] The processing flow will be explained below.

[0427] Career Design Assistant

[0428] Step 1:

[0429] The user uses the device to enter their skills, experience, and interests. For example, they might enter "Software development, 5 years, interested in AI technology."

[0430] Step 2:

[0431] The terminal converts the input data into JSON format and sends it to the server.

[0432] Step 3:

[0433] The server parses the received JSON data.

[0434] Step 4:

[0435] The server uses an emotion engine to recognize and evaluate the user's emotions based on the input data, such as interest and confidence, based on facial expressions and input content.

[0436] Step 5:

[0437] The server passes the user's data and emotional evaluation results to the generation AI module, which uses this data to create a list of suitable jobs and companies.

[0438] Step 6:

[0439] The server converts the generated results into JSON format and sends them to the terminal.

[0440] Step 7:

[0441] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[0442] Resume creation support

[0443] Step 1:

[0444] A user uses a terminal to enter their work history, for example, "Server-side development, Java, Python, project management."

[0445] Step 2:

[0446] The terminal converts the input data into JSON format and sends it to the server.

[0447] Step 3:

[0448] The server parses the received JSON data.

[0449] Step 4:

[0450] The server uses an emotion engine to evaluate the input data and the user's reaction in real time, for example, by analyzing the user's facial expressions and typing speed to assess their level of nervousness or confidence.

[0451] Step 5:

[0452] The server passes the user's data and the sentiment assessment results to the generation AI module, which then uses this data to automatically generate compelling resume sentences. For example, it might generate a sentence like, "I have five years of server-side development experience and excellent project management skills using Java and Python."

[0453] Step 6:

[0454] The server converts the generated resume text into JSON format and sends it to the terminal.

[0455] Step 7:

[0456] The terminal analyzes the received resume text and displays it to the user, who can then check the generated resume text.

[0457] Interview support

[0458] Step 1:

[0459] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[0460] Step 2:

[0461] The device converts the request into JSON format and sends it to the server.

[0462] Step 3:

[0463] The server analyzes the received request.

[0464] Step 4:

[0465] The server uses an emotion engine to assess emotions based on the user's selection, for example by analyzing facial expressions and tone of voice at the start of a practice interview.

[0466] Step 5:

[0467] The server passes the data to a generation AI module to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[0468] Step 6:

[0469] The server converts the generated question into JSON format and sends it to the terminal.

[0470] Step 7:

[0471] The terminal displays the received question to the user.

[0472] Step 8:

[0473] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[0474] Step 9:

[0475] The server analyzes the received responses, and the emotion engine evaluates the emotion based on the user's response data, for example, by analyzing the user's facial expression and tone along with the content of the response.

[0476] Step 10:

[0477] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[0478] Step 11:

[0479] The server converts the generated feedback into JSON format and sends it to the device.

[0480] Step 12:

[0481] The device will display the received feedback to the user, who can review it and learn from it to improve.

[0482] Job Changer Network

[0483] Step 1:

[0484] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[0485] Step 2:

[0486] Based on the accumulated data, the server generates a list of success stories for job seekers.

[0487] Step 3:

[0488] The user opens a job seeker network page using a device.

[0489] Step 4:

[0490] The device sends a request to the server.

[0491] Step 5:

[0492] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[0493] Step 6:

[0494] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[0495] In this way, each function is realized through specific processing steps, and by making full use of generative AI and an emotion engine, comprehensive support is provided to job seekers.

[0496] Example 2

[0497] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0498] Conventional career change support systems often list job types and companies based solely on the job seeker's skills and experience, making it difficult to provide customized suggestions that take into account the job seeker's feelings and interests. It also makes it difficult to provide optimized feedback for each individual job seeker when helping them create resumes or practice for interviews.

[0499] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0500] In this invention, the server includes means for receiving skills, experience, and interests input by job seekers, means for converting the input data into a structured data format and transmitting it to the server, and means for analyzing the job seeker's data using a generative AI and a sentiment analysis engine to list the most suitable jobs and companies. This makes it possible to utilize the generative AI and the sentiment analysis engine to propose industries and companies customized for the job seeker.

[0501] A "job seeker" is someone who wishes to change jobs and receives proposals for different jobs and companies.

[0502] "Skills" refers to the occupational or work-related skills and abilities possessed by job seekers.

[0503] "Experience" refers to the job seeker's past occupations and work history, as well as the knowledge and skills they have acquired through them.

[0504] "Interest" refers to the interest or preference that a job seeker has in a particular job type, job role, or field.

[0505] A "structured data format" refers to organizing data based on specific rules and putting it into a format that can be easily analyzed by a computer.

[0506] "Server" refers to a computer system that analyzes data received from job seekers and processes it using generative AI and an emotion analysis engine.

[0507] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze input data and generate specific outputs (e.g., job suggestions or resume generation).

[0508] "Sentiment analysis engine" refers to a software system for assessing emotions based on user input data and responses.

[0509] "Shortlisting jobs and companies" refers to creating a list of jobs and companies that best fit the job seeker's skills, experience, and interests.

[0510] A "resume" is a document that describes a job seeker's past work experience and skills and is used to highlight the job seeker's abilities.

[0511] "Feedback" refers to response information including evaluation and advice regarding the job seeker's input and response.

[0512] This invention is a system that provides customized career change support to job seekers, and is realized by combining generative AI and a sentiment analysis engine. The system analyzes the skills, experience, and interests entered by job seekers via their devices and can list the most suitable jobs and companies. It also provides support for creating resumes and practicing for interviews, providing comprehensive support to job seekers.

[0513] First, the user uses the device to input their skills, experience, and interests. For example, they might input "Software development, 5 years, interested in AI technology." This input data is converted into a structured data format (e.g., JSON format) by the device and sent to the server. The server analyzes the received data and evaluates the user's emotions using a sentiment analysis engine (e.g., general sentiment analysis software).

[0514] The server then passes the user's data and emotional evaluation to a generative AI model (e.g., a general generative AI model) to generate a list of suitable jobs and companies. This list reflects the user's input data and emotional evaluation results. For example, the following prompt sentence can be input to the generative AI model:

[0515] text

[0516] User Skills: Software Development, Experience: 5 years, Interests: AI Technology

[0517] Make a list of the jobs and companies that are best for you.

[0518] The generative AI model generates a list of results and returns it to the server. The server then converts it back into structured data format and sends it to the device. The device then analyzes the received data and displays suggestions to the user, such as "AI project manager" or "data scientist."

[0519] Next, we will explain how a user creates a resume. The user enters their past work experience through the terminal. For example, they enter data such as "server-side development, Java, Python, project management." This data is also converted into a structured data format by the terminal and sent to the server. The server analyzes the received data and evaluates the user's emotions using a sentiment analysis engine.

[0520] Next, the server asks the generative AI model to create a resume based on the evaluation results and data. For example, the following prompt sentence is input to the generative AI model:

[0521] text

[0522] Previous work experience: Server-side development, Languages ​​used: Java, Python, Role: Project management

[0523] Create an attractive resume.

[0524] The generative AI model generates compelling resume text and returns it to the server, which converts it into a structured data format and sends it to the device, which analyzes the received data and displays the generated resume text to the user.

[0525] Finally, we will explain interview practice support. The user uses the device to select the interview practice they wish to use. For example, they select "interview practice for an AI engineer position." This request is converted into a structured data format by the device and sent to the server. The server analyzes the request and evaluates the user's emotions using an emotion analysis engine.

[0526] The server then asks the generative AI model to create interview questions based on the evaluation results and the request. For example, the following prompt sentences are input to the generative AI model:

[0527] text

[0528] Desired job: AI engineer

[0529] Create practice interview questions.

[0530] The generative AI model generates interview questions and returns them to the server, which converts them into a structured data format and sends them to the device, which then displays the received questions to the user.

[0531] The user answers questions through their device, converts the answers into structured data format, and sends them to the server. The server analyzes the answers and evaluates their emotions using a sentiment analysis engine. The server then requests the generative AI model to create feedback based on the evaluation results and data. For example, the following prompt sentence could be input to the generative AI model:

[0532] text

[0533] User Answer: The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models.

[0534] Please provide feedback on this answer.

[0535] The generative AI model generates feedback and returns it to the server, which converts it into a structured data format and sends it to the device, where it displays the received feedback to the user, who can review it and learn from it.

[0536] In this way, the system utilizes generative AI and a sentiment analysis engine to provide customized assistance to job seekers.

[0537] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0538] Career Design Assistant

[0539] Step 1:

[0540] Users enter their skills, experience, and interests into a terminal.

[0541] Input: Users enter their skills, experience, and interests into an input form.

[0542] Output: The input data is stored in the device's memory.

[0543] Specific operation: The user enters "Software development, 5 years, interested in AI technology" into the input form on the device.

[0544] Step 2:

[0545] The terminal converts the input data into JSON format and sends it to the server.

[0546] Input: Skills, experience, and interest data entered.

[0547] Output: The data converted to JSON format is sent to the server.

[0548] Specific operation: The device generates the following JSON data and sends it to the server.

[0549] json

[0550] {

[0551] "skill": "software development",

[0552] "experience": "5 years",

[0553] "interest": "AI technology"

[0554] }

[0555] Step 3:

[0556] The server analyzes the received data.

[0557] Input: JSON formatted data received by the server.

[0558] Output: Parsed skills, experiences, and interests are extracted.

[0559] What happens: The server parses the JSON data and extracts the individual fields (skill, experience, interest).

[0560] Step 4:

[0561] The server performs emotion assessment using an emotion analysis engine.

[0562] Input: Parsed data (skills, experience, interests).

[0563] Output: User's emotion evaluation result.

[0564] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "excited" or "cautious" based on the user's input.

[0565] Step 5:

[0566] The server passes the data to a generative AI module, which then lists the most suitable jobs and companies.

[0567] Inputs: Skills, experience, interests, plus emotional assessment results.

[0568] Output: A list of suitable jobs and companies.

[0569] How it works: The server passes the following prompt to the generative AI model:

[0570] text

[0571] User Skills: Software Development, Experience: 5 years, Interests: AI Technology

[0572] Make a list of the jobs and companies that are best for you.

[0573] The generative AI model will list job titles such as "AI project manager" and "data scientist."

[0574] Step 6:

[0575] The server converts the list results into JSON format and sends it to the terminal.

[0576] Input: Listed job and company data.

[0577] Output: A list converted to JSON format.

[0578] Specific operation: The server generates the following JSON data and sends it to the terminal.

[0579] json

[0580] {

[0581] "suggestions": [

[0582] "AI Project Manager",

[0583] "Data Scientist"

[0584] ]

[0585] }

[0586] Step 7:

[0587] The device parses the JSON data and displays it to the user.

[0588] Input: List data in JSON format sent from the server.

[0589] Output: The list of analyzed jobs and companies will be displayed on the screen.

[0590] Specific operation: The device parses the received JSON data and displays it to the user as an "AI project manager" and "data scientist."

[0591] Resume creation support

[0592] Step 1:

[0593] The user inputs his / her past work history into the terminal.

[0594] Input: The user enters their work history into an input form.

[0595] Output: The input data is stored in the device's memory.

[0596] Specific operation: The user enters "Server-side development, Java, Python, project management" into the input form on the terminal.

[0597] Step 2:

[0598] The terminal converts the input data into JSON format and sends it to the server.

[0599] Input: Work history data entered.

[0600] Output: The data converted to JSON format is sent to the server.

[0601] Specific operation: The device generates the following JSON data and sends it to the server.

[0602] json

[0603] {

[0604] "experience": "server-side development",

[0605] "skills": ["Java", "Python"],

[0606] "roles": ["Project Management"]

[0607] }

[0608] Step 3:

[0609] The server analyzes the received data.

[0610] Input: JSON formatted data received by the server.

[0611] Output: The analyzed work history, skills, and roles are extracted.

[0612] What happens: The server parses the JSON data and extracts the individual fields (experience, skills, roles).

[0613] Step 4:

[0614] The server performs emotion assessment using an emotion analysis engine.

[0615] Input: Parsed data (work history, skills, roles).

[0616] Output: User's emotion evaluation result.

[0617] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "confident" or "anxious" based on the user's input.

[0618] Step 5:

[0619] The server passes the data to a generation AI module, which automatically generates the resume text.

[0620] Input: Work history, skills, role, and emotional assessment results.

[0621] Output: The generated resume text.

[0622] How it works: The server passes the following prompt to the generative AI model:

[0623] text

[0624] Previous work experience: Server-side development, Languages ​​used: Java, Python, Role: Project management

[0625] Create an attractive resume.

[0626] For example, the generative AI model generates a sentence such as, "He has five years of server-side development experience and has excellent project management skills using Java and Python."

[0627] Step 6:

[0628] The server converts the generated text into JSON format and sends it to the terminal.

[0629] Input: Generated resume text.

[0630] Output: The resume text converted to JSON format.

[0631] Specific operation: The server converts the generated resume text into JSON data as follows and sends it to the terminal.

[0632] json

[0633] {

[0634] "resume": "Five years of server-side development experience and excellent project management skills using Java and Python."

[0635] }

[0636] Step 7:

[0637] The device parses the JSON data and displays it to the user.

[0638] Input: JSON format resume text sent from the server.

[0639] Output: The parsed resume text is displayed on the screen.

[0640] Specific operation: The device parses the received JSON data and displays the generated resume text to the user. The user can review the displayed resume text and make any necessary corrections.

[0641] Interview support

[0642] Step 1:

[0643] The user uses the terminal to select the interview practice he / she wishes to take.

[0644] Input: The user selects the position they would like to interview for.

[0645] Output: The selected job type is stored in the device's memory.

[0646] Specific operation: The user selects "AI engineer job interview practice" from the device screen.

[0647] Step 2:

[0648] The device converts the request into JSON format and sends it to the server.

[0649] Input: Data for the selected interview position.

[0650] Output: The request data converted to JSON format is sent to the server.

[0651] Specific operation: The device generates the following JSON data and sends it to the server.

[0652] json

[0653] {

[0654] "interview_position": "AI Engineer"

[0655] }

[0656] Step 3:

[0657] The server parses the incoming request.

[0658] Input: The request data received by the server in JSON format.

[0659] Output: Parsed interview job data.

[0660] Specific behavior: The server parses the JSON data and extracts the interview position.

[0661] Step 4:

[0662] The server performs emotion assessment using an emotion analysis engine.

[0663] Input: Parsed interview job data.

[0664] Output: User's emotion evaluation result.

[0665] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "excited" or "cautious" based on the user's interview job selection.

[0666] Step 5:

[0667] The server passes the data to a generation AI module, which automatically generates interview questions.

[0668] Input: Interview job data, emotion evaluation results.

[0669] Output: Generated interview questions.

[0670] How it works: The server passes the following prompt to the generative AI model:

[0671] text

[0672] Desired job: AI engineer

[0673] Create practice interview questions.

[0674] The generative AI model generates questions such as, "What was the biggest challenge in your previous projects?"

[0675] Step 6:

[0676] The server converts the generated question into JSON format and sends it to the terminal.

[0677] Input: Generated interview questions.

[0678] Output: Interview questions converted to JSON format.

[0679] Specific operation: The server converts the generated interview questions into JSON data as follows and sends it to the terminal.

[0680] json

[0681] {

[0682] "questions": ["What has been the biggest challenge in your previous projects?"]

[0683] }

[0684] Step 7:

[0685] The terminal displays the question to the user.

[0686] Input: Question data in JSON format sent from the server.

[0687] Output: The parsed question is displayed on the screen.

[0688] Specific operation: The device parses the received JSON data and displays the question to the user.

[0689] Step 8:

[0690] The user answers the questions through the terminal and sends them to the server.

[0691] Input: The user's answer.

[0692] Output: Response data converted to JSON format.

[0693] Specific operation: The user answers, "The biggest challenge in my projects so far has been introducing new algorithms to improve the performance of the AI ​​model," and the device converts this into JSON format and sends it to the server.

[0694] json

[0695] {

[0696] "answer": "The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models."

[0697] }

[0698] Step 9:

[0699] The server analyzes the received responses and performs emotion evaluation.

[0700] Input: Received response data in JSON format.

[0701] Output: User's emotion evaluation result.

[0702] Specific operation: The server parses and analyzes the response data, and evaluates the sentiment based on the response using a sentiment analysis engine.

[0703] Step 10:

[0704] The server passes the data to the generation AI module, which generates feedback.

[0705] Input: User response data and sentiment evaluation results.

[0706] Output: The generated feedback.

[0707] How it works: The server passes the following prompt to the generative AI model:

[0708] text

[0709] User Answer: The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models.

[0710] Please provide feedback on this answer.

[0711] The generative AI model generates feedback such as, "This answer is not specific enough, so it would be better to include more specific examples."

[0712] Step 11:

[0713] The server converts the feedback into JSON format and sends it to the device.

[0714] Input: Generated feedback.

[0715] Output: Feedback converted to JSON format.

[0716] Specific operation: The server converts the generated feedback into JSON data as follows and sends it to the device.

[0717] json

[0718] {

[0719] "feedback": "This answer is not specific enough. Please provide some examples."

[0720] }

[0721] Step 12:

[0722] The device displays the feedback to the user.

[0723] Input: Feedback data sent from the server in JSON format.

[0724] Output: Parsed feedback is displayed on the screen.

[0725] Specific behavior: The device parses the received JSON data and displays feedback to the user, who can review the feedback and learn from it to improve.

[0726] (Application example 2)

[0727] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0728] The main function of conventional job change support systems was to consider a job seeker's skills, experience, and interests and list the most suitable jobs and companies. However, this alone was not enough to provide job seekers with the best job opportunities. In addition, marketing and advertising for job seekers only provided general information and was not customized based on individual profiles. This made it difficult to fully meet the needs of job seekers.

[0729] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0730] In this invention, the server includes means for receiving skills, experience, and interests entered by a job seeker, means for analyzing the job seeker's data using a generation AI and listing optimal jobs and companies, means for presenting the listed jobs and companies to the job seeker, means for generating a customized advertisement based on the job seeker's profile information and emotion assessment results, and means for displaying the generated advertisement to the job seeker. This makes it possible to provide not only a list of optimal jobs and companies based on the job seeker's individual profile, but also customized advertisements tailored to the job seeker's interests and emotions.

[0731] A "job seeker" is an individual who wishes to change jobs in search of a new occupation or work environment.

[0732] "Skills" refers to specialized abilities and techniques related to a profession or job.

[0733] "Experience" refers to knowledge and abilities acquired through previous work or duties.

[0734] "Interest" refers to a special interest or curiosity in a particular field or activity.

[0735] "Generative AI" refers to artificial intelligence technology that automatically generates new information and content based on input data.

[0736] "Means for analyzing data" refers to techniques and methods for analyzing collected data and extracting meaningful information.

[0737] "Job type" refers to a type of job or occupation with specific job duties or work content.

[0738] "Enterprise" refers to a legal entity or company that carries on a particular business activity.

[0739] "Means of listing" refers to techniques or methods for listing items or data based on specific conditions or criteria.

[0740] "Profile Information" refers to detailed information about an individual, including their skills, experience, interests, personality traits, etc.

[0741] "Emotional assessment" refers to measuring an individual's emotional state or psychological response and making an assessment based on that.

[0742] "Customized advertising" refers to advertising whose content is optimized to suit the characteristics and interests of a specific individual.

[0743] "Display means" refers to the techniques and methods for visually presenting the generated information or data to the user.

[0744] This invention is a system that combines a generative AI model and an emotion evaluation engine to provide customized job-hunting support to job seekers. The system receives skills, experience, and interests entered by job seekers and lists the most suitable jobs and companies based on them. Furthermore, the system uses generative AI to generate customized advertisements and display them to job seekers.

[0745] The server implements this system using a program that includes the following steps:

[0746] First, the user uses the input interface to input their skills, experience, and interests. For example, they enter specific information such as "Software development, 5 years, interested in AI technology." The device converts the input data into JSON format and sends it to the server.

[0747] The server parses the received JSON data and uses an emotion engine, such as Hume AI or Affectiva AI, to evaluate the job seeker's emotions based on the input data.

[0748] The server then passes the user's data and sentiment evaluation results to a generative AI module, which uses a generative AI model (such as OpenAI's GPT-4) to create a list of suitable jobs and companies, while simultaneously generating customized advertisements based on the job seeker's profile information and sentiment evaluation results.

[0749] The results are then converted back into JSON format and sent to the device, which then parses the data and displays a list of jobs, companies, and customized ads to the user, such as suggestions for "AI project manager" or "data scientist."

[0750] For example, you might use the following prompts for a generative AI model:

[0751] User skills: Software development, project management

[0752] Experience: 5 years

[0753] Interests: AI, data science

[0754] Emotional rating: Positive

[0755] Use the above information to generate ads that are appealing to users.

[0756] In this way, the server can analyze user profile information and emotion evaluation results with high accuracy, and provide job seekers with the most appropriate information. This system also enables the provision of individually customized advertisements to job seekers.

[0757] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0758] Step 1:

[0759] The user uses the input interface to input their skills, experience, and interests. For example, they can input specific information such as "Software development, 5 years, interested in AI technology." This results in input data. The output is the specific profile information entered by the user.

[0760] Step 2:

[0761] The device converts the input data into JSON format, which structures the data and makes it a format that can be communicated. The input is the profile information entered by the user, and the output is JSON format data.

[0762] Step 3:

[0763] The terminal sends data converted to JSON format to the server. HTTP or WebSocket is used as the communication protocol. The input is JSON format data, and the output is transmission completion to the server.

[0764] Step 4:

[0765] The server analyzes the JSON data received. Specifically, it receives the data using a Python framework (Flask or Django) and performs the analysis process. The input is JSON format data and the output is the analysis result.

[0766] Step 5:

[0767] The server uses an emotion engine to evaluate emotions based on the input data. Hume AI and Affectiva AI are used as emotion engines. The input is the analyzed data, and the output is the emotion evaluation result.

[0768] Step 6:

[0769] The server passes the user's data and sentiment evaluation results to the generation AI module, which then lists the most suitable jobs and companies. OpenAI's GPT-4 is used as the generation AI model. The prompt used is something like, "User skills: software development, project management Experience: 5 years Interests: AI, data science Sentiment evaluation: positive Please generate an advertisement that will appeal to the user based on the above information." The input is user data and sentiment evaluation results, and the output is the most suitable jobs and companies and a customized advertisement.

[0770] Step 7:

[0771] The generated results (best jobs, companies, customized ads) are then converted back into JSON format by the server, which makes it possible to send the results to the device. The input is the output data from the generative AI model, and the output is JSON format data.

[0772] Step 8:

[0773] The server converts the data into JSON format and sends it to the terminal. The input is JSON format data, and the output is sent to the terminal.

[0774] Step 9:

[0775] The device analyzes the received JSON data and displays a list of jobs, companies, and customized advertisements to the user. Specifically, it displays the job listings, companies, and customized advertisements in a user interface using HTML and JavaScript. The input is JSON-formatted data, and the output is a visual presentation to the user. For example, suggestions such as "AI project manager" or "data scientist" are displayed.

[0776] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0777] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0778] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0779] [Second embodiment]

[0780] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0781] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0782] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0783] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0784] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0785] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0786] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0787] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0788] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0789] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0790] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0791] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0792] This invention is a customized system that supports job changes by suggesting the most suitable jobs and companies based on the skills, experience, and interests of job seekers. The system is mainly composed of a device used by job seekers, a server that processes data, and multiple modules that use generation AI to automatically generate resumes, generate interview questions and feedback, and suggest job information.

[0793] Career Design Assistant

[0794] 1. The user enters their skills, experience, and interests through the terminal. For example, they enter information such as "Software development, 5 years of experience, interested in AI technology."

[0795] 2. The device sends the entered data to the server in JSON format.

[0796] 3. The server uses generative AI to analyze the user's data and create a list of suitable jobs and companies.

[0797] 4. The server sends the result to the terminal again in JSON format.

[0798] 5. The device analyzes the results received from the server and displays them to the user. For example, it displays a list of job titles and companies, such as "AI project manager" or "data scientist."

[0799] Resume creation support

[0800] 1. The user enters their past work experience into the terminal. For example, they enter "Server-side development, Java, Python, project management."

[0801] 2. The device sends the entered data to the server in JSON format.

[0802] 3. The server uses AI to generate compelling resume sentences. Based on the input data, the AI ​​automatically generates sentences such as, "I have five years of server-side development experience and excellent project management skills using Java and Python."

[0803] 4. The server sends the generated results to the terminal in JSON format.

[0804] 5. The device displays a preview of the received resume to the user.

[0805] Interview support

[0806] 1. The user selects the interview practice they wish to participate in on their device. For example, they can select "Interview practice for an AI engineer position."

[0807] 2. The device sends the request to the server in JSON format.

[0808] 3. The server uses generative AI to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[0809] 4. The server sends the generated question to the device in JSON format.

[0810] 5. The user answers the questions through the terminal and sends the answers to the server.

[0811] 6. The server analyzes the answer and generates feedback using generative AI, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[0812] 7. The server sends the generated feedback in JSON format to the device.

[0813] 8. The device displays the received feedback to the user.

[0814] Job Changer Network

[0815] 1. The server periodically collects introductions and interview articles of successful job-changers. It collects and organizes successful job-change cases from various data sources and stores them in a database.

[0816] 2. When a user opens the job seeker network page on their device, the device retrieves the relevant list from the server.

[0817] 3. The server sends a list of successful job changes in JSON format to the device.

[0818] 4. The device displays the received success stories and interview articles to the user. For example, a specific success story such as "A former engineer becomes a project manager and is successful in remote work" is presented.

[0819] In this way, the present invention is a system that uses various generative AIs to provide comprehensive support to job seekers. Each module provides information tailored to the needs of job seekers, allowing users to make appropriate career choices.

[0820] The processing flow will be explained below.

[0821] Career Design Assistant

[0822] Step 1:

[0823] The user uses the device to input their skills, experience, and interests. For example, the user inputs "Software development, 5 years, interested in AI technology."

[0824] Step 2:

[0825] The terminal converts the input data into JSON format and sends it to the server.

[0826] Step 3:

[0827] The server parses the received JSON data.

[0828] Step 4:

[0829] The server passes the user's data to the generation AI module, which analyzes it and creates a list of suitable jobs and companies.

[0830] Step 5:

[0831] The server converts the generated results into JSON format and sends them to the terminal.

[0832] Step 6:

[0833] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[0834] Resume creation support

[0835] Step 1:

[0836] A user uses a terminal to enter their work history, for example, "Server-side development, Java, Python, project management."

[0837] Step 2:

[0838] The terminal converts the input data into JSON format and sends it to the server.

[0839] Step 3:

[0840] The server parses the received JSON data.

[0841] Step 4:

[0842] The server passes the user's data to the generation AI module, which analyzes it and automatically generates an attractive resume.

[0843] Step 5:

[0844] The server converts the generated resume text into JSON format and sends it to the terminal.

[0845] Step 6:

[0846] The device analyzes the resume text received and displays it to the user. For example, a preview such as "Has five years of server-side development experience and has excellent project management skills using Java and Python" is displayed.

[0847] Interview support

[0848] Step 1:

[0849] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[0850] Step 2:

[0851] The device converts the request into JSON format and sends it to the server.

[0852] Step 3:

[0853] The server analyzes the received request.

[0854] Step 4:

[0855] The server passes the data to a generation AI module to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[0856] Step 5:

[0857] The server converts the generated question into JSON format and sends it to the terminal.

[0858] Step 6:

[0859] The terminal displays the received question to the user.

[0860] Step 7:

[0861] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[0862] Step 8:

[0863] The server analyzes the received response.

[0864] Step 9:

[0865] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more concrete examples."

[0866] Step 10:

[0867] The server converts the generated feedback into JSON format and sends it to the device.

[0868] Step 11:

[0869] The terminal displays the received feedback to the user.

[0870] Job Changer Network

[0871] Step 1:

[0872] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[0873] Step 2:

[0874] Based on the data accumulated by the server, a list of success stories is generated for job seekers.

[0875] Step 3:

[0876] The user opens a job seeker network page using a device.

[0877] Step 4:

[0878] The device sends a request to the server.

[0879] Step 5:

[0880] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[0881] Step 6:

[0882] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[0883] In this way, each function is realized through specific processing steps.

[0884] Example 1

[0885] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0886] Currently, job seekers must expend a great deal of time and effort to find the job and company that best suits them. They also have to create resumes and prepare for interviews on their own, making it difficult to conduct a job search efficiently.

[0887] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0888] In this invention, the server includes a means for analyzing data of job seekers and listing the most suitable jobs and companies, a means for generating attractive resumes, and a means for generating interview questions and generating feedback on the answers, thereby enabling job seekers to effectively find the most suitable jobs and companies and efficiently conduct their job search.

[0889] A "job seeker" is an individual who wants to leave their current occupation and take up a new one.

[0890] "Skills" refers to the specialized techniques and knowledge possessed by job seekers.

[0891] "Experience" refers to the achievements and experiences that a job seeker has gained in the job or work they have previously performed.

[0892] "Interest" refers to the job seeker's interest in the job or industry.

[0893] "Data terminal" refers to the electronic device used by job seekers to enter information.

[0894] "JSON format" refers to a JavaScript Object Notation data structure used for exchanging and storing data.

[0895] "Server" refers to a central processing unit that analyzes data, generates resumes, creates interview questions, etc.

[0896] "Generative AI" refers to an artificial intelligence model that performs natural language processing based on large amounts of data.

[0897] "Job type" refers to the type of work within a particular industry or field.

[0898] "Company" refers to the corporate organization where the job seeker seeks employment.

[0899] A resume is a document submitted by a job seeker listing their skills and experience.

[0900] "Interview questions" refer to questions that job seekers prepare in anticipation of being asked in an interview with a company.

[0901] "Feedback" refers to evaluation of the job seeker's answers and instructions, including areas for improvement.

[0902] This invention is a system that supports job-hunting activities by suggesting the most suitable jobs and companies for job seekers based on their skills, experience, and interests. The system provides various support functions using data terminals used by job seekers, a server that processes data, and a generative AI model.

[0903] The system's hardware configuration includes a data terminal (e.g., PC, smartphone, tablet, etc.) for job seekers to input data, and a server for analyzing the data and providing information. The software uses a generative AI model, such as OpenAI GPT-4.

[0904] The system provides the following main functions:

[0905] 1. Career design assistant function

[0906] Users input their skills, experience, and interests through a terminal. For example, they input information such as "Software development, 5 years of experience, interested in AI technology."

[0907] The terminal converts the input data into JSON format and sends it to the server.

[0908] The server uses a generative AI to analyze the user's data and create a list of suitable jobs and companies. For example, the generative AI uses the following prompt:

[0909] Generate the following prompt based on the user's skills, experience, and interests: 'Software development, 5 years of experience, interested in AI technology'

[0910] The server sends the listed results back to the terminal in JSON format.

[0911] The device analyzes the results received from the server and displays them to the user. For example, it displays a list of job titles and companies, such as "AI project manager" and "data scientist."

[0912] 2. Resume creation support function

[0913] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[0914] The terminal converts the input data into JSON format and sends it to the server.

[0915] The server uses generative AI to generate compelling resume prompts based on the input data. Examples of prompts used include:

[0916] Generate compelling resume copy based on your work experience: 'Server-side development, Java, Python, Project management'

[0917] The server sends the generated resume text in JSON format to the terminal.

[0918] The terminal displays a preview of the received resume to the user.

[0919] 3. Interview support function

[0920] The user selects the interview practice they wish to have on their device, such as "Interview practice for an AI engineer position."

[0921] The device sends the request to the server in JSON format.

[0922] The server generates interview questions using a generation AI. Examples of prompts include:

[0923] Generate AI engineer job questions for interview practice

[0924] The server sends the generated question in JSON format to the device.

[0925] The user answers the questions through the terminal and sends the answers to the server.

[0926] The server analyzes the answers and generates feedback using generative AI. Example prompts for generating feedback:

[0927] Generate feedback based on the user's answer. Example: The answer is vague, so it would be helpful to include more examples.

[0928] The server sends the generated feedback in JSON format to the device.

[0929] The terminal displays the received feedback to the user.

[0930] Each of these functions is designed to comprehensively support users' job-hunting activities and enable them to make efficient and effective career choices. The purpose of this invention is to provide customized support tailored to the individual circumstances of job seekers and improve the quality of the entire job-hunting process.

[0931] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0932] Career Design Assistant

[0933] Step 1: Data entry

[0934] Users input their skills, experience, and interests through a terminal. For example, they input information such as "Software development, 5 years of experience, interested in AI technology."

[0935] Input: User's skills, experience, and interests (in text format)

[0936] Output: User input data to the terminal (text format)

[0937] Step 2: Send data

[0938] The terminal converts the input data into JSON format and sends it to the server.

[0939] Specifically, the device extracts the information entered by the user and generates JSON data like this:

[0940] json

[0941] {

[0942] "skill": "software development",

[0943] "experience": "5 years",

[0944] "interest": "AI technology"

[0945] }

[0946] Input: User's skills, experience, and interests (in text format)

[0947] Output: JSON format data to the server

[0948] Step 3: Propose a job

[0949] The server uses generative AI to analyze the data it receives and create a list of the most suitable jobs and companies.

[0950] Input: JSON format data sent from the terminal

[0951] Specific operation: The server uses a generative AI (e.g., OpenAI GPT-4) and inputs the following prompt into the generative AI:

[0952] Generate the following prompt based on the user's skills, experience, and interests: 'Software development, 5 years of experience, interested in AI technology'

[0953] Data processing: Generative AI analyzes and creates a list of suitable jobs and companies

[0954] Output: Listing results in JSON format

[0955] Step 4: Send results

[0956] The server generates the results and sends them to the terminal in JSON format. The data from the server is sent in the following format:

[0957] json

[0958] {

[0959] "recommendations": [

[0960] {"position": "AI Project Manager", "company": "Technology Company"},

[0961] {"position": "Data Scientist", "company": "Data Solutions Inc."}

[0962] ]

[0963] }

[0964] Input: Generated list result (JSON format)

[0965] Output: JSON format data to the terminal

[0966] Step 5: View the results

[0967] The terminal analyzes the received results and displays them to the user.

[0968] Example: The terminal parses the received JSON data and displays it in the GUI:

[0969] AI Project Manager - Technology Company

[0970] Data Scientist - Data Solutions Inc.

[0971] Input: Result sent from the server (JSON format)

[0972] Output: What is displayed to the user (GUI format)

[0973] Resume creation support

[0974] Step 1: Enter your work history

[0975] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[0976] Input: User's work history (text format)

[0977] Output: User input data to the terminal (text format)

[0978] Step 2: Send data

[0979] The terminal converts the input data into JSON format and sends it to the server.

[0980] Specifically, the data is prepared as follows:

[0981] json

[0982] {

[0983] "employment_history": [

[0984] {"role": "Server-side development", "skills": ["Java", "Python", "Project management"]}

[0985] ]

[0986] }

[0987] Input: User's work history (text format)

[0988] Output: JSON format data to the server

[0989] Step 3: Generate resume text

[0990] The server uses generation AI to generate resume text.

[0991] Specific operation: The server inputs the following prompt to the generation AI:

[0992] Generate compelling resume copy based on your work experience: 'Server-side development, Java, Python, Project management'

[0993] Input: JSON format data sent from the terminal

[0994] Data processing: Generative AI creates text

[0995] Output: Generated resume text (JSON format)

[0996] Step 4: Sending the generated results

[0997] The server sends the generated resume text in JSON format to the terminal.

[0998] Input: Generated resume text (JSON format)

[0999] Output: JSON format data to the terminal

[1000] Step 5: Preview your resume

[1001] The terminal displays the contents of the received resume to the user.

[1002] Example: The device parses the received JSON data and displays it to the user as follows:

[1003] He has 5 years of server-side development experience and is skilled in project management using Java and Python.

[1004] Input: Result sent from the server (JSON format)

[1005] Output: What is displayed to the user (GUI format)

[1006] Interview support

[1007] Step 1: Enter your interview practice preferences

[1008] The user selects the interview practice they wish to have on their device, such as "Interview practice for an AI engineer position."

[1009] Input: Interview practice request (text format)

[1010] Output: User input data to the terminal (text format)

[1011] Step 2: Submitting a request

[1012] The device sends the request to the server in JSON format.

[1013] Specifically, the data is prepared as follows:

[1014] json

[1015] {

[1016] "interview_practice": "AI engineer position"

[1017] }

[1018] Input: Interview practice request (text format)

[1019] Output: JSON format data to the server

[1020] Step 3: Generate interview questions

[1021] The server generates interview questions using a generation AI.

[1022] Specific operation: The server inputs the following prompt to the generation AI:

[1023] Generate AI engineer job questions for interview practice

[1024] Input: JSON format data sent from the terminal

[1025] Data manipulation: Generative AI creates interview questions

[1026] Output: Generated questions (JSON format)

[1027] Step 4: Submit your question

[1028] The server sends the generated question in JSON format to the device.

[1029] Input: Generated question (JSON format)

[1030] Output: JSON format data to the terminal

[1031] Step 5: Answer the questions

[1032] The user answers the questions through the terminal and sends the answers to the server.

[1033] Input: User's answers to interview questions (in text format)

[1034] Output: JSON format data to the server

[1035] Step 6: Feedback generation

[1036] The server analyzes the answers and generates feedback using generative AI.

[1037] Specific action: The server inputs the following prompt to the generation AI:

[1038] Generate feedback based on the user's answer. Example: The answer is vague, so it would be helpful to include more examples.

[1039] Input: User's answer (in text format)

[1040] Data manipulation: Generative AI creates feedback

[1041] Output: Generated feedback (JSON format)

[1042] Step 7: Submit your feedback

[1043] The server sends the generated feedback in JSON format to the device.

[1044] Input: Generated feedback (JSON format)

[1045] Output: JSON format data to the terminal

[1046] Step 8: Feedback display

[1047] The terminal displays the received feedback to the user.

[1048] Example: The device parses the received JSON data and displays it to the user as follows:

[1049] This answer is a bit vague, so it would be helpful to include some more examples.

[1050] Input: Result sent from the server (JSON format)

[1051] Output: What is displayed to the user (GUI format)

[1052] Job Changer Network

[1053] Step 1: Collecting success stories

[1054] The server regularly collects profiles and interviews of successful job seekers.

[1055] What it does: Collects data using web scraping or APIs and stores it in a database.

[1056] Input: Successful career change cases from various data sources (text format)

[1057] Output: Accumulation in the database on the server

[1058] Step 2: Page display request

[1059] When a user opens a job-changer network page on their device, the device retrieves the relevant list from the server.

[1060] Input: User request (text format)

[1061] Output: Request to server (text format)

[1062] Step 3: Submit your success story list

[1063] The server sends a list of successful job change cases in JSON format to the terminal.

[1064] Input: List of success stories in the database

[1065] Output: JSON format data to the terminal

[1066] Step 4: View success stories

[1067] The terminal displays the received success stories to the user.

[1068] Specific example: Specific success stories such as "A former engineer transitioned to project manager and is now thriving in remote work" are displayed.

[1069] Input: Result sent from the server (JSON format)

[1070] Output: What is displayed to the user (GUI format)

[1071] The above is the specific processing flow of this system.

[1072] (Application example 1)

[1073] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1074] This invention relates to a system for providing advanced job change support and customer service in brick-and-mortar stores. Conventional job change support systems can suggest optimal job types and companies for individual job seekers, but they have the problem that the subsequent interview preparation, resume preparation support, and customer service in brick-and-mortar stores take time and effort. In particular, it has been difficult to grasp the interests and preferences of customers in brick-and-mortar stores in real time and quickly make suggestions based on them.

[1075] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1076] In this invention, the server includes a means for receiving skills, experience, and interests entered by job seekers, a means for analyzing data on job seekers using a generation AI and listing optimal jobs and companies, a means for presenting the listed jobs and companies to job seekers, and a means related to a device for store staff to use the generation AI to analyze interests and preferences from customer comments and actions and propose optimal products and services. This not only enables comprehensive job change support for job seekers, but also makes it possible to grasp customer needs in real time and make appropriate proposals in physical stores.

[1077] A "job seeker" is an individual who wishes to change jobs from their current position to another position.

[1078] "Skills" refer to the techniques and abilities required for a particular job or task.

[1079] "Experience" refers to the track record and history of past work and tasks.

[1080] "Interest" refers to a concern or desire for a particular field or activity.

[1081] "Generative AI" refers to a system that uses artificial intelligence technology to generate and analyze data.

[1082] "Listing" refers to the act of compiling multiple candidates into a list.

[1083] "Job type" refers to specific job content or work category.

[1084] An "enterprise" is an organization that provides goods and services.

[1085] An "apparatus" is a machine or device configured to perform a particular function.

[1086] "Customer" means an individual or entity that purchases or uses goods or services.

[1087] "Statement" refers to verbal expressions of intent or opinions.

[1088] "Behavior" refers to human movements or activities that are carried out with a specific purpose.

[1089] "Interests and preferences" refer to the areas or products in which a customer has particular preferences or interests.

[1090] A "suggestion" is the act of presenting a particular idea or option.

[1091] "Store staff" refers to employees who deal with customers and provide services in physical stores.

[1092] "Real-time" means that events or actions occur almost immediately, corresponding to actual time.

[1093] The present invention is a system that proposes the most suitable job types and companies based on data on job seekers, and supports resume creation, interview practice, and customer service in brick-and-mortar stores. Specific embodiments of the system are described below.

[1094] 1. Implementation of the job change support system

[1095] The system of this invention is composed of a terminal used by job seekers, a server that processes data, and a generation AI. When a user enters their skills, experience, and interests through the terminal, the terminal sends the entered data in JSON format to the server. The server uses the generation AI to analyze the user data, lists the most suitable jobs and companies, and sends the results back to the terminal in JSON format. The user can then check the listed jobs and companies on the terminal.

[1096] 2. Resume creation support

[1097] When a user enters their work history into the device, the device sends the data in JSON format to the server. The server uses a generative AI to generate an attractive resume and sends the generated results in JSON format to the device. The device then displays a preview of the received resume to the user.

[1098] 3. Interview practice support

[1099] When a user selects their preference for interview practice on their device, the device sends a request to the server in JSON format. The server uses a generation AI to generate interview questions and sends the generated questions to the device in JSON format. The user answers the questions through their device and sends the answers to the server. The server analyzes the answers, generates feedback using the generation AI, and sends the generated feedback to the device in JSON format. The user then checks the received feedback on their device.

[1100] 4. Physical store applications

[1101] Store staff wear smart glasses to gather information about customers' interests and preferences from their comments and behavior. The smart glasses capture what customers say and send the data to a server in real time. The server then uses generative AI to analyze the customer data and generate information to recommend optimal products and services. The generated recommendation information is then sent back to the smart glasses, where staff can view the suggestions on the glasses' display and make them to the customer.

[1102] Hardware and software used

[1103] Device: The device on which the user enters information (smartphone, tablet, PC, etc.)

[1104] Server: Receives and transmits data, and executes AI generation (e.g., cloud server)

[1105] Generative AI: Data analysis, resume generation, interview question generation, feedback generation (e.g., OpenAI's GPT-3)

[1106] Smart glasses: Used for customer service in physical stores (smart glasses from specific manufacturers)

[1107] Examples and prompts

[1108] As a specific example, if a store staff member wearing smart glasses captures a customer saying, "I've been interested in the outdoors lately," the program will respond as follows:

[1109] Example prompt sentence:

[1110] text

[1111] A customer might say: 'I've been interested in the outdoors lately'. Suggest the best products based on that.

[1112] The generated suggestions are displayed on the smart glasses in the form of, for example, "If you're interested in the outdoors, we recommend the latest compact tent and waterproof jacket. In particular, outdoor gear on sale this week is very popular."

[1113] In this way, a system can be realized that can provide advanced information and support in real time to a variety of users, including not only job seekers but also store staff.

[1114] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1115] Step 1:

[1116] The device receives input data from the user, such as skills, experience, and interests. The user enters specific information, such as "software development, 5 years of experience, interested in AI technology." The device converts this data into JSON format and sends it to the server.

[1117] Step 2:

[1118] The server receives the JSON data sent from the device and analyzes the data using a generative AI model (e.g., GPT-3). The generative AI model analyzes the user data and lists the most suitable jobs and companies. The analysis results are structured in JSON format and sent back from the server to the device.

[1119] Step 3:

[1120] The device analyzes the JSON data of the analysis results received from the server and displays a list of jobs and companies that are most suitable for the user. For example, it may list jobs such as "data scientist" or "AI project manager."

[1121] Step 4:

[1122] The user enters their work experience, such as "server-side development, Java, Python, project management," into the terminal. The terminal then sends this data in JSON format to the server.

[1123] Step 5:

[1124] The server receives the JSON data sent from the device and generates resume text using a generative AI model. Based on the input data, the generative AI model automatically generates an appealing resume text such as "Has five years of server-side development experience and has excellent project management skills using Java and Python," and sends it in JSON format to the device.

[1125] Step 6:

[1126] The device parses the JSON data of the resume text received from the server and displays a preview to the user. The user can then check the generated resume and make any necessary corrections.

[1127] Step 7:

[1128] The user selects the interview practice they wish to do on their device. For example, they select "Interview practice for an AI engineer position." The device then sends the selected information to the server in JSON format.

[1129] Step 8:

[1130] The server receives the JSON data sent from the device and generates interview questions using a generative AI model, such as "What was the biggest challenge in your previous projects?", and sends them to the device in JSON format.

[1131] Step 9:

[1132] The device parses the JSON data of the interview questions received from the server and displays the questions to the user. The user answers the questions and sends the answers back to the server via the device.

[1133] Step 10:

[1134] The server receives the user's response data and generates feedback using a generative AI model. The generative AI model analyzes the response data and generates feedback such as "This response is not specific enough, so it would be better to include more specific examples," and sends it to the device in JSON format.

[1135] Step 11:

[1136] The device parses the JSON data of the feedback received from the server and displays the feedback to the user, who can then improve their answer based on the feedback.

[1137] Step 12:

[1138] In a physical store, store staff wearing smart glasses capture customer speech. For example, if a customer says, "I've recently become interested in outdoor activities," the smart glasses receive the speech data and send it to a server in real time.

[1139] Step 13:

[1140] The server receives the voice data sent from the smart glasses, analyzes the customer's interests and preferences using a generative AI model, and generates information to recommend the most suitable products and services. For example, it generates information such as "If you're interested in outdoor activities, we recommend the latest compact tent and waterproof jacket," and sends it back to the smart glasses in JSON format.

[1141] Step 14:

[1142] The smart glasses display the received recommendation information and store staff make suggestions to customers, enabling them to provide optimal products and services to customers in real time.

[1143] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1144] This invention uses a system that combines generative AI and an emotion engine to provide customized career change support to job seekers. The system analyzes the skills, experience, and interests entered by job seekers, lists the most suitable jobs and companies, and also creates resumes, provides interview practice, and generates feedback.

[1145] Career Design Assistant

[1146] Users input their skills, experience, and interests through the terminal. For example, they could input "Software development, 5 years, interested in AI technology."

[1147] The terminal converts the input data into JSON format and sends it to the server.

[1148] The server parses the received JSON data and uses an emotion engine to recognize and evaluate the user's emotion based on the input data.

[1149] The server passes the user's data and emotional evaluation results to the generation AI module, which then uses this information to create a list of suitable jobs and companies.

[1150] The server converts the generated results into JSON format and sends them to the terminal.

[1151] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[1152] Resume creation support

[1153] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[1154] The terminal converts the input data into JSON format and sends it to the server.

[1155] The server parses the received JSON data and uses an emotion engine to evaluate the input data and the user's reaction in real time.

[1156] The server passes the user's data and sentiment assessment to the generation AI module, which then uses this data to automatically generate compelling resume sentences, such as "I have five years of server-side development experience and excellent project management skills using Java and Python."

[1157] The server converts the generated resume text into JSON format and sends it to the terminal.

[1158] The terminal analyzes the received resume text and displays it to the user, who can then check the generated resume text.

[1159] Interview support

[1160] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[1161] The device converts the request into JSON format and sends it to the server.

[1162] The server analyzes the received request.

[1163] The server uses an emotion engine to evaluate emotions based on the user's selection and passes the data to a generative AI module, which then automatically generates questions such as, "What was the biggest challenge in your previous project?"

[1164] The server converts the generated question into JSON format and sends it to the terminal.

[1165] The terminal displays the received question to the user.

[1166] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[1167] The server analyzes the received answers, and the emotion engine evaluates the emotion based on the user's answer data.

[1168] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[1169] The server converts the generated feedback into JSON format and sends it to the device.

[1170] The device will display the received feedback to the user, who can review it and learn from it to improve.

[1171] Job Changer Network

[1172] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[1173] Based on the accumulated data, the server generates a list of success stories for job seekers.

[1174] The user opens a job seeker network page using a device.

[1175] The device sends a request to the server.

[1176] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[1177] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[1178] In this way, each function is realized through a specific processing flow, and by making full use of generative AI and an emotion engine, comprehensive support is provided to job seekers.

[1179] The processing flow will be explained below.

[1180] Career Design Assistant

[1181] Step 1:

[1182] The user uses the device to enter their skills, experience, and interests. For example, they might enter "Software development, 5 years, interested in AI technology."

[1183] Step 2:

[1184] The terminal converts the input data into JSON format and sends it to the server.

[1185] Step 3:

[1186] The server parses the received JSON data.

[1187] Step 4:

[1188] The server uses an emotion engine to recognize and evaluate the user's emotions based on the input data, such as interest and confidence, based on facial expressions and input content.

[1189] Step 5:

[1190] The server passes the user's data and emotional evaluation results to the generation AI module, which uses this data to create a list of suitable jobs and companies.

[1191] Step 6:

[1192] The server converts the generated results into JSON format and sends them to the terminal.

[1193] Step 7:

[1194] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[1195] Resume creation support

[1196] Step 1:

[1197] A user uses a terminal to enter their work history, for example, "Server-side development, Java, Python, project management."

[1198] Step 2:

[1199] The terminal converts the input data into JSON format and sends it to the server.

[1200] Step 3:

[1201] The server parses the received JSON data.

[1202] Step 4:

[1203] The server uses an emotion engine to evaluate the input data and the user's reaction in real time, for example, by analyzing the user's facial expressions and typing speed to assess their level of nervousness or confidence.

[1204] Step 5:

[1205] The server passes the user's data and the sentiment assessment results to the generation AI module, which then uses this data to automatically generate compelling resume sentences. For example, it might generate a sentence like, "I have five years of server-side development experience and excellent project management skills using Java and Python."

[1206] Step 6:

[1207] The server converts the generated resume text into JSON format and sends it to the terminal.

[1208] Step 7:

[1209] The terminal analyzes the received resume text and displays it to the user, who can then check the generated resume text.

[1210] Interview support

[1211] Step 1:

[1212] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[1213] Step 2:

[1214] The device converts the request into JSON format and sends it to the server.

[1215] Step 3:

[1216] The server analyzes the received request.

[1217] Step 4:

[1218] The server uses an emotion engine to assess emotions based on the user's selection, for example by analyzing facial expressions and tone of voice at the start of a practice interview.

[1219] Step 5:

[1220] The server passes the data to a generation AI module to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[1221] Step 6:

[1222] The server converts the generated question into JSON format and sends it to the terminal.

[1223] Step 7:

[1224] The terminal displays the received question to the user.

[1225] Step 8:

[1226] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[1227] Step 9:

[1228] The server analyzes the received responses, and the emotion engine evaluates the emotion based on the user's response data, for example, by analyzing the user's facial expression and tone along with the content of the response.

[1229] Step 10:

[1230] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[1231] Step 11:

[1232] The server converts the generated feedback into JSON format and sends it to the device.

[1233] Step 12:

[1234] The device will display the received feedback to the user, who can review it and learn from it to improve.

[1235] Job Changer Network

[1236] Step 1:

[1237] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[1238] Step 2:

[1239] Based on the accumulated data, the server generates a list of success stories for job seekers.

[1240] Step 3:

[1241] The user opens a job seeker network page using a device.

[1242] Step 4:

[1243] The device sends a request to the server.

[1244] Step 5:

[1245] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[1246] Step 6:

[1247] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[1248] In this way, each function is realized through specific processing steps, and by making full use of generative AI and an emotion engine, comprehensive support is provided to job seekers.

[1249] Example 2

[1250] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1251] Conventional career change support systems often list job types and companies based solely on the job seeker's skills and experience, making it difficult to provide customized suggestions that take into account the job seeker's feelings and interests. It also makes it difficult to provide optimized feedback for each individual job seeker when helping them create resumes or practice for interviews.

[1252] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1253] In this invention, the server includes means for receiving skills, experience, and interests input by job seekers, means for converting the input data into a structured data format and transmitting it to the server, and means for analyzing the job seeker's data using a generative AI and a sentiment analysis engine to list the most suitable jobs and companies. This makes it possible to utilize the generative AI and the sentiment analysis engine to propose industries and companies customized for the job seeker.

[1254] A "job seeker" is someone who wishes to change jobs and receives proposals for different jobs and companies.

[1255] "Skills" refers to the occupational or work-related skills and abilities possessed by job seekers.

[1256] "Experience" refers to the job seeker's past occupations and work history, as well as the knowledge and skills they have acquired through them.

[1257] "Interest" refers to the interest or preference that a job seeker has in a particular job type, job role, or field.

[1258] A "structured data format" refers to organizing data based on specific rules and putting it into a format that can be easily analyzed by a computer.

[1259] "Server" refers to a computer system that analyzes data received from job seekers and processes it using generative AI and an emotion analysis engine.

[1260] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze input data and generate specific outputs (e.g., job suggestions or resume generation).

[1261] "Sentiment analysis engine" refers to a software system for assessing emotions based on user input data and responses.

[1262] "Shortlisting jobs and companies" refers to creating a list of jobs and companies that best fit the job seeker's skills, experience, and interests.

[1263] A "resume" is a document that describes a job seeker's past work experience and skills and is used to highlight the job seeker's abilities.

[1264] "Feedback" refers to response information including evaluation and advice regarding the job seeker's input and response.

[1265] This invention is a system that provides customized career change support to job seekers, and is realized by combining generative AI and a sentiment analysis engine. The system analyzes the skills, experience, and interests entered by job seekers via their devices and can list the most suitable jobs and companies. It also provides support for creating resumes and practicing for interviews, providing comprehensive support to job seekers.

[1266] First, the user uses the device to input their skills, experience, and interests. For example, they might input "Software development, 5 years, interested in AI technology." This input data is converted into a structured data format (e.g., JSON format) by the device and sent to the server. The server analyzes the received data and evaluates the user's emotions using a sentiment analysis engine (e.g., general sentiment analysis software).

[1267] The server then passes the user's data and emotional evaluation to a generative AI model (e.g., a general generative AI model) to generate a list of suitable jobs and companies. This list reflects the user's input data and emotional evaluation results. For example, the following prompt sentence can be input to the generative AI model:

[1268] text

[1269] User Skills: Software Development, Experience: 5 years, Interests: AI Technology

[1270] Make a list of the jobs and companies that are best for you.

[1271] The generative AI model generates a list of results and returns it to the server. The server then converts it back into structured data format and sends it to the device. The device then analyzes the received data and displays suggestions to the user, such as "AI project manager" or "data scientist."

[1272] Next, we will explain how a user creates a resume. The user enters their past work experience through the terminal. For example, they enter data such as "server-side development, Java, Python, project management." This data is also converted into a structured data format by the terminal and sent to the server. The server analyzes the received data and evaluates the user's emotions using a sentiment analysis engine.

[1273] Next, the server asks the generative AI model to create a resume based on the evaluation results and data. For example, the following prompt sentence is input to the generative AI model:

[1274] text

[1275] Previous work experience: Server-side development, Languages ​​used: Java, Python, Role: Project management

[1276] Create an attractive resume.

[1277] The generative AI model generates compelling resume text and returns it to the server, which converts it into a structured data format and sends it to the device, which analyzes the received data and displays the generated resume text to the user.

[1278] Finally, we will explain interview practice support. The user uses the device to select the interview practice they wish to use. For example, they select "interview practice for an AI engineer position." This request is converted into a structured data format by the device and sent to the server. The server analyzes the request and evaluates the user's emotions using an emotion analysis engine.

[1279] The server then asks the generative AI model to create interview questions based on the evaluation results and the request. For example, the following prompt sentences are input to the generative AI model:

[1280] text

[1281] Desired job: AI engineer

[1282] Create practice interview questions.

[1283] The generative AI model generates interview questions and returns them to the server, which converts them into a structured data format and sends them to the device, which then displays the received questions to the user.

[1284] The user answers questions through their device, converts the answers into structured data format, and sends them to the server. The server analyzes the answers and evaluates their emotions using a sentiment analysis engine. The server then requests the generative AI model to create feedback based on the evaluation results and data. For example, the following prompt sentence could be input to the generative AI model:

[1285] text

[1286] User Answer: The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models.

[1287] Please provide feedback on this answer.

[1288] The generative AI model generates feedback and returns it to the server, which converts it into a structured data format and sends it to the device, where it displays the received feedback to the user, who can review it and learn from it.

[1289] In this way, the system utilizes generative AI and a sentiment analysis engine to provide customized assistance to job seekers.

[1290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1291] Career Design Assistant

[1292] Step 1:

[1293] Users enter their skills, experience, and interests into a terminal.

[1294] Input: Users enter their skills, experience, and interests into an input form.

[1295] Output: The input data is stored in the device's memory.

[1296] Specific operation: The user enters "Software development, 5 years, interested in AI technology" into the input form on the device.

[1297] Step 2:

[1298] The terminal converts the input data into JSON format and sends it to the server.

[1299] Input: Skills, experience, and interest data entered.

[1300] Output: The data converted to JSON format is sent to the server.

[1301] Specific operation: The device generates the following JSON data and sends it to the server.

[1302] json

[1303] {

[1304] "skill": "software development",

[1305] "experience": "5 years",

[1306] "interest": "AI technology"

[1307] }

[1308] Step 3:

[1309] The server analyzes the received data.

[1310] Input: JSON formatted data received by the server.

[1311] Output: Parsed skills, experiences, and interests are extracted.

[1312] What happens: The server parses the JSON data and extracts the individual fields (skill, experience, interest).

[1313] Step 4:

[1314] The server performs emotion assessment using an emotion analysis engine.

[1315] Input: Parsed data (skills, experience, interests).

[1316] Output: User's emotion evaluation result.

[1317] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "excited" or "cautious" based on the user's input.

[1318] Step 5:

[1319] The server passes the data to a generative AI module, which then lists the most suitable jobs and companies.

[1320] Inputs: Skills, experience, interests, plus emotional assessment results.

[1321] Output: A list of suitable jobs and companies.

[1322] How it works: The server passes the following prompt to the generative AI model:

[1323] text

[1324] User Skills: Software Development, Experience: 5 years, Interests: AI Technology

[1325] Make a list of the jobs and companies that are best for you.

[1326] The generative AI model will list job titles such as "AI project manager" and "data scientist."

[1327] Step 6:

[1328] The server converts the list results into JSON format and sends it to the terminal.

[1329] Input: Listed job and company data.

[1330] Output: A list converted to JSON format.

[1331] Specific operation: The server generates the following JSON data and sends it to the terminal.

[1332] json

[1333] {

[1334] "suggestions": [

[1335] "AI Project Manager",

[1336] "Data Scientist"

[1337] ]

[1338] }

[1339] Step 7:

[1340] The device parses the JSON data and displays it to the user.

[1341] Input: List data in JSON format sent from the server.

[1342] Output: The list of analyzed jobs and companies will be displayed on the screen.

[1343] Specific operation: The device parses the received JSON data and displays it to the user as an "AI project manager" and "data scientist."

[1344] Resume creation support

[1345] Step 1:

[1346] The user inputs his / her past work history into the terminal.

[1347] Input: The user enters their work history into an input form.

[1348] Output: The input data is stored in the device's memory.

[1349] Specific operation: The user enters "Server-side development, Java, Python, project management" into the input form on the terminal.

[1350] Step 2:

[1351] The terminal converts the input data into JSON format and sends it to the server.

[1352] Input: Work history data entered.

[1353] Output: The data converted to JSON format is sent to the server.

[1354] Specific operation: The device generates the following JSON data and sends it to the server.

[1355] json

[1356] {

[1357] "experience": "server-side development",

[1358] "skills": ["Java", "Python"],

[1359] "roles": ["Project Management"]

[1360] }

[1361] Step 3:

[1362] The server analyzes the received data.

[1363] Input: JSON formatted data received by the server.

[1364] Output: The analyzed work history, skills, and roles are extracted.

[1365] What happens: The server parses the JSON data and extracts the individual fields (experience, skills, roles).

[1366] Step 4:

[1367] The server performs emotion assessment using an emotion analysis engine.

[1368] Input: Parsed data (work history, skills, roles).

[1369] Output: User's emotion evaluation result.

[1370] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "confident" or "anxious" based on the user's input.

[1371] Step 5:

[1372] The server passes the data to a generation AI module, which automatically generates the resume text.

[1373] Input: Work history, skills, role, and emotional assessment results.

[1374] Output: The generated resume text.

[1375] How it works: The server passes the following prompt to the generative AI model:

[1376] text

[1377] Previous work experience: Server-side development, Languages ​​used: Java, Python, Role: Project management

[1378] Create an attractive resume.

[1379] For example, the generative AI model generates a sentence such as, "He has five years of server-side development experience and has excellent project management skills using Java and Python."

[1380] Step 6:

[1381] The server converts the generated text into JSON format and sends it to the terminal.

[1382] Input: Generated resume text.

[1383] Output: The resume text converted to JSON format.

[1384] Specific operation: The server converts the generated resume text into JSON data as follows and sends it to the terminal.

[1385] json

[1386] {

[1387] "resume": "Five years of server-side development experience and excellent project management skills using Java and Python."

[1388] }

[1389] Step 7:

[1390] The device parses the JSON data and displays it to the user.

[1391] Input: JSON format resume text sent from the server.

[1392] Output: The parsed resume text is displayed on the screen.

[1393] Specific operation: The device parses the received JSON data and displays the generated resume text to the user. The user can review the displayed resume text and make any necessary corrections.

[1394] Interview support

[1395] Step 1:

[1396] The user uses the terminal to select the interview practice he / she wishes to take.

[1397] Input: The user selects the position they would like to interview for.

[1398] Output: The selected job type is stored in the device's memory.

[1399] Specific operation: The user selects "AI engineer job interview practice" from the device screen.

[1400] Step 2:

[1401] The device converts the request into JSON format and sends it to the server.

[1402] Input: Data for the selected interview position.

[1403] Output: The request data converted to JSON format is sent to the server.

[1404] Specific operation: The device generates the following JSON data and sends it to the server.

[1405] json

[1406] {

[1407] "interview_position": "AI Engineer"

[1408] }

[1409] Step 3:

[1410] The server parses the incoming request.

[1411] Input: The request data received by the server in JSON format.

[1412] Output: Parsed interview job data.

[1413] Specific behavior: The server parses the JSON data and extracts the interview position.

[1414] Step 4:

[1415] The server performs emotion assessment using an emotion analysis engine.

[1416] Input: Parsed interview job data.

[1417] Output: User's emotion evaluation result.

[1418] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "excited" or "cautious" based on the user's interview job selection.

[1419] Step 5:

[1420] The server passes the data to a generation AI module, which automatically generates interview questions.

[1421] Input: Interview job data, emotion evaluation results.

[1422] Output: Generated interview questions.

[1423] How it works: The server passes the following prompt to the generative AI model:

[1424] text

[1425] Desired job: AI engineer

[1426] Create practice interview questions.

[1427] The generative AI model generates questions such as, "What was the biggest challenge in your previous projects?"

[1428] Step 6:

[1429] The server converts the generated question into JSON format and sends it to the terminal.

[1430] Input: Generated interview questions.

[1431] Output: Interview questions converted to JSON format.

[1432] Specific operation: The server converts the generated interview questions into JSON data as follows and sends it to the terminal.

[1433] json

[1434] {

[1435] "questions": ["What has been the biggest challenge in your previous projects?"]

[1436] }

[1437] Step 7:

[1438] The terminal displays the question to the user.

[1439] Input: Question data in JSON format sent from the server.

[1440] Output: The parsed question is displayed on the screen.

[1441] Specific operation: The device parses the received JSON data and displays the question to the user.

[1442] Step 8:

[1443] The user answers the questions through the terminal and sends them to the server.

[1444] Input: The user's answer.

[1445] Output: Response data converted to JSON format.

[1446] Specific operation: The user answers, "The biggest challenge in my projects so far has been introducing new algorithms to improve the performance of the AI ​​model," and the device converts this into JSON format and sends it to the server.

[1447] json

[1448] {

[1449] "answer": "The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models."

[1450] }

[1451] Step 9:

[1452] The server analyzes the received responses and performs emotion evaluation.

[1453] Input: Received response data in JSON format.

[1454] Output: User's emotion evaluation result.

[1455] Specific operation: The server parses and analyzes the response data, and evaluates the sentiment based on the response using a sentiment analysis engine.

[1456] Step 10:

[1457] The server passes the data to the generation AI module, which generates feedback.

[1458] Input: User response data and sentiment evaluation results.

[1459] Output: The generated feedback.

[1460] How it works: The server passes the following prompt to the generative AI model:

[1461] text

[1462] User Answer: The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models.

[1463] Please provide feedback on this answer.

[1464] The generative AI model generates feedback such as, "This answer is not specific enough, so it would be better to include more specific examples."

[1465] Step 11:

[1466] The server converts the feedback into JSON format and sends it to the device.

[1467] Input: Generated feedback.

[1468] Output: Feedback converted to JSON format.

[1469] Specific operation: The server converts the generated feedback into JSON data as follows and sends it to the device.

[1470] json

[1471] {

[1472] "feedback": "This answer is not specific enough. Please provide some examples."

[1473] }

[1474] Step 12:

[1475] The device displays the feedback to the user.

[1476] Input: Feedback data sent from the server in JSON format.

[1477] Output: Parsed feedback is displayed on the screen.

[1478] Specific behavior: The device parses the received JSON data and displays feedback to the user, who can review the feedback and learn from it to improve.

[1479] (Application example 2)

[1480] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1481] The main function of conventional job change support systems was to consider a job seeker's skills, experience, and interests and list the most suitable jobs and companies. However, this alone was not enough to provide job seekers with the best job opportunities. In addition, marketing and advertising for job seekers only provided general information and was not customized based on individual profiles. This made it difficult to fully meet the needs of job seekers.

[1482] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1483] In this invention, the server includes means for receiving skills, experience, and interests entered by a job seeker, means for analyzing the job seeker's data using a generation AI and listing optimal jobs and companies, means for presenting the listed jobs and companies to the job seeker, means for generating a customized advertisement based on the job seeker's profile information and emotion assessment results, and means for displaying the generated advertisement to the job seeker. This makes it possible to provide not only a list of optimal jobs and companies based on the job seeker's individual profile, but also customized advertisements tailored to the job seeker's interests and emotions.

[1484] A "job seeker" is an individual who wishes to change jobs in search of a new occupation or work environment.

[1485] "Skills" refers to specialized abilities and techniques related to a profession or job.

[1486] "Experience" refers to knowledge and abilities acquired through previous work or duties.

[1487] "Interest" refers to a special interest or curiosity in a particular field or activity.

[1488] "Generative AI" refers to artificial intelligence technology that automatically generates new information and content based on input data.

[1489] "Means for analyzing data" refers to techniques and methods for analyzing collected data and extracting meaningful information.

[1490] "Job type" refers to a type of job or occupation with specific job duties or work content.

[1491] "Enterprise" refers to a legal entity or company that carries on a particular business activity.

[1492] "Means of listing" refers to techniques or methods for listing items or data based on specific conditions or criteria.

[1493] "Profile Information" refers to detailed information about an individual, including their skills, experience, interests, personality traits, etc.

[1494] "Emotional assessment" refers to measuring an individual's emotional state or psychological response and making an assessment based on that.

[1495] "Customized advertising" refers to advertising whose content is optimized to suit the characteristics and interests of a specific individual.

[1496] "Display means" refers to the techniques and methods for visually presenting the generated information or data to the user.

[1497] This invention is a system that combines a generative AI model and an emotion evaluation engine to provide customized job-hunting support to job seekers. The system receives skills, experience, and interests entered by job seekers and lists the most suitable jobs and companies based on them. Furthermore, the system uses generative AI to generate customized advertisements and display them to job seekers.

[1498] The server implements this system using a program that includes the following steps:

[1499] First, the user uses the input interface to input their skills, experience, and interests. For example, they enter specific information such as "Software development, 5 years, interested in AI technology." The device converts the input data into JSON format and sends it to the server.

[1500] The server parses the received JSON data and uses an emotion engine, such as Hume AI or Affectiva AI, to evaluate the job seeker's emotions based on the input data.

[1501] The server then passes the user's data and sentiment evaluation results to a generative AI module, which uses a generative AI model (such as OpenAI's GPT-4) to create a list of suitable jobs and companies, while simultaneously generating customized advertisements based on the job seeker's profile information and sentiment evaluation results.

[1502] The results are then converted back into JSON format and sent to the device, which then parses the data and displays a list of jobs, companies, and customized ads to the user, such as suggestions for "AI project manager" or "data scientist."

[1503] For example, you might use the following prompts for a generative AI model:

[1504] User skills: Software development, project management

[1505] Experience: 5 years

[1506] Interests: AI, data science

[1507] Emotional rating: Positive

[1508] Use the above information to generate ads that are appealing to users.

[1509] In this way, the server can analyze user profile information and emotion evaluation results with high accuracy, and provide job seekers with the most appropriate information. This system also enables the provision of individually customized advertisements to job seekers.

[1510] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1511] Step 1:

[1512] The user uses the input interface to input their skills, experience, and interests. For example, they can input specific information such as "Software development, 5 years, interested in AI technology." This results in input data. The output is the specific profile information entered by the user.

[1513] Step 2:

[1514] The device converts the input data into JSON format, which structures the data and makes it a format that can be communicated. The input is the profile information entered by the user, and the output is JSON format data.

[1515] Step 3:

[1516] The terminal sends data converted to JSON format to the server. HTTP or WebSocket is used as the communication protocol. The input is JSON format data, and the output is transmission completion to the server.

[1517] Step 4:

[1518] The server analyzes the JSON data received. Specifically, it receives the data using a Python framework (Flask or Django) and performs the analysis process. The input is JSON format data and the output is the analysis result.

[1519] Step 5:

[1520] The server uses an emotion engine to evaluate emotions based on the input data. Hume AI and Affectiva AI are used as emotion engines. The input is the analyzed data, and the output is the emotion evaluation result.

[1521] Step 6:

[1522] The server passes the user's data and sentiment evaluation results to the generation AI module, which then lists the most suitable jobs and companies. OpenAI's GPT-4 is used as the generation AI model. The prompt used is something like, "User skills: software development, project management Experience: 5 years Interests: AI, data science Sentiment evaluation: positive Please generate an advertisement that will appeal to the user based on the above information." The input is user data and sentiment evaluation results, and the output is the most suitable jobs and companies and a customized advertisement.

[1523] Step 7:

[1524] The generated results (best jobs, companies, customized ads) are then converted back into JSON format by the server, which makes it possible to send the results to the device. The input is the output data from the generative AI model, and the output is JSON format data.

[1525] Step 8:

[1526] The server converts the data into JSON format and sends it to the terminal. The input is JSON format data, and the output is sent to the terminal.

[1527] Step 9:

[1528] The device analyzes the received JSON data and displays a list of jobs, companies, and customized advertisements to the user. Specifically, it displays the job listings, companies, and customized advertisements in a user interface using HTML and JavaScript. The input is JSON-formatted data, and the output is a visual presentation to the user. For example, suggestions such as "AI project manager" or "data scientist" are displayed.

[1529] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1530] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1531] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1532] [Third embodiment]

[1533] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1534] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1535] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1536] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1537] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1538] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1539] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1540] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1541] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1542] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1543] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1544] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1545] This invention is a customized system that supports job changes by suggesting the most suitable jobs and companies based on the skills, experience, and interests of job seekers. The system is mainly composed of a device used by job seekers, a server that processes data, and multiple modules that use generation AI to automatically generate resumes, generate interview questions and feedback, and suggest job information.

[1546] Career Design Assistant

[1547] 1. The user enters their skills, experience, and interests through the terminal. For example, they enter information such as "Software development, 5 years of experience, interested in AI technology."

[1548] 2. The device sends the entered data to the server in JSON format.

[1549] 3. The server uses generative AI to analyze the user's data and create a list of suitable jobs and companies.

[1550] 4. The server sends the result to the terminal again in JSON format.

[1551] 5. The device analyzes the results received from the server and displays them to the user. For example, it displays a list of job titles and companies, such as "AI project manager" or "data scientist."

[1552] Resume creation support

[1553] 1. The user enters their past work experience into the terminal. For example, they enter "Server-side development, Java, Python, project management."

[1554] 2. The device sends the entered data to the server in JSON format.

[1555] 3. The server uses AI to generate compelling resume sentences. Based on the input data, the AI ​​automatically generates sentences such as, "I have five years of server-side development experience and excellent project management skills using Java and Python."

[1556] 4. The server sends the generated results to the terminal in JSON format.

[1557] 5. The device displays a preview of the received resume to the user.

[1558] Interview support

[1559] 1. The user selects the interview practice they wish to participate in on their device. For example, they can select "Interview practice for an AI engineer position."

[1560] 2. The device sends the request to the server in JSON format.

[1561] 3. The server uses generative AI to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[1562] 4. The server sends the generated question to the device in JSON format.

[1563] 5. The user answers the questions through the terminal and sends the answers to the server.

[1564] 6. The server analyzes the answer and generates feedback using generative AI, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[1565] 7. The server sends the generated feedback in JSON format to the device.

[1566] 8. The device displays the received feedback to the user.

[1567] Job Changer Network

[1568] 1. The server periodically collects introductions and interview articles of successful job-changers. It collects and organizes successful job-change cases from various data sources and stores them in a database.

[1569] 2. When a user opens the job seeker network page on their device, the device retrieves the relevant list from the server.

[1570] 3. The server sends a list of successful job changes in JSON format to the device.

[1571] 4. The device displays the received success stories and interview articles to the user. For example, a specific success story such as "A former engineer becomes a project manager and is successful in remote work" is presented.

[1572] In this way, the present invention is a system that uses various generative AIs to provide comprehensive support to job seekers. Each module provides information tailored to the needs of job seekers, allowing users to make appropriate career choices.

[1573] The processing flow will be explained below.

[1574] Career Design Assistant

[1575] Step 1:

[1576] The user uses the device to input their skills, experience, and interests. For example, the user inputs "Software development, 5 years, interested in AI technology."

[1577] Step 2:

[1578] The terminal converts the input data into JSON format and sends it to the server.

[1579] Step 3:

[1580] The server parses the received JSON data.

[1581] Step 4:

[1582] The server passes the user's data to the generation AI module, which analyzes it and creates a list of suitable jobs and companies.

[1583] Step 5:

[1584] The server converts the generated results into JSON format and sends them to the terminal.

[1585] Step 6:

[1586] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[1587] Resume creation support

[1588] Step 1:

[1589] A user uses a terminal to enter their work history, for example, "Server-side development, Java, Python, project management."

[1590] Step 2:

[1591] The terminal converts the input data into JSON format and sends it to the server.

[1592] Step 3:

[1593] The server parses the received JSON data.

[1594] Step 4:

[1595] The server passes the user's data to the generation AI module, which analyzes it and automatically generates an attractive resume.

[1596] Step 5:

[1597] The server converts the generated resume text into JSON format and sends it to the terminal.

[1598] Step 6:

[1599] The device analyzes the resume text received and displays it to the user. For example, a preview such as "Has five years of server-side development experience and has excellent project management skills using Java and Python" is displayed.

[1600] Interview support

[1601] Step 1:

[1602] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[1603] Step 2:

[1604] The device converts the request into JSON format and sends it to the server.

[1605] Step 3:

[1606] The server analyzes the received request.

[1607] Step 4:

[1608] The server passes the data to a generation AI module to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[1609] Step 5:

[1610] The server converts the generated question into JSON format and sends it to the terminal.

[1611] Step 6:

[1612] The terminal displays the received question to the user.

[1613] Step 7:

[1614] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[1615] Step 8:

[1616] The server analyzes the received response.

[1617] Step 9:

[1618] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more concrete examples."

[1619] Step 10:

[1620] The server converts the generated feedback into JSON format and sends it to the device.

[1621] Step 11:

[1622] The terminal displays the received feedback to the user.

[1623] Job Changer Network

[1624] Step 1:

[1625] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[1626] Step 2:

[1627] Based on the data accumulated by the server, a list of success stories is generated for job seekers.

[1628] Step 3:

[1629] The user opens a job seeker network page using a device.

[1630] Step 4:

[1631] The device sends a request to the server.

[1632] Step 5:

[1633] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[1634] Step 6:

[1635] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[1636] In this way, each function is realized through specific processing steps.

[1637] Example 1

[1638] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1639] Currently, job seekers must expend a great deal of time and effort to find the job and company that best suits them. They also have to create resumes and prepare for interviews on their own, making it difficult to conduct a job search efficiently.

[1640] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1641] In this invention, the server includes a means for analyzing data of job seekers and listing the most suitable jobs and companies, a means for generating attractive resumes, and a means for generating interview questions and generating feedback on the answers, thereby enabling job seekers to effectively find the most suitable jobs and companies and efficiently conduct their job search.

[1642] A "job seeker" is an individual who wants to leave their current occupation and take up a new one.

[1643] "Skills" refers to the specialized techniques and knowledge possessed by job seekers.

[1644] "Experience" refers to the achievements and experiences that a job seeker has gained in the job or work they have previously performed.

[1645] "Interest" refers to the job seeker's interest in the job or industry.

[1646] "Data terminal" refers to the electronic device used by job seekers to enter information.

[1647] "JSON format" refers to a JavaScript Object Notation data structure used for exchanging and storing data.

[1648] "Server" refers to a central processing unit that analyzes data, generates resumes, creates interview questions, etc.

[1649] "Generative AI" refers to an artificial intelligence model that performs natural language processing based on large amounts of data.

[1650] "Job type" refers to the type of work within a particular industry or field.

[1651] "Company" refers to the corporate organization where the job seeker seeks employment.

[1652] A resume is a document submitted by a job seeker listing their skills and experience.

[1653] "Interview questions" refer to questions that job seekers prepare in anticipation of being asked in an interview with a company.

[1654] "Feedback" refers to evaluation of the job seeker's answers and instructions, including areas for improvement.

[1655] This invention is a system that supports job-hunting activities by suggesting the most suitable jobs and companies for job seekers based on their skills, experience, and interests. The system provides various support functions using data terminals used by job seekers, a server that processes data, and a generative AI model.

[1656] The system's hardware configuration includes a data terminal (e.g., PC, smartphone, tablet, etc.) for job seekers to input data, and a server for analyzing the data and providing information. The software uses a generative AI model, such as OpenAI GPT-4.

[1657] The system provides the following main functions:

[1658] 1. Career design assistant function

[1659] Users input their skills, experience, and interests through a terminal. For example, they input information such as "Software development, 5 years of experience, interested in AI technology."

[1660] The terminal converts the input data into JSON format and sends it to the server.

[1661] The server uses a generative AI to analyze the user's data and create a list of suitable jobs and companies. For example, the generative AI uses the following prompt:

[1662] Generate the following prompt based on the user's skills, experience, and interests: 'Software development, 5 years of experience, interested in AI technology'

[1663] The server sends the listed results back to the terminal in JSON format.

[1664] The device analyzes the results received from the server and displays them to the user. For example, it displays a list of job titles and companies, such as "AI project manager" and "data scientist."

[1665] 2. Resume creation support function

[1666] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[1667] The terminal converts the input data into JSON format and sends it to the server.

[1668] The server uses generative AI to generate compelling resume prompts based on the input data. Examples of prompts used include:

[1669] Generate compelling resume copy based on your work experience: 'Server-side development, Java, Python, Project management'

[1670] The server sends the generated resume text in JSON format to the terminal.

[1671] The terminal displays a preview of the received resume to the user.

[1672] 3. Interview support function

[1673] The user selects the interview practice they wish to have on their device, such as "Interview practice for an AI engineer position."

[1674] The device sends the request to the server in JSON format.

[1675] The server generates interview questions using a generation AI. Examples of prompts include:

[1676] Generate AI engineer job questions for interview practice

[1677] The server sends the generated question in JSON format to the device.

[1678] The user answers the questions through the terminal and sends the answers to the server.

[1679] The server analyzes the answers and generates feedback using generative AI. Example prompts for generating feedback:

[1680] Generate feedback based on the user's answer. Example: The answer is vague, so it would be helpful to include more examples.

[1681] The server sends the generated feedback in JSON format to the device.

[1682] The terminal displays the received feedback to the user.

[1683] Each of these functions is designed to comprehensively support users' job-hunting activities and enable them to make efficient and effective career choices. The purpose of this invention is to provide customized support tailored to the individual circumstances of job seekers and improve the quality of the entire job-hunting process.

[1684] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1685] Career Design Assistant

[1686] Step 1: Data entry

[1687] Users input their skills, experience, and interests through a terminal. For example, they input information such as "Software development, 5 years of experience, interested in AI technology."

[1688] Input: User's skills, experience, and interests (in text format)

[1689] Output: User input data to the terminal (text format)

[1690] Step 2: Send data

[1691] The terminal converts the input data into JSON format and sends it to the server.

[1692] Specifically, the device extracts the information entered by the user and generates JSON data like this:

[1693] json

[1694] {

[1695] "skill": "software development",

[1696] "experience": "5 years",

[1697] "interest": "AI technology"

[1698] }

[1699] Input: User's skills, experience, and interests (in text format)

[1700] Output: JSON format data to the server

[1701] Step 3: Propose a job

[1702] The server uses generative AI to analyze the data it receives and create a list of the most suitable jobs and companies.

[1703] Input: JSON format data sent from the terminal

[1704] Specific operation: The server uses a generative AI (e.g., OpenAI GPT-4) and inputs the following prompt into the generative AI:

[1705] Generate the following prompt based on the user's skills, experience, and interests: 'Software development, 5 years of experience, interested in AI technology'

[1706] Data processing: Generative AI analyzes and creates a list of suitable jobs and companies

[1707] Output: Listing results in JSON format

[1708] Step 4: Send results

[1709] The server generates the results and sends them to the terminal in JSON format. The data from the server is sent in the following format:

[1710] json

[1711] {

[1712] "recommendations": [

[1713] {"position": "AI Project Manager", "company": "Technology Company"},

[1714] {"position": "Data Scientist", "company": "Data Solutions Inc."}

[1715] ]

[1716] }

[1717] Input: Generated list result (JSON format)

[1718] Output: JSON format data to the terminal

[1719] Step 5: View the results

[1720] The terminal analyzes the received results and displays them to the user.

[1721] Example: The terminal parses the received JSON data and displays it in the GUI:

[1722] AI Project Manager - Technology Company

[1723] Data Scientist - Data Solutions Inc.

[1724] Input: Result sent from the server (JSON format)

[1725] Output: What is displayed to the user (GUI format)

[1726] Resume creation support

[1727] Step 1: Enter your work history

[1728] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[1729] Input: User's work history (text format)

[1730] Output: User input data to the terminal (text format)

[1731] Step 2: Send data

[1732] The terminal converts the input data into JSON format and sends it to the server.

[1733] Specifically, the data is prepared as follows:

[1734] json

[1735] {

[1736] "employment_history": [

[1737] {"role": "Server-side development", "skills": ["Java", "Python", "Project management"]}

[1738] ]

[1739] }

[1740] Input: User's work history (text format)

[1741] Output: JSON format data to the server

[1742] Step 3: Generate resume text

[1743] The server uses generation AI to generate resume text.

[1744] Specific operation: The server inputs the following prompt to the generation AI:

[1745] Generate compelling resume copy based on your work experience: 'Server-side development, Java, Python, Project management'

[1746] Input: JSON format data sent from the terminal

[1747] Data processing: Generative AI creates text

[1748] Output: Generated resume text (JSON format)

[1749] Step 4: Sending the generated results

[1750] The server sends the generated resume text in JSON format to the terminal.

[1751] Input: Generated resume text (JSON format)

[1752] Output: JSON format data to the terminal

[1753] Step 5: Preview your resume

[1754] The terminal displays the contents of the received resume to the user.

[1755] Example: The device parses the received JSON data and displays it to the user as follows:

[1756] He has 5 years of server-side development experience and is skilled in project management using Java and Python.

[1757] Input: Result sent from the server (JSON format)

[1758] Output: What is displayed to the user (GUI format)

[1759] Interview support

[1760] Step 1: Enter your interview practice preferences

[1761] The user selects the interview practice they wish to have on their device, such as "Interview practice for an AI engineer position."

[1762] Input: Interview practice request (text format)

[1763] Output: User input data to the terminal (text format)

[1764] Step 2: Submitting a request

[1765] The device sends the request to the server in JSON format.

[1766] Specifically, the data is prepared as follows:

[1767] json

[1768] {

[1769] "interview_practice": "AI engineer position"

[1770] }

[1771] Input: Interview practice request (text format)

[1772] Output: JSON format data to the server

[1773] Step 3: Generate interview questions

[1774] The server generates interview questions using a generation AI.

[1775] Specific operation: The server inputs the following prompt to the generation AI:

[1776] Generate AI engineer job questions for interview practice

[1777] Input: JSON format data sent from the terminal

[1778] Data manipulation: Generative AI creates interview questions

[1779] Output: Generated questions (JSON format)

[1780] Step 4: Submit your question

[1781] The server sends the generated question in JSON format to the device.

[1782] Input: Generated question (JSON format)

[1783] Output: JSON format data to the terminal

[1784] Step 5: Answer the questions

[1785] The user answers the questions through the terminal and sends the answers to the server.

[1786] Input: User's answers to interview questions (in text format)

[1787] Output: JSON format data to the server

[1788] Step 6: Feedback generation

[1789] The server analyzes the answers and generates feedback using generative AI.

[1790] Specific action: The server inputs the following prompt to the generation AI:

[1791] Generate feedback based on the user's answer. Example: The answer is vague, so it would be helpful to include more examples.

[1792] Input: User's answer (in text format)

[1793] Data manipulation: Generative AI creates feedback

[1794] Output: Generated feedback (JSON format)

[1795] Step 7: Submit your feedback

[1796] The server sends the generated feedback in JSON format to the device.

[1797] Input: Generated feedback (JSON format)

[1798] Output: JSON format data to the terminal

[1799] Step 8: Feedback display

[1800] The terminal displays the received feedback to the user.

[1801] Example: The device parses the received JSON data and displays it to the user as follows:

[1802] This answer is a bit vague, so it would be helpful to include some more examples.

[1803] Input: Result sent from the server (JSON format)

[1804] Output: What is displayed to the user (GUI format)

[1805] Job Changer Network

[1806] Step 1: Collecting success stories

[1807] The server regularly collects profiles and interviews of successful job seekers.

[1808] What it does: Collects data using web scraping or APIs and stores it in a database.

[1809] Input: Successful career change cases from various data sources (text format)

[1810] Output: Accumulation in the database on the server

[1811] Step 2: Page display request

[1812] When a user opens a job-changer network page on their device, the device retrieves the relevant list from the server.

[1813] Input: User request (text format)

[1814] Output: Request to server (text format)

[1815] Step 3: Submit your success story list

[1816] The server sends a list of successful job change cases in JSON format to the terminal.

[1817] Input: List of success stories in the database

[1818] Output: JSON format data to the terminal

[1819] Step 4: View success stories

[1820] The terminal displays the received success stories to the user.

[1821] Specific example: Specific success stories such as "A former engineer transitioned to project manager and is now thriving in remote work" are displayed.

[1822] Input: Result sent from the server (JSON format)

[1823] Output: What is displayed to the user (GUI format)

[1824] The above is the specific processing flow of this system.

[1825] (Application example 1)

[1826] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1827] This invention relates to a system for providing advanced job change support and customer service in brick-and-mortar stores. Conventional job change support systems can suggest optimal job types and companies for individual job seekers, but they have the problem that the subsequent interview preparation, resume preparation support, and customer service in brick-and-mortar stores take time and effort. In particular, it has been difficult to grasp the interests and preferences of customers in brick-and-mortar stores in real time and quickly make suggestions based on them.

[1828] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1829] In this invention, the server includes a means for receiving skills, experience, and interests entered by job seekers, a means for analyzing data on job seekers using a generation AI and listing optimal jobs and companies, a means for presenting the listed jobs and companies to job seekers, and a means related to a device for store staff to use the generation AI to analyze interests and preferences from customer comments and actions and propose optimal products and services. This not only enables comprehensive job change support for job seekers, but also makes it possible to grasp customer needs in real time and make appropriate proposals in physical stores.

[1830] A "job seeker" is an individual who wishes to change jobs from their current position to another position.

[1831] "Skills" refer to the techniques and abilities required for a particular job or task.

[1832] "Experience" refers to the track record and history of past work and tasks.

[1833] "Interest" refers to a concern or desire for a particular field or activity.

[1834] "Generative AI" refers to a system that uses artificial intelligence technology to generate and analyze data.

[1835] "Listing" refers to the act of compiling multiple candidates into a list.

[1836] "Job type" refers to specific job content or work category.

[1837] An "enterprise" is an organization that provides goods and services.

[1838] An "apparatus" is a machine or device configured to perform a particular function.

[1839] "Customer" means an individual or entity that purchases or uses goods or services.

[1840] "Statement" refers to verbal expressions of intent or opinions.

[1841] "Behavior" refers to human movements or activities that are carried out with a specific purpose.

[1842] "Interests and preferences" refer to the areas or products in which a customer has particular preferences or interests.

[1843] A "suggestion" is the act of presenting a particular idea or option.

[1844] "Store staff" refers to employees who deal with customers and provide services in physical stores.

[1845] "Real-time" means that events or actions occur almost immediately, corresponding to actual time.

[1846] The present invention is a system that proposes the most suitable job types and companies based on data on job seekers, and supports resume creation, interview practice, and customer service in brick-and-mortar stores. Specific embodiments of the system are described below.

[1847] 1. Implementation of the job change support system

[1848] The system of this invention is composed of a terminal used by job seekers, a server that processes data, and a generation AI. When a user enters their skills, experience, and interests through the terminal, the terminal sends the entered data in JSON format to the server. The server uses the generation AI to analyze the user data, lists the most suitable jobs and companies, and sends the results back to the terminal in JSON format. The user can then check the listed jobs and companies on the terminal.

[1849] 2. Resume creation support

[1850] When a user enters their work history into the device, the device sends the data in JSON format to the server. The server uses a generative AI to generate an attractive resume and sends the generated results in JSON format to the device. The device then displays a preview of the received resume to the user.

[1851] 3. Interview practice support

[1852] When a user selects their preference for interview practice on their device, the device sends a request to the server in JSON format. The server uses a generation AI to generate interview questions and sends the generated questions to the device in JSON format. The user answers the questions through their device and sends the answers to the server. The server analyzes the answers, generates feedback using the generation AI, and sends the generated feedback to the device in JSON format. The user then checks the received feedback on their device.

[1853] 4. Physical store applications

[1854] Store staff wear smart glasses to gather information about customers' interests and preferences from their comments and behavior. The smart glasses capture what customers say and send the data to a server in real time. The server then uses generative AI to analyze the customer data and generate information to recommend optimal products and services. The generated recommendation information is then sent back to the smart glasses, where staff can view the suggestions on the glasses' display and make them to the customer.

[1855] Hardware and software used

[1856] Device: The device on which the user enters information (smartphone, tablet, PC, etc.)

[1857] Server: Receives and transmits data, and executes AI generation (e.g., cloud server)

[1858] Generative AI: Data analysis, resume generation, interview question generation, feedback generation (e.g., OpenAI's GPT-3)

[1859] Smart glasses: Used for customer service in physical stores (smart glasses from specific manufacturers)

[1860] Examples and prompts

[1861] As a specific example, if a store staff member wearing smart glasses captures a customer saying, "I've been interested in the outdoors lately," the program will respond as follows:

[1862] Example prompt sentence:

[1863] text

[1864] A customer might say: 'I've been interested in the outdoors lately'. Suggest the best products based on that.

[1865] The generated suggestions are displayed on the smart glasses in the form of, for example, "If you're interested in the outdoors, we recommend the latest compact tent and waterproof jacket. In particular, outdoor gear on sale this week is very popular."

[1866] In this way, a system can be realized that can provide advanced information and support in real time to a variety of users, including not only job seekers but also store staff.

[1867] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1868] Step 1:

[1869] The device receives input data from the user, such as skills, experience, and interests. The user enters specific information, such as "software development, 5 years of experience, interested in AI technology." The device converts this data into JSON format and sends it to the server.

[1870] Step 2:

[1871] The server receives the JSON data sent from the device and analyzes the data using a generative AI model (e.g., GPT-3). The generative AI model analyzes the user data and lists the most suitable jobs and companies. The analysis results are structured in JSON format and sent back from the server to the device.

[1872] Step 3:

[1873] The device analyzes the JSON data of the analysis results received from the server and displays a list of jobs and companies that are most suitable for the user. For example, it may list jobs such as "data scientist" or "AI project manager."

[1874] Step 4:

[1875] The user enters their work experience, such as "server-side development, Java, Python, project management," into the terminal. The terminal then sends this data in JSON format to the server.

[1876] Step 5:

[1877] The server receives the JSON data sent from the device and generates resume text using a generative AI model. Based on the input data, the generative AI model automatically generates an appealing resume text such as "Has five years of server-side development experience and has excellent project management skills using Java and Python," and sends it in JSON format to the device.

[1878] Step 6:

[1879] The device parses the JSON data of the resume text received from the server and displays a preview to the user. The user can then check the generated resume and make any necessary corrections.

[1880] Step 7:

[1881] The user selects the interview practice they wish to do on their device. For example, they select "Interview practice for an AI engineer position." The device then sends the selected information to the server in JSON format.

[1882] Step 8:

[1883] The server receives the JSON data sent from the device and generates interview questions using a generative AI model, such as "What was the biggest challenge in your previous projects?", and sends them to the device in JSON format.

[1884] Step 9:

[1885] The device parses the JSON data of the interview questions received from the server and displays the questions to the user. The user answers the questions and sends the answers back to the server via the device.

[1886] Step 10:

[1887] The server receives the user's response data and generates feedback using a generative AI model. The generative AI model analyzes the response data and generates feedback such as "This response is not specific enough, so it would be better to include more specific examples," and sends it to the device in JSON format.

[1888] Step 11:

[1889] The device parses the JSON data of the feedback received from the server and displays the feedback to the user, who can then improve their answer based on the feedback.

[1890] Step 12:

[1891] In a physical store, store staff wearing smart glasses capture customer speech. For example, if a customer says, "I've recently become interested in outdoor activities," the smart glasses receive the speech data and send it to a server in real time.

[1892] Step 13:

[1893] The server receives the voice data sent from the smart glasses, analyzes the customer's interests and preferences using a generative AI model, and generates information to recommend the most suitable products and services. For example, it generates information such as "If you're interested in outdoor activities, we recommend the latest compact tent and waterproof jacket," and sends it back to the smart glasses in JSON format.

[1894] Step 14:

[1895] The smart glasses display the received recommendation information and store staff make suggestions to customers, enabling them to provide optimal products and services to customers in real time.

[1896] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1897] This invention uses a system that combines generative AI and an emotion engine to provide customized career change support to job seekers. The system analyzes the skills, experience, and interests entered by job seekers, lists the most suitable jobs and companies, and also creates resumes, provides interview practice, and generates feedback.

[1898] Career Design Assistant

[1899] Users input their skills, experience, and interests through the terminal. For example, they could input "Software development, 5 years, interested in AI technology."

[1900] The terminal converts the input data into JSON format and sends it to the server.

[1901] The server parses the received JSON data and uses an emotion engine to recognize and evaluate the user's emotion based on the input data.

[1902] The server passes the user's data and emotional evaluation results to the generation AI module, which then uses this information to create a list of suitable jobs and companies.

[1903] The server converts the generated results into JSON format and sends them to the terminal.

[1904] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[1905] Resume creation support

[1906] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[1907] The terminal converts the input data into JSON format and sends it to the server.

[1908] The server parses the received JSON data and uses an emotion engine to evaluate the input data and the user's reaction in real time.

[1909] The server passes the user's data and sentiment assessment to the generation AI module, which then uses this data to automatically generate compelling resume sentences, such as "I have five years of server-side development experience and excellent project management skills using Java and Python."

[1910] The server converts the generated resume text into JSON format and sends it to the terminal.

[1911] The terminal analyzes the received resume text and displays it to the user, who can then check the generated resume text.

[1912] Interview support

[1913] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[1914] The device converts the request into JSON format and sends it to the server.

[1915] The server analyzes the received request.

[1916] The server uses an emotion engine to evaluate emotions based on the user's selection and passes the data to a generative AI module, which then automatically generates questions such as, "What was the biggest challenge in your previous project?"

[1917] The server converts the generated question into JSON format and sends it to the terminal.

[1918] The terminal displays the received question to the user.

[1919] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[1920] The server analyzes the received answers, and the emotion engine evaluates the emotion based on the user's answer data.

[1921] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[1922] The server converts the generated feedback into JSON format and sends it to the device.

[1923] The device will display the received feedback to the user, who can review it and learn from it to improve.

[1924] Job Changer Network

[1925] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[1926] Based on the accumulated data, the server generates a list of success stories for job seekers.

[1927] The user opens a job seeker network page using a device.

[1928] The device sends a request to the server.

[1929] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[1930] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[1931] In this way, each function is realized through a specific processing flow, and by making full use of generative AI and an emotion engine, comprehensive support is provided to job seekers.

[1932] The processing flow will be explained below.

[1933] Career Design Assistant

[1934] Step 1:

[1935] The user uses the device to enter their skills, experience, and interests. For example, they might enter "Software development, 5 years, interested in AI technology."

[1936] Step 2:

[1937] The terminal converts the input data into JSON format and sends it to the server.

[1938] Step 3:

[1939] The server parses the received JSON data.

[1940] Step 4:

[1941] The server uses an emotion engine to recognize and evaluate the user's emotions based on the input data, such as interest and confidence, based on facial expressions and input content.

[1942] Step 5:

[1943] The server passes the user's data and emotional evaluation results to the generation AI module, which uses this data to create a list of suitable jobs and companies.

[1944] Step 6:

[1945] The server converts the generated results into JSON format and sends them to the terminal.

[1946] Step 7:

[1947] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[1948] Resume creation support

[1949] Step 1:

[1950] A user uses a terminal to enter their work history, for example, "Server-side development, Java, Python, project management."

[1951] Step 2:

[1952] The terminal converts the input data into JSON format and sends it to the server.

[1953] Step 3:

[1954] The server parses the received JSON data.

[1955] Step 4:

[1956] The server uses an emotion engine to evaluate the input data and the user's reaction in real time, for example, by analyzing the user's facial expressions and typing speed to assess their level of nervousness or confidence.

[1957] Step 5:

[1958] The server passes the user's data and the sentiment assessment results to the generation AI module, which then uses this data to automatically generate compelling resume sentences. For example, it might generate a sentence like, "I have five years of server-side development experience and excellent project management skills using Java and Python."

[1959] Step 6:

[1960] The server converts the generated resume text into JSON format and sends it to the terminal.

[1961] Step 7:

[1962] The terminal analyzes the received resume text and displays it to the user, who can then check the generated resume text.

[1963] Interview support

[1964] Step 1:

[1965] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[1966] Step 2:

[1967] The device converts the request into JSON format and sends it to the server.

[1968] Step 3:

[1969] The server analyzes the received request.

[1970] Step 4:

[1971] The server uses an emotion engine to assess emotions based on the user's selection, for example by analyzing facial expressions and tone of voice at the start of a practice interview.

[1972] Step 5:

[1973] The server passes the data to a generation AI module to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[1974] Step 6:

[1975] The server converts the generated question into JSON format and sends it to the terminal.

[1976] Step 7:

[1977] The terminal displays the received question to the user.

[1978] Step 8:

[1979] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[1980] Step 9:

[1981] The server analyzes the received responses, and the emotion engine evaluates the emotion based on the user's response data, for example, by analyzing the user's facial expression and tone along with the content of the response.

[1982] Step 10:

[1983] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[1984] Step 11:

[1985] The server converts the generated feedback into JSON format and sends it to the device.

[1986] Step 12:

[1987] The device will display the received feedback to the user, who can review it and learn from it to improve.

[1988] Job Changer Network

[1989] Step 1:

[1990] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[1991] Step 2:

[1992] Based on the accumulated data, the server generates a list of success stories for job seekers.

[1993] Step 3:

[1994] The user opens a job seeker network page using a device.

[1995] Step 4:

[1996] The device sends a request to the server.

[1997] Step 5:

[1998] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[1999] Step 6:

[2000] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[2001] In this way, each function is realized through specific processing steps, and by making full use of generative AI and an emotion engine, comprehensive support is provided to job seekers.

[2002] Example 2

[2003] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2004] Conventional career change support systems often list job types and companies based solely on the job seeker's skills and experience, making it difficult to provide customized suggestions that take into account the job seeker's feelings and interests. It also makes it difficult to provide optimized feedback for each individual job seeker when helping them create resumes or practice for interviews.

[2005] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2006] In this invention, the server includes means for receiving skills, experience, and interests input by job seekers, means for converting the input data into a structured data format and transmitting it to the server, and means for analyzing the job seeker's data using a generative AI and a sentiment analysis engine to list the most suitable jobs and companies. This makes it possible to utilize the generative AI and the sentiment analysis engine to propose industries and companies customized for the job seeker.

[2007] A "job seeker" is someone who wishes to change jobs and receives proposals for different jobs and companies.

[2008] "Skills" refers to the occupational or work-related skills and abilities possessed by job seekers.

[2009] "Experience" refers to the job seeker's past occupations and work history, as well as the knowledge and skills they have acquired through them.

[2010] "Interest" refers to the interest or preference that a job seeker has in a particular job type, job role, or field.

[2011] A "structured data format" refers to organizing data based on specific rules and putting it into a format that can be easily analyzed by a computer.

[2012] "Server" refers to a computer system that analyzes data received from job seekers and processes it using generative AI and an emotion analysis engine.

[2013] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze input data and generate specific outputs (e.g., job suggestions or resume generation).

[2014] "Sentiment analysis engine" refers to a software system for assessing emotions based on user input data and responses.

[2015] "Shortlisting jobs and companies" refers to creating a list of jobs and companies that best fit the job seeker's skills, experience, and interests.

[2016] A "resume" is a document that describes a job seeker's past work experience and skills and is used to highlight the job seeker's abilities.

[2017] "Feedback" refers to response information including evaluation and advice regarding the job seeker's input and response.

[2018] This invention is a system that provides customized career change support to job seekers, and is realized by combining generative AI and a sentiment analysis engine. The system analyzes the skills, experience, and interests entered by job seekers via their devices and can list the most suitable jobs and companies. It also provides support for creating resumes and practicing for interviews, providing comprehensive support to job seekers.

[2019] First, the user uses the device to input their skills, experience, and interests. For example, they might input "Software development, 5 years, interested in AI technology." This input data is converted into a structured data format (e.g., JSON format) by the device and sent to the server. The server analyzes the received data and evaluates the user's emotions using a sentiment analysis engine (e.g., general sentiment analysis software).

[2020] The server then passes the user's data and emotional evaluation to a generative AI model (e.g., a general generative AI model) to generate a list of suitable jobs and companies. This list reflects the user's input data and emotional evaluation results. For example, the following prompt sentence can be input to the generative AI model:

[2021] text

[2022] User Skills: Software Development, Experience: 5 years, Interests: AI Technology

[2023] Make a list of the jobs and companies that are best for you.

[2024] The generative AI model generates a list of results and returns it to the server. The server then converts it back into structured data format and sends it to the device. The device then analyzes the received data and displays suggestions to the user, such as "AI project manager" or "data scientist."

[2025] Next, we will explain how a user creates a resume. The user enters their past work experience through the terminal. For example, they enter data such as "server-side development, Java, Python, project management." This data is also converted into a structured data format by the terminal and sent to the server. The server analyzes the received data and evaluates the user's emotions using a sentiment analysis engine.

[2026] Next, the server asks the generative AI model to create a resume based on the evaluation results and data. For example, the following prompt sentence is input to the generative AI model:

[2027] text

[2028] Previous work experience: Server-side development, Languages ​​used: Java, Python, Role: Project management

[2029] Create an attractive resume.

[2030] The generative AI model generates compelling resume text and returns it to the server, which converts it into a structured data format and sends it to the device, which analyzes the received data and displays the generated resume text to the user.

[2031] Finally, we will explain interview practice support. The user uses the device to select the interview practice they wish to use. For example, they select "interview practice for an AI engineer position." This request is converted into a structured data format by the device and sent to the server. The server analyzes the request and evaluates the user's emotions using an emotion analysis engine.

[2032] The server then asks the generative AI model to create interview questions based on the evaluation results and the request. For example, the following prompt sentences are input to the generative AI model:

[2033] text

[2034] Desired job: AI engineer

[2035] Create practice interview questions.

[2036] The generative AI model generates interview questions and returns them to the server, which converts them into a structured data format and sends them to the device, which then displays the received questions to the user.

[2037] The user answers questions through their device, converts the answers into structured data format, and sends them to the server. The server analyzes the answers and evaluates their emotions using a sentiment analysis engine. The server then requests the generative AI model to create feedback based on the evaluation results and data. For example, the following prompt sentence could be input to the generative AI model:

[2038] text

[2039] User Answer: The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models.

[2040] Please provide feedback on this answer.

[2041] The generative AI model generates feedback and returns it to the server, which converts it into a structured data format and sends it to the device, where it displays the received feedback to the user, who can review it and learn from it.

[2042] In this way, the system utilizes generative AI and a sentiment analysis engine to provide customized assistance to job seekers.

[2043] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2044] Career Design Assistant

[2045] Step 1:

[2046] Users enter their skills, experience, and interests into a terminal.

[2047] Input: Users enter their skills, experience, and interests into an input form.

[2048] Output: The input data is stored in the device's memory.

[2049] Specific operation: The user enters "Software development, 5 years, interested in AI technology" into the input form on the device.

[2050] Step 2:

[2051] The terminal converts the input data into JSON format and sends it to the server.

[2052] Input: Skills, experience, and interest data entered.

[2053] Output: The data converted to JSON format is sent to the server.

[2054] Specific operation: The device generates the following JSON data and sends it to the server.

[2055] json

[2056] {

[2057] "skill": "software development",

[2058] "experience": "5 years",

[2059] "interest": "AI technology"

[2060] }

[2061] Step 3:

[2062] The server analyzes the received data.

[2063] Input: JSON formatted data received by the server.

[2064] Output: Parsed skills, experiences, and interests are extracted.

[2065] What happens: The server parses the JSON data and extracts the individual fields (skill, experience, interest).

[2066] Step 4:

[2067] The server performs emotion assessment using an emotion analysis engine.

[2068] Input: Parsed data (skills, experience, interests).

[2069] Output: User's emotion evaluation result.

[2070] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "excited" or "cautious" based on the user's input.

[2071] Step 5:

[2072] The server passes the data to a generative AI module, which then lists the most suitable jobs and companies.

[2073] Inputs: Skills, experience, interests, plus emotional assessment results.

[2074] Output: A list of suitable jobs and companies.

[2075] How it works: The server passes the following prompt to the generative AI model:

[2076] text

[2077] User Skills: Software Development, Experience: 5 years, Interests: AI Technology

[2078] Make a list of the jobs and companies that are best for you.

[2079] The generative AI model will list job titles such as "AI project manager" and "data scientist."

[2080] Step 6:

[2081] The server converts the list results into JSON format and sends it to the terminal.

[2082] Input: Listed job and company data.

[2083] Output: A list converted to JSON format.

[2084] Specific operation: The server generates the following JSON data and sends it to the terminal.

[2085] json

[2086] {

[2087] "suggestions": [

[2088] "AI Project Manager",

[2089] "Data Scientist"

[2090] ]

[2091] }

[2092] Step 7:

[2093] The device parses the JSON data and displays it to the user.

[2094] Input: List data in JSON format sent from the server.

[2095] Output: The list of analyzed jobs and companies will be displayed on the screen.

[2096] Specific operation: The device parses the received JSON data and displays it to the user as an "AI project manager" and "data scientist."

[2097] Resume creation support

[2098] Step 1:

[2099] The user inputs his / her past work history into the terminal.

[2100] Input: The user enters their work history into an input form.

[2101] Output: The input data is stored in the device's memory.

[2102] Specific operation: The user enters "Server-side development, Java, Python, project management" into the input form on the terminal.

[2103] Step 2:

[2104] The terminal converts the input data into JSON format and sends it to the server.

[2105] Input: Work history data entered.

[2106] Output: The data converted to JSON format is sent to the server.

[2107] Specific operation: The device generates the following JSON data and sends it to the server.

[2108] json

[2109] {

[2110] "experience": "server-side development",

[2111] "skills": ["Java", "Python"],

[2112] "roles": ["Project Management"]

[2113] }

[2114] Step 3:

[2115] The server analyzes the received data.

[2116] Input: JSON formatted data received by the server.

[2117] Output: The analyzed work history, skills, and roles are extracted.

[2118] What happens: The server parses the JSON data and extracts the individual fields (experience, skills, roles).

[2119] Step 4:

[2120] The server performs emotion assessment using an emotion analysis engine.

[2121] Input: Parsed data (work history, skills, roles).

[2122] Output: User's emotion evaluation result.

[2123] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "confident" or "anxious" based on the user's input.

[2124] Step 5:

[2125] The server passes the data to a generation AI module, which automatically generates the resume text.

[2126] Input: Work history, skills, role, and emotional assessment results.

[2127] Output: The generated resume text.

[2128] How it works: The server passes the following prompt to the generative AI model:

[2129] text

[2130] Previous work experience: Server-side development, Languages ​​used: Java, Python, Role: Project management

[2131] Create an attractive resume.

[2132] For example, the generative AI model generates a sentence such as, "He has five years of server-side development experience and has excellent project management skills using Java and Python."

[2133] Step 6:

[2134] The server converts the generated text into JSON format and sends it to the terminal.

[2135] Input: Generated resume text.

[2136] Output: The resume text converted to JSON format.

[2137] Specific operation: The server converts the generated resume text into JSON data as follows and sends it to the terminal.

[2138] json

[2139] {

[2140] "resume": "Five years of server-side development experience and excellent project management skills using Java and Python."

[2141] }

[2142] Step 7:

[2143] The device parses the JSON data and displays it to the user.

[2144] Input: JSON format resume text sent from the server.

[2145] Output: The parsed resume text is displayed on the screen.

[2146] Specific operation: The device parses the received JSON data and displays the generated resume text to the user. The user can review the displayed resume text and make any necessary corrections.

[2147] Interview support

[2148] Step 1:

[2149] The user uses the terminal to select the interview practice he / she wishes to take.

[2150] Input: The user selects the position they would like to interview for.

[2151] Output: The selected job type is stored in the device's memory.

[2152] Specific operation: The user selects "AI engineer job interview practice" from the device screen.

[2153] Step 2:

[2154] The device converts the request into JSON format and sends it to the server.

[2155] Input: Data for the selected interview position.

[2156] Output: The request data converted to JSON format is sent to the server.

[2157] Specific operation: The device generates the following JSON data and sends it to the server.

[2158] json

[2159] {

[2160] "interview_position": "AI Engineer"

[2161] }

[2162] Step 3:

[2163] The server parses the incoming request.

[2164] Input: The request data received by the server in JSON format.

[2165] Output: Parsed interview job data.

[2166] Specific behavior: The server parses the JSON data and extracts the interview position.

[2167] Step 4:

[2168] The server performs emotion assessment using an emotion analysis engine.

[2169] Input: Parsed interview job data.

[2170] Output: User's emotion evaluation result.

[2171] Specific operation: The server uses an emotion analysis engine to evaluate emotions such as "excited" or "cautious" based on the user's interview job selection.

[2172] Step 5:

[2173] The server passes the data to a generation AI module, which automatically generates interview questions.

[2174] Input: Interview job data, emotion evaluation results.

[2175] Output: Generated interview questions.

[2176] How it works: The server passes the following prompt to the generative AI model:

[2177] text

[2178] Desired job: AI engineer

[2179] Create practice interview questions.

[2180] The generative AI model generates questions such as, "What was the biggest challenge in your previous projects?"

[2181] Step 6:

[2182] The server converts the generated question into JSON format and sends it to the terminal.

[2183] Input: Generated interview questions.

[2184] Output: Interview questions converted to JSON format.

[2185] Specific operation: The server converts the generated interview questions into JSON data as follows and sends it to the terminal.

[2186] json

[2187] {

[2188] "questions": ["What has been the biggest challenge in your previous projects?"]

[2189] }

[2190] Step 7:

[2191] The terminal displays the question to the user.

[2192] Input: Question data in JSON format sent from the server.

[2193] Output: The parsed question is displayed on the screen.

[2194] Specific operation: The device parses the received JSON data and displays the question to the user.

[2195] Step 8:

[2196] The user answers the questions through the terminal and sends them to the server.

[2197] Input: The user's answer.

[2198] Output: Response data converted to JSON format.

[2199] Specific operation: The user answers, "The biggest challenge in my projects so far has been introducing new algorithms to improve the performance of the AI ​​model," and the device converts this into JSON format and sends it to the server.

[2200] json

[2201] {

[2202] "answer": "The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models."

[2203] }

[2204] Step 9:

[2205] The server analyzes the received responses and performs emotion evaluation.

[2206] Input: Received response data in JSON format.

[2207] Output: User's emotion evaluation result.

[2208] Specific operation: The server parses and analyzes the response data, and evaluates the sentiment based on the response using a sentiment analysis engine.

[2209] Step 10:

[2210] The server passes the data to the generation AI module, which generates feedback.

[2211] Input: User response data and sentiment evaluation results.

[2212] Output: The generated feedback.

[2213] How it works: The server passes the following prompt to the generative AI model:

[2214] text

[2215] User Answer: The biggest challenge in my projects so far has been implementing new algorithms to improve the performance of AI models.

[2216] Please provide feedback on this answer.

[2217] The generative AI model generates feedback such as, "This answer is not specific enough, so it would be better to include more specific examples."

[2218] Step 11:

[2219] The server converts the feedback into JSON format and sends it to the device.

[2220] Input: Generated feedback.

[2221] Output: Feedback converted to JSON format.

[2222] Specific operation: The server converts the generated feedback into JSON data as follows and sends it to the device.

[2223] json

[2224] {

[2225] "feedback": "This answer is not specific enough. Please provide some examples."

[2226] }

[2227] Step 12:

[2228] The device displays the feedback to the user.

[2229] Input: Feedback data sent from the server in JSON format.

[2230] Output: Parsed feedback is displayed on the screen.

[2231] Specific behavior: The device parses the received JSON data and displays feedback to the user, who can review the feedback and learn from it to improve.

[2232] (Application example 2)

[2233] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2234] The main function of conventional job change support systems was to consider a job seeker's skills, experience, and interests and list the most suitable jobs and companies. However, this alone was not enough to provide job seekers with the best job opportunities. In addition, marketing and advertising for job seekers only provided general information and was not customized based on individual profiles. This made it difficult to fully meet the needs of job seekers.

[2235] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2236] In this invention, the server includes means for receiving skills, experience, and interests entered by a job seeker, means for analyzing the job seeker's data using a generation AI and listing optimal jobs and companies, means for presenting the listed jobs and companies to the job seeker, means for generating a customized advertisement based on the job seeker's profile information and emotion assessment results, and means for displaying the generated advertisement to the job seeker. This makes it possible to provide not only a list of optimal jobs and companies based on the job seeker's individual profile, but also customized advertisements tailored to the job seeker's interests and emotions.

[2237] A "job seeker" is an individual who wishes to change jobs in search of a new occupation or work environment.

[2238] "Skills" refers to specialized abilities and techniques related to a profession or job.

[2239] "Experience" refers to knowledge and abilities acquired through previous work or duties.

[2240] "Interest" refers to a special interest or curiosity in a particular field or activity.

[2241] "Generative AI" refers to artificial intelligence technology that automatically generates new information and content based on input data.

[2242] "Means for analyzing data" refers to techniques and methods for analyzing collected data and extracting meaningful information.

[2243] "Job type" refers to a type of job or occupation with specific job duties or work content.

[2244] "Enterprise" refers to a legal entity or company that carries on a particular business activity.

[2245] "Means of listing" refers to techniques or methods for listing items or data based on specific conditions or criteria.

[2246] "Profile Information" refers to detailed information about an individual, including their skills, experience, interests, personality traits, etc.

[2247] "Emotional assessment" refers to measuring an individual's emotional state or psychological response and making an assessment based on that.

[2248] "Customized advertising" refers to advertising whose content is optimized to suit the characteristics and interests of a specific individual.

[2249] "Display means" refers to the techniques and methods for visually presenting the generated information or data to the user.

[2250] This invention is a system that combines a generative AI model and an emotion evaluation engine to provide customized job-hunting support to job seekers. The system receives skills, experience, and interests entered by job seekers and lists the most suitable jobs and companies based on them. Furthermore, the system uses generative AI to generate customized advertisements and display them to job seekers.

[2251] The server implements this system using a program that includes the following steps:

[2252] First, the user uses the input interface to input their skills, experience, and interests. For example, they enter specific information such as "Software development, 5 years, interested in AI technology." The device converts the input data into JSON format and sends it to the server.

[2253] The server parses the received JSON data and uses an emotion engine, such as Hume AI or Affectiva AI, to evaluate the job seeker's emotions based on the input data.

[2254] The server then passes the user's data and sentiment evaluation results to a generative AI module, which uses a generative AI model (such as OpenAI's GPT-4) to create a list of suitable jobs and companies, while simultaneously generating customized advertisements based on the job seeker's profile information and sentiment evaluation results.

[2255] The results are then converted back into JSON format and sent to the device, which then parses the data and displays a list of jobs, companies, and customized ads to the user, such as suggestions for "AI project manager" or "data scientist."

[2256] For example, you might use the following prompts for a generative AI model:

[2257] User skills: Software development, project management

[2258] Experience: 5 years

[2259] Interests: AI, data science

[2260] Emotional rating: Positive

[2261] Use the above information to generate ads that are appealing to users.

[2262] In this way, the server can analyze user profile information and emotion evaluation results with high accuracy, and provide job seekers with the most appropriate information. This system also enables the provision of individually customized advertisements to job seekers.

[2263] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2264] Step 1:

[2265] The user uses the input interface to input their skills, experience, and interests. For example, they can input specific information such as "Software development, 5 years, interested in AI technology." This results in input data. The output is the specific profile information entered by the user.

[2266] Step 2:

[2267] The device converts the input data into JSON format, which structures the data and makes it a format that can be communicated. The input is the profile information entered by the user, and the output is JSON format data.

[2268] Step 3:

[2269] The terminal sends data converted to JSON format to the server. HTTP or WebSocket is used as the communication protocol. The input is JSON format data, and the output is transmission completion to the server.

[2270] Step 4:

[2271] The server analyzes the JSON data received. Specifically, it receives the data using a Python framework (Flask or Django) and performs the analysis process. The input is JSON format data and the output is the analysis result.

[2272] Step 5:

[2273] The server uses an emotion engine to evaluate emotions based on the input data. Hume AI and Affectiva AI are used as emotion engines. The input is the analyzed data, and the output is the emotion evaluation result.

[2274] Step 6:

[2275] The server passes the user's data and sentiment evaluation results to the generation AI module, which then lists the most suitable jobs and companies. OpenAI's GPT-4 is used as the generation AI model. The prompt used is something like, "User skills: software development, project management Experience: 5 years Interests: AI, data science Sentiment evaluation: positive Please generate an advertisement that will appeal to the user based on the above information." The input is user data and sentiment evaluation results, and the output is the most suitable jobs and companies and a customized advertisement.

[2276] Step 7:

[2277] The generated results (best jobs, companies, customized ads) are then converted back into JSON format by the server, which makes it possible to send the results to the device. The input is the output data from the generative AI model, and the output is JSON format data.

[2278] Step 8:

[2279] The server converts the data into JSON format and sends it to the terminal. The input is JSON format data, and the output is sent to the terminal.

[2280] Step 9:

[2281] The device analyzes the received JSON data and displays a list of jobs, companies, and customized advertisements to the user. Specifically, it displays the job listings, companies, and customized advertisements in a user interface using HTML and JavaScript. The input is JSON-formatted data, and the output is a visual presentation to the user. For example, suggestions such as "AI project manager" or "data scientist" are displayed.

[2282] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2283] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2284] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2285] [Fourth embodiment]

[2286] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2287] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2288] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2289] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2290] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2291] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2292] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2293] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2294] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2295] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2296] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2297] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2298] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2299] This invention is a customized system that supports job changes by suggesting the most suitable jobs and companies based on the skills, experience, and interests of job seekers. The system is mainly composed of a device used by job seekers, a server that processes data, and multiple modules that use generation AI to automatically generate resumes, generate interview questions and feedback, and suggest job information.

[2300] Career Design Assistant

[2301] 1. The user enters their skills, experience, and interests through the terminal. For example, they enter information such as "Software development, 5 years of experience, interested in AI technology."

[2302] 2. The device sends the entered data to the server in JSON format.

[2303] 3. The server uses generative AI to analyze the user's data and create a list of suitable jobs and companies.

[2304] 4. The server sends the result to the terminal again in JSON format.

[2305] 5. The device analyzes the results received from the server and displays them to the user. For example, it displays a list of job titles and companies, such as "AI project manager" or "data scientist."

[2306] Resume creation support

[2307] 1. The user enters their past work experience into the terminal. For example, they enter "Server-side development, Java, Python, project management."

[2308] 2. The device sends the entered data to the server in JSON format.

[2309] 3. The server uses AI to generate compelling resume sentences. Based on the input data, the AI ​​automatically generates sentences such as, "I have five years of server-side development experience and excellent project management skills using Java and Python."

[2310] 4. The server sends the generated results to the terminal in JSON format.

[2311] 5. The device displays a preview of the received resume to the user.

[2312] Interview support

[2313] 1. The user selects the interview practice they wish to participate in on their device. For example, they can select "Interview practice for an AI engineer position."

[2314] 2. The device sends the request to the server in JSON format.

[2315] 3. The server uses generative AI to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[2316] 4. The server sends the generated question to the device in JSON format.

[2317] 5. The user answers the questions through the terminal and sends the answers to the server.

[2318] 6. The server analyzes the answer and generates feedback using generative AI, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[2319] 7. The server sends the generated feedback in JSON format to the device.

[2320] 8. The device displays the received feedback to the user.

[2321] Job Changer Network

[2322] 1. The server periodically collects introductions and interview articles of successful job-changers. It collects and organizes successful job-change cases from various data sources and stores them in a database.

[2323] 2. When a user opens the job seeker network page on their device, the device retrieves the relevant list from the server.

[2324] 3. The server sends a list of successful job changes in JSON format to the device.

[2325] 4. The device displays the received success stories and interview articles to the user. For example, a specific success story such as "A former engineer becomes a project manager and is successful in remote work" is presented.

[2326] In this way, the present invention is a system that uses various generative AIs to provide comprehensive support to job seekers. Each module provides information tailored to the needs of job seekers, allowing users to make appropriate career choices.

[2327] The processing flow will be explained below.

[2328] Career Design Assistant

[2329] Step 1:

[2330] The user uses the device to input their skills, experience, and interests. For example, the user inputs "Software development, 5 years, interested in AI technology."

[2331] Step 2:

[2332] The terminal converts the input data into JSON format and sends it to the server.

[2333] Step 3:

[2334] The server parses the received JSON data.

[2335] Step 4:

[2336] The server passes the user's data to the generation AI module, which analyzes it and creates a list of suitable jobs and companies.

[2337] Step 5:

[2338] The server converts the generated results into JSON format and sends them to the terminal.

[2339] Step 6:

[2340] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[2341] Resume creation support

[2342] Step 1:

[2343] A user uses a terminal to enter their work history, for example, "Server-side development, Java, Python, project management."

[2344] Step 2:

[2345] The terminal converts the input data into JSON format and sends it to the server.

[2346] Step 3:

[2347] The server parses the received JSON data.

[2348] Step 4:

[2349] The server passes the user's data to the generation AI module, which analyzes it and automatically generates an attractive resume.

[2350] Step 5:

[2351] The server converts the generated resume text into JSON format and sends it to the terminal.

[2352] Step 6:

[2353] The device analyzes the resume text received and displays it to the user. For example, a preview such as "Has five years of server-side development experience and has excellent project management skills using Java and Python" is displayed.

[2354] Interview support

[2355] Step 1:

[2356] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[2357] Step 2:

[2358] The device converts the request into JSON format and sends it to the server.

[2359] Step 3:

[2360] The server analyzes the received request.

[2361] Step 4:

[2362] The server passes the data to a generation AI module to generate interview questions, such as "What was the biggest challenge in your previous projects?"

[2363] Step 5:

[2364] The server converts the generated question into JSON format and sends it to the terminal.

[2365] Step 6:

[2366] The terminal displays the received question to the user.

[2367] Step 7:

[2368] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[2369] Step 8:

[2370] The server analyzes the received response.

[2371] Step 9:

[2372] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more concrete examples."

[2373] Step 10:

[2374] The server converts the generated feedback into JSON format and sends it to the device.

[2375] Step 11:

[2376] The terminal displays the received feedback to the user.

[2377] Job Changer Network

[2378] Step 1:

[2379] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[2380] Step 2:

[2381] Based on the data accumulated by the server, a list of success stories is generated for job seekers.

[2382] Step 3:

[2383] The user opens a job seeker network page using a device.

[2384] Step 4:

[2385] The device sends a request to the server.

[2386] Step 5:

[2387] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[2388] Step 6:

[2389] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[2390] In this way, each function is realized through specific processing steps.

[2391] Example 1

[2392] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2393] Currently, job seekers must expend a great deal of time and effort to find the job and company that best suits them. They also have to create resumes and prepare for interviews on their own, making it difficult to conduct a job search efficiently.

[2394] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2395] In this invention, the server includes a means for analyzing data of job seekers and listing the most suitable jobs and companies, a means for generating attractive resumes, and a means for generating interview questions and generating feedback on the answers, thereby enabling job seekers to effectively find the most suitable jobs and companies and efficiently conduct their job search.

[2396] A "job seeker" is an individual who wants to leave their current occupation and take up a new one.

[2397] "Skills" refers to the specialized techniques and knowledge possessed by job seekers.

[2398] "Experience" refers to the achievements and experiences that a job seeker has gained in the job or work they have previously performed.

[2399] "Interest" refers to the job seeker's interest in the job or industry.

[2400] "Data terminal" refers to the electronic device used by job seekers to enter information.

[2401] "JSON format" refers to a JavaScript Object Notation data structure used for exchanging and storing data.

[2402] "Server" refers to a central processing unit that analyzes data, generates resumes, creates interview questions, etc.

[2403] "Generative AI" refers to an artificial intelligence model that performs natural language processing based on large amounts of data.

[2404] "Job type" refers to the type of work within a particular industry or field.

[2405] "Company" refers to the corporate organization where the job seeker seeks employment.

[2406] A resume is a document submitted by a job seeker listing their skills and experience.

[2407] "Interview questions" refer to questions that job seekers prepare in anticipation of being asked in an interview with a company.

[2408] "Feedback" refers to evaluation of the job seeker's answers and instructions, including areas for improvement.

[2409] This invention is a system that supports job-hunting activities by suggesting the most suitable jobs and companies for job seekers based on their skills, experience, and interests. The system provides various support functions using data terminals used by job seekers, a server that processes data, and a generative AI model.

[2410] The system's hardware configuration includes a data terminal (e.g., PC, smartphone, tablet, etc.) for job seekers to input data, and a server for analyzing the data and providing information. The software uses a generative AI model, such as OpenAI GPT-4.

[2411] The system provides the following main functions:

[2412] 1. Career design assistant function

[2413] Users input their skills, experience, and interests through a terminal. For example, they input information such as "Software development, 5 years of experience, interested in AI technology."

[2414] The terminal converts the input data into JSON format and sends it to the server.

[2415] The server uses a generative AI to analyze the user's data and create a list of suitable jobs and companies. For example, the generative AI uses the following prompt:

[2416] Generate the following prompt based on the user's skills, experience, and interests: 'Software development, 5 years of experience, interested in AI technology'

[2417] The server sends the listed results back to the terminal in JSON format.

[2418] The device analyzes the results received from the server and displays them to the user. For example, it displays a list of job titles and companies, such as "AI project manager" and "data scientist."

[2419] 2. Resume creation support function

[2420] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[2421] The terminal converts the input data into JSON format and sends it to the server.

[2422] The server uses generative AI to generate compelling resume prompts based on the input data. Examples of prompts used include:

[2423] Generate compelling resume copy based on your work experience: 'Server-side development, Java, Python, Project management'

[2424] The server sends the generated resume text in JSON format to the terminal.

[2425] The terminal displays a preview of the received resume to the user.

[2426] 3. Interview support function

[2427] The user selects the interview practice they wish to have on their device, such as "Interview practice for an AI engineer position."

[2428] The device sends the request to the server in JSON format.

[2429] The server generates interview questions using a generation AI. Examples of prompts include:

[2430] Generate AI engineer job questions for interview practice

[2431] The server sends the generated question in JSON format to the device.

[2432] The user answers the questions through the terminal and sends the answers to the server.

[2433] The server analyzes the answers and generates feedback using generative AI. Example prompts for generating feedback:

[2434] Generate feedback based on the user's answer. Example: The answer is vague, so it would be helpful to include more examples.

[2435] The server sends the generated feedback in JSON format to the device.

[2436] The terminal displays the received feedback to the user.

[2437] Each of these functions is designed to comprehensively support users' job-hunting activities and enable them to make efficient and effective career choices. The purpose of this invention is to provide customized support tailored to the individual circumstances of job seekers and improve the quality of the entire job-hunting process.

[2438] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2439] Career Design Assistant

[2440] Step 1: Data entry

[2441] Users input their skills, experience, and interests through a terminal. For example, they input information such as "Software development, 5 years of experience, interested in AI technology."

[2442] Input: User's skills, experience, and interests (in text format)

[2443] Output: User input data to the terminal (text format)

[2444] Step 2: Send data

[2445] The terminal converts the input data into JSON format and sends it to the server.

[2446] Specifically, the device extracts the information entered by the user and generates JSON data like this:

[2447] json

[2448] {

[2449] "skill": "software development",

[2450] "experience": "5 years",

[2451] "interest": "AI technology"

[2452] }

[2453] Input: User's skills, experience, and interests (in text format)

[2454] Output: JSON format data to the server

[2455] Step 3: Propose a job

[2456] The server uses generative AI to analyze the data it receives and create a list of the most suitable jobs and companies.

[2457] Input: JSON format data sent from the terminal

[2458] Specific operation: The server uses a generative AI (e.g., OpenAI GPT-4) and inputs the following prompt into the generative AI:

[2459] Generate the following prompt based on the user's skills, experience, and interests: 'Software development, 5 years of experience, interested in AI technology'

[2460] Data processing: Generative AI analyzes and creates a list of suitable jobs and companies

[2461] Output: Listing results in JSON format

[2462] Step 4: Send results

[2463] The server generates the results and sends them to the terminal in JSON format. The data from the server is sent in the following format:

[2464] json

[2465] {

[2466] "recommendations": [

[2467] {"position": "AI Project Manager", "company": "Technology Company"},

[2468] {"position": "Data Scientist", "company": "Data Solutions Inc."}

[2469] ]

[2470] }

[2471] Input: Generated list result (JSON format)

[2472] Output: JSON format data to the terminal

[2473] Step 5: View the results

[2474] The terminal analyzes the received results and displays them to the user.

[2475] Example: The terminal parses the received JSON data and displays it in the GUI:

[2476] AI Project Manager - Technology Company

[2477] Data Scientist - Data Solutions Inc.

[2478] Input: Result sent from the server (JSON format)

[2479] Output: What is displayed to the user (GUI format)

[2480] Resume creation support

[2481] Step 1: Enter your work history

[2482] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[2483] Input: User's work history (text format)

[2484] Output: User input data to the terminal (text format)

[2485] Step 2: Send data

[2486] The terminal converts the input data into JSON format and sends it to the server.

[2487] Specifically, the data is prepared as follows:

[2488] json

[2489] {

[2490] "employment_history": [

[2491] {"role": "Server-side development", "skills": ["Java", "Python", "Project management"]}

[2492] ]

[2493] }

[2494] Input: User's work history (text format)

[2495] Output: JSON format data to the server

[2496] Step 3: Generate resume text

[2497] The server uses generation AI to generate resume text.

[2498] Specific operation: The server inputs the following prompt to the generation AI:

[2499] Generate compelling resume copy based on your work experience: 'Server-side development, Java, Python, Project management'

[2500] Input: JSON format data sent from the terminal

[2501] Data processing: Generative AI creates text

[2502] Output: Generated resume text (JSON format)

[2503] Step 4: Sending the generated results

[2504] The server sends the generated resume text in JSON format to the terminal.

[2505] Input: Generated resume text (JSON format)

[2506] Output: JSON format data to the terminal

[2507] Step 5: Preview your resume

[2508] The terminal displays the contents of the received resume to the user.

[2509] Example: The device parses the received JSON data and displays it to the user as follows:

[2510] He has 5 years of server-side development experience and is skilled in project management using Java and Python.

[2511] Input: Result sent from the server (JSON format)

[2512] Output: What is displayed to the user (GUI format)

[2513] Interview support

[2514] Step 1: Enter your interview practice preferences

[2515] The user selects the interview practice they wish to have on their device, such as "Interview practice for an AI engineer position."

[2516] Input: Interview practice request (text format)

[2517] Output: User input data to the terminal (text format)

[2518] Step 2: Submitting a request

[2519] The device sends the request to the server in JSON format.

[2520] Specifically, the data is prepared as follows:

[2521] json

[2522] {

[2523] "interview_practice": "AI engineer position"

[2524] }

[2525] Input: Interview practice request (text format)

[2526] Output: JSON format data to the server

[2527] Step 3: Generate interview questions

[2528] The server generates interview questions using a generation AI.

[2529] Specific operation: The server inputs the following prompt to the generation AI:

[2530] Generate AI engineer job questions for interview practice

[2531] Input: JSON format data sent from the terminal

[2532] Data manipulation: Generative AI creates interview questions

[2533] Output: Generated questions (JSON format)

[2534] Step 4: Submit your question

[2535] The server sends the generated question in JSON format to the device.

[2536] Input: Generated question (JSON format)

[2537] Output: JSON format data to the terminal

[2538] Step 5: Answer the questions

[2539] The user answers the questions through the terminal and sends the answers to the server.

[2540] Input: User's answers to interview questions (in text format)

[2541] Output: JSON format data to the server

[2542] Step 6: Feedback generation

[2543] The server analyzes the answers and generates feedback using generative AI.

[2544] Specific action: The server inputs the following prompt to the generation AI:

[2545] Generate feedback based on the user's answer. Example: The answer is vague, so it would be helpful to include more examples.

[2546] Input: User's answer (in text format)

[2547] Data manipulation: Generative AI creates feedback

[2548] Output: Generated feedback (JSON format)

[2549] Step 7: Submit your feedback

[2550] The server sends the generated feedback in JSON format to the device.

[2551] Input: Generated feedback (JSON format)

[2552] Output: JSON format data to the terminal

[2553] Step 8: Feedback display

[2554] The terminal displays the received feedback to the user.

[2555] Example: The device parses the received JSON data and displays it to the user as follows:

[2556] This answer is a bit vague, so it would be helpful to include some more examples.

[2557] Input: Result sent from the server (JSON format)

[2558] Output: What is displayed to the user (GUI format)

[2559] Job Changer Network

[2560] Step 1: Collecting success stories

[2561] The server regularly collects profiles and interviews of successful job seekers.

[2562] What it does: Collects data using web scraping or APIs and stores it in a database.

[2563] Input: Successful career change cases from various data sources (text format)

[2564] Output: Accumulation in the database on the server

[2565] Step 2: Page display request

[2566] When a user opens a job-changer network page on their device, the device retrieves the relevant list from the server.

[2567] Input: User request (text format)

[2568] Output: Request to server (text format)

[2569] Step 3: Submit your success story list

[2570] The server sends a list of successful job change cases in JSON format to the terminal.

[2571] Input: List of success stories in the database

[2572] Output: JSON format data to the terminal

[2573] Step 4: View success stories

[2574] The terminal displays the received success stories to the user.

[2575] Specific example: Specific success stories such as "A former engineer transitioned to project manager and is now thriving in remote work" are displayed.

[2576] Input: Result sent from the server (JSON format)

[2577] Output: What is displayed to the user (GUI format)

[2578] The above is the specific processing flow of this system.

[2579] (Application example 1)

[2580] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2581] This invention relates to a system for providing advanced job change support and customer service in brick-and-mortar stores. Conventional job change support systems can suggest optimal job types and companies for individual job seekers, but they have the problem that the subsequent interview preparation, resume preparation support, and customer service in brick-and-mortar stores take time and effort. In particular, it has been difficult to grasp the interests and preferences of customers in brick-and-mortar stores in real time and quickly make suggestions based on them.

[2582] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2583] In this invention, the server includes a means for receiving skills, experience, and interests entered by job seekers, a means for analyzing data on job seekers using a generation AI and listing optimal jobs and companies, a means for presenting the listed jobs and companies to job seekers, and a means related to a device for store staff to use the generation AI to analyze interests and preferences from customer comments and actions and propose optimal products and services. This not only enables comprehensive job change support for job seekers, but also makes it possible to grasp customer needs in real time and make appropriate proposals in physical stores.

[2584] A "job seeker" is an individual who wishes to change jobs from their current position to another position.

[2585] "Skills" refer to the techniques and abilities required for a particular job or task.

[2586] "Experience" refers to the track record and history of past work and tasks.

[2587] "Interest" refers to a concern or desire for a particular field or activity.

[2588] "Generative AI" refers to a system that uses artificial intelligence technology to generate and analyze data.

[2589] "Listing" refers to the act of compiling multiple candidates into a list.

[2590] "Job type" refers to specific job content or work category.

[2591] An "enterprise" is an organization that provides goods and services.

[2592] An "apparatus" is a machine or device configured to perform a particular function.

[2593] "Customer" means an individual or entity that purchases or uses goods or services.

[2594] "Statement" refers to verbal expressions of intent or opinions.

[2595] "Behavior" refers to human movements or activities that are carried out with a specific purpose.

[2596] "Interests and preferences" refer to the areas or products in which a customer has particular preferences or interests.

[2597] A "suggestion" is the act of presenting a particular idea or option.

[2598] "Store staff" refers to employees who deal with customers and provide services in physical stores.

[2599] "Real-time" means that events or actions occur almost immediately, corresponding to actual time.

[2600] The present invention is a system that proposes the most suitable job types and companies based on data on job seekers, and supports resume creation, interview practice, and customer service in brick-and-mortar stores. Specific embodiments of the system are described below.

[2601] 1. Implementation of the job change support system

[2602] The system of this invention is composed of a terminal used by job seekers, a server that processes data, and a generation AI. When a user enters their skills, experience, and interests through the terminal, the terminal sends the entered data in JSON format to the server. The server uses the generation AI to analyze the user data, lists the most suitable jobs and companies, and sends the results back to the terminal in JSON format. The user can then check the listed jobs and companies on the terminal.

[2603] 2. Resume creation support

[2604] When a user enters their work history into the device, the device sends the data in JSON format to the server. The server uses a generative AI to generate an attractive resume and sends the generated results in JSON format to the device. The device then displays a preview of the received resume to the user.

[2605] 3. Interview practice support

[2606] When a user selects their preference for interview practice on their device, the device sends a request to the server in JSON format. The server uses a generation AI to generate interview questions and sends the generated questions to the device in JSON format. The user answers the questions through their device and sends the answers to the server. The server analyzes the answers, generates feedback using the generation AI, and sends the generated feedback to the device in JSON format. The user then checks the received feedback on their device.

[2607] 4. Physical store applications

[2608] Store staff wear smart glasses to gather information about customers' interests and preferences from their comments and behavior. The smart glasses capture what customers say and send the data to a server in real time. The server then uses generative AI to analyze the customer data and generate information to recommend optimal products and services. The generated recommendation information is then sent back to the smart glasses, where staff can view the suggestions on the glasses' display and make them to the customer.

[2609] Hardware and software used

[2610] Device: The device on which the user enters information (smartphone, tablet, PC, etc.)

[2611] Server: Receives and transmits data, and executes AI generation (e.g., cloud server)

[2612] Generative AI: Data analysis, resume generation, interview question generation, feedback generation (e.g., OpenAI's GPT-3)

[2613] Smart glasses: Used for customer service in physical stores (smart glasses from specific manufacturers)

[2614] Examples and prompts

[2615] As a specific example, if a store staff member wearing smart glasses captures a customer saying, "I've been interested in the outdoors lately," the program will respond as follows:

[2616] Example prompt sentence:

[2617] text

[2618] A customer might say: 'I've been interested in the outdoors lately'. Suggest the best products based on that.

[2619] The generated suggestions are displayed on the smart glasses in the form of, for example, "If you're interested in the outdoors, we recommend the latest compact tent and waterproof jacket. In particular, outdoor gear on sale this week is very popular."

[2620] In this way, a system can be realized that can provide advanced information and support in real time to a variety of users, including not only job seekers but also store staff.

[2621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2622] Step 1:

[2623] The device receives input data from the user, such as skills, experience, and interests. The user enters specific information, such as "software development, 5 years of experience, interested in AI technology." The device converts this data into JSON format and sends it to the server.

[2624] Step 2:

[2625] The server receives the JSON data sent from the device and analyzes the data using a generative AI model (e.g., GPT-3). The generative AI model analyzes the user data and lists the most suitable jobs and companies. The analysis results are structured in JSON format and sent back from the server to the device.

[2626] Step 3:

[2627] The device analyzes the JSON data of the analysis results received from the server and displays a list of jobs and companies that are most suitable for the user. For example, it may list jobs such as "data scientist" or "AI project manager."

[2628] Step 4:

[2629] The user enters their work experience, such as "server-side development, Java, Python, project management," into the terminal. The terminal then sends this data in JSON format to the server.

[2630] Step 5:

[2631] The server receives the JSON data sent from the device and generates resume text using a generative AI model. Based on the input data, the generative AI model automatically generates an appealing resume text such as "Has five years of server-side development experience and has excellent project management skills using Java and Python," and sends it in JSON format to the device.

[2632] Step 6:

[2633] The device parses the JSON data of the resume text received from the server and displays a preview to the user. The user can then check the generated resume and make any necessary corrections.

[2634] Step 7:

[2635] The user selects the interview practice they wish to do on their device. For example, they select "Interview practice for an AI engineer position." The device then sends the selected information to the server in JSON format.

[2636] Step 8:

[2637] The server receives the JSON data sent from the device and generates interview questions using a generative AI model, such as "What was the biggest challenge in your previous projects?", and sends them to the device in JSON format.

[2638] Step 9:

[2639] The device parses the JSON data of the interview questions received from the server and displays the questions to the user. The user answers the questions and sends the answers back to the server via the device.

[2640] Step 10:

[2641] The server receives the user's response data and generates feedback using a generative AI model. The generative AI model analyzes the response data and generates feedback such as "This response is not specific enough, so it would be better to include more specific examples," and sends it to the device in JSON format.

[2642] Step 11:

[2643] The device parses the JSON data of the feedback received from the server and displays the feedback to the user, who can then improve their answer based on the feedback.

[2644] Step 12:

[2645] In a physical store, store staff wearing smart glasses capture customer speech. For example, if a customer says, "I've recently become interested in outdoor activities," the smart glasses receive the speech data and send it to a server in real time.

[2646] Step 13:

[2647] The server receives the voice data sent from the smart glasses, analyzes the customer's interests and preferences using a generative AI model, and generates information to recommend the most suitable products and services. For example, it generates information such as "If you're interested in outdoor activities, we recommend the latest compact tent and waterproof jacket," and sends it back to the smart glasses in JSON format.

[2648] Step 14:

[2649] The smart glasses display the received recommendation information and store staff make suggestions to customers, enabling them to provide optimal products and services to customers in real time.

[2650] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2651] This invention uses a system that combines generative AI and an emotion engine to provide customized career change support to job seekers. The system analyzes the skills, experience, and interests entered by job seekers, lists the most suitable jobs and companies, and also creates resumes, provides interview practice, and generates feedback.

[2652] Career Design Assistant

[2653] Users input their skills, experience, and interests through the terminal. For example, they could input "Software development, 5 years, interested in AI technology."

[2654] The terminal converts the input data into JSON format and sends it to the server.

[2655] The server parses the received JSON data and uses an emotion engine to recognize and evaluate the user's emotion based on the input data.

[2656] The server passes the user's data and emotional evaluation results to the generation AI module, which then uses this information to create a list of suitable jobs and companies.

[2657] The server converts the generated results into JSON format and sends them to the terminal.

[2658] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[2659] Resume creation support

[2660] The user enters their past work experience into the terminal. For example, they enter "server-side development, Java, Python, project management."

[2661] The terminal converts the input data into JSON format and sends it to the server.

[2662] The server parses the received JSON data and uses an emotion engine to evaluate the input data and the user's reaction in real time.

[2663] The server passes the user's data and sentiment assessment to the generation AI module, which then uses this data to automatically generate compelling resume sentences, such as "I have five years of server-side development experience and excellent project management skills using Java and Python."

[2664] The server converts the generated resume text into JSON format and sends it to the terminal.

[2665] The terminal analyzes the received resume text and displays it to the user, who can then check the generated resume text.

[2666] Interview support

[2667] The user selects the interview practice they want to do using the device. For example, they select "interview practice for an AI engineer position."

[2668] The device converts the request into JSON format and sends it to the server.

[2669] The server analyzes the received request.

[2670] The server uses an emotion engine to evaluate emotions based on the user's selection and passes the data to a generative AI module, which then automatically generates questions such as, "What was the biggest challenge in your previous project?"

[2671] The server converts the generated question into JSON format and sends it to the terminal.

[2672] The terminal displays the received question to the user.

[2673] The user answers questions through the terminal, and the answers are converted into JSON format and sent to the server.

[2674] The server analyzes the received answers, and the emotion engine evaluates the emotion based on the user's answer data.

[2675] The server passes the data to the generation AI module, which generates feedback, such as "This answer is not specific enough, so it would be better if you included more specific examples."

[2676] The server converts the generated feedback into JSON format and sends it to the device.

[2677] The device will display the received feedback to the user, who can review it and learn from it to improve.

[2678] Job Changer Network

[2679] The server periodically collects introductions and interviews of successful job seekers, collecting information from various data sources and storing it in a database.

[2680] Based on the accumulated data, the server generates a list of success stories for job seekers.

[2681] The user opens a job seeker network page using a device.

[2682] The device sends a request to the server.

[2683] The server analyzes the received request, converts the list of successful job change cases into JSON format, and sends it to the terminal.

[2684] The device analyzes the received JSON data and displays success stories and interview articles to the user. For example, an article such as "Former engineer becomes project manager and thrives in remote work" may be displayed.

[2685] In this way, each function is realized through a specific processing flow, and by making full use of generative AI and an emotion engine, comprehensive support is provided to job seekers.

[2686] The processing flow will be explained below.

[2687] Career Design Assistant

[2688] Step 1:

[2689] The user uses the device to enter their skills, experience, and interests. For example, they might enter "Software development, 5 years, interested in AI technology."

[2690] Step 2:

[2691] The terminal converts the input data into JSON format and sends it to the server.

[2692] Step 3:

[2693] The server parses the received JSON data.

[2694] Step 4:

[2695] The server uses an emotion engine to recognize and evaluate the user's emotions based on the input data, such as interest and confidence, based on facial expressions and input content.

[2696] Step 5:

[2697] The server passes the user's data and emotional evaluation results to the generation AI module, which uses this data to create a list of suitable jobs and companies.

[2698] Step 6:

[2699] The server converts the generated results into JSON format and sends them to the terminal.

[2700] Step 7:

[2701] The device parses the received JSON data and displays it to the user, suggesting jobs such as "AI project manager" or "data scientist."

[2702] Resume creation support

[2703] Step 1:

[2704] A user uses a terminal to enter their work history, for example, "Server-side development, Java, Python, project management."

[2705] Step 2:

[2706] The terminal converts the input data into JSON format and sends it to the server.

[2707] Step 3:

[2708] The server parses the received JSON data.

[2709] Step 4:

[2710] The ...

Claims

1. A means to receive the skills, experience, and interests entered by job seekers; A method to analyze data on job seekers using generative AI and create a list of the most suitable jobs and companies. A way to present the listed jobs and companies to job seekers, A system including:

2. A means of collecting input data from job seekers and generating attractive resumes; A means for presenting the generated resume text to job seekers; The system of claim 1 further comprising:

3. A means for generating interview questions for job seekers using generative AI; A means for receiving responses from job seekers and generating feedback using a generation AI; A means for presenting the generated feedback to job seekers; The system of claim 1 further comprising:

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