system

Abstract

Provide a system. A means for users to input information about their work history, skill set, and the industry or job type they are aiming for; means for transmitting the input information to a server; A means for the server to generate resume text using a natural language processing algorithm based on the input information; means for transmitting the resume text generated by the server to a terminal; means for displaying the resume text to the user on the terminal; A system including:

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 face challenges in finding the best fit for their job and company based on their work history, skill set, and interests. Resume preparation and interview preparation often require a lot of time and effort. Furthermore, limited opportunities for appropriate feedback make it difficult to improve interview skills. An effective system to solve these problems is needed. [Means for solving the problem]

[0005] The present invention provides a system including: a means for a user to input information regarding their work history, skill set, and desired industry and occupation; a means for transmitting the input information to a server; a means for the server to generate resume text based on the input information using a natural language processing algorithm; a means for the server to transmit the generated resume text to a terminal; and a means for the terminal to display the resume text to the user. The system also includes a means for the user to input an instruction to the terminal to start interview practice; a means for transmitting the user's profile data to the server; a means for the server to generate interview questions based on the profile data and transmit them to the terminal; a means for the user to input answers to the interview questions to the terminal and for the terminal to transmit the answers to the server; a means for the server to evaluate the answers, generate feedback, and transmit it to the terminal; and a means for the server to generate new interview questions and transmit it to the terminal. The system also includes a means for the server to recommend optimal jobs and companies for the individual based on the user's input data; and a means for the server to transmit the recommendation results to the terminal and display them to the user. This allows users to effectively create resumes, prepare for interviews, and find optimal jobs and companies.

[0006] "User" refers to an individual who uses the system to input information such as work history, skill set, and interests to create a resume and practice for interviews.

[0007] "Work history" refers to information such as the job content, role, and period of work the user has performed in the past.

[0008] A "skill set" refers to a collection of specific skills or abilities that a user possesses.

[0009] "Desired industry or job type" refers to the specific industry or job type to which the user wishes to transfer.

[0010] "Means" refers to hardware or software components for performing a specific function within a system.

[0011] "Terminal" means a device through which a User accesses the System and inputs and receives information.

[0012] "Server" refers to a central processing unit that receives and processes data sent by users.

[0013] A "natural language processing algorithm" refers to a computational method for analyzing text data entered by a user and converting it into a format that humans can understand.

[0014] "Resume text" refers to a document intended for recruiters that is generated based on a user's work history and skill set.

[0015] "Profile Data" refers to detailed information about a user's personal and career information.

[0016] "Interview questions" refer to questions that are likely to be asked during an interview and are generated by the server based on the user's profile data.

[0017] An "answer" refers to a response that a user inputs to an interview question.

[0018] "Feedback" refers to information that allows the server to evaluate the user's answers and provide suggestions for improvement or advice.

[0019] "Recommendation" refers to the act of the server suggesting the most suitable job or company based on the data entered by the user. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram 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

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

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

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

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

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

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

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

[0028] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0041] The present invention relates to a system for supporting users in their job hunting activities. The main features of this system are a resume creation function and an interview support function. Detailed embodiments of each function and the operation of the entire system are described below.

[0042] 1. Resume creation function

[0043] The user uses a device to input information about their past work experience, skill set, and the industry and job they are aiming for. The device then sends this input data to a server, which then analyzes the received data using a natural language processing algorithm to generate the optimal resume text that will impress hiring managers.

[0044] Example: A user enters "5 years of experience as a software engineer. My main skills are Python and Java. I'm interested in AI." The device sends this information to the server, which then generates a resume that reads, "I've worked as a software engineer for 5 years and have successfully completed multiple projects using mainly Python and Java. I'm particularly interested in AI-related technologies and would like to further my career in that field." The user can then review and edit this resume displayed on the device.

[0045] 2. Interview support function

[0046] When a user inputs a command to start interview practice into the device, the device sends the user's profile data to the server. Based on this data, the server generates appropriate interview questions related to the industry and job type the user is aiming for and sends them to the device. The user then inputs answers to the questions displayed on the device and sends them to the server.

[0047] The server analyzes and evaluates the user's answers, generates suggestions for improvement and specific feedback, and sends them to the device.The server also generates new questions based on the user's responses and sends them to the device.

[0048] For example, if a user says, "I'm looking for a data scientist position," the server generates questions like, "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the server evaluates the answer and provides feedback like, "Your answer is a bit vague. Please briefly describe the specific dataset, the analytical tools used, and the results." This process is repeated until the user improves their interview skills.

[0049] 3. Recommendations for suitable jobs and companies

[0050] Based on the user's input data, the server recommends the most suitable jobs and companies for the user. The server analyzes the user's work history, skill set, and interests and provides appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[0051] For example, if a user enters "I have skills in Python and Java and am interested in AI-related jobs," the server will recommend job information for software engineering positions in the AI ​​field based on this information. Recommendations such as "AI software engineer jobs: Company X" and "Data scientist jobs: Company Y" will be displayed on the device.

[0052] As described above, the job change support system of the present invention supports users in effectively creating resumes, preparing for interviews, and finding the most suitable job type and company, which is expected to improve the efficiency and success rate of job change activities.

[0053] The processing flow will be explained below.

[0054] 1. Resume creation function

[0055] Step 1:

[0056] The user uses the terminal to enter information about their past work experience, skill set, and the industry and job type they are aiming for.

[0057] Step 2:

[0058] The terminal transmits the user's input data to the server.

[0059] Step 3:

[0060] The server runs the received data through a natural language processing algorithm to generate the most suitable resume text.

[0061] Step 4:

[0062] The server sends the generated resume text to the terminal.

[0063] Step 5:

[0064] The terminal displays the resume content received from the server to the user.

[0065] Step 6:

[0066] The user checks the resume displayed on the terminal and makes corrections as necessary.

[0067] 2. Interview support function

[0068] Step 1:

[0069] To start the interview practice, the user issues a command to start the simulation from the terminal.

[0070] Step 2:

[0071] The terminal transmits the user's profile data to the server.

[0072] Step 3:

[0073] The server generates appropriate interview questions based on the profile data and sends them to the terminal.

[0074] Step 4:

[0075] The user inputs answers to the interview questions displayed on the terminal.

[0076] Step 5:

[0077] The terminal sends the user's answer to the server.

[0078] Step 6:

[0079] The server analyzes and evaluates the user's answers and generates feedback.

[0080] Step 7:

[0081] The server generates feedback and sends it to the device.

[0082] Step 8:

[0083] The device displays the feedback to the user.

[0084] Step 9:

[0085] The server generates a new question based on the user's response and sends it to the terminal.

[0086] 3. Recommendations for suitable jobs and companies

[0087] Step 1:

[0088] The user uses the terminal to input data such as work history, skill set, and interests.

[0089] Step 2:

[0090] The terminal transmits the input data to the server.

[0091] Step 3:

[0092] The server analyzes the input data and recommends the most suitable jobs and companies.

[0093] Step 4:

[0094] The server sends the recommendation results to the terminal.

[0095] Step 5:

[0096] The terminal displays the recommendation results to the user.

[0097] Example 1

[0098] 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."

[0099] Conventional job-hunting support systems make it difficult for users to efficiently create resumes, prepare for interviews, and find the most suitable jobs and companies. Specifically, resume generation and interview questions are mechanical and not optimized for each user's individual profile. Furthermore, feedback during interview practice sessions is not specific, making it difficult for users to improve their interview skills. Furthermore, many of the job recommendations do not match the user's skill set or interests.

[0100] 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.

[0101] In this invention, the server includes means for a user to input information about his / her work history, skill set, and the industry and occupation he / she is aiming for, means for transmitting the input information to the server, means for the server to generate resume text using a natural language processing algorithm based on the input information, means for transmitting the resume text generated by the server to a terminal, means for the terminal to display the resume text to the user, means for the user to input an instruction to the terminal to start interview practice, means for transmitting profile data of the user to the server, means for the server to generate interview questions based on the profile data and transmit them to the terminal, and means for the user to input answers to the interview questions to the terminal and for the terminal to display the answers. The system includes means for transmitting answers to a server, means for the server to evaluate the answers and generate feedback and transmit it to the terminal, means for the server to generate new interview questions and transmit them to the terminal, means for the server to recommend the most suitable job types and companies for the individual based on the user's input data, means for the server to transmit the recommendation results to the terminal and display them to the user, means for the server to use a generative AI model as a natural language processing algorithm in the resume creation function and provide prompt sentences to the generative AI model, and means for the server to use the generative AI model when evaluating the user's answers and generate new questions and feedback based on the evaluation results in the interview support function. This enables the creation of resumes tailored to the individual needs of the user, specific and effective interview practice, and recommendations for the most suitable job types and companies.

[0102] "User" refers to an individual who uses this system to create a resume, practice interviews, or receive job recommendations.

[0103] "Work history" refers to the work history and work that the user has been involved in up to now.

[0104] A "skill set" refers to the skills and knowledge a user has acquired and their ability to apply them.

[0105] "Server" refers to a computer system that receives, analyzes, and processes information entered by a user.

[0106] A "natural language processing algorithm" refers to a program that analyzes text data entered by a user and converts it into natural language sentences.

[0107] "Resume text" refers to the text content of the resume generated by the server based on the user's work history and skill set.

[0108] A "terminal" is a device through which a user inputs information, and refers to a computer or smartphone that sends and receives information.

[0109] "Profile Data" refers to data including a user's personal information, work history, and skill set.

[0110] "Interview questions" refer to predicted questions that may be asked in an interview that are generated by the server based on the user's profile data.

[0111] An "answer" refers to a response that a user inputs to an interview question.

[0112] "Feedback" refers to the server's evaluation of the user's answers, including suggestions for improvement and advice.

[0113] "Recommendation results" refers to information on occupations and companies suitable for a user, which is analyzed by the server based on the user's input data.

[0114] "Generative AI model" refers to an artificial intelligence model used for natural language processing, question generation, and feedback evaluation.

[0115] A "prompt" refers to an instruction statement that instructs a generative AI model to perform a specific process.

[0116] The present invention relates to a system that supports users in their job-hunting activities. The main features of this system are a resume creation function, an interview support function, and a job / company recommendation function. Specific embodiments for carrying out the invention are described below.

[0117] 1. Resume creation function

[0118] Users use a device (such as a PC or smartphone) to enter information about their work history, skill set, and the industry and job they are aiming for into a dedicated form. This input data is sent from the device to a server (such as a cloud server). The server uses a generative AI model (e.g., GPT-4 (registered trademark)) based on the received data to generate resume text.

[0119] Hardware and software used:

[0120] Device: PC or smartphone

[0121] Server: Cloud server (e.g. AWS (registered trademark) EC2)

[0122] Generative AI model: GPT-4

[0123] Examples:

[0124] When a user inputs "I have 5 years of experience as a software engineer. My main skills are Python and Java. I am interested in AI," the device sends this information to the server. The server then inputs the following prompt into the generative AI model: "Please create a professional resume based on this information." As a result, the server generates the following sentence: "I have worked as a software engineer for 5 years and have successfully completed multiple projects, primarily using Python and Java. I am particularly interested in AI-related technologies and would like to further my career in this field." and sends it to the device. The user can review this sentence and modify it as necessary.

[0125] 2. Interview support function

[0126] When a user inputs a command to start interview practice on their device, the device sends the user's profile data to a server. Based on this data, the server generates appropriate interview questions related to the user's desired industry and job type and sends them to the device. The user enters answers to the questions displayed on the device and sends them to the server. The server analyzes the user's answers using a generative AI model, evaluates the content, generates areas for improvement and specific feedback, and sends them to the device. Furthermore, the server generates new interview questions based on the user's responses and sends them to the device.

[0127] Hardware and software used:

[0128] Device: PC or smartphone

[0129] Server: Cloud server (e.g. AWS EC2)

[0130] Generative AI model: Various interview support AI (e.g., GPT-4)

[0131] Examples:

[0132] If the user "wants to work as a data scientist," the server inputs the prompt: "Please create an appropriate interview question for a user who wants to work as a data scientist" into the generative AI model, and generates the question: "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the server evaluates the content and provides feedback saying, "Your answer is a bit vague. Please briefly describe the specific dataset, analysis tools used, and results." This process is repeated until the user is satisfied.

[0133] 3. Recommendations for suitable jobs and companies

[0134] When a user inputs information about their desired job type and skills into the device, the device sends the data to the server. The server analyzes the user's data and uses a generative AI model to generate job information that recommends the most suitable job types and companies. The results are sent to the device and displayed to the user.

[0135] Hardware and software used:

[0136] Device: PC or smartphone

[0137] Server: Cloud server (e.g. AWS EC2)

[0138] Generative AI model: Job recommendation AI (e.g., recommendation system)

[0139] Examples:

[0140] When a user enters "I have Python and Java skills and am interested in AI-related jobs," the server inputs the prompt: "Please recommend the best job information for users who have Python and Java skills and an interest in the AI ​​field" into the generative AI model, generating recommendation results such as "AI software engineer job: XX company" and "Data scientist job: XX company" and sending them to the device. The user can review these and consider applying.

[0141] As described above, the system of the present invention uses a generative AI model based on data entered by the user to create resumes, assist with interview practice, and recommend appropriate jobs and companies, thereby improving the efficiency and success rate of job hunting.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Resume creation function

[0144] Step 1:

[0145] A user uses a terminal to enter resume information.

[0146] Input: Information about the user's work history, skill set, and desired industry or job type

[0147] Output: Resume information in text format

[0148] Specific operation: The user enters the following into a dedicated form on the device: "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI."

[0149] Step 2:

[0150] The terminal sends the input data to the server.

[0151] Input: Resume information in text format

[0152] Output: Send to server

[0153] Specific operation: The device sends data to the server using the HTTPS protocol.

[0154] Step 3:

[0155] The server inputs the received data into a generative AI model.

[0156] Input: Resume information in text format

[0157] Output: Analysis results from the generative AI model

[0158] Specific operation: The server inputs the prompt "Please create a professional resume based on this information" into a generative AI model (e.g., GPT-4).

[0159] Step 4:

[0160] The server generates the optimal resume text and sends it to the terminal.

[0161] Input: Analysis results from generative AI model

[0162] Output: Optimized resume text

[0163] What it does: The server generates the following sentence and sends it to the device: "I have worked as a software engineer for five years and have successfully completed multiple projects using primarily Python and Java. I am particularly interested in AI-related technologies and would like to further my career in this field."

[0164] Step 5:

[0165] The terminal displays the generated resume text to the user.

[0166] Input: Optimized resume text

[0167] Output: User confirmation screen

[0168] Specific operation: The terminal displays the resume text to the user, who can then review and edit it.

[0169] Interview support function

[0170] Step 1:

[0171] The user inputs an instruction to start the interview practice into the terminal.

[0172] Input: Instruction to start interview practice

[0173] Output: Data sent to the server

[0174] Specific action: The user clicks the "Start Practice" button on the device.

[0175] Step 2:

[0176] The terminal transmits the user's profile data to the server.

[0177] Input: User profile data

[0178] Output: Send to server

[0179] What happens: The device sends profile data using the HTTPS protocol.

[0180] Step 3:

[0181] The server generates interview questions based on the profile data.

[0182] Input: User profile data

[0183] Output: Generated interview questions

[0184] Specific operation: The server inputs the prompt "Please create appropriate interview questions for users seeking a data scientist position" into a generation AI model (e.g., GPT-4), which then generates the question "How did you choose data analysis methods in past projects?"

[0185] Step 4:

[0186] The terminal displays the generated interview questions to the user.

[0187] Input: Generated interview questions

[0188] Output: User response screen

[0189] Specific operation: The terminal displays the interview questions to the user.

[0190] Step 5:

[0191] The user enters answers to the interview questions and the terminal transmits them to the server.

[0192] Input: Answers to interview questions

[0193] Output: Send to server

[0194] Specific action: The user types into the terminal, "Based on the project requirements, I selected a data cleaning technique using Pandas in Python" and submits.

[0195] Step 6:

[0196] The server analyzes the answers and generates feedback.

[0197] Input: User's answer

[0198] Output: Feedback

[0199] Specific behavior: The server uses a generative AI model (e.g., GPT-4) to evaluate the answer and generate feedback such as, "Your answer is somewhat vague. Please briefly describe the specific dataset, analytical tools used, and results."

[0200] Step 7:

[0201] The terminal displays the generated feedback to the user.

[0202] Input: Feedback

[0203] Output: User confirmation screen

[0204] Specific behavior: The device displays feedback to the user.

[0205] Step 8:

[0206] The server generates new interview questions and sends them to the terminal.

[0207] Input: Evaluation result

[0208] Output: New interview question

[0209] Specific operation: The server generates a new question, "How did the analysis results contribute to the outcome of the project?" and sends it to the device.

[0210] Recommendations for suitable jobs and companies

[0211] Step 1:

[0212] The user enters information about their skills and desired job type into the terminal and submits it.

[0213] Input: Information about the user's skills and desired job

[0214] Output: Send to server

[0215] Specific behavior: The user enters "I have skills in Python and Java and am interested in AI-related jobs" and submits.

[0216] Step 2:

[0217] The server analyzes the user's input data and searches for appropriate jobs and companies.

[0218] Input: Information about the user's skills and desired job

[0219] Output: Search results (suitable jobs and companies)

[0220] Specific operation: The server inputs the prompt statement "Please recommend the best job listings for users who have Python and Java skills and an interest in the field of AI" into the generative AI model (e.g., a recommendation system).

[0221] Step 3:

[0222] The server generates recommendations and sends them to the device.

[0223] Input: Analysis results from generative AI model

[0224] Output: Recommendation results

[0225] Specific operation: The server generates recommendation results such as "AI software engineer job: XX company" and "Data scientist job: △△ company" and sends them to the device.

[0226] Step 4:

[0227] The terminal displays the recommendation results to the user.

[0228] Input: Recommendation results

[0229] Output: User confirmation screen

[0230] Specific operation: The terminal displays the recommendation results to the user, who then confirms them.

[0231] (Application example 1)

[0232] 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."

[0233] While existing resume creation and interview support systems support general work history and skill sets, they lack functionality tailored to specific industries, particularly the food delivery industry. As a result, users have difficulty efficiently conducting job searches in the food delivery industry, and it takes a great deal of effort to find suitable jobs and companies. Furthermore, interview practice sessions lack questions and feedback specific to the food delivery industry, preventing users from fully improving their interview skills.

[0234] 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.

[0235] In this invention, the server includes: a means for a user to input information about their work history, skill set, and desired industry and job type; a means for transmitting the input information to the server; a means for the server to generate resume text using a natural language processing algorithm based on the input information; a means for the server to transmit the generated resume text to a terminal; a means for the terminal to display the resume text to the user; a means for generating optimal questions based on the user's desired job type and position using data related to the food delivery industry; a means for recommending appropriate jobs and companies based on the user's input data; a means for displaying the relevant questions and recommendation results on the terminal; and a means for generating and transmitting feedback to support the user's interview practice. This enables effective and efficient job hunting in the food delivery industry. Furthermore, users can improve their interview skills by receiving interview practice and feedback tailored to the food delivery industry.

[0236] "A means for users to input information about their work experience, skill set, and desired industry or job type" refers to an interface or device that allows users to electronically input information about their past work experience, skills, and desired industry or job type.

[0237] The "means for transmitting the input information to the server" is a communication function for transmitting the data input by the user to the server via a network.

[0238] "The means by which the server generates resume text using a natural language processing algorithm based on the input information" refers to a function in which the server analyzes the data it receives, converts it into appropriate words and structures using natural language processing technology, and automatically generates resume text.

[0239] The "means for transmitting the resume text generated by the server to the terminal" is a communication function that enables the server to transmit the generated resume text to the user's terminal.

[0240] The "means for the terminal to display the resume text to the user" is a function for the user's terminal to display the resume text received on the screen.

[0241] "A means of using data related to the food delivery industry to generate optimal questions for the job type or position desired by the user" is a function that utilizes knowledge and data in the food delivery industry to automatically generate interview questions suitable for the job type or position the user is aiming for.

[0242] "Means of recommending appropriate jobs and companies based on user input data" refers to a function that uses an algorithm to identify and recommend suitable jobs and companies based on information provided by the user.

[0243] The "means for displaying the relevant questions and recommendation results on the terminal" is a function for displaying the generated interview questions and company recommendation results on the user's terminal.

[0244] The "means for generating feedback to assist the user in practicing for an interview and sending it to the terminal" is a function for evaluating the interview answers entered by the user, generating feedback providing suggestions for improvement and specific advice, and sending it to the terminal.

[0245] The "means for inputting an instruction to start interview practice into the terminal" is an interface that allows the user to input into the terminal his / her intention to start interview practice.

[0246] The "means for transmitting user profile data to a server" is a communication function for transmitting user profile information to a server.

[0247] The "means for generating interview questions and transmitting them to the terminal" is a function for generating appropriate interview questions based on the user's profile data and transmitting them to the terminal.

[0248] "Means for inputting answers to interview questions and for the terminal to transmit the answers to a server" is a function that allows a user to input answers to interview questions and transmit the input answers to a server.

[0249] The "means for evaluating the answer, generating feedback, and transmitting the feedback to the terminal" is a function in which the server evaluates the user's answer, generates feedback, and transmits the feedback to the terminal.

[0250] The "means for generating new interview questions and transmitting them to the terminal" is a function for referencing the content of the user's answers, generating further interview questions, and transmitting them to the terminal.

[0251] "Means for considering recommendation results specialized for job types and positions in the food delivery industry" refers to a function that provides optimal recommendation results specialized for various job types and positions in the food delivery industry.

[0252] The present invention relates to a system for supporting job hunting in the food delivery industry. Specific embodiments are described in detail below.

[0253] System Configuration

[0254] This system consists of a user terminal and a server. Using the terminal, users input information about their work history, skill set, and the industry and job type they are aiming for. The input information is sent to the server, which uses a natural language processing algorithm to generate resume text based on that information and sends it to the user's terminal. When the user inputs a command to start interview practice into the terminal, the server generates new interview questions based on the user's profile data and sends them to the terminal. The server then recommends the most suitable jobs and companies based on the user's input data and displays them on the terminal.

[0255] Hardware and software used

[0256] Hardware:

[0257] User device: Input device such as a smartphone or PC

[0258] Server: A cloud server with high-performance processing capabilities (e.g., Amazon Web Services, Google Cloud Platform)

[0259] software:

[0260] Natural Language Processing Algorithms: OpenAI® generative AI models (e.g., GPT-3®)

[0261] Communication protocol: HTTPS ensures secure data transmission

[0262] Data processing and calculation

[0263] Resume creation

[0264] 1. Obtaining input data:

[0265] Users enter information about their work history, skill set, and the industry or job they are aiming for on the device.

[0266] 2. Data transmission:

[0267] Data entered on the terminal is sent to the server.

[0268] 3. Natural Language Processing:

[0269] The server analyzes the received data and generates resume text using a generative AI model.

[0270] 4. Data return:

[0271] The generated resume text is sent to the user's terminal and displayed on the screen.

[0272] As a specific example, if a user enters "3 years of experience as a food delivery person. Main skills are quick delivery and customer service," the server generates a resume sentence that reads, "I have worked as a food delivery person for 3 years and have excellent skills in quick delivery and customer service."

[0273] Interview support

[0274] 1. Start your interview practice:

[0275] The user inputs an instruction to start the interview practice into the terminal.

[0276] 2. Sending profile data:

[0277] The user's profile data is sent to the server.

[0278] 3. Generate interview questions:

[0279] The server generates appropriate interview questions based on the profile data and sends them to the terminal.

[0280] 4. Enter and evaluate your answers:

[0281] The user enters an answer into the terminal and sends it to the server, which evaluates the answer and generates feedback.

[0282] 5. Generate new questions:

[0283] Based on the feedback, the server generates new interview questions and sends them to the device, and this process is repeated until the user improves their interviewing skills.

[0284] For example, if a user enters "Desired job: Area manager," the server generates a question such as "Tell us about your past team management experience." If the user answers, "I reduced delivery time by 30% and improved customer satisfaction," the server provides feedback such as, "It would be even better if you provided specific methods and results."

[0285] Job / company recommendations

[0286] 1. Data Analysis:

[0287] Analyzes user input data to identify the most suitable job types and companies.

[0288] 2. Submit your recommendation:

[0289] The server sends the recommendation results to the terminal and displays them to the user.

[0290] For example, if a user enters "I'm interested in an area manager position with the skills to deliver quickly and handle customers," the server will display recommendations such as "Area Manager Position: XX Delivery Company."

[0291] Prompt Sentence Examples

[0292] Here are some examples of prompts for generative AI models:

[0293] Resume writing prompt:

[0294] text

[0295] User-specified work experience and skill set: 3 years of experience as a food delivery driver. Key skills include fast delivery and customer service. Create your food delivery resume.

[0296] Interview question generation prompt:

[0297] text

[0298] User Profile: Desired Job: Area Manager. Generate appropriate interview questions related to the food delivery industry.

[0299] Interview response evaluation prompt:

[0300] text

[0301] User Interview Response: We've reduced delivery time by 30% and increased customer satisfaction in past projects. Please rate this response and provide suggestions for improvement.

[0302] In this way, users can receive support in effectively pursuing a job in the food delivery industry.

[0303] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0304] Step 1:

[0305] Obtaining input data

[0306] The user uses the device to input information about their work history, skill set, and the industry and job they are aiming for. All input data is saved in text format on the device. For example, a user might input "3 years of experience as a food delivery person. My main skills are quick delivery and customer service."

[0307] Step 2:

[0308] Data transmission

[0309] The device sends the acquired input data to the server. The data is securely transmitted using the HTTPS protocol. The input data consists of text information such as work history, skill set, and desired job type.

[0310] Step 3:

[0311] Resume generation using natural language processing

[0312] The server uses a natural language processing algorithm to generate resume text based on the received input data. Specifically, it uses a generative AI model (e.g., GPT-3) to analyze and process the input data. Resume text is generated according to the prompt, and output in the form of "I have worked as a food delivery driver for three years and have excellent skills in prompt delivery and customer service."

[0313] Step 4:

[0314] Returning generated resume data

[0315] The server sends the generated resume text back to the device, which also sends it securely using the HTTPS protocol. The device then saves the received resume text data.

[0316] Step 5:

[0317] View resume

[0318] The terminal displays the resume text received from the server to the user, who can then log in and check and edit the resume displayed on the screen.

[0319] Step 6:

[0320] Interview practice begins

[0321] The user inputs an instruction to start the interview practice on the terminal. Information about the desired job type and position is also input as the initial setting. For example, the user inputs "Desired job type: Area manager."

[0322] Step 7:

[0323] Sending profile data

[0324] The terminal transmits an instruction to start the interview practice and the user's profile data to the server.

[0325] Step 8:

[0326] Generate and submit interview questions

[0327] The server generates interview questions using a generative AI model based on the received profile data. The generated questions are sent to the device. For example, a question such as "Tell us about your past team management experience" is generated.

[0328] Step 9:

[0329] User answer input

[0330] The user enters answers to the interview questions displayed on the terminal. The answers are saved in text format on the terminal. For example, the user might enter "We reduced delivery time by 30% and improved customer satisfaction."

[0331] Step 10:

[0332] Submitting and rating answers

[0333] The device sends the input answers to the server, which uses a natural language processing algorithm to evaluate the received answers and generate feedback, such as "It would be even better if you provided specific methods and results."

[0334] Step 11:

[0335] Sending and Viewing Feedback

[0336] The server sends the generated feedback to the terminal, and the terminal displays the received feedback to the user, who can then further refine the content of their answer based on the feedback.

[0337] Step 12:

[0338] Generate new interview questions

[0339] The server generates new interview questions based on the user's past answers and sends them to the device, and this process is repeated until the user has sufficiently improved their interviewing skills.

[0340] Step 13:

[0341] Job / company recommendations

[0342] The server analyzes the data entered by the user and identifies suitable job types and companies in the food delivery industry. The server then sends the recommendation results to the device and displays them to the user. For example, a recommendation such as "Area Manager: XX Delivery Company" may be displayed. The user can use this as a reference to advance their job search.

[0343] Through the above process, the present invention provides comprehensive support for users to efficiently advance their job search in the food delivery industry.

[0344] 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.

[0345] The present invention relates to a system that supports users in their job hunting activities. This system recognizes the user's emotions by combining emotion engines and optimizes resume creation and interview support based on those emotions. Detailed embodiments of each function, the operation of the entire system, and specific examples are described below.

[0346] 1. Resume creation function

[0347] The user uses a device to input information about their past work experience, skill set, and the industry and job type they are aiming for. The emotion engine recognizes the user's emotions as they enter their information, and the device sends this data to a server. The server uses the received information and the user's emotional data to optimize a natural language processing algorithm and generate resume text. The generated resume is sent to the device, which displays it to the user.

[0348] Example: A user enters "5 years of experience as a software engineer. My main skills are Python and Java. I'm interested in AI" into a device, and the emotion engine recognizes the user's emotion as "confident." The device then sends the information and emotion data to the server, which then generates the following sentence: "I've worked as a software engineer for 5 years and have successfully completed multiple projects using mainly Python and Java. I'm particularly interested in AI-related technologies and aspire to further growth in that field." and sends it to the device. The user can then review and edit this sentence displayed on the device.

[0349] 2. Interview support function

[0350] To begin interview practice, the user issues a command to start the simulation from their device. The emotion engine sends the user's profile data and emotion data to the server in real time. The server generates appropriate interview questions based on the profile data and emotion data and sends them to the device. The user enters answers to the interview questions displayed on the device, and the device sends the answers and emotion data to the server. The server analyzes and evaluates them, generates feedback and the next question, and sends them to the device.

[0351] For example, if a user says, "I'm looking for a data scientist position," the server generates a question such as, "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the emotion engine recognizes this as "nervous." The server analyzes the content and emotion data and provides feedback such as, "Your answer is a little vague. Please briefly describe the specific dataset, analysis tools used, and results." Based on this emotion data, the next question can be generated in a more appropriate format.

[0352] 3. Recommendations for suitable jobs and companies

[0353] Based on the user's input data and emotional data, the server recommends the most suitable jobs and companies for the user. The server analyzes the user's work history, skill set, interests, and emotional data to provide appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[0354] Example: A user enters "I have skills in Python and Java and am interested in AI-related jobs," and the emotion engine recognizes this as "full of expectations." Based on this, the server recommends job listings for software engineering positions in the AI ​​field, and displays recommended results on the device, such as "AI software engineer positions: Company X" and "Data scientist positions: Company YY."

[0355] As described above, the job change support system of the present invention allows users to effectively create resumes, prepare for interviews, and find the most suitable jobs and companies while taking into consideration their feelings, which allows users to smoothly progress in their job change activities and obtain highly satisfying results.

[0356] The processing flow will be explained below.

[0357] 1. Resume creation function

[0358] Step 1:

[0359] Users use the terminal to enter information about their past work experience, skill set, and the industry and job type they are aiming for.

[0360] Step 2:

[0361] The emotion engine recognizes the emotion the user is feeling when inputting and transmits the emotion data to the terminal.

[0362] Step 3:

[0363] The terminal transmits the user's input data and emotion data to the server.

[0364] Step 4:

[0365] The server then runs the received data through a natural language processing algorithm to generate the optimal resume text, taking into account emotional data to adjust the tone and content of the text.

[0366] Step 5:

[0367] The server sends the generated resume text to the terminal.

[0368] Step 6:

[0369] The terminal displays the resume content received from the server to the user.

[0370] Step 7:

[0371] The user checks the resume displayed on the terminal and makes corrections as necessary.

[0372] 2. Interview support function

[0373] Step 1:

[0374] To start the interview practice, the user issues a command to start the simulation from the terminal.

[0375] Step 2:

[0376] The emotion engine recognizes the user's profile data and emotion data in real time and transmits it to the terminal.

[0377] Step 3:

[0378] The terminal transmits the user's profile data and emotion data to the server.

[0379] Step 4:

[0380] The server generates appropriate interview questions based on the profile data and emotion data and transmits them to the terminal.

[0381] Step 5:

[0382] The user inputs answers to the interview questions displayed on the terminal.

[0383] Step 6:

[0384] The emotion engine recognizes the user's emotions in real time when answering and transmits them to the device.

[0385] Step 7:

[0386] The device sends the user's response and emotion data to the server.

[0387] Step 8:

[0388] The server analyzes the received responses and emotional data and generates feedback, adjusting the content and tone of the feedback based on the emotional data.

[0389] Step 9:

[0390] The server generates feedback and sends it to the device.

[0391] Step 10:

[0392] The device displays the feedback to the user.

[0393] Step 11:

[0394] The server generates new interview questions based on the feedback and transmits them to the terminal.

[0395] 3. Recommendations for suitable jobs and companies

[0396] Step 1:

[0397] The user uses the terminal to input data such as work history, skill set, and interests.

[0398] Step 2:

[0399] The terminal sends the input data to the server.

[0400] Step 3:

[0401] The emotion engine recognizes the emotion the user is feeling when inputting and transmits the emotion data to the terminal.

[0402] Step 4:

[0403] The device transmits the emotion data to the server.

[0404] Step 5:

[0405] The server analyzes the input data and emotional data and recommends the most suitable jobs and companies.

[0406] Step 6:

[0407] The server sends the recommendation results to the terminal.

[0408] Step 7:

[0409] The terminal displays the recommendation results to the user.

[0410] Example 2

[0411] 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."

[0412] Conventional job-hunting support systems provide support for resume creation and interview practice without considering the user's emotions, which means that they are unable to provide optimal support tailored to the user's individual situation and emotions. As a result, users often receive suggestions and support that do not match their emotional state, which can cause their job hunting to stall or result in unsatisfactory results.

[0413] 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.

[0414] In this invention, the server includes means for a user to input information about their work history, skill set, and desired industry and occupation, means for transmitting the input information to the server, means for recognizing the user's emotions using an emotion engine and generating data, means for the server to generate resume text using a generative AI model based on the input information and emotion recognition data, means for transmitting the resume text generated by the server to a terminal, and means for the terminal to display the resume text to the user. This supports job hunting while taking the user's emotions into consideration, enabling effective resume creation, interview preparation, and recommendations for suitable occupations and companies.

[0415] "User" refers to an individual who uses the system to search for a new job.

[0416] "Terminal" means an electronic device through which a user inputs information and receives responses from a system. Examples include computers, smartphones, and tablets.

[0417] "Server" refers to a computer system that receives data sent from a user, processes it, and sends the generated results to a terminal.

[0418] "Work history" refers to information indicating the job content and work history of the user.

[0419] "Skill set" refers to information that lists the skills, knowledge, qualifications, etc. that a user possesses.

[0420] "Desired industry and occupation" refers to information about the industry and occupation to which the user wishes to transfer.

[0421] An "emotion engine" refers to software or algorithms that recognize a user's emotional state from their input or behavior and generate emotional data.

[0422] "Emotion recognition data" refers to information generated by the emotion engine regarding the user's emotional state.

[0423] A "generative AI model" refers to an artificial intelligence algorithm that generates appropriate sentences and answers based on user input information and emotion recognition data.

[0424] "Resume Text" refers to the textual content of the resume created by the generative AI model using user input and emotion recognition data.

[0425] "Interview Questions" refers to questions used in practice interviews that are generated by the server based on the user's profile data.

[0426] "Feedback" refers to a response that evaluates the user's practice interview answers and includes suggestions for improvement and advice.

[0427] "Recommendation results" refer to information about the most suitable jobs and companies for a user, generated by the server based on the user's input data and emotion recognition data.

[0428] The present invention relates to a system that supports users in their job hunting activities, and in particular, a system that recognizes the user's emotions by combining an emotion engine and a generative AI model, and then creates a resume, supports interviews, and recommends the most suitable jobs and companies based on those emotions. A specific embodiment of the present invention is described below.

[0429] Users input information about their work history, skill set, and desired industry or occupation into the device. The device temporarily stores this input information, then activates an emotion engine to recognize the user's emotions and generate emotion data. This emotion data and the input information are then sent to the server.

[0430] The server analyzes the received input information and emotional data, and generates resume text using a generative AI model (e.g., GPT-4 or an equivalent natural language processing algorithm). This generated resume text is then sent back to the device, where it is displayed to the user.

[0431] As a specific example, if a user inputs information such as "Five years of experience as a software engineer. Main skills are Python and Java. Interested in AI" into the device and the emotion engine recognizes this as "confident," the server will generate a sentence based on the received data: "I have worked as a software engineer for five years and have successfully completed multiple projects using mainly Python and Java. I am particularly interested in AI-related technologies and am aiming to further grow in that field." and send this to the device. The user can review and edit the displayed resume text.

[0432] Furthermore, when a user starts an interview practice, they can issue a command to start the simulation from their device, which causes the emotion engine to recognize and update the user's emotions in real time and send them to the server along with the user's profile data. The server then generates appropriate interview questions based on the user's profile data and emotion data and sends them to the device. When the user answers the questions, the answers and emotion data are sent back to the server, which analyzes them to generate feedback and the next question, which are then sent to the device.

[0433] For example, if a question such as "How did you choose your data analysis method in past projects?" is generated for a user hoping to become a data scientist, and the user answers "Based on the project requirements, I chose a data cleaning method using Python's Pandas," and the emotion engine recognizes this as "nervous," the server will generate feedback such as "Your answer is a little vague. Please briefly describe the specific dataset, the analysis tools used, and the results." In this way, the next question can be generated in a more appropriate format based on the emotion data.

[0434] Furthermore, the server recommends the most suitable jobs and companies for each individual based on the user's input data and emotional data. Specifically, it analyzes work history, skill set, interests, and emotional data to provide appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[0435] For example, if a user enters "I have skills in Python and Java and am interested in AI-related jobs" and the emotion engine recognizes this as "full of expectations," the server will generate recommendation results such as "AI software engineer jobs" and "data scientist jobs" and send them to the device.

[0436] As described above, the job change support system of the present invention can effectively create a resume, prepare for an interview, and find the most suitable job type and company while taking into consideration the user's feelings, which allows the user's job change search to proceed more smoothly and achieve a highly satisfying result.

[0437] Prompt Sentence Examples

[0438] 1. Example resume prompts:

[0439] "A user confidently enters his background as a software engineer. Use a natural language processing algorithm to generate a resume based on his experience."

[0440] 2. Example prompts for interview support:

[0441] "A user is nervous about attending a practice interview. Generate appropriate interview questions based on his past work and provide feedback."

[0442] 3. Example job recommendation prompts:

[0443] "Users are excited and interested in AI-related jobs. Please recommend suitable jobs and companies for users with Python and Java skill sets."

[0444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0445] Step 1: User enters information using the terminal

[0446] The user enters information about their work history, skill set, and desired industry and job type into an input form on the device. The entered information is temporarily stored on the device. The input data includes specific details such as "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI." This input data is used for processing in the next step.

[0447] Step 2: Emotion recognition by the emotion engine

[0448] The device activates the emotion engine, which analyzes the user's input data and recognizes emotions. The emotion engine uses input speed and data from activated sensors to quantify the user's emotions. This emotion recognition data indicates a specific emotional state, such as "confident." This data is then sent to the server in the next step.

[0449] Step 3: The device sends the data to the server

[0450] The device sends the user's input information and emotion recognition data to the server. By sending the input data and emotion recognition data together, the server can process the data comprehensively. For example, data such as "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI" and "Full of confidence" are sent.

[0451] Step 4: Analyze data on the server and use generative AI models

[0452] The server analyzes the input data and emotion recognition data it receives and uses a generative AI model to generate resume sentences. Based on the input data and emotion recognition data, a natural language processing algorithm such as GPT-4 runs and generates appropriate sentences. For example, the server might generate a sentence like, "I have worked as a software engineer for five years, primarily using Python and Java, and have successfully completed multiple projects. I am particularly interested in AI-related technologies and hope to further my growth in this field."

[0453] Step 5: Send the server-generated text to the terminal

[0454] The server sends the generated resume text to the device. The generated text is converted into a format that the device can display and sent so that the user can understand it. For example, a text like "I have worked as a software engineer for five years and have successfully completed multiple projects using mainly Python and Java. I am particularly interested in AI-related technologies and hope to further grow in that field." is sent to the device.

[0455] Step 6: The terminal displays the generated text to the user.

[0456] The device displays the resume text received from the server to the user. The user can review the generated text and enter corrections or additional information as needed. For example, if the user wants to add specific project experience related to AI, they can edit that section. This correction information is sent back to the server and used for the next data processing.

[0457] The above is the specific flow of processing, from when a user inputs information about their work history, skill set, and desired industry and job type, to when emotions are recognized by the emotion engine, and when resume text is generated and displayed using a generative AI model on the server, users can easily create the optimal resume that takes their emotions into account.

[0458] (Application example 2)

[0459] 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."

[0460] Conventional job-change support systems do not take the user's emotions into consideration, which can result in suboptimal resumes and interview support. Furthermore, they are unable to properly address the user's nervousness and anxiety during interview practice, making it difficult to conduct effective practice. Furthermore, because they do not take the user's emotions into consideration when recommending optimal jobs and companies, the user may not be satisfied with the results. To address these issues, the present invention aims to provide a system that recognizes the user's emotions and effectively supports job-change activities based on those emotions.

[0461] 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.

[0462] In this invention, the server includes: means for a user to input information about their work history, skill set, and desired industry and occupation; means for transmitting the input information to the server; means for the server to generate resume text using a natural language processing algorithm based on the input information; means for the server to transmit the generated resume text to a terminal; means for the terminal to display the resume text to the user; emotion recognition means for recognizing the user's emotions; means for transmitting emotion data obtained by the emotion recognition means to the server; and means for the server to optimize resume creation and interview support based on the input information and the emotion data. This enables effective job change support that takes the user's emotions into consideration, allowing the user to obtain more satisfactory results.

[0463] "Work history" refers to the history of the jobs and positions held by the user in the past, and is information including the specific work content and period of time.

[0464] A "skill set" is a collection of specialized skills and knowledge that a user possesses, and refers to skills that are useful in the workplace in a particular job or industry.

[0465] The "target industry or occupation" refers to the industry or occupation the user wishes to work in in the future, and includes specific job content and type of industry.

[0466] A "natural language processing algorithm" is a computer program that analyzes input text data and performs appropriate document creation and information extraction.

[0467] "Emotion recognition means" refers to technology or devices for detecting emotions from a user's facial expressions, voice, actions, etc., and analyzing that data.

[0468] A "resume text" is a document that describes a user's career history, skills, etc. for use in job hunting, and is generated using a natural language processing algorithm.

[0469] "Interview Practice" is a simulation activity that allows users to improve their question-answering and communication skills in preparation for a real interview.

[0470] "Feedback" refers to evaluations and advice on the user's actions and answers, and is information indicating the next action or areas for improvement.

[0471] The "recommendation results" are a list of occupations and companies that the server suggests based on the user's aptitude and desires.

[0472] This invention will specifically explain a system that combines emotion recognition technology to help users effectively conduct job hunting. The system is configured as follows:

[0473] First, the user inputs information about their work history, skill set, and the industry and job type they are aiming for into their terminal. The terminal then sends this input information to the server. The server uses a natural language processing algorithm based on the information to generate resume text. The generated resume text is then sent from the server to the terminal and displayed to the user. At this time, emotion recognition means is used to analyze the user's emotions in real time. For example, if emotion data indicating that the user is "confident" is obtained when creating a resume, the server will generate resume text using positive expressions that correspond to that emotion.

[0474] Next, if the user wants to practice for an interview, they input instructions for the interview practice into the terminal. The terminal sends the user's profile data and emotional data to the server. The server generates appropriate interview questions based on this data and sends them to the terminal. When the user answers the interview questions, the terminal sends the answers to the server. The server analyzes the answers and emotional data, generates feedback, and sends it to the terminal. For example, if the user's emotion is recognized as "nervous," the server provides advice to ease the tension or the next question to ask.

[0475] Finally, the server recommends suitable jobs and companies based on the user's input data and emotional data. The server then sends these recommendations to the user's device and displays them to the user. For example, if a user inputs "I'm interested in AI" and the emotion is recognized as "full of hope," the server will prioritize job listings related to AI.

[0476] The system of the present invention is realized using multiple hardware and software components. Emotion recognition uses hardware such as an emotion recognition camera and microphone, as well as an emotion recognition module. Natural language processing algorithms are used for natural language processing. A voice synthesis library is also used for voice feedback. This system recognizes users' emotions in real time and effectively supports job hunting.

[0477] For example, if a factory line worker is tired, the emotion recognition means will recognize this and the robot will ask, "Would you like to take a short break?" The robot will also reduce the burden on the worker by appropriately adjusting the work speed.

[0478] Examples of prompts to input to a generative AI model include:

[0479] "Generate example code for an emotion-aware robot assistant that 'prompts a worker when they are tired and adjusts their work speed.'"

[0480] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0481] Step 1:

[0482] The user inputs information about their work experience, skill set, and desired industry and occupation into the device. The device then compiles this information and sends it to the server. Specifically, the user inputs text-based information through the input interface on the device, which is then sent to the server in JSON format. The input includes work experience, skill set, and desired industry and occupation, and becomes the input data for the server.

[0483] Step 2:

[0484] The server receives the input information and generates resume text using a natural language processing algorithm. First, it analyzes the received data and applies a natural language processing algorithm to generate the resume text. Specific processes include text analysis, grammar formatting, and skill matching. The generated resume draft is obtained as an output.

[0485] Step 3:

[0486] The server sends the generated resume text to the terminal. The data is sent in a normal text file or HTML format, and is sent to the terminal in a format suitable for display. This allows the terminal to receive the data needed to display the generated resume to the user.

[0487] Step 4:

[0488] The terminal displays the resume text to the user. The received data is visualized through a display interface and presented in a format that is easy for the user to review. The user can then check the resume content on the display of their smartphone or computer. Here, they can also correct or edit the resume.

[0489] Step 5:

[0490] The emotion recognition means recognizes the emotions expressed by the user when creating a resume. The hardware used is an emotion recognition camera and microphone, which analyzes the user's facial expressions and voice in real time. The recognized emotion data is sent from the device to the server in JSON format. The emotion recognition data is obtained as input.

[0491] Step 6:

[0492] The server receives the emotion data and optimizes resume creation or interview support based on the user's input information and the emotion data. The emotion data is analyzed and the resume style and wording are adjusted according to the user's emotional state. The generation of interview questions and feedback are also optimized based on the emotion data. The optimized resume and interview questions are obtained as output.

[0493] Step 7:

[0494] When a user wants to practice for an interview, he or she inputs instructions for the interview into the terminal. The instructions are given through an interface such as a touch screen or keyboard, and the terminal receives the instructions.

[0495] Step 8:

[0496] The terminal transmits the user's profile data and emotion data to the server, which includes the user's profile information and emotion data acquired in real time, and the data is used by the server to generate and optimize interview questions.

[0497] Step 9:

[0498] The server generates appropriate interview questions based on the profile data and emotion data, and sends them to the terminal. The server generates interview questions using a natural language processing algorithm and sends them back to the terminal. The generated interview questions are obtained as an output.

[0499] Step 10:

[0500] When the user answers the interview questions, the device sends the answers to the server. The answers are entered in text or voice format and sent to the server. The user's answer data is obtained as input.

[0501] Step 11:

[0502] The server analyzes the response, generates feedback, and transmits it to the terminal. The server analyzes the response based on the content and emotion data of the response and generates appropriate feedback. Feedback data is obtained as an output.

[0503] Step 12:

[0504] The server recommends the most suitable jobs and companies based on the user's input data and emotional data. This includes the process of extracting suitable job information based on the user's background, skills, and emotional data. The server then sends the recommendation results to the terminal and displays them to the user. The output is a list of recommended jobs and companies.

[0505] 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.

[0506] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0507] 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.

[0508] [Second embodiment]

[0509] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0510] 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.

[0511] 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).

[0512] 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.

[0513] 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.

[0514] 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).

[0515] 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. 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.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] 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."

[0521] The present invention relates to a system for supporting users in their job hunting activities. The main features of this system are a resume creation function and an interview support function. Detailed embodiments of each function and the operation of the entire system are described below.

[0522] 1. Resume creation function

[0523] The user uses a device to input information about their past work experience, skill set, and the industry and job they are aiming for. The device then sends this input data to a server, which then analyzes the received data using a natural language processing algorithm to generate the optimal resume text that will impress hiring managers.

[0524] Example: A user enters "5 years of experience as a software engineer. My main skills are Python and Java. I'm interested in AI" into a terminal. When the terminal sends this information to the server, the server generates a resume statement that reads, "I've worked as a software engineer for 5 years and have successfully completed multiple projects using mainly Python and Java. I'm particularly interested in AI-related technologies and would like to further my career in this field." and sends it to the terminal. The user can review and edit this statement displayed on the terminal.

[0525] 2. Interview support function

[0526] When a user inputs a command to start interview practice into the device, the device sends the user's profile data to the server. Based on this data, the server generates appropriate interview questions related to the industry and job type the user is aiming for and sends them to the device. The user then inputs answers to the questions displayed on the device and sends them to the server.

[0527] The server analyzes and evaluates the user's answers, generates suggestions for improvement and specific feedback, and sends them to the device.The server also generates new questions based on the user's responses and sends them to the device.

[0528] For example, if a user says, "I'm looking for a data scientist position," the server generates questions like, "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the server evaluates the answer and provides feedback like, "Your answer is a bit vague. Please briefly describe the specific dataset, the analytical tools used, and the results." This process is repeated until the user improves their interview skills.

[0529] 3. Recommendations for suitable jobs and companies

[0530] Based on the user's input data, the server recommends the most suitable jobs and companies for the user. The server analyzes the user's work history, skill set, and interests and provides appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[0531] For example, if a user enters "I have skills in Python and Java and am interested in AI-related jobs," the server will recommend job information for software engineering positions in the AI ​​field based on this information. Recommendations such as "AI software engineer jobs: Company X" and "Data scientist jobs: Company Y" will be displayed on the device.

[0532] As described above, the job change support system of the present invention supports users in effectively creating resumes, preparing for interviews, and finding the most suitable job type and company, which is expected to improve the efficiency and success rate of job change activities.

[0533] The processing flow will be explained below.

[0534] 1. Resume creation function

[0535] Step 1:

[0536] The user uses the terminal to enter information about their past work experience, skill set, and the industry and job type they are aiming for.

[0537] Step 2:

[0538] The terminal transmits the user's input data to the server.

[0539] Step 3:

[0540] The server runs the received data through a natural language processing algorithm to generate the most suitable resume text.

[0541] Step 4:

[0542] The server sends the generated resume text to the terminal.

[0543] Step 5:

[0544] The terminal displays the resume content received from the server to the user.

[0545] Step 6:

[0546] The user checks the resume displayed on the terminal and makes corrections as necessary.

[0547] 2. Interview support function

[0548] Step 1:

[0549] To start the interview practice, the user issues a command to start the simulation from the terminal.

[0550] Step 2:

[0551] The terminal transmits the user's profile data to the server.

[0552] Step 3:

[0553] The server generates appropriate interview questions based on the profile data and sends them to the terminal.

[0554] Step 4:

[0555] The user inputs answers to the interview questions displayed on the terminal.

[0556] Step 5:

[0557] The terminal sends the user's answer to the server.

[0558] Step 6:

[0559] The server analyzes and evaluates the user's answers and generates feedback.

[0560] Step 7:

[0561] The server generates feedback and sends it to the device.

[0562] Step 8:

[0563] The device displays the feedback to the user.

[0564] Step 9:

[0565] The server generates a new question based on the user's response and sends it to the terminal.

[0566] 3. Recommendations for suitable jobs and companies

[0567] Step 1:

[0568] The user uses the terminal to input data such as work history, skill set, and interests.

[0569] Step 2:

[0570] The terminal transmits the input data to the server.

[0571] Step 3:

[0572] The server analyzes the input data and recommends the most suitable jobs and companies.

[0573] Step 4:

[0574] The server sends the recommendation results to the terminal.

[0575] Step 5:

[0576] The terminal displays the recommendation results to the user.

[0577] Example 1

[0578] 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."

[0579] Conventional job-hunting support systems make it difficult for users to efficiently create resumes, prepare for interviews, and find the most suitable jobs and companies. Specifically, resume generation and interview questions are mechanical and not optimized for each user's individual profile. Furthermore, feedback during interview practice sessions is not specific, making it difficult for users to improve their interview skills. Furthermore, many of the job recommendations do not match the user's skill set or interests.

[0580] 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.

[0581] In this invention, the server includes means for a user to input information about his / her work history, skill set, and the industry and occupation he / she is aiming for, means for transmitting the input information to the server, means for the server to generate resume text using a natural language processing algorithm based on the input information, means for transmitting the resume text generated by the server to a terminal, means for the terminal to display the resume text to the user, means for the user to input an instruction to the terminal to start interview practice, means for transmitting profile data of the user to the server, means for the server to generate interview questions based on the profile data and transmit them to the terminal, and means for the user to input answers to the interview questions to the terminal and for the terminal to display the answers. The system includes means for transmitting answers to a server, means for the server to evaluate the answers and generate feedback and transmit it to the terminal, means for the server to generate new interview questions and transmit them to the terminal, means for the server to recommend the most suitable job types and companies for the individual based on the user's input data, means for the server to transmit the recommendation results to the terminal and display them to the user, means for the server to use a generative AI model as a natural language processing algorithm in the resume creation function and provide prompt sentences to the generative AI model, and means for the server to use the generative AI model when evaluating the user's answers and generate new questions and feedback based on the evaluation results in the interview support function. This enables the creation of resumes tailored to the individual needs of the user, specific and effective interview practice, and recommendations for the most suitable job types and companies.

[0582] "User" refers to an individual who uses this system to create a resume, practice interviews, or receive job recommendations.

[0583] "Work history" refers to the work history and work that the user has been involved in up to now.

[0584] A "skill set" refers to the skills and knowledge a user has acquired and their ability to apply them.

[0585] "Server" refers to a computer system that receives, analyzes, and processes information entered by a user.

[0586] A "natural language processing algorithm" refers to a program that analyzes text data entered by a user and converts it into natural language sentences.

[0587] "Resume text" refers to the text content of the resume generated by the server based on the user's work history and skill set.

[0588] A "terminal" is a device through which a user inputs information, and refers to a computer or smartphone that sends and receives information.

[0589] "Profile Data" refers to data including a user's personal information, work history, and skill set.

[0590] "Interview questions" refer to predicted questions that may be asked in an interview that are generated by the server based on the user's profile data.

[0591] An "answer" refers to a response that a user inputs to an interview question.

[0592] "Feedback" refers to the server's evaluation of the user's answers, including suggestions for improvement and advice.

[0593] "Recommendation results" refers to information on occupations and companies suitable for a user, which is analyzed by the server based on the user's input data.

[0594] "Generative AI model" refers to an artificial intelligence model used for natural language processing, question generation, and feedback evaluation.

[0595] A "prompt" refers to an instruction statement that instructs a generative AI model to perform a specific process.

[0596] The present invention relates to a system that supports users in their job-hunting activities. The main features of this system are a resume creation function, an interview support function, and a job / company recommendation function. Specific embodiments for carrying out the invention are described below.

[0597] 1. Resume creation function

[0598] Users use a device (such as a PC or smartphone) to enter information about their work history, skill set, and the industry and job they are aiming for into a dedicated form. This input data is sent from the device to a server (such as a cloud server). The server uses a generative AI model (e.g., GPT-4) based on the received data to generate resume text.

[0599] Hardware and software used:

[0600] Device: PC or smartphone

[0601] Server: Cloud server (e.g. AWS EC2)

[0602] Generative AI model: GPT-4

[0603] Examples:

[0604] When a user inputs "I have 5 years of experience as a software engineer. My main skills are Python and Java. I am interested in AI," the device sends this information to the server. The server then inputs the following prompt into the generative AI model: "Please create a professional resume based on this information." As a result, the server generates the following sentence: "I have worked as a software engineer for 5 years and have successfully completed multiple projects, primarily using Python and Java. I am particularly interested in AI-related technologies and would like to further my career in this field." and sends it to the device. The user can review this sentence and modify it as necessary.

[0605] 2. Interview support function

[0606] When a user inputs a command to start interview practice on their device, the device sends the user's profile data to a server. Based on this data, the server generates appropriate interview questions related to the user's desired industry and job type and sends them to the device. The user enters answers to the questions displayed on the device and sends them to the server. The server analyzes the user's answers using a generative AI model, evaluates the content, generates areas for improvement and specific feedback, and sends them to the device. Furthermore, the server generates new interview questions based on the user's responses and sends them to the device.

[0607] Hardware and software used:

[0608] Device: PC or smartphone

[0609] Server: Cloud server (e.g. AWS EC2)

[0610] Generative AI model: Various interview support AI (e.g., GPT-4)

[0611] Examples:

[0612] If the user "wants to work as a data scientist," the server inputs the prompt: "Please create an appropriate interview question for a user who wants to work as a data scientist" into the generative AI model, and generates the question: "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the server evaluates the content and provides feedback saying, "Your answer is a bit vague. Please briefly describe the specific dataset, analysis tools used, and results." This process is repeated until the user is satisfied.

[0613] 3. Recommendations for suitable jobs and companies

[0614] When a user inputs information about their desired job type and skills into the device, the device sends the data to the server. The server analyzes the user's data and uses a generative AI model to generate job information that recommends the most suitable job types and companies. The results are sent to the device and displayed to the user.

[0615] Hardware and software used:

[0616] Device: PC or smartphone

[0617] Server: Cloud server (e.g. AWS EC2)

[0618] Generative AI model: Job recommendation AI (e.g., recommendation system)

[0619] Examples:

[0620] When a user enters "I have Python and Java skills and am interested in AI-related jobs," the server inputs the prompt: "Please recommend the best job information for users who have Python and Java skills and an interest in the AI ​​field" into the generative AI model, generating recommendation results such as "AI software engineer job: XX company" and "Data scientist job: XX company" and sending them to the device. The user can review these and consider applying.

[0621] As described above, the system of the present invention uses a generative AI model based on data entered by the user to create resumes, assist with interview practice, and recommend appropriate jobs and companies, thereby improving the efficiency and success rate of job hunting.

[0622] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0623] Resume creation function

[0624] Step 1:

[0625] A user uses a terminal to enter resume information.

[0626] Input: Information about the user's work history, skill set, and desired industry or job type

[0627] Output: Resume information in text format

[0628] Specific operation: The user enters the following into a dedicated form on the device: "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI."

[0629] Step 2:

[0630] The terminal sends the input data to the server.

[0631] Input: Resume information in text format

[0632] Output: Send to server

[0633] Specific operation: The device sends data to the server using the HTTPS protocol.

[0634] Step 3:

[0635] The server inputs the received data into a generative AI model.

[0636] Input: Resume information in text format

[0637] Output: Analysis results from the generative AI model

[0638] Specific operation: The server inputs the prompt "Please create a professional resume based on this information" into a generative AI model (e.g., GPT-4).

[0639] Step 4:

[0640] The server generates the optimal resume text and sends it to the terminal.

[0641] Input: Analysis results from generative AI model

[0642] Output: Optimized resume text

[0643] What it does: The server generates the following sentence and sends it to the device: "I have worked as a software engineer for five years and have successfully completed multiple projects using primarily Python and Java. I am particularly interested in AI-related technologies and would like to further my career in this field."

[0644] Step 5:

[0645] The terminal displays the generated resume text to the user.

[0646] Input: Optimized resume text

[0647] Output: User confirmation screen

[0648] Specific operation: The terminal displays the resume text to the user, who can then review and edit it.

[0649] Interview support function

[0650] Step 1:

[0651] The user inputs an instruction to start the interview practice into the terminal.

[0652] Input: Instruction to start interview practice

[0653] Output: Data sent to the server

[0654] Specific action: The user clicks the "Start Practice" button on the device.

[0655] Step 2:

[0656] The terminal transmits the user's profile data to the server.

[0657] Input: User profile data

[0658] Output: Send to server

[0659] What happens: The device sends profile data using the HTTPS protocol.

[0660] Step 3:

[0661] The server generates interview questions based on the profile data.

[0662] Input: User profile data

[0663] Output: Generated interview questions

[0664] Specific operation: The server inputs the prompt "Please create appropriate interview questions for users seeking a data scientist position" into a generation AI model (e.g., GPT-4), which then generates the question "How did you choose data analysis methods in past projects?"

[0665] Step 4:

[0666] The terminal displays the generated interview questions to the user.

[0667] Input: Generated interview questions

[0668] Output: User response screen

[0669] Specific operation: The terminal displays the interview questions to the user.

[0670] Step 5:

[0671] The user enters answers to the interview questions and the terminal transmits them to the server.

[0672] Input: Answers to interview questions

[0673] Output: Send to server

[0674] Specific action: The user types into the terminal, "Based on the project requirements, I selected a data cleaning technique using Pandas in Python" and submits.

[0675] Step 6:

[0676] The server analyzes the answers and generates feedback.

[0677] Input: User's answer

[0678] Output: Feedback

[0679] Specific behavior: The server uses a generative AI model (e.g., GPT-4) to evaluate the answer and generate feedback such as, "Your answer is somewhat vague. Please briefly describe the specific dataset, analytical tools used, and results."

[0680] Step 7:

[0681] The terminal displays the generated feedback to the user.

[0682] Input: Feedback

[0683] Output: User confirmation screen

[0684] Specific behavior: The device displays feedback to the user.

[0685] Step 8:

[0686] The server generates new interview questions and sends them to the terminal.

[0687] Input: Evaluation result

[0688] Output: New interview question

[0689] Specific operation: The server generates a new question, "How did the analysis results contribute to the outcome of the project?" and sends it to the device.

[0690] Recommendations for suitable jobs and companies

[0691] Step 1:

[0692] The user enters information about their skills and desired job type into the terminal and submits it.

[0693] Input: Information about the user's skills and desired job

[0694] Output: Send to server

[0695] Specific behavior: The user enters "I have skills in Python and Java and am interested in AI-related jobs" and submits.

[0696] Step 2:

[0697] The server analyzes the user's input data and searches for appropriate jobs and companies.

[0698] Input: Information about the user's skills and desired job

[0699] Output: Search results (suitable jobs and companies)

[0700] Specific operation: The server inputs the prompt statement "Please recommend the best job listings for users who have Python and Java skills and an interest in the field of AI" into the generative AI model (e.g., a recommendation system).

[0701] Step 3:

[0702] The server generates recommendations and sends them to the device.

[0703] Input: Analysis results from generative AI model

[0704] Output: Recommendation results

[0705] Specific operation: The server generates recommendation results such as "AI software engineer job: XX company" and "Data scientist job: △△ company" and sends them to the device.

[0706] Step 4:

[0707] The terminal displays the recommendation results to the user.

[0708] Input: Recommendation results

[0709] Output: User confirmation screen

[0710] Specific operation: The terminal displays the recommendation results to the user, who then confirms them.

[0711] (Application example 1)

[0712] 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."

[0713] While existing resume creation and interview support systems support general work history and skill sets, they lack functionality tailored to specific industries, particularly the food delivery industry. As a result, users have difficulty efficiently conducting job searches in the food delivery industry, and it takes a great deal of effort to find suitable jobs and companies. Furthermore, interview practice sessions lack questions and feedback specific to the food delivery industry, preventing users from fully improving their interview skills.

[0714] 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.

[0715] In this invention, the server includes: a means for a user to input information about their work history, skill set, and desired industry and job type; a means for transmitting the input information to the server; a means for the server to generate resume text using a natural language processing algorithm based on the input information; a means for the server to transmit the generated resume text to a terminal; a means for the terminal to display the resume text to the user; a means for generating optimal questions based on the user's desired job type and position using data related to the food delivery industry; a means for recommending appropriate jobs and companies based on the user's input data; a means for displaying the relevant questions and recommendation results on the terminal; and a means for generating and transmitting feedback to support the user's interview practice. This enables effective and efficient job hunting in the food delivery industry. Furthermore, users can improve their interview skills by receiving interview practice and feedback tailored to the food delivery industry.

[0716] "A means for users to input information about their work experience, skill set, and desired industry or job type" refers to an interface or device that allows users to electronically input information about their past work experience, skills, and desired industry or job type.

[0717] The "means for transmitting the input information to the server" is a communication function for transmitting the data input by the user to the server via a network.

[0718] "The means by which the server generates resume text using a natural language processing algorithm based on the input information" refers to a function in which the server analyzes the data it receives, converts it into appropriate words and structures using natural language processing technology, and automatically generates resume text.

[0719] The "means for transmitting the resume text generated by the server to the terminal" is a communication function that enables the server to transmit the generated resume text to the user's terminal.

[0720] The "means for the terminal to display the resume text to the user" is a function for the user's terminal to display the resume text received on the screen.

[0721] "A means of using data related to the food delivery industry to generate optimal questions for the job type or position desired by the user" is a function that utilizes knowledge and data in the food delivery industry to automatically generate interview questions suitable for the job type or position the user is aiming for.

[0722] "Means of recommending appropriate jobs and companies based on user input data" refers to a function that uses an algorithm to identify and recommend suitable jobs and companies based on information provided by the user.

[0723] The "means for displaying the relevant questions and recommendation results on the terminal" is a function for displaying the generated interview questions and company recommendation results on the user's terminal.

[0724] The "means for generating feedback to assist the user in practicing for an interview and sending it to the terminal" is a function for evaluating the interview answers entered by the user, generating feedback providing suggestions for improvement and specific advice, and sending it to the terminal.

[0725] The "means for inputting an instruction to start interview practice into the terminal" is an interface that allows the user to input into the terminal his / her intention to start interview practice.

[0726] The "means for transmitting user profile data to a server" is a communication function for transmitting user profile information to a server.

[0727] The "means for generating interview questions and transmitting them to the terminal" is a function for generating appropriate interview questions based on the user's profile data and transmitting them to the terminal.

[0728] "Means for inputting answers to interview questions and for the terminal to transmit the answers to a server" is a function that allows a user to input answers to interview questions and transmit the input answers to a server.

[0729] The "means for evaluating the answer, generating feedback, and transmitting the feedback to the terminal" is a function in which the server evaluates the user's answer, generates feedback, and transmits the feedback to the terminal.

[0730] The "means for generating new interview questions and transmitting them to the terminal" is a function for referencing the content of the user's answers, generating further interview questions, and transmitting them to the terminal.

[0731] "Means for considering recommendation results specialized for job types and positions in the food delivery industry" refers to a function that provides optimal recommendation results specialized for various job types and positions in the food delivery industry.

[0732] The present invention relates to a system for supporting job hunting in the food delivery industry. Specific embodiments are described in detail below.

[0733] System Configuration

[0734] This system consists of a user terminal and a server. Using the terminal, users input information about their work history, skill set, and the industry and job type they are aiming for. The input information is sent to the server, which uses a natural language processing algorithm to generate resume text based on that information and sends it to the user's terminal. When the user inputs a command to start interview practice into the terminal, the server generates new interview questions based on the user's profile data and sends them to the terminal. The server then recommends the most suitable jobs and companies based on the user's input data and displays them on the terminal.

[0735] Hardware and software used

[0736] Hardware:

[0737] User device: Input device such as a smartphone or PC

[0738] Server: A cloud server with high-performance processing power (e.g., Amazon Web Services, Google Cloud Platform)

[0739] software:

[0740] Natural Language Processing algorithms: OpenAI generative AI models (e.g., GPT-3)

[0741] Communication protocol: HTTPS ensures secure data transmission

[0742] Data processing and calculation

[0743] Resume creation

[0744] 1. Obtaining input data:

[0745] Users enter information about their work history, skill set, and the industry or job they are aiming for on the device.

[0746] 2. Data transmission:

[0747] Data entered on the terminal is sent to the server.

[0748] 3. Natural Language Processing:

[0749] The server analyzes the received data and generates resume text using a generative AI model.

[0750] 4. Data return:

[0751] The generated resume text is sent to the user's terminal and displayed on the screen.

[0752] As a specific example, if a user enters "3 years of experience as a food delivery person. Main skills are quick delivery and customer service," the server generates a resume sentence that reads, "I have worked as a food delivery person for 3 years and have excellent skills in quick delivery and customer service."

[0753] Interview support

[0754] 1. Start your interview practice:

[0755] The user inputs an instruction to start the interview practice into the terminal.

[0756] 2. Sending profile data:

[0757] The user's profile data is sent to the server.

[0758] 3. Generate interview questions:

[0759] The server generates appropriate interview questions based on the profile data and sends them to the terminal.

[0760] 4. Enter and evaluate your answers:

[0761] The user enters an answer into the terminal and sends it to the server, which evaluates the answer and generates feedback.

[0762] 5. Generate new questions:

[0763] Based on the feedback, the server generates new interview questions and sends them to the device, and this process is repeated until the user improves their interviewing skills.

[0764] For example, if a user enters "Desired job: Area manager," the server generates a question such as "Tell us about your past team management experience." If the user answers, "I reduced delivery time by 30% and improved customer satisfaction," the server provides feedback such as, "It would be even better if you provided specific methods and results."

[0765] Job / company recommendations

[0766] 1. Data Analysis:

[0767] Analyzes user input data to identify the most suitable job types and companies.

[0768] 2. Submit your recommendation:

[0769] The server sends the recommendation results to the terminal and displays them to the user.

[0770] For example, if a user enters "I'm interested in an area manager position with the skills to deliver quickly and handle customers," the server will display recommendations such as "Area Manager Position: XX Delivery Company."

[0771] Prompt Sentence Examples

[0772] Here are some examples of prompts for generative AI models:

[0773] Resume writing prompt:

[0774] text

[0775] User-specified work experience and skill set: 3 years of experience as a food delivery driver. Key skills include fast delivery and customer service. Create your food delivery resume.

[0776] Interview question generation prompt:

[0777] text

[0778] User Profile: Desired Job: Area Manager. Generate appropriate interview questions related to the food delivery industry.

[0779] Interview response evaluation prompt:

[0780] text

[0781] User Interview Response: We've reduced delivery time by 30% and increased customer satisfaction in past projects. Please rate this response and provide suggestions for improvement.

[0782] In this way, users can receive support in effectively pursuing a job in the food delivery industry.

[0783] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0784] Step 1:

[0785] Obtaining input data

[0786] The user uses the device to input information about their work history, skill set, and the industry and job they are aiming for. All input data is saved in text format on the device. For example, a user might input "3 years of experience as a food delivery person. My main skills are quick delivery and customer service."

[0787] Step 2:

[0788] Data transmission

[0789] The device sends the acquired input data to the server. The data is securely transmitted using the HTTPS protocol. The input data consists of text information such as work history, skill set, and desired job type.

[0790] Step 3:

[0791] Resume generation using natural language processing

[0792] The server uses a natural language processing algorithm to generate resume text based on the received input data. Specifically, it uses a generative AI model (e.g., GPT-3) to analyze and process the input data. Resume text is generated according to the prompt, and output in the form of "I have worked as a food delivery driver for three years and have excellent skills in prompt delivery and customer service."

[0793] Step 4:

[0794] Returning generated resume data

[0795] The server sends the generated resume text back to the device, which also sends it securely using the HTTPS protocol. The device then saves the received resume text data.

[0796] Step 5:

[0797] View resume

[0798] The terminal displays the resume text received from the server to the user, who can then log in and check and edit the resume displayed on the screen.

[0799] Step 6:

[0800] Interview practice begins

[0801] The user inputs an instruction to start the interview practice on the terminal. Information about the desired job type and position is also input as the initial setting. For example, the user inputs "Desired job type: Area manager."

[0802] Step 7:

[0803] Sending profile data

[0804] The terminal transmits an instruction to start the interview practice and the user's profile data to the server.

[0805] Step 8:

[0806] Generate and submit interview questions

[0807] The server generates interview questions using a generative AI model based on the received profile data. The generated questions are sent to the device. For example, a question such as "Tell us about your past team management experience" is generated.

[0808] Step 9:

[0809] User answer input

[0810] The user enters answers to the interview questions displayed on the terminal. The answers are saved in text format on the terminal. For example, the user might enter "We reduced delivery time by 30% and improved customer satisfaction."

[0811] Step 10:

[0812] Submitting and rating answers

[0813] The device sends the input answers to the server, which uses a natural language processing algorithm to evaluate the received answers and generate feedback, such as "It would be even better if you provided specific methods and results."

[0814] Step 11:

[0815] Sending and Viewing Feedback

[0816] The server sends the generated feedback to the terminal, and the terminal displays the received feedback to the user, who can then further refine the content of their answer based on the feedback.

[0817] Step 12:

[0818] Generate new interview questions

[0819] The server generates new interview questions based on the user's past answers and sends them to the device, and this process is repeated until the user has sufficiently improved their interviewing skills.

[0820] Step 13:

[0821] Job / company recommendations

[0822] The server analyzes the data entered by the user and identifies suitable job types and companies in the food delivery industry. The server then sends the recommendation results to the device and displays them to the user. For example, a recommendation such as "Area Manager: XX Delivery Company" may be displayed. The user can use this as a reference to advance their job search.

[0823] Through the above process, the present invention provides comprehensive support for users to efficiently advance their job search in the food delivery industry.

[0824] 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.

[0825] The present invention relates to a system that supports users in their job hunting activities. This system recognizes the user's emotions by combining emotion engines and optimizes resume creation and interview support based on those emotions. Detailed embodiments of each function, the operation of the entire system, and specific examples are described below.

[0826] 1. Resume creation function

[0827] The user uses a device to input information about their past work experience, skill set, and the industry and job type they are aiming for. The emotion engine recognizes the user's emotions as they enter their information, and the device sends this data to a server. The server uses the received information and the user's emotional data to optimize a natural language processing algorithm and generate resume text. The generated resume is sent to the device, which displays it to the user.

[0828] Example: A user enters "5 years of experience as a software engineer. My main skills are Python and Java. I'm interested in AI" into a device, and the emotion engine recognizes the user's emotion as "confident." The device then sends the information and emotion data to the server, which then generates the following sentence: "I've worked as a software engineer for 5 years and have successfully completed multiple projects using mainly Python and Java. I'm particularly interested in AI-related technologies and aspire to further growth in that field." and sends it to the device. The user can then review and edit this sentence displayed on the device.

[0829] 2. Interview support function

[0830] To begin interview practice, the user issues a command to start the simulation from their device. The emotion engine sends the user's profile data and emotion data to the server in real time. The server generates appropriate interview questions based on the profile data and emotion data and sends them to the device. The user enters answers to the interview questions displayed on the device, and the device sends the answers and emotion data to the server. The server analyzes and evaluates them, generates feedback and the next question, and sends them to the device.

[0831] For example, if a user says, "I'm looking for a data scientist position," the server generates a question such as, "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the emotion engine recognizes this as "nervous." The server analyzes the content and emotion data and provides feedback such as, "Your answer is a little vague. Please briefly describe the specific dataset, analysis tools used, and results." Based on this emotion data, the next question can be generated in a more appropriate format.

[0832] 3. Recommendations for suitable jobs and companies

[0833] Based on the user's input data and emotional data, the server recommends the most suitable jobs and companies for the user. The server analyzes the user's work history, skill set, interests, and emotional data to provide appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[0834] Example: A user enters "I have skills in Python and Java and am interested in AI-related jobs," and the emotion engine recognizes this as "full of expectations." Based on this, the server recommends job listings for software engineering positions in the AI ​​field, and displays recommended results on the device, such as "AI software engineer positions: Company X" and "Data scientist positions: Company YY."

[0835] As described above, the job change support system of the present invention allows users to effectively create resumes, prepare for interviews, and find the most suitable jobs and companies while taking into consideration their feelings, which allows users to smoothly progress in their job change activities and obtain highly satisfying results.

[0836] The processing flow will be explained below.

[0837] 1. Resume creation function

[0838] Step 1:

[0839] Users use the terminal to enter information about their past work experience, skill set, and the industry and job type they are aiming for.

[0840] Step 2:

[0841] The emotion engine recognizes the emotion the user is feeling when inputting and transmits the emotion data to the terminal.

[0842] Step 3:

[0843] The terminal transmits the user's input data and emotion data to the server.

[0844] Step 4:

[0845] The server then runs the received data through a natural language processing algorithm to generate the optimal resume text, taking into account emotional data to adjust the tone and content of the text.

[0846] Step 5:

[0847] The server sends the generated resume text to the terminal.

[0848] Step 6:

[0849] The terminal displays the resume content received from the server to the user.

[0850] Step 7:

[0851] The user checks the resume displayed on the terminal and makes corrections as necessary.

[0852] 2. Interview support function

[0853] Step 1:

[0854] To start the interview practice, the user issues a command to start the simulation from the terminal.

[0855] Step 2:

[0856] The emotion engine recognizes the user's profile data and emotion data in real time and transmits it to the terminal.

[0857] Step 3:

[0858] The terminal transmits the user's profile data and emotion data to the server.

[0859] Step 4:

[0860] The server generates appropriate interview questions based on the profile data and emotion data and transmits them to the terminal.

[0861] Step 5:

[0862] The user inputs answers to the interview questions displayed on the terminal.

[0863] Step 6:

[0864] The emotion engine recognizes the user's emotions in real time when answering and transmits them to the device.

[0865] Step 7:

[0866] The device sends the user's response and emotion data to the server.

[0867] Step 8:

[0868] The server analyzes the received responses and emotional data and generates feedback, adjusting the content and tone of the feedback based on the emotional data.

[0869] Step 9:

[0870] The server generates feedback and sends it to the device.

[0871] Step 10:

[0872] The device displays the feedback to the user.

[0873] Step 11:

[0874] The server generates new interview questions based on the feedback and transmits them to the terminal.

[0875] 3. Recommendations for suitable jobs and companies

[0876] Step 1:

[0877] The user uses the terminal to input data such as work history, skill set, and interests.

[0878] Step 2:

[0879] The terminal sends the input data to the server.

[0880] Step 3:

[0881] The emotion engine recognizes the emotion the user is feeling when inputting and transmits the emotion data to the terminal.

[0882] Step 4:

[0883] The device transmits the emotion data to the server.

[0884] Step 5:

[0885] The server analyzes the input data and emotional data and recommends the most suitable jobs and companies.

[0886] Step 6:

[0887] The server sends the recommendation results to the terminal.

[0888] Step 7:

[0889] The terminal displays the recommendation results to the user.

[0890] Example 2

[0891] 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."

[0892] Conventional job-hunting support systems provide support for resume creation and interview practice without considering the user's emotions, which means that they are unable to provide optimal support tailored to the user's individual situation and emotions. As a result, users often receive suggestions and support that do not match their emotional state, which can cause their job hunting to stall or result in unsatisfactory results.

[0893] 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.

[0894] In this invention, the server includes means for a user to input information about their work history, skill set, and desired industry and occupation, means for transmitting the input information to the server, means for recognizing the user's emotions using an emotion engine and generating data, means for the server to generate resume text using a generative AI model based on the input information and emotion recognition data, means for transmitting the resume text generated by the server to a terminal, and means for the terminal to display the resume text to the user. This supports job hunting while taking the user's emotions into consideration, enabling effective resume creation, interview preparation, and recommendations for suitable occupations and companies.

[0895] "User" refers to an individual who uses the system to search for a new job.

[0896] "Terminal" means an electronic device through which a user inputs information and receives responses from a system. Examples include computers, smartphones, and tablets.

[0897] "Server" refers to a computer system that receives data sent from a user, processes it, and sends the generated results to a terminal.

[0898] "Work history" refers to information indicating the job content and work history of the user.

[0899] "Skill set" refers to information that lists the skills, knowledge, qualifications, etc. that a user possesses.

[0900] "Desired industry and occupation" refers to information about the industry and occupation to which the user wishes to transfer.

[0901] An "emotion engine" refers to software or algorithms that recognize a user's emotional state from their input or behavior and generate emotional data.

[0902] "Emotion recognition data" refers to information generated by the emotion engine regarding the user's emotional state.

[0903] A "generative AI model" refers to an artificial intelligence algorithm that generates appropriate sentences and answers based on user input information and emotion recognition data.

[0904] "Resume Text" refers to the textual content of the resume created by the generative AI model using user input and emotion recognition data.

[0905] "Interview Questions" refers to questions used in practice interviews that are generated by the server based on the user's profile data.

[0906] "Feedback" refers to a response that evaluates the user's practice interview answers and includes suggestions for improvement and advice.

[0907] "Recommendation results" refer to information about the most suitable jobs and companies for a user, generated by the server based on the user's input data and emotion recognition data.

[0908] The present invention relates to a system that supports users in their job hunting activities, and in particular, a system that recognizes the user's emotions by combining an emotion engine and a generative AI model, and then creates a resume, supports interviews, and recommends the most suitable jobs and companies based on those emotions. A specific embodiment of the present invention is described below.

[0909] Users input information about their work history, skill set, and desired industry or occupation into the device. The device temporarily stores this input information, then activates an emotion engine to recognize the user's emotions and generate emotion data. This emotion data and the input information are then sent to the server.

[0910] The server analyzes the received input information and emotional data, and generates resume text using a generative AI model (e.g., GPT-4 or an equivalent natural language processing algorithm). This generated resume text is then sent back to the device, where it is displayed to the user.

[0911] As a specific example, if a user inputs information such as "Five years of experience as a software engineer. Main skills are Python and Java. Interested in AI" into the device and the emotion engine recognizes this as "confident," the server will generate a sentence based on the received data: "I have worked as a software engineer for five years and have successfully completed multiple projects using mainly Python and Java. I am particularly interested in AI-related technologies and am aiming to further grow in that field." and send this to the device. The user can review and edit the displayed resume text.

[0912] Furthermore, when a user starts an interview practice, they can issue a command to start the simulation from their device, which causes the emotion engine to recognize and update the user's emotions in real time and send them to the server along with the user's profile data. The server then generates appropriate interview questions based on the user's profile data and emotion data and sends them to the device. When the user answers the questions, the answers and emotion data are sent back to the server, which analyzes them to generate feedback and the next question, which are then sent to the device.

[0913] For example, if a question such as "How did you choose your data analysis method in past projects?" is generated for a user hoping to become a data scientist, and the user answers "Based on the project requirements, I chose a data cleaning method using Python's Pandas," and the emotion engine recognizes this as "nervous," the server will generate feedback such as "Your answer is a little vague. Please briefly describe the specific dataset, the analysis tools used, and the results." In this way, the next question can be generated in a more appropriate format based on the emotion data.

[0914] Furthermore, the server recommends the most suitable jobs and companies for each individual based on the user's input data and emotional data. Specifically, it analyzes work history, skill set, interests, and emotional data to provide appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[0915] For example, if a user enters "I have skills in Python and Java and am interested in AI-related jobs" and the emotion engine recognizes this as "full of expectations," the server will generate recommendation results such as "AI software engineer jobs" and "data scientist jobs" and send them to the device.

[0916] As described above, the job change support system of the present invention can effectively create a resume, prepare for an interview, and find the most suitable job type and company while taking into consideration the user's feelings, which allows the user's job change search to proceed more smoothly and achieve a highly satisfying result.

[0917] Prompt Sentence Examples

[0918] 1. Example resume prompts:

[0919] "A user confidently enters his background as a software engineer. Use a natural language processing algorithm to generate a resume based on his experience."

[0920] 2. Example prompts for interview support:

[0921] "A user is nervous about attending a practice interview. Generate appropriate interview questions based on his past work and provide feedback."

[0922] 3. Example job recommendation prompts:

[0923] "Users are excited and interested in AI-related jobs. Please recommend suitable jobs and companies for users with Python and Java skill sets."

[0924] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0925] Step 1: User enters information using the terminal

[0926] The user enters information about their work history, skill set, and desired industry and job type into an input form on the device. The entered information is temporarily stored on the device. The input data includes specific details such as "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI." This input data is used for processing in the next step.

[0927] Step 2: Emotion recognition by the emotion engine

[0928] The device activates the emotion engine, which analyzes the user's input data and recognizes emotions. The emotion engine uses input speed and data from activated sensors to quantify the user's emotions. This emotion recognition data indicates a specific emotional state, such as "confident." This data is then sent to the server in the next step.

[0929] Step 3: The device sends the data to the server

[0930] The device sends the user's input information and emotion recognition data to the server. By sending the input data and emotion recognition data together, the server can process the data comprehensively. For example, data such as "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI" and "Full of confidence" are sent.

[0931] Step 4: Analyze data on the server and use generative AI models

[0932] The server analyzes the input data and emotion recognition data it receives and uses a generative AI model to generate resume sentences. Based on the input data and emotion recognition data, a natural language processing algorithm such as GPT-4 runs and generates appropriate sentences. For example, the server might generate a sentence like, "I have worked as a software engineer for five years, primarily using Python and Java, and have successfully completed multiple projects. I am particularly interested in AI-related technologies and hope to further my growth in this field."

[0933] Step 5: Send the server-generated text to the terminal

[0934] The server sends the generated resume text to the device. The generated text is converted into a format that the device can display and sent so that the user can understand it. For example, a text like "I have worked as a software engineer for five years and have successfully completed multiple projects using mainly Python and Java. I am particularly interested in AI-related technologies and hope to further grow in that field." is sent to the device.

[0935] Step 6: The terminal displays the generated text to the user.

[0936] The device displays the resume text received from the server to the user. The user can review the generated text and enter corrections or additional information as needed. For example, if the user wants to add specific project experience related to AI, they can edit that section. This correction information is sent back to the server and used for the next data processing.

[0937] The above is the specific flow of processing, from when a user inputs information about their work history, skill set, and desired industry and job type, to when emotions are recognized by the emotion engine, and when resume text is generated and displayed using a generative AI model on the server, users can easily create the optimal resume that takes their emotions into account.

[0938] (Application example 2)

[0939] 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."

[0940] Conventional job-change support systems do not take the user's emotions into consideration, which can result in suboptimal resumes and interview support. Furthermore, they are unable to properly address the user's nervousness and anxiety during interview practice, making it difficult to conduct effective practice. Furthermore, because they do not take the user's emotions into consideration when recommending optimal jobs and companies, the user may not be satisfied with the results. To address these issues, the present invention aims to provide a system that recognizes the user's emotions and effectively supports job-change activities based on those emotions.

[0941] 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.

[0942] In this invention, the server includes: means for a user to input information about their work history, skill set, and desired industry and occupation; means for transmitting the input information to the server; means for the server to generate resume text using a natural language processing algorithm based on the input information; means for the server to transmit the generated resume text to a terminal; means for the terminal to display the resume text to the user; emotion recognition means for recognizing the user's emotions; means for transmitting emotion data obtained by the emotion recognition means to the server; and means for the server to optimize resume creation and interview support based on the input information and the emotion data. This enables effective job change support that takes the user's emotions into consideration, allowing the user to obtain more satisfactory results.

[0943] "Work history" refers to the history of the jobs and positions held by the user in the past, and is information including the specific work content and period of time.

[0944] A "skill set" is a collection of specialized skills and knowledge that a user possesses, and refers to skills that are useful in the workplace in a particular job or industry.

[0945] The "target industry or occupation" refers to the industry or occupation the user wishes to work in in the future, and includes specific job content and type of industry.

[0946] A "natural language processing algorithm" is a computer program that analyzes input text data and performs appropriate document creation and information extraction.

[0947] "Emotion recognition means" refers to technology or devices for detecting emotions from a user's facial expressions, voice, actions, etc., and analyzing that data.

[0948] A "resume text" is a document that describes a user's career history, skills, etc. for use in job hunting, and is generated using a natural language processing algorithm.

[0949] "Interview Practice" is a simulation activity that allows users to improve their question-answering and communication skills in preparation for a real interview.

[0950] "Feedback" refers to evaluations and advice on the user's actions and answers, and is information indicating the next action or areas for improvement.

[0951] The "recommendation results" are a list of occupations and companies that the server suggests based on the user's aptitude and desires.

[0952] This invention will specifically explain a system that combines emotion recognition technology to help users effectively conduct job hunting. The system is configured as follows:

[0953] First, the user inputs information about their work history, skill set, and the industry and job type they are aiming for into their terminal. The terminal then sends this input information to the server. The server uses a natural language processing algorithm based on the information to generate resume text. The generated resume text is then sent from the server to the terminal and displayed to the user. At this time, emotion recognition means is used to analyze the user's emotions in real time. For example, if emotion data indicating that the user is "confident" is obtained when creating a resume, the server will generate resume text using positive expressions that correspond to that emotion.

[0954] Next, if the user wants to practice for an interview, they input instructions for the interview practice into the terminal. The terminal sends the user's profile data and emotional data to the server. The server generates appropriate interview questions based on this data and sends them to the terminal. When the user answers the interview questions, the terminal sends the answers to the server. The server analyzes the answers and emotional data, generates feedback, and sends it to the terminal. For example, if the user's emotion is recognized as "nervous," the server provides advice to ease the tension or the next question to ask.

[0955] Finally, the server recommends suitable jobs and companies based on the user's input data and emotional data. The server then sends these recommendations to the user's device and displays them to the user. For example, if a user inputs "I'm interested in AI" and the emotion is recognized as "full of hope," the server will prioritize job listings related to AI.

[0956] The system of the present invention is realized using multiple hardware and software components. Emotion recognition uses hardware such as an emotion recognition camera and microphone, as well as an emotion recognition module. Natural language processing algorithms are used for natural language processing. A voice synthesis library is also used for voice feedback. This system recognizes users' emotions in real time and effectively supports job hunting.

[0957] For example, if a factory line worker is tired, the emotion recognition means will recognize this and the robot will ask, "Would you like to take a short break?" The robot will also reduce the burden on the worker by appropriately adjusting the work speed.

[0958] Examples of prompts to input to a generative AI model include:

[0959] "Generate example code for an emotion-aware robot assistant that 'prompts a worker when they are tired and adjusts their work speed.'"

[0960] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0961] Step 1:

[0962] The user inputs information about their work experience, skill set, and desired industry and occupation into the device. The device then compiles this information and sends it to the server. Specifically, the user inputs text-based information through the input interface on the device, which is then sent to the server in JSON format. The input includes work experience, skill set, and desired industry and occupation, and becomes the input data for the server.

[0963] Step 2:

[0964] The server receives the input information and generates resume text using a natural language processing algorithm. First, it analyzes the received data and applies a natural language processing algorithm to generate the resume text. Specific processes include text analysis, grammar formatting, and skill matching. The generated resume draft is obtained as an output.

[0965] Step 3:

[0966] The server sends the generated resume text to the terminal. The data is sent in a normal text file or HTML format, and is sent to the terminal in a format suitable for display. This allows the terminal to receive the data needed to display the generated resume to the user.

[0967] Step 4:

[0968] The terminal displays the resume text to the user. The received data is visualized through a display interface and presented in a format that is easy for the user to review. The user can then check the resume content on the display of their smartphone or computer. Here, they can also correct or edit the resume.

[0969] Step 5:

[0970] The emotion recognition means recognizes the emotions expressed by the user when creating a resume. The hardware used is an emotion recognition camera and microphone, which analyzes the user's facial expressions and voice in real time. The recognized emotion data is sent from the device to the server in JSON format. The emotion recognition data is obtained as input.

[0971] Step 6:

[0972] The server receives the emotion data and optimizes resume creation or interview support based on the user's input information and the emotion data. The emotion data is analyzed and the resume style and wording are adjusted according to the user's emotional state. The generation of interview questions and feedback are also optimized based on the emotion data. The optimized resume and interview questions are obtained as output.

[0973] Step 7:

[0974] When a user wants to practice for an interview, he or she inputs instructions for the interview into the terminal. The instructions are given through an interface such as a touch screen or keyboard, and the terminal receives the instructions.

[0975] Step 8:

[0976] The terminal transmits the user's profile data and emotion data to the server, which includes the user's profile information and emotion data acquired in real time, and the data is used by the server to generate and optimize interview questions.

[0977] Step 9:

[0978] The server generates appropriate interview questions based on the profile data and emotion data, and sends them to the terminal. The server generates interview questions using a natural language processing algorithm and sends them back to the terminal. The generated interview questions are obtained as an output.

[0979] Step 10:

[0980] When the user answers the interview questions, the device sends the answers to the server. The answers are entered in text or voice format and sent to the server. The user's answer data is obtained as input.

[0981] Step 11:

[0982] The server analyzes the response, generates feedback, and transmits it to the terminal. The server analyzes the response based on the content and emotion data of the response and generates appropriate feedback. Feedback data is obtained as an output.

[0983] Step 12:

[0984] The server recommends the most suitable jobs and companies based on the user's input data and emotional data. This includes the process of extracting suitable job information based on the user's background, skills, and emotional data. The server then sends the recommendation results to the terminal and displays them to the user. The output is a list of recommended jobs and companies.

[0985] 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.

[0986] 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.

[0987] 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.

[0988] [Third embodiment]

[0989] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0990] 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.

[0991] 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).

[0992] 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.

[0993] 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.

[0994] 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).

[0995] 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. 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.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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."

[1001] The present invention relates to a system for supporting users in their job hunting activities. The main features of this system are a resume creation function and an interview support function. Detailed embodiments of each function and the operation of the entire system are described below.

[1002] 1. Resume creation function

[1003] The user uses a device to input information about their past work experience, skill set, and the industry and job they are aiming for. The device then sends this input data to a server, which then analyzes the received data using a natural language processing algorithm to generate the optimal resume text that will impress hiring managers.

[1004] Example: A user enters "5 years of experience as a software engineer. My main skills are Python and Java. I'm interested in AI" into a terminal. When the terminal sends this information to the server, the server generates a resume statement that reads, "I've worked as a software engineer for 5 years and have successfully completed multiple projects using mainly Python and Java. I'm particularly interested in AI-related technologies and would like to further my career in this field." and sends it to the terminal. The user can review and edit this statement displayed on the terminal.

[1005] 2. Interview support function

[1006] When a user inputs a command to start interview practice into the device, the device sends the user's profile data to the server. Based on this data, the server generates appropriate interview questions related to the industry and job type the user is aiming for and sends them to the device. The user then inputs answers to the questions displayed on the device and sends them to the server.

[1007] The server analyzes and evaluates the user's answers, generates suggestions for improvement and specific feedback, and sends them to the device.The server also generates new questions based on the user's responses and sends them to the device.

[1008] For example, if a user says, "I'm looking for a data scientist position," the server generates questions like, "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the server evaluates the answer and provides feedback like, "Your answer is a bit vague. Please briefly describe the specific dataset, the analytical tools used, and the results." This process is repeated until the user improves their interview skills.

[1009] 3. Recommendations for suitable jobs and companies

[1010] Based on the user's input data, the server recommends the most suitable jobs and companies for the user. The server analyzes the user's work history, skill set, and interests and provides appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[1011] For example, if a user enters "I have skills in Python and Java and am interested in AI-related jobs," the server will recommend job information for software engineering positions in the AI ​​field based on this information. Recommendations such as "AI software engineer jobs: Company X" and "Data scientist jobs: Company Y" will be displayed on the device.

[1012] As described above, the job change support system of the present invention supports users in effectively creating resumes, preparing for interviews, and finding the most suitable job type and company, which is expected to improve the efficiency and success rate of job change activities.

[1013] The processing flow will be explained below.

[1014] 1. Resume creation function

[1015] Step 1:

[1016] The user uses the terminal to enter information about their past work experience, skill set, and the industry and job type they are aiming for.

[1017] Step 2:

[1018] The terminal transmits the user's input data to the server.

[1019] Step 3:

[1020] The server runs the received data through a natural language processing algorithm to generate the most suitable resume text.

[1021] Step 4:

[1022] The server sends the generated resume text to the terminal.

[1023] Step 5:

[1024] The terminal displays the resume content received from the server to the user.

[1025] Step 6:

[1026] The user checks the resume displayed on the terminal and makes corrections as necessary.

[1027] 2. Interview support function

[1028] Step 1:

[1029] To start the interview practice, the user issues a command to start the simulation from the terminal.

[1030] Step 2:

[1031] The terminal transmits the user's profile data to the server.

[1032] Step 3:

[1033] The server generates appropriate interview questions based on the profile data and sends them to the terminal.

[1034] Step 4:

[1035] The user inputs answers to the interview questions displayed on the terminal.

[1036] Step 5:

[1037] The terminal sends the user's answer to the server.

[1038] Step 6:

[1039] The server analyzes and evaluates the user's answers and generates feedback.

[1040] Step 7:

[1041] The server generates feedback and sends it to the device.

[1042] Step 8:

[1043] The device displays the feedback to the user.

[1044] Step 9:

[1045] The server generates a new question based on the user's response and sends it to the terminal.

[1046] 3. Recommendations for suitable jobs and companies

[1047] Step 1:

[1048] The user uses the terminal to input data such as work history, skill set, and interests.

[1049] Step 2:

[1050] The terminal transmits the input data to the server.

[1051] Step 3:

[1052] The server analyzes the input data and recommends the most suitable jobs and companies.

[1053] Step 4:

[1054] The server sends the recommendation results to the terminal.

[1055] Step 5:

[1056] The terminal displays the recommendation results to the user.

[1057] Example 1

[1058] 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."

[1059] Conventional job-hunting support systems make it difficult for users to efficiently create resumes, prepare for interviews, and find the most suitable jobs and companies. Specifically, resume generation and interview questions are mechanical and not optimized for each user's individual profile. Furthermore, feedback during interview practice sessions is not specific, making it difficult for users to improve their interview skills. Furthermore, many of the job recommendations do not match the user's skill set or interests.

[1060] 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.

[1061] In this invention, the server includes means for a user to input information about his / her work history, skill set, and the industry and occupation he / she is aiming for, means for transmitting the input information to the server, means for the server to generate resume text using a natural language processing algorithm based on the input information, means for transmitting the resume text generated by the server to a terminal, means for the terminal to display the resume text to the user, means for the user to input an instruction to the terminal to start interview practice, means for transmitting profile data of the user to the server, means for the server to generate interview questions based on the profile data and transmit them to the terminal, and means for the user to input answers to the interview questions to the terminal and for the terminal to display the answers. The system includes means for transmitting answers to a server, means for the server to evaluate the answers and generate feedback and transmit it to the terminal, means for the server to generate new interview questions and transmit them to the terminal, means for the server to recommend the most suitable job types and companies for the individual based on the user's input data, means for the server to transmit the recommendation results to the terminal and display them to the user, means for the server to use a generative AI model as a natural language processing algorithm in the resume creation function and provide prompt sentences to the generative AI model, and means for the server to use the generative AI model when evaluating the user's answers and generate new questions and feedback based on the evaluation results in the interview support function. This enables the creation of resumes tailored to the individual needs of the user, specific and effective interview practice, and recommendations for the most suitable job types and companies.

[1062] "User" refers to an individual who uses this system to create a resume, practice interviews, or receive job recommendations.

[1063] "Work history" refers to the work history and work that the user has been involved in up to now.

[1064] A "skill set" refers to the skills and knowledge a user has acquired and their ability to apply them.

[1065] "Server" refers to a computer system that receives, analyzes, and processes information entered by a user.

[1066] A "natural language processing algorithm" refers to a program that analyzes text data entered by a user and converts it into natural language sentences.

[1067] "Resume text" refers to the text content of the resume generated by the server based on the user's work history and skill set.

[1068] A "terminal" is a device through which a user inputs information, and refers to a computer or smartphone that sends and receives information.

[1069] "Profile Data" refers to data including a user's personal information, work history, and skill set.

[1070] "Interview questions" refer to predicted questions that may be asked in an interview that are generated by the server based on the user's profile data.

[1071] An "answer" refers to a response that a user inputs to an interview question.

[1072] "Feedback" refers to the server's evaluation of the user's answers, including suggestions for improvement and advice.

[1073] "Recommendation results" refers to information on occupations and companies suitable for a user, which is analyzed by the server based on the user's input data.

[1074] "Generative AI model" refers to an artificial intelligence model used for natural language processing, question generation, and feedback evaluation.

[1075] A "prompt" refers to an instruction statement that instructs a generative AI model to perform a specific process.

[1076] The present invention relates to a system that supports users in their job-hunting activities. The main features of this system are a resume creation function, an interview support function, and a job / company recommendation function. Specific embodiments for carrying out the invention are described below.

[1077] 1. Resume creation function

[1078] Users use a device (such as a PC or smartphone) to enter information about their work history, skill set, and the industry and job they are aiming for into a dedicated form. This input data is sent from the device to a server (such as a cloud server). The server uses a generative AI model (e.g., GPT-4) based on the received data to generate resume text.

[1079] Hardware and software used:

[1080] Device: PC or smartphone

[1081] Server: Cloud server (e.g. AWS EC2)

[1082] Generative AI model: GPT-4

[1083] Examples:

[1084] When a user inputs "I have 5 years of experience as a software engineer. My main skills are Python and Java. I am interested in AI," the device sends this information to the server. The server then inputs the following prompt into the generative AI model: "Please create a professional resume based on this information." As a result, the server generates the following sentence: "I have worked as a software engineer for 5 years and have successfully completed multiple projects, primarily using Python and Java. I am particularly interested in AI-related technologies and would like to further my career in this field." and sends it to the device. The user can review this sentence and modify it as necessary.

[1085] 2. Interview support function

[1086] When a user inputs a command to start interview practice on their device, the device sends the user's profile data to a server. Based on this data, the server generates appropriate interview questions related to the user's desired industry and job type and sends them to the device. The user enters answers to the questions displayed on the device and sends them to the server. The server analyzes the user's answers using a generative AI model, evaluates the content, generates areas for improvement and specific feedback, and sends them to the device. Furthermore, the server generates new interview questions based on the user's responses and sends them to the device.

[1087] Hardware and software used:

[1088] Device: PC or smartphone

[1089] Server: Cloud server (e.g. AWS EC2)

[1090] Generative AI model: Various interview support AI (e.g., GPT-4)

[1091] Examples:

[1092] If the user "wants to work as a data scientist," the server inputs the prompt: "Please create an appropriate interview question for a user who wants to work as a data scientist" into the generative AI model, and generates the question: "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the server evaluates the content and provides feedback saying, "Your answer is a bit vague. Please briefly describe the specific dataset, analysis tools used, and results." This process is repeated until the user is satisfied.

[1093] 3. Recommendations for suitable jobs and companies

[1094] When a user inputs information about their desired job type and skills into the device, the device sends the data to the server. The server analyzes the user's data and uses a generative AI model to generate job information that recommends the most suitable job types and companies. The results are sent to the device and displayed to the user.

[1095] Hardware and software used:

[1096] Device: PC or smartphone

[1097] Server: Cloud server (e.g. AWS EC2)

[1098] Generative AI model: Job recommendation AI (e.g., recommendation system)

[1099] Examples:

[1100] When a user enters "I have Python and Java skills and am interested in AI-related jobs," the server inputs the prompt: "Please recommend the best job information for users who have Python and Java skills and an interest in the AI ​​field" into the generative AI model, generating recommendation results such as "AI software engineer job: XX company" and "Data scientist job: XX company" and sending them to the device. The user can review these and consider applying.

[1101] As described above, the system of the present invention uses a generative AI model based on data entered by the user to create resumes, assist with interview practice, and recommend appropriate jobs and companies, thereby improving the efficiency and success rate of job hunting.

[1102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1103] Resume creation function

[1104] Step 1:

[1105] A user uses a terminal to enter resume information.

[1106] Input: Information about the user's work history, skill set, and desired industry or job type

[1107] Output: Resume information in text format

[1108] Specific operation: The user enters the following into a dedicated form on the device: "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI."

[1109] Step 2:

[1110] The terminal sends the input data to the server.

[1111] Input: Resume information in text format

[1112] Output: Send to server

[1113] Specific operation: The device sends data to the server using the HTTPS protocol.

[1114] Step 3:

[1115] The server inputs the received data into a generative AI model.

[1116] Input: Resume information in text format

[1117] Output: Analysis results from the generative AI model

[1118] Specific operation: The server inputs the prompt "Please create a professional resume based on this information" into a generative AI model (e.g., GPT-4).

[1119] Step 4:

[1120] The server generates the optimal resume text and sends it to the terminal.

[1121] Input: Analysis results from generative AI model

[1122] Output: Optimized resume text

[1123] What it does: The server generates the following sentence and sends it to the device: "I have worked as a software engineer for five years and have successfully completed multiple projects using primarily Python and Java. I am particularly interested in AI-related technologies and would like to further my career in this field."

[1124] Step 5:

[1125] The terminal displays the generated resume text to the user.

[1126] Input: Optimized resume text

[1127] Output: User confirmation screen

[1128] Specific operation: The terminal displays the resume text to the user, who can then review and edit it.

[1129] Interview support function

[1130] Step 1:

[1131] The user inputs an instruction to start the interview practice into the terminal.

[1132] Input: Instruction to start interview practice

[1133] Output: Data sent to the server

[1134] Specific action: The user clicks the "Start Practice" button on the device.

[1135] Step 2:

[1136] The terminal transmits the user's profile data to the server.

[1137] Input: User profile data

[1138] Output: Send to server

[1139] What happens: The device sends profile data using the HTTPS protocol.

[1140] Step 3:

[1141] The server generates interview questions based on the profile data.

[1142] Input: User profile data

[1143] Output: Generated interview questions

[1144] Specific operation: The server inputs the prompt "Please create appropriate interview questions for users seeking a data scientist position" into a generation AI model (e.g., GPT-4), which then generates the question "How did you choose data analysis methods in past projects?"

[1145] Step 4:

[1146] The terminal displays the generated interview questions to the user.

[1147] Input: Generated interview questions

[1148] Output: User response screen

[1149] Specific operation: The terminal displays the interview questions to the user.

[1150] Step 5:

[1151] The user enters answers to the interview questions and the terminal transmits them to the server.

[1152] Input: Answers to interview questions

[1153] Output: Send to server

[1154] Specific action: The user types into the terminal, "Based on the project requirements, I selected a data cleaning technique using Pandas in Python" and submits.

[1155] Step 6:

[1156] The server analyzes the answers and generates feedback.

[1157] Input: User's answer

[1158] Output: Feedback

[1159] Specific behavior: The server uses a generative AI model (e.g., GPT-4) to evaluate the answer and generate feedback such as, "Your answer is somewhat vague. Please briefly describe the specific dataset, analytical tools used, and results."

[1160] Step 7:

[1161] The terminal displays the generated feedback to the user.

[1162] Input: Feedback

[1163] Output: User confirmation screen

[1164] Specific behavior: The device displays feedback to the user.

[1165] Step 8:

[1166] The server generates new interview questions and sends them to the terminal.

[1167] Input: Evaluation result

[1168] Output: New interview question

[1169] Specific operation: The server generates a new question, "How did the analysis results contribute to the outcome of the project?" and sends it to the device.

[1170] Recommendations for suitable jobs and companies

[1171] Step 1:

[1172] The user enters information about their skills and desired job type into the terminal and submits it.

[1173] Input: Information about the user's skills and desired job

[1174] Output: Send to server

[1175] Specific behavior: The user enters "I have skills in Python and Java and am interested in AI-related jobs" and submits.

[1176] Step 2:

[1177] The server analyzes the user's input data and searches for appropriate jobs and companies.

[1178] Input: Information about the user's skills and desired job

[1179] Output: Search results (suitable jobs and companies)

[1180] Specific operation: The server inputs the prompt statement "Please recommend the best job listings for users who have Python and Java skills and an interest in the field of AI" into the generative AI model (e.g., a recommendation system).

[1181] Step 3:

[1182] The server generates recommendations and sends them to the device.

[1183] Input: Analysis results from generative AI model

[1184] Output: Recommendation results

[1185] Specific operation: The server generates recommendation results such as "AI software engineer job: XX company" and "Data scientist job: △△ company" and sends them to the device.

[1186] Step 4:

[1187] The terminal displays the recommendation results to the user.

[1188] Input: Recommendation results

[1189] Output: User confirmation screen

[1190] Specific operation: The terminal displays the recommendation results to the user, who then confirms them.

[1191] (Application example 1)

[1192] 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."

[1193] While existing resume creation and interview support systems support general work history and skill sets, they lack functionality tailored to specific industries, particularly the food delivery industry. As a result, users have difficulty efficiently conducting job searches in the food delivery industry, and it takes a great deal of effort to find suitable jobs and companies. Furthermore, interview practice sessions lack questions and feedback specific to the food delivery industry, preventing users from fully improving their interview skills.

[1194] 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.

[1195] In this invention, the server includes: a means for a user to input information about their work history, skill set, and desired industry and job type; a means for transmitting the input information to the server; a means for the server to generate resume text using a natural language processing algorithm based on the input information; a means for the server to transmit the generated resume text to a terminal; a means for the terminal to display the resume text to the user; a means for generating optimal questions based on the user's desired job type and position using data related to the food delivery industry; a means for recommending appropriate jobs and companies based on the user's input data; a means for displaying the relevant questions and recommendation results on the terminal; and a means for generating and transmitting feedback to support the user's interview practice. This enables effective and efficient job hunting in the food delivery industry. Furthermore, users can improve their interview skills by receiving interview practice and feedback tailored to the food delivery industry.

[1196] "A means for users to input information about their work experience, skill set, and desired industry or job type" refers to an interface or device that allows users to electronically input information about their past work experience, skills, and desired industry or job type.

[1197] The "means for transmitting the input information to the server" is a communication function for transmitting the data input by the user to the server via a network.

[1198] "The means by which the server generates resume text using a natural language processing algorithm based on the input information" refers to a function in which the server analyzes the data it receives, converts it into appropriate words and structures using natural language processing technology, and automatically generates resume text.

[1199] The "means for transmitting the resume text generated by the server to the terminal" is a communication function that enables the server to transmit the generated resume text to the user's terminal.

[1200] The "means for the terminal to display the resume text to the user" is a function for the user's terminal to display the resume text received on the screen.

[1201] "A means of using data related to the food delivery industry to generate optimal questions for the job type or position desired by the user" is a function that utilizes knowledge and data in the food delivery industry to automatically generate interview questions suitable for the job type or position the user is aiming for.

[1202] "Means of recommending appropriate jobs and companies based on user input data" refers to a function that uses an algorithm to identify and recommend suitable jobs and companies based on information provided by the user.

[1203] The "means for displaying the relevant questions and recommendation results on the terminal" is a function for displaying the generated interview questions and company recommendation results on the user's terminal.

[1204] The "means for generating feedback to assist the user in practicing for an interview and sending it to the terminal" is a function for evaluating the interview answers entered by the user, generating feedback providing suggestions for improvement and specific advice, and sending it to the terminal.

[1205] The "means for inputting an instruction to start interview practice into the terminal" is an interface that allows the user to input into the terminal his / her intention to start interview practice.

[1206] The "means for transmitting user profile data to a server" is a communication function for transmitting user profile information to a server.

[1207] The "means for generating interview questions and transmitting them to the terminal" is a function for generating appropriate interview questions based on the user's profile data and transmitting them to the terminal.

[1208] "Means for inputting answers to interview questions and for the terminal to transmit the answers to a server" is a function that allows a user to input answers to interview questions and transmit the input answers to a server.

[1209] The "means for evaluating the answer, generating feedback, and transmitting the feedback to the terminal" is a function in which the server evaluates the user's answer, generates feedback, and transmits the feedback to the terminal.

[1210] The "means for generating new interview questions and transmitting them to the terminal" is a function for referencing the content of the user's answers, generating further interview questions, and transmitting them to the terminal.

[1211] "Means for considering recommendation results specialized for job types and positions in the food delivery industry" refers to a function that provides optimal recommendation results specialized for various job types and positions in the food delivery industry.

[1212] The present invention relates to a system for supporting job hunting in the food delivery industry. Specific embodiments are described in detail below.

[1213] System Configuration

[1214] This system consists of a user terminal and a server. Using the terminal, users input information about their work history, skill set, and the industry and job type they are aiming for. The input information is sent to the server, which uses a natural language processing algorithm to generate resume text based on that information and sends it to the user's terminal. When the user inputs a command to start interview practice into the terminal, the server generates new interview questions based on the user's profile data and sends them to the terminal. The server then recommends the most suitable jobs and companies based on the user's input data and displays them on the terminal.

[1215] Hardware and software used

[1216] Hardware:

[1217] User device: Input device such as a smartphone or PC

[1218] Server: A cloud server with high-performance processing power (e.g., Amazon Web Services, Google Cloud Platform)

[1219] software:

[1220] Natural Language Processing algorithms: OpenAI generative AI models (e.g., GPT-3)

[1221] Communication protocol: HTTPS ensures secure data transmission

[1222] Data processing and calculation

[1223] Resume creation

[1224] 1. Obtaining input data:

[1225] Users enter information about their work history, skill set, and the industry or job they are aiming for on the device.

[1226] 2. Data transmission:

[1227] Data entered on the terminal is sent to the server.

[1228] 3. Natural Language Processing:

[1229] The server analyzes the received data and generates resume text using a generative AI model.

[1230] 4. Data return:

[1231] The generated resume text is sent to the user's terminal and displayed on the screen.

[1232] As a specific example, if a user enters "3 years of experience as a food delivery person. Main skills are quick delivery and customer service," the server generates a resume sentence that reads, "I have worked as a food delivery person for 3 years and have excellent skills in quick delivery and customer service."

[1233] Interview support

[1234] 1. Start your interview practice:

[1235] The user inputs an instruction to start the interview practice into the terminal.

[1236] 2. Sending profile data:

[1237] The user's profile data is sent to the server.

[1238] 3. Generate interview questions:

[1239] The server generates appropriate interview questions based on the profile data and sends them to the terminal.

[1240] 4. Enter and evaluate your answers:

[1241] The user enters an answer into the terminal and sends it to the server, which evaluates the answer and generates feedback.

[1242] 5. Generate new questions:

[1243] Based on the feedback, the server generates new interview questions and sends them to the device, and this process is repeated until the user improves their interviewing skills.

[1244] For example, if a user enters "Desired job: Area manager," the server generates a question such as "Tell us about your past team management experience." If the user answers, "I reduced delivery time by 30% and improved customer satisfaction," the server provides feedback such as, "It would be even better if you provided specific methods and results."

[1245] Job / company recommendations

[1246] 1. Data Analysis:

[1247] Analyzes user input data to identify the most suitable job types and companies.

[1248] 2. Submit your recommendation:

[1249] The server sends the recommendation results to the terminal and displays them to the user.

[1250] For example, if a user enters "I'm interested in an area manager position with the skills to deliver quickly and handle customers," the server will display recommendations such as "Area Manager Position: XX Delivery Company."

[1251] Prompt Sentence Examples

[1252] Here are some examples of prompts for generative AI models:

[1253] Resume writing prompt:

[1254] text

[1255] User-specified work experience and skill set: 3 years of experience as a food delivery driver. Key skills include fast delivery and customer service. Create your food delivery resume.

[1256] Interview question generation prompt:

[1257] text

[1258] User Profile: Desired Job: Area Manager. Generate appropriate interview questions related to the food delivery industry.

[1259] Interview response evaluation prompt:

[1260] text

[1261] User Interview Response: We've reduced delivery time by 30% and increased customer satisfaction in past projects. Please rate this response and provide suggestions for improvement.

[1262] In this way, users can receive support in effectively pursuing a job in the food delivery industry.

[1263] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1264] Step 1:

[1265] Obtaining input data

[1266] The user uses the device to input information about their work history, skill set, and the industry and job they are aiming for. All input data is saved in text format on the device. For example, a user might input "3 years of experience as a food delivery person. My main skills are quick delivery and customer service."

[1267] Step 2:

[1268] Data transmission

[1269] The device sends the acquired input data to the server. The data is securely transmitted using the HTTPS protocol. The input data consists of text information such as work history, skill set, and desired job type.

[1270] Step 3:

[1271] Resume generation using natural language processing

[1272] The server uses a natural language processing algorithm to generate resume text based on the received input data. Specifically, it uses a generative AI model (e.g., GPT-3) to analyze and process the input data. Resume text is generated according to the prompt, and output in the form of "I have worked as a food delivery driver for three years and have excellent skills in prompt delivery and customer service."

[1273] Step 4:

[1274] Returning generated resume data

[1275] The server sends the generated resume text back to the device, which also sends it securely using the HTTPS protocol. The device then saves the received resume text data.

[1276] Step 5:

[1277] View resume

[1278] The terminal displays the resume text received from the server to the user, who can then log in and check and edit the resume displayed on the screen.

[1279] Step 6:

[1280] Interview practice begins

[1281] The user inputs an instruction to start the interview practice on the terminal. Information about the desired job type and position is also input as the initial setting. For example, the user inputs "Desired job type: Area manager."

[1282] Step 7:

[1283] Sending profile data

[1284] The terminal transmits an instruction to start the interview practice and the user's profile data to the server.

[1285] Step 8:

[1286] Generate and submit interview questions

[1287] The server generates interview questions using a generative AI model based on the received profile data. The generated questions are sent to the device. For example, a question such as "Tell us about your past team management experience" is generated.

[1288] Step 9:

[1289] User answer input

[1290] The user enters answers to the interview questions displayed on the terminal. The answers are saved in text format on the terminal. For example, the user might enter "We reduced delivery time by 30% and improved customer satisfaction."

[1291] Step 10:

[1292] Submitting and rating answers

[1293] The device sends the input answers to the server, which uses a natural language processing algorithm to evaluate the received answers and generate feedback, such as "It would be even better if you provided specific methods and results."

[1294] Step 11:

[1295] Sending and Viewing Feedback

[1296] The server sends the generated feedback to the terminal, and the terminal displays the received feedback to the user, who can then further refine the content of their answer based on the feedback.

[1297] Step 12:

[1298] Generate new interview questions

[1299] The server generates new interview questions based on the user's past answers and sends them to the device, and this process is repeated until the user has sufficiently improved their interviewing skills.

[1300] Step 13:

[1301] Job / company recommendations

[1302] The server analyzes the data entered by the user and identifies suitable job types and companies in the food delivery industry. The server then sends the recommendation results to the device and displays them to the user. For example, a recommendation such as "Area Manager: XX Delivery Company" may be displayed. The user can use this as a reference to advance their job search.

[1303] Through the above process, the present invention provides comprehensive support for users to efficiently advance their job search in the food delivery industry.

[1304] 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.

[1305] The present invention relates to a system that supports users in their job hunting activities. This system recognizes the user's emotions by combining emotion engines and optimizes resume creation and interview support based on those emotions. Detailed embodiments of each function, the operation of the entire system, and specific examples are described below.

[1306] 1. Resume creation function

[1307] The user uses a device to input information about their past work experience, skill set, and the industry and job type they are aiming for. The emotion engine recognizes the user's emotions as they enter their information, and the device sends this data to a server. The server uses the received information and the user's emotional data to optimize a natural language processing algorithm and generate resume text. The generated resume is sent to the device, which displays it to the user.

[1308] Example: A user enters "5 years of experience as a software engineer. My main skills are Python and Java. I'm interested in AI" into a device, and the emotion engine recognizes the user's emotion as "confident." The device then sends the information and emotion data to the server, which then generates the following sentence: "I've worked as a software engineer for 5 years and have successfully completed multiple projects using mainly Python and Java. I'm particularly interested in AI-related technologies and aspire to further growth in that field." and sends it to the device. The user can then review and edit this sentence displayed on the device.

[1309] 2. Interview support function

[1310] To begin interview practice, the user issues a command to start the simulation from their device. The emotion engine sends the user's profile data and emotion data to the server in real time. The server generates appropriate interview questions based on the profile data and emotion data and sends them to the device. The user enters answers to the interview questions displayed on the device, and the device sends the answers and emotion data to the server. The server analyzes and evaluates them, generates feedback and the next question, and sends them to the device.

[1311] For example, if a user says, "I'm looking for a data scientist position," the server generates a question such as, "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the emotion engine recognizes this as "nervous." The server analyzes the content and emotion data and provides feedback such as, "Your answer is a little vague. Please briefly describe the specific dataset, analysis tools used, and results." Based on this emotion data, the next question can be generated in a more appropriate format.

[1312] 3. Recommendations for suitable jobs and companies

[1313] Based on the user's input data and emotional data, the server recommends the most suitable jobs and companies for the user. The server analyzes the user's work history, skill set, interests, and emotional data to provide appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[1314] Example: A user enters "I have skills in Python and Java and am interested in AI-related jobs," and the emotion engine recognizes this as "full of expectations." Based on this, the server recommends job listings for software engineering positions in the AI ​​field, and displays recommended results on the device, such as "AI software engineer positions: Company X" and "Data scientist positions: Company YY."

[1315] As described above, the job change support system of the present invention allows users to effectively create resumes, prepare for interviews, and find the most suitable jobs and companies while taking into consideration their feelings, which allows users to smoothly progress in their job change activities and obtain highly satisfying results.

[1316] The processing flow will be explained below.

[1317] 1. Resume creation function

[1318] Step 1:

[1319] Users use the terminal to enter information about their past work experience, skill set, and the industry and job type they are aiming for.

[1320] Step 2:

[1321] The emotion engine recognizes the emotion the user is feeling when inputting and transmits the emotion data to the terminal.

[1322] Step 3:

[1323] The terminal transmits the user's input data and emotion data to the server.

[1324] Step 4:

[1325] The server then runs the received data through a natural language processing algorithm to generate the optimal resume text, taking into account emotional data to adjust the tone and content of the text.

[1326] Step 5:

[1327] The server sends the generated resume text to the terminal.

[1328] Step 6:

[1329] The terminal displays the resume content received from the server to the user.

[1330] Step 7:

[1331] The user checks the resume displayed on the terminal and makes corrections as necessary.

[1332] 2. Interview support function

[1333] Step 1:

[1334] To start the interview practice, the user issues a command to start the simulation from the terminal.

[1335] Step 2:

[1336] The emotion engine recognizes the user's profile data and emotion data in real time and transmits it to the terminal.

[1337] Step 3:

[1338] The terminal transmits the user's profile data and emotion data to the server.

[1339] Step 4:

[1340] The server generates appropriate interview questions based on the profile data and emotion data and transmits them to the terminal.

[1341] Step 5:

[1342] The user inputs answers to the interview questions displayed on the terminal.

[1343] Step 6:

[1344] The emotion engine recognizes the user's emotions in real time when answering and transmits them to the device.

[1345] Step 7:

[1346] The device sends the user's response and emotion data to the server.

[1347] Step 8:

[1348] The server analyzes the received responses and emotional data and generates feedback, adjusting the content and tone of the feedback based on the emotional data.

[1349] Step 9:

[1350] The server generates feedback and sends it to the device.

[1351] Step 10:

[1352] The device displays the feedback to the user.

[1353] Step 11:

[1354] The server generates new interview questions based on the feedback and transmits them to the terminal.

[1355] 3. Recommendations for suitable jobs and companies

[1356] Step 1:

[1357] The user uses the terminal to input data such as work history, skill set, and interests.

[1358] Step 2:

[1359] The terminal sends the input data to the server.

[1360] Step 3:

[1361] The emotion engine recognizes the emotion the user is feeling when inputting and transmits the emotion data to the terminal.

[1362] Step 4:

[1363] The device transmits the emotion data to the server.

[1364] Step 5:

[1365] The server analyzes the input data and emotional data and recommends the most suitable jobs and companies.

[1366] Step 6:

[1367] The server sends the recommendation results to the terminal.

[1368] Step 7:

[1369] The terminal displays the recommendation results to the user.

[1370] Example 2

[1371] 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."

[1372] Conventional job-hunting support systems provide support for resume creation and interview practice without considering the user's emotions, which means that they are unable to provide optimal support tailored to the user's individual situation and emotions. As a result, users often receive suggestions and support that do not match their emotional state, which can cause their job hunting to stall or result in unsatisfactory results.

[1373] 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.

[1374] In this invention, the server includes means for a user to input information about their work history, skill set, and desired industry and occupation, means for transmitting the input information to the server, means for recognizing the user's emotions using an emotion engine and generating data, means for the server to generate resume text using a generative AI model based on the input information and emotion recognition data, means for transmitting the resume text generated by the server to a terminal, and means for the terminal to display the resume text to the user. This supports job hunting while taking the user's emotions into consideration, enabling effective resume creation, interview preparation, and recommendations for suitable occupations and companies.

[1375] "User" refers to an individual who uses the system to search for a new job.

[1376] "Terminal" means an electronic device through which a user inputs information and receives responses from a system. Examples include computers, smartphones, and tablets.

[1377] "Server" refers to a computer system that receives data sent from a user, processes it, and sends the generated results to a terminal.

[1378] "Work history" refers to information indicating the job content and work history of the user.

[1379] "Skill set" refers to information that lists the skills, knowledge, qualifications, etc. that a user possesses.

[1380] "Desired industry and occupation" refers to information about the industry and occupation to which the user wishes to transfer.

[1381] An "emotion engine" refers to software or algorithms that recognize a user's emotional state from their input or behavior and generate emotional data.

[1382] "Emotion recognition data" refers to information generated by the emotion engine regarding the user's emotional state.

[1383] A "generative AI model" refers to an artificial intelligence algorithm that generates appropriate sentences and answers based on user input information and emotion recognition data.

[1384] "Resume Text" refers to the textual content of the resume created by the generative AI model using user input and emotion recognition data.

[1385] "Interview Questions" refers to questions used in practice interviews that are generated by the server based on the user's profile data.

[1386] "Feedback" refers to a response that evaluates the user's practice interview answers and includes suggestions for improvement and advice.

[1387] "Recommendation results" refer to information about the most suitable jobs and companies for a user, generated by the server based on the user's input data and emotion recognition data.

[1388] The present invention relates to a system that supports users in their job hunting activities, and in particular, a system that recognizes the user's emotions by combining an emotion engine and a generative AI model, and then creates a resume, supports interviews, and recommends the most suitable jobs and companies based on those emotions. A specific embodiment of the present invention is described below.

[1389] Users input information about their work history, skill set, and desired industry or occupation into the device. The device temporarily stores this input information, then activates an emotion engine to recognize the user's emotions and generate emotion data. This emotion data and the input information are then sent to the server.

[1390] The server analyzes the received input information and emotional data, and generates resume text using a generative AI model (e.g., GPT-4 or an equivalent natural language processing algorithm). This generated resume text is then sent back to the device, where it is displayed to the user.

[1391] As a specific example, if a user inputs information such as "Five years of experience as a software engineer. Main skills are Python and Java. Interested in AI" into the device and the emotion engine recognizes this as "confident," the server will generate a sentence based on the received data: "I have worked as a software engineer for five years and have successfully completed multiple projects using mainly Python and Java. I am particularly interested in AI-related technologies and am aiming to further grow in that field." and send this to the device. The user can review and edit the displayed resume text.

[1392] Furthermore, when a user starts an interview practice, they can issue a command to start the simulation from their device, which causes the emotion engine to recognize and update the user's emotions in real time and send them to the server along with the user's profile data. The server then generates appropriate interview questions based on the user's profile data and emotion data and sends them to the device. When the user answers the questions, the answers and emotion data are sent back to the server, which analyzes them to generate feedback and the next question, which are then sent to the device.

[1393] For example, if a question such as "How did you choose your data analysis method in past projects?" is generated for a user hoping to become a data scientist, and the user answers "Based on the project requirements, I chose a data cleaning method using Python's Pandas," and the emotion engine recognizes this as "nervous," the server will generate feedback such as "Your answer is a little vague. Please briefly describe the specific dataset, the analysis tools used, and the results." In this way, the next question can be generated in a more appropriate format based on the emotion data.

[1394] Furthermore, the server recommends the most suitable jobs and companies for each individual based on the user's input data and emotional data. Specifically, it analyzes work history, skill set, interests, and emotional data to provide appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[1395] For example, if a user enters "I have skills in Python and Java and am interested in AI-related jobs" and the emotion engine recognizes this as "full of expectations," the server will generate recommendation results such as "AI software engineer jobs" and "data scientist jobs" and send them to the device.

[1396] As described above, the job change support system of the present invention can effectively create a resume, prepare for an interview, and find the most suitable job type and company while taking into consideration the user's feelings, which allows the user's job change search to proceed more smoothly and achieve a highly satisfying result.

[1397] Prompt Sentence Examples

[1398] 1. Example resume prompts:

[1399] "A user confidently enters his background as a software engineer. Use a natural language processing algorithm to generate a resume based on his experience."

[1400] 2. Example prompts for interview support:

[1401] "A user is nervous about attending a practice interview. Generate appropriate interview questions based on his past work and provide feedback."

[1402] 3. Example job recommendation prompts:

[1403] "Users are excited and interested in AI-related jobs. Please recommend suitable jobs and companies for users with Python and Java skill sets."

[1404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1405] Step 1: User enters information using the terminal

[1406] The user enters information about their work history, skill set, and desired industry and job type into an input form on the device. The entered information is temporarily stored on the device. The input data includes specific details such as "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI." This input data is used for processing in the next step.

[1407] Step 2: Emotion recognition by the emotion engine

[1408] The device activates the emotion engine, which analyzes the user's input data and recognizes emotions. The emotion engine uses input speed and data from activated sensors to quantify the user's emotions. This emotion recognition data indicates a specific emotional state, such as "confident." This data is then sent to the server in the next step.

[1409] Step 3: The device sends the data to the server

[1410] The device sends the user's input information and emotion recognition data to the server. By sending the input data and emotion recognition data together, the server can process the data comprehensively. For example, data such as "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI" and "Full of confidence" are sent.

[1411] Step 4: Analyze data on the server and use generative AI models

[1412] The server analyzes the input data and emotion recognition data it receives and uses a generative AI model to generate resume sentences. Based on the input data and emotion recognition data, a natural language processing algorithm such as GPT-4 runs and generates appropriate sentences. For example, the server might generate a sentence like, "I have worked as a software engineer for five years, primarily using Python and Java, and have successfully completed multiple projects. I am particularly interested in AI-related technologies and hope to further my growth in this field."

[1413] Step 5: Send the server-generated text to the terminal

[1414] The server sends the generated resume text to the device. The generated text is converted into a format that the device can display and sent so that the user can understand it. For example, a text like "I have worked as a software engineer for five years and have successfully completed multiple projects using mainly Python and Java. I am particularly interested in AI-related technologies and hope to further grow in that field." is sent to the device.

[1415] Step 6: The terminal displays the generated text to the user.

[1416] The device displays the resume text received from the server to the user. The user can review the generated text and enter corrections or additional information as needed. For example, if the user wants to add specific project experience related to AI, they can edit that section. This correction information is sent back to the server and used for the next data processing.

[1417] The above is the specific flow of processing, from when a user inputs information about their work history, skill set, and desired industry and job type, to when emotions are recognized by the emotion engine, and when resume text is generated and displayed using a generative AI model on the server, users can easily create the optimal resume that takes their emotions into account.

[1418] (Application example 2)

[1419] 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."

[1420] Conventional job-change support systems do not take the user's emotions into consideration, which can result in suboptimal resumes and interview support. Furthermore, they are unable to properly address the user's nervousness and anxiety during interview practice, making it difficult to conduct effective practice. Furthermore, because they do not take the user's emotions into consideration when recommending optimal jobs and companies, the user may not be satisfied with the results. To address these issues, the present invention aims to provide a system that recognizes the user's emotions and effectively supports job-change activities based on those emotions.

[1421] 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.

[1422] In this invention, the server includes: means for a user to input information about their work history, skill set, and desired industry and occupation; means for transmitting the input information to the server; means for the server to generate resume text using a natural language processing algorithm based on the input information; means for the server to transmit the generated resume text to a terminal; means for the terminal to display the resume text to the user; emotion recognition means for recognizing the user's emotions; means for transmitting emotion data obtained by the emotion recognition means to the server; and means for the server to optimize resume creation and interview support based on the input information and the emotion data. This enables effective job change support that takes the user's emotions into consideration, allowing the user to obtain more satisfactory results.

[1423] "Work history" refers to the history of the jobs and positions held by the user in the past, and is information including the specific work content and period of time.

[1424] A "skill set" is a collection of specialized skills and knowledge that a user possesses, and refers to skills that are useful in the workplace in a particular job or industry.

[1425] The "target industry or occupation" refers to the industry or occupation the user wishes to work in in the future, and includes specific job content and type of industry.

[1426] A "natural language processing algorithm" is a computer program that analyzes input text data and performs appropriate document creation and information extraction.

[1427] "Emotion recognition means" refers to technology or devices for detecting emotions from a user's facial expressions, voice, actions, etc., and analyzing that data.

[1428] A "resume text" is a document that describes a user's career history, skills, etc. for use in job hunting, and is generated using a natural language processing algorithm.

[1429] "Interview Practice" is a simulation activity that allows users to improve their question-answering and communication skills in preparation for a real interview.

[1430] "Feedback" refers to evaluations and advice on the user's actions and answers, and is information indicating the next action or areas for improvement.

[1431] The "recommendation results" are a list of occupations and companies that the server suggests based on the user's aptitude and desires.

[1432] This invention will specifically explain a system that combines emotion recognition technology to help users effectively conduct job hunting. The system is configured as follows:

[1433] First, the user inputs information about their work history, skill set, and the industry and job type they are aiming for into their terminal. The terminal then sends this input information to the server. The server uses a natural language processing algorithm based on the information to generate resume text. The generated resume text is then sent from the server to the terminal and displayed to the user. At this time, emotion recognition means is used to analyze the user's emotions in real time. For example, if emotion data indicating that the user is "confident" is obtained when creating a resume, the server will generate resume text using positive expressions that correspond to that emotion.

[1434] Next, if the user wants to practice for an interview, they input instructions for the interview practice into the terminal. The terminal sends the user's profile data and emotional data to the server. The server generates appropriate interview questions based on this data and sends them to the terminal. When the user answers the interview questions, the terminal sends the answers to the server. The server analyzes the answers and emotional data, generates feedback, and sends it to the terminal. For example, if the user's emotion is recognized as "nervous," the server provides advice to ease the tension or the next question to ask.

[1435] Finally, the server recommends suitable jobs and companies based on the user's input data and emotional data. The server then sends these recommendations to the user's device and displays them to the user. For example, if a user inputs "I'm interested in AI" and the emotion is recognized as "full of hope," the server will prioritize job listings related to AI.

[1436] The system of the present invention is realized using multiple hardware and software components. Emotion recognition uses hardware such as an emotion recognition camera and microphone, as well as an emotion recognition module. Natural language processing algorithms are used for natural language processing. A voice synthesis library is also used for voice feedback. This system recognizes users' emotions in real time and effectively supports job hunting.

[1437] For example, if a factory line worker is tired, the emotion recognition means will recognize this and the robot will ask, "Would you like to take a short break?" The robot will also reduce the burden on the worker by appropriately adjusting the work speed.

[1438] Examples of prompts to input to a generative AI model include:

[1439] "Generate example code for an emotion-aware robot assistant that 'prompts a worker when they are tired and adjusts their work speed.'"

[1440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1441] Step 1:

[1442] The user inputs information about their work experience, skill set, and desired industry and occupation into the device. The device then compiles this information and sends it to the server. Specifically, the user inputs text-based information through the input interface on the device, which is then sent to the server in JSON format. The input includes work experience, skill set, and desired industry and occupation, and becomes the input data for the server.

[1443] Step 2:

[1444] The server receives the input information and generates resume text using a natural language processing algorithm. First, it analyzes the received data and applies a natural language processing algorithm to generate the resume text. Specific processes include text analysis, grammar formatting, and skill matching. The generated resume draft is obtained as an output.

[1445] Step 3:

[1446] The server sends the generated resume text to the terminal. The data is sent in a normal text file or HTML format, and is sent to the terminal in a format suitable for display. This allows the terminal to receive the data needed to display the generated resume to the user.

[1447] Step 4:

[1448] The terminal displays the resume text to the user. The received data is visualized through a display interface and presented in a format that is easy for the user to review. The user can then check the resume content on the display of their smartphone or computer. Here, they can also correct or edit the resume.

[1449] Step 5:

[1450] The emotion recognition means recognizes the emotions expressed by the user when creating a resume. The hardware used is an emotion recognition camera and microphone, which analyzes the user's facial expressions and voice in real time. The recognized emotion data is sent from the device to the server in JSON format. The emotion recognition data is obtained as input.

[1451] Step 6:

[1452] The server receives the emotion data and optimizes resume creation or interview support based on the user's input information and the emotion data. The emotion data is analyzed and the resume style and wording are adjusted according to the user's emotional state. The generation of interview questions and feedback are also optimized based on the emotion data. The optimized resume and interview questions are obtained as output.

[1453] Step 7:

[1454] When a user wants to practice for an interview, he or she inputs instructions for the interview into the terminal. The instructions are given through an interface such as a touch screen or keyboard, and the terminal receives the instructions.

[1455] Step 8:

[1456] The terminal transmits the user's profile data and emotion data to the server, which includes the user's profile information and emotion data acquired in real time, and the data is used by the server to generate and optimize interview questions.

[1457] Step 9:

[1458] The server generates appropriate interview questions based on the profile data and emotion data, and sends them to the terminal. The server generates interview questions using a natural language processing algorithm and sends them back to the terminal. The generated interview questions are obtained as an output.

[1459] Step 10:

[1460] When the user answers the interview questions, the device sends the answers to the server. The answers are entered in text or voice format and sent to the server. The user's answer data is obtained as input.

[1461] Step 11:

[1462] The server analyzes the response, generates feedback, and transmits it to the terminal. The server analyzes the response based on the content and emotion data of the response and generates appropriate feedback. Feedback data is obtained as an output.

[1463] Step 12:

[1464] The server recommends the most suitable jobs and companies based on the user's input data and emotional data. This includes the process of extracting suitable job information based on the user's background, skills, and emotional data. The server then sends the recommendation results to the terminal and displays them to the user. The output is a list of recommended jobs and companies.

[1465] 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.

[1466] 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.

[1467] 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.

[1468] [Fourth embodiment]

[1469] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1470] 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.

[1471] 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).

[1472] 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.

[1473] 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.

[1474] 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).

[1475] 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. 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.

[1476] 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.

[1477] 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.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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."

[1482] The present invention relates to a system for supporting users in their job hunting activities. The main features of this system are a resume creation function and an interview support function. Detailed embodiments of each function and the operation of the entire system are described below.

[1483] 1. Resume creation function

[1484] The user uses a device to input information about their past work experience, skill set, and the industry and job they are aiming for. The device then sends this input data to a server, which then analyzes the received data using a natural language processing algorithm to generate the optimal resume text that will impress hiring managers.

[1485] Example: A user enters "5 years of experience as a software engineer. My main skills are Python and Java. I'm interested in AI" into a terminal. When the terminal sends this information to the server, the server generates a resume statement that reads, "I've worked as a software engineer for 5 years and have successfully completed multiple projects using mainly Python and Java. I'm particularly interested in AI-related technologies and would like to further my career in this field." and sends it to the terminal. The user can review and edit this statement displayed on the terminal.

[1486] 2. Interview support function

[1487] When a user inputs a command to start interview practice into the device, the device sends the user's profile data to the server. Based on this data, the server generates appropriate interview questions related to the industry and job type the user is aiming for and sends them to the device. The user then inputs answers to the questions displayed on the device and sends them to the server.

[1488] The server analyzes and evaluates the user's answers, generates suggestions for improvement and specific feedback, and sends them to the device.The server also generates new questions based on the user's responses and sends them to the device.

[1489] For example, if a user says, "I'm looking for a data scientist position," the server generates questions like, "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the server evaluates the answer and provides feedback like, "Your answer is a bit vague. Please briefly describe the specific dataset, the analytical tools used, and the results." This process is repeated until the user improves their interview skills.

[1490] 3. Recommendations for suitable jobs and companies

[1491] Based on the user's input data, the server recommends the most suitable jobs and companies for the user. The server analyzes the user's work history, skill set, and interests and provides appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[1492] For example, if a user enters "I have skills in Python and Java and am interested in AI-related jobs," the server will recommend job information for software engineering positions in the AI ​​field based on this information. Recommendations such as "AI software engineer jobs: Company X" and "Data scientist jobs: Company Y" will be displayed on the device.

[1493] As described above, the job change support system of the present invention supports users in effectively creating resumes, preparing for interviews, and finding the most suitable job type and company, which is expected to improve the efficiency and success rate of job change activities.

[1494] The processing flow will be explained below.

[1495] 1. Resume creation function

[1496] Step 1:

[1497] The user uses the terminal to enter information about their past work experience, skill set, and the industry and job type they are aiming for.

[1498] Step 2:

[1499] The terminal transmits the user's input data to the server.

[1500] Step 3:

[1501] The server runs the received data through a natural language processing algorithm to generate the most suitable resume text.

[1502] Step 4:

[1503] The server sends the generated resume text to the terminal.

[1504] Step 5:

[1505] The terminal displays the resume content received from the server to the user.

[1506] Step 6:

[1507] The user checks the resume displayed on the terminal and makes corrections as necessary.

[1508] 2. Interview support function

[1509] Step 1:

[1510] To start the interview practice, the user issues a command to start the simulation from the terminal.

[1511] Step 2:

[1512] The terminal transmits the user's profile data to the server.

[1513] Step 3:

[1514] The server generates appropriate interview questions based on the profile data and sends them to the terminal.

[1515] Step 4:

[1516] The user inputs answers to the interview questions displayed on the terminal.

[1517] Step 5:

[1518] The terminal sends the user's answer to the server.

[1519] Step 6:

[1520] The server analyzes and evaluates the user's answers and generates feedback.

[1521] Step 7:

[1522] The server generates feedback and sends it to the device.

[1523] Step 8:

[1524] The device displays the feedback to the user.

[1525] Step 9:

[1526] The server generates a new question based on the user's response and sends it to the terminal.

[1527] 3. Recommendations for suitable jobs and companies

[1528] Step 1:

[1529] The user uses the terminal to input data such as work history, skill set, and interests.

[1530] Step 2:

[1531] The terminal transmits the input data to the server.

[1532] Step 3:

[1533] The server analyzes the input data and recommends the most suitable jobs and companies.

[1534] Step 4:

[1535] The server sends the recommendation results to the terminal.

[1536] Step 5:

[1537] The terminal displays the recommendation results to the user.

[1538] Example 1

[1539] 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."

[1540] Conventional job-hunting support systems make it difficult for users to efficiently create resumes, prepare for interviews, and find the most suitable jobs and companies. Specifically, resume generation and interview questions are mechanical and not optimized for each user's individual profile. Furthermore, feedback during interview practice sessions is not specific, making it difficult for users to improve their interview skills. Furthermore, many of the job recommendations do not match the user's skill set or interests.

[1541] 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.

[1542] In this invention, the server includes means for a user to input information about his / her work history, skill set, and the industry and occupation he / she is aiming for, means for transmitting the input information to the server, means for the server to generate resume text using a natural language processing algorithm based on the input information, means for transmitting the resume text generated by the server to a terminal, means for the terminal to display the resume text to the user, means for the user to input an instruction to the terminal to start interview practice, means for transmitting profile data of the user to the server, means for the server to generate interview questions based on the profile data and transmit them to the terminal, and means for the user to input answers to the interview questions to the terminal and for the terminal to display the answers. The system includes means for transmitting answers to a server, means for the server to evaluate the answers and generate feedback and transmit it to the terminal, means for the server to generate new interview questions and transmit them to the terminal, means for the server to recommend the most suitable job types and companies for the individual based on the user's input data, means for the server to transmit the recommendation results to the terminal and display them to the user, means for the server to use a generative AI model as a natural language processing algorithm in the resume creation function and provide prompt sentences to the generative AI model, and means for the server to use the generative AI model when evaluating the user's answers and generate new questions and feedback based on the evaluation results in the interview support function. This enables the creation of resumes tailored to the individual needs of the user, specific and effective interview practice, and recommendations for the most suitable job types and companies.

[1543] "User" refers to an individual who uses this system to create a resume, practice interviews, or receive job recommendations.

[1544] "Work history" refers to the work history and work that the user has been involved in up to now.

[1545] A "skill set" refers to the skills and knowledge a user has acquired and their ability to apply them.

[1546] "Server" refers to a computer system that receives, analyzes, and processes information entered by a user.

[1547] A "natural language processing algorithm" refers to a program that analyzes text data entered by a user and converts it into natural language sentences.

[1548] "Resume text" refers to the text content of the resume generated by the server based on the user's work history and skill set.

[1549] A "terminal" is a device through which a user inputs information, and refers to a computer or smartphone that sends and receives information.

[1550] "Profile Data" refers to data including a user's personal information, work history, and skill set.

[1551] "Interview questions" refer to predicted questions that may be asked in an interview that are generated by the server based on the user's profile data.

[1552] An "answer" refers to a response that a user inputs to an interview question.

[1553] "Feedback" refers to the server's evaluation of the user's answers, including suggestions for improvement and advice.

[1554] "Recommendation results" refers to information on occupations and companies suitable for a user, which is analyzed by the server based on the user's input data.

[1555] "Generative AI model" refers to an artificial intelligence model used for natural language processing, question generation, and feedback evaluation.

[1556] A "prompt" refers to an instruction statement that instructs a generative AI model to perform a specific process.

[1557] The present invention relates to a system that supports users in their job-hunting activities. The main features of this system are a resume creation function, an interview support function, and a job / company recommendation function. Specific embodiments for carrying out the invention are described below.

[1558] 1. Resume creation function

[1559] Users use a device (such as a PC or smartphone) to enter information about their work history, skill set, and the industry and job they are aiming for into a dedicated form. This input data is sent from the device to a server (such as a cloud server). The server uses a generative AI model (e.g., GPT-4) based on the received data to generate resume text.

[1560] Hardware and software used:

[1561] Device: PC or smartphone

[1562] Server: Cloud server (e.g. AWS EC2)

[1563] Generative AI model: GPT-4

[1564] Examples:

[1565] When a user inputs "I have 5 years of experience as a software engineer. My main skills are Python and Java. I am interested in AI," the device sends this information to the server. The server then inputs the following prompt into the generative AI model: "Please create a professional resume based on this information." As a result, the server generates the following sentence: "I have worked as a software engineer for 5 years and have successfully completed multiple projects, primarily using Python and Java. I am particularly interested in AI-related technologies and would like to further my career in this field." and sends it to the device. The user can review this sentence and modify it as necessary.

[1566] 2. Interview support function

[1567] When a user inputs a command to start interview practice on their device, the device sends the user's profile data to a server. Based on this data, the server generates appropriate interview questions related to the user's desired industry and job type and sends them to the device. The user enters answers to the questions displayed on the device and sends them to the server. The server analyzes the user's answers using a generative AI model, evaluates the content, generates areas for improvement and specific feedback, and sends them to the device. Furthermore, the server generates new interview questions based on the user's responses and sends them to the device.

[1568] Hardware and software used:

[1569] Device: PC or smartphone

[1570] Server: Cloud server (e.g. AWS EC2)

[1571] Generative AI model: Various interview support AI (e.g., GPT-4)

[1572] Examples:

[1573] If the user "wants to work as a data scientist," the server inputs the prompt: "Please create an appropriate interview question for a user who wants to work as a data scientist" into the generative AI model, and generates the question: "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the server evaluates the content and provides feedback saying, "Your answer is a bit vague. Please briefly describe the specific dataset, analysis tools used, and results." This process is repeated until the user is satisfied.

[1574] 3. Recommendations for suitable jobs and companies

[1575] When a user inputs information about their desired job type and skills into the device, the device sends the data to the server. The server analyzes the user's data and uses a generative AI model to generate job information that recommends the most suitable job types and companies. The results are sent to the device and displayed to the user.

[1576] Hardware and software used:

[1577] Device: PC or smartphone

[1578] Server: Cloud server (e.g. AWS EC2)

[1579] Generative AI model: Job recommendation AI (e.g., recommendation system)

[1580] Examples:

[1581] When a user enters "I have Python and Java skills and am interested in AI-related jobs," the server inputs the prompt: "Please recommend the best job information for users who have Python and Java skills and an interest in the AI ​​field" into the generative AI model, generating recommendation results such as "AI software engineer job: XX company" and "Data scientist job: XX company" and sending them to the device. The user can review these and consider applying.

[1582] As described above, the system of the present invention uses a generative AI model based on data entered by the user to create resumes, assist with interview practice, and recommend appropriate jobs and companies, thereby improving the efficiency and success rate of job hunting.

[1583] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1584] Resume creation function

[1585] Step 1:

[1586] A user uses a terminal to enter resume information.

[1587] Input: Information about the user's work history, skill set, and desired industry or job type

[1588] Output: Resume information in text format

[1589] Specific operation: The user enters the following into a dedicated form on the device: "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI."

[1590] Step 2:

[1591] The terminal sends the input data to the server.

[1592] Input: Resume information in text format

[1593] Output: Send to server

[1594] Specific operation: The device sends data to the server using the HTTPS protocol.

[1595] Step 3:

[1596] The server inputs the received data into a generative AI model.

[1597] Input: Resume information in text format

[1598] Output: Analysis results from the generative AI model

[1599] Specific operation: The server inputs the prompt "Please create a professional resume based on this information" into a generative AI model (e.g., GPT-4).

[1600] Step 4:

[1601] The server generates the optimal resume text and sends it to the terminal.

[1602] Input: Analysis results from generative AI model

[1603] Output: Optimized resume text

[1604] What it does: The server generates the following sentence and sends it to the device: "I have worked as a software engineer for five years and have successfully completed multiple projects using primarily Python and Java. I am particularly interested in AI-related technologies and would like to further my career in this field."

[1605] Step 5:

[1606] The terminal displays the generated resume text to the user.

[1607] Input: Optimized resume text

[1608] Output: User confirmation screen

[1609] Specific operation: The terminal displays the resume text to the user, who can then review and edit it.

[1610] Interview support function

[1611] Step 1:

[1612] The user inputs an instruction to start the interview practice into the terminal.

[1613] Input: Instruction to start interview practice

[1614] Output: Data sent to the server

[1615] Specific action: The user clicks the "Start Practice" button on the device.

[1616] Step 2:

[1617] The terminal transmits the user's profile data to the server.

[1618] Input: User profile data

[1619] Output: Send to server

[1620] What happens: The device sends profile data using the HTTPS protocol.

[1621] Step 3:

[1622] The server generates interview questions based on the profile data.

[1623] Input: User profile data

[1624] Output: Generated interview questions

[1625] Specific operation: The server inputs the prompt "Please create appropriate interview questions for users seeking a data scientist position" into a generation AI model (e.g., GPT-4), which then generates the question "How did you choose data analysis methods in past projects?"

[1626] Step 4:

[1627] The terminal displays the generated interview questions to the user.

[1628] Input: Generated interview questions

[1629] Output: User response screen

[1630] Specific operation: The terminal displays the interview questions to the user.

[1631] Step 5:

[1632] The user enters answers to the interview questions and the terminal transmits them to the server.

[1633] Input: Answers to interview questions

[1634] Output: Send to server

[1635] Specific action: The user types into the terminal, "Based on the project requirements, I selected a data cleaning technique using Pandas in Python" and submits.

[1636] Step 6:

[1637] The server analyzes the answers and generates feedback.

[1638] Input: User's answer

[1639] Output: Feedback

[1640] Specific behavior: The server uses a generative AI model (e.g., GPT-4) to evaluate the answer and generate feedback such as, "Your answer is somewhat vague. Please briefly describe the specific dataset, analytical tools used, and results."

[1641] Step 7:

[1642] The terminal displays the generated feedback to the user.

[1643] Input: Feedback

[1644] Output: User confirmation screen

[1645] Specific behavior: The device displays feedback to the user.

[1646] Step 8:

[1647] The server generates new interview questions and sends them to the terminal.

[1648] Input: Evaluation result

[1649] Output: New interview question

[1650] Specific operation: The server generates a new question, "How did the analysis results contribute to the outcome of the project?" and sends it to the device.

[1651] Recommendations for suitable jobs and companies

[1652] Step 1:

[1653] The user enters information about their skills and desired job type into the terminal and submits it.

[1654] Input: Information about the user's skills and desired job

[1655] Output: Send to server

[1656] Specific behavior: The user enters "I have skills in Python and Java and am interested in AI-related jobs" and submits.

[1657] Step 2:

[1658] The server analyzes the user's input data and searches for appropriate jobs and companies.

[1659] Input: Information about the user's skills and desired job

[1660] Output: Search results (suitable jobs and companies)

[1661] Specific operation: The server inputs the prompt statement "Please recommend the best job listings for users who have Python and Java skills and an interest in the field of AI" into the generative AI model (e.g., a recommendation system).

[1662] Step 3:

[1663] The server generates recommendations and sends them to the device.

[1664] Input: Analysis results from generative AI model

[1665] Output: Recommendation results

[1666] Specific operation: The server generates recommendation results such as "AI software engineer job: XX company" and "Data scientist job: △△ company" and sends them to the device.

[1667] Step 4:

[1668] The terminal displays the recommendation results to the user.

[1669] Input: Recommendation results

[1670] Output: User confirmation screen

[1671] Specific operation: The terminal displays the recommendation results to the user, who then confirms them.

[1672] (Application example 1)

[1673] 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."

[1674] While existing resume creation and interview support systems support general work history and skill sets, they lack functionality tailored to specific industries, particularly the food delivery industry. As a result, users have difficulty efficiently conducting job searches in the food delivery industry, and it takes a great deal of effort to find suitable jobs and companies. Furthermore, interview practice sessions lack questions and feedback specific to the food delivery industry, preventing users from fully improving their interview skills.

[1675] 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.

[1676] In this invention, the server includes: a means for a user to input information about their work history, skill set, and desired industry and job type; a means for transmitting the input information to the server; a means for the server to generate resume text using a natural language processing algorithm based on the input information; a means for the server to transmit the generated resume text to a terminal; a means for the terminal to display the resume text to the user; a means for generating optimal questions based on the user's desired job type and position using data related to the food delivery industry; a means for recommending appropriate jobs and companies based on the user's input data; a means for displaying the relevant questions and recommendation results on the terminal; and a means for generating and transmitting feedback to support the user's interview practice. This enables effective and efficient job hunting in the food delivery industry. Furthermore, users can improve their interview skills by receiving interview practice and feedback tailored to the food delivery industry.

[1677] "A means for users to input information about their work experience, skill set, and desired industry or job type" refers to an interface or device that allows users to electronically input information about their past work experience, skills, and desired industry or job type.

[1678] The "means for transmitting the input information to the server" is a communication function for transmitting the data input by the user to the server via a network.

[1679] "The means by which the server generates resume text using a natural language processing algorithm based on the input information" refers to a function in which the server analyzes the data it receives, converts it into appropriate words and structures using natural language processing technology, and automatically generates resume text.

[1680] The "means for transmitting the resume text generated by the server to the terminal" is a communication function that enables the server to transmit the generated resume text to the user's terminal.

[1681] The "means for the terminal to display the resume text to the user" is a function for the user's terminal to display the resume text received on the screen.

[1682] "A means of using data related to the food delivery industry to generate optimal questions for the job type or position desired by the user" is a function that utilizes knowledge and data in the food delivery industry to automatically generate interview questions suitable for the job type or position the user is aiming for.

[1683] "Means of recommending appropriate jobs and companies based on user input data" refers to a function that uses an algorithm to identify and recommend suitable jobs and companies based on information provided by the user.

[1684] The "means for displaying the relevant questions and recommendation results on the terminal" is a function for displaying the generated interview questions and company recommendation results on the user's terminal.

[1685] The "means for generating feedback to assist the user in practicing for an interview and sending it to the terminal" is a function for evaluating the interview answers entered by the user, generating feedback providing suggestions for improvement and specific advice, and sending it to the terminal.

[1686] The "means for inputting an instruction to start interview practice into the terminal" is an interface that allows the user to input into the terminal his / her intention to start interview practice.

[1687] The "means for transmitting user profile data to a server" is a communication function for transmitting user profile information to a server.

[1688] The "means for generating interview questions and transmitting them to the terminal" is a function for generating appropriate interview questions based on the user's profile data and transmitting them to the terminal.

[1689] "Means for inputting answers to interview questions and for the terminal to transmit the answers to a server" is a function that allows a user to input answers to interview questions and transmit the input answers to a server.

[1690] The "means for evaluating the answer, generating feedback, and transmitting the feedback to the terminal" is a function in which the server evaluates the user's answer, generates feedback, and transmits the feedback to the terminal.

[1691] The "means for generating new interview questions and transmitting them to the terminal" is a function for referencing the content of the user's answers, generating further interview questions, and transmitting them to the terminal.

[1692] "Means for considering recommendation results specialized for job types and positions in the food delivery industry" refers to a function that provides optimal recommendation results specialized for various job types and positions in the food delivery industry.

[1693] The present invention relates to a system for supporting job hunting in the food delivery industry. Specific embodiments are described in detail below.

[1694] System Configuration

[1695] This system consists of a user terminal and a server. Using the terminal, users input information about their work history, skill set, and the industry and job type they are aiming for. The input information is sent to the server, which uses a natural language processing algorithm to generate resume text based on that information and sends it to the user's terminal. When the user inputs a command to start interview practice into the terminal, the server generates new interview questions based on the user's profile data and sends them to the terminal. The server then recommends the most suitable jobs and companies based on the user's input data and displays them on the terminal.

[1696] Hardware and software used

[1697] Hardware:

[1698] User device: Input device such as a smartphone or PC

[1699] Server: A cloud server with high-performance processing power (e.g., Amazon Web Services, Google Cloud Platform)

[1700] software:

[1701] Natural Language Processing algorithms: OpenAI generative AI models (e.g., GPT-3)

[1702] Communication protocol: HTTPS ensures secure data transmission

[1703] Data processing and calculation

[1704] Resume creation

[1705] 1. Obtaining input data:

[1706] Users enter information about their work history, skill set, and the industry or job they are aiming for on the device.

[1707] 2. Data transmission:

[1708] Data entered on the terminal is sent to the server.

[1709] 3. Natural Language Processing:

[1710] The server analyzes the received data and generates resume text using a generative AI model.

[1711] 4. Data return:

[1712] The generated resume text is sent to the user's terminal and displayed on the screen.

[1713] As a specific example, if a user enters "3 years of experience as a food delivery person. Main skills are quick delivery and customer service," the server generates a resume sentence that reads, "I have worked as a food delivery person for 3 years and have excellent skills in quick delivery and customer service."

[1714] Interview support

[1715] 1. Start your interview practice:

[1716] The user inputs an instruction to start the interview practice into the terminal.

[1717] 2. Sending profile data:

[1718] The user's profile data is sent to the server.

[1719] 3. Generate interview questions:

[1720] The server generates appropriate interview questions based on the profile data and sends them to the terminal.

[1721] 4. Enter and evaluate your answers:

[1722] The user enters an answer into the terminal and sends it to the server, which evaluates the answer and generates feedback.

[1723] 5. Generate new questions:

[1724] Based on the feedback, the server generates new interview questions and sends them to the device, and this process is repeated until the user improves their interviewing skills.

[1725] For example, if a user enters "Desired job: Area manager," the server generates a question such as "Tell us about your past team management experience." If the user answers, "I reduced delivery time by 30% and improved customer satisfaction," the server provides feedback such as, "It would be even better if you provided specific methods and results."

[1726] Job / company recommendations

[1727] 1. Data Analysis:

[1728] Analyzes user input data to identify the most suitable job types and companies.

[1729] 2. Submit your recommendation:

[1730] The server sends the recommendation results to the terminal and displays them to the user.

[1731] For example, if a user enters "I'm interested in an area manager position with the skills to deliver quickly and handle customers," the server will display recommendations such as "Area Manager Position: XX Delivery Company."

[1732] Prompt Sentence Examples

[1733] Here are some examples of prompts for generative AI models:

[1734] Resume writing prompt:

[1735] text

[1736] User-specified work experience and skill set: 3 years of experience as a food delivery driver. Key skills include fast delivery and customer service. Create your food delivery resume.

[1737] Interview question generation prompt:

[1738] text

[1739] User Profile: Desired Job: Area Manager. Generate appropriate interview questions related to the food delivery industry.

[1740] Interview response evaluation prompt:

[1741] text

[1742] User Interview Response: We've reduced delivery time by 30% and increased customer satisfaction in past projects. Please rate this response and provide suggestions for improvement.

[1743] In this way, users can receive support in effectively pursuing a job in the food delivery industry.

[1744] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1745] Step 1:

[1746] Obtaining input data

[1747] The user uses the device to input information about their work history, skill set, and the industry and job they are aiming for. All input data is saved in text format on the device. For example, a user might input "3 years of experience as a food delivery person. My main skills are quick delivery and customer service."

[1748] Step 2:

[1749] Data transmission

[1750] The device sends the acquired input data to the server. The data is securely transmitted using the HTTPS protocol. The input data consists of text information such as work history, skill set, and desired job type.

[1751] Step 3:

[1752] Resume generation using natural language processing

[1753] The server uses a natural language processing algorithm to generate resume text based on the received input data. Specifically, it uses a generative AI model (e.g., GPT-3) to analyze and process the input data. Resume text is generated according to the prompt, and output in the form of "I have worked as a food delivery driver for three years and have excellent skills in prompt delivery and customer service."

[1754] Step 4:

[1755] Returning generated resume data

[1756] The server sends the generated resume text back to the device, which also sends it securely using the HTTPS protocol. The device then saves the received resume text data.

[1757] Step 5:

[1758] View resume

[1759] The terminal displays the resume text received from the server to the user, who can then log in and check and edit the resume displayed on the screen.

[1760] Step 6:

[1761] Interview practice begins

[1762] The user inputs an instruction to start the interview practice on the terminal. Information about the desired job type and position is also input as the initial setting. For example, the user inputs "Desired job type: Area manager."

[1763] Step 7:

[1764] Sending profile data

[1765] The terminal transmits an instruction to start the interview practice and the user's profile data to the server.

[1766] Step 8:

[1767] Generate and submit interview questions

[1768] The server generates interview questions using a generative AI model based on the received profile data. The generated questions are sent to the device. For example, a question such as "Tell us about your past team management experience" is generated.

[1769] Step 9:

[1770] User answer input

[1771] The user enters answers to the interview questions displayed on the terminal. The answers are saved in text format on the terminal. For example, the user might enter "We reduced delivery time by 30% and improved customer satisfaction."

[1772] Step 10:

[1773] Submitting and rating answers

[1774] The device sends the input answers to the server, which uses a natural language processing algorithm to evaluate the received answers and generate feedback, such as "It would be even better if you provided specific methods and results."

[1775] Step 11:

[1776] Sending and Viewing Feedback

[1777] The server sends the generated feedback to the terminal, and the terminal displays the received feedback to the user, who can then further refine the content of their answer based on the feedback.

[1778] Step 12:

[1779] Generate new interview questions

[1780] The server generates new interview questions based on the user's past answers and sends them to the device, and this process is repeated until the user has sufficiently improved their interviewing skills.

[1781] Step 13:

[1782] Job / company recommendations

[1783] The server analyzes the data entered by the user and identifies suitable job types and companies in the food delivery industry. The server then sends the recommendation results to the device and displays them to the user. For example, a recommendation such as "Area Manager: XX Delivery Company" may be displayed. The user can use this as a reference to advance their job search.

[1784] Through the above process, the present invention provides comprehensive support for users to efficiently advance their job search in the food delivery industry.

[1785] 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.

[1786] The present invention relates to a system that supports users in their job hunting activities. This system recognizes the user's emotions by combining emotion engines and optimizes resume creation and interview support based on those emotions. Detailed embodiments of each function, the operation of the entire system, and specific examples are described below.

[1787] 1. Resume creation function

[1788] The user uses a device to input information about their past work experience, skill set, and the industry and job type they are aiming for. The emotion engine recognizes the user's emotions as they enter their information, and the device sends this data to a server. The server uses the received information and the user's emotional data to optimize a natural language processing algorithm and generate resume text. The generated resume is sent to the device, which displays it to the user.

[1789] Example: A user enters "5 years of experience as a software engineer. My main skills are Python and Java. I'm interested in AI" into a device, and the emotion engine recognizes the user's emotion as "confident." The device then sends the information and emotion data to the server, which then generates the following sentence: "I've worked as a software engineer for 5 years and have successfully completed multiple projects using mainly Python and Java. I'm particularly interested in AI-related technologies and aspire to further growth in that field." and sends it to the device. The user can then review and edit this sentence displayed on the device.

[1790] 2. Interview support function

[1791] To begin interview practice, the user issues a command to start the simulation from their device. The emotion engine sends the user's profile data and emotion data to the server in real time. The server generates appropriate interview questions based on the profile data and emotion data and sends them to the device. The user enters answers to the interview questions displayed on the device, and the device sends the answers and emotion data to the server. The server analyzes and evaluates them, generates feedback and the next question, and sends them to the device.

[1792] For example, if a user says, "I'm looking for a data scientist position," the server generates a question such as, "How did you choose your data analysis method in your past projects?" If the user answers, "Based on the project requirements, I chose a data cleaning method using Pandas in Python," the emotion engine recognizes this as "nervous." The server analyzes the content and emotion data and provides feedback such as, "Your answer is a little vague. Please briefly describe the specific dataset, analysis tools used, and results." Based on this emotion data, the next question can be generated in a more appropriate format.

[1793] 3. Recommendations for suitable jobs and companies

[1794] Based on the user's input data and emotional data, the server recommends the most suitable jobs and companies for the user. The server analyzes the user's work history, skill set, interests, and emotional data to provide appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[1795] Example: A user enters "I have skills in Python and Java and am interested in AI-related jobs," and the emotion engine recognizes this as "full of expectations." Based on this, the server recommends job listings for software engineering positions in the AI ​​field, and displays recommended results on the device, such as "AI software engineer positions: Company X" and "Data scientist positions: Company YY."

[1796] As described above, the job change support system of the present invention allows users to effectively create resumes, prepare for interviews, and find the most suitable jobs and companies while taking into consideration their feelings, which allows users to smoothly progress in their job change activities and obtain highly satisfying results.

[1797] The processing flow will be explained below.

[1798] 1. Resume creation function

[1799] Step 1:

[1800] Users use the terminal to enter information about their past work experience, skill set, and the industry and job type they are aiming for.

[1801] Step 2:

[1802] The emotion engine recognizes the emotion the user is feeling when inputting and transmits the emotion data to the terminal.

[1803] Step 3:

[1804] The terminal transmits the user's input data and emotion data to the server.

[1805] Step 4:

[1806] The server then runs the received data through a natural language processing algorithm to generate the optimal resume text, taking into account emotional data to adjust the tone and content of the text.

[1807] Step 5:

[1808] The server sends the generated resume text to the terminal.

[1809] Step 6:

[1810] The terminal displays the resume content received from the server to the user.

[1811] Step 7:

[1812] The user checks the resume displayed on the terminal and makes corrections as necessary.

[1813] 2. Interview support function

[1814] Step 1:

[1815] To start the interview practice, the user issues a command to start the simulation from the terminal.

[1816] Step 2:

[1817] The emotion engine recognizes the user's profile data and emotion data in real time and transmits it to the terminal.

[1818] Step 3:

[1819] The terminal transmits the user's profile data and emotion data to the server.

[1820] Step 4:

[1821] The server generates appropriate interview questions based on the profile data and emotion data and transmits them to the terminal.

[1822] Step 5:

[1823] The user inputs answers to the interview questions displayed on the terminal.

[1824] Step 6:

[1825] The emotion engine recognizes the user's emotions in real time when answering and transmits them to the device.

[1826] Step 7:

[1827] The device sends the user's response and emotion data to the server.

[1828] Step 8:

[1829] The server analyzes the received responses and emotional data and generates feedback, adjusting the content and tone of the feedback based on the emotional data.

[1830] Step 9:

[1831] The server generates feedback and sends it to the device.

[1832] Step 10:

[1833] The device displays the feedback to the user.

[1834] Step 11:

[1835] The server generates new interview questions based on the feedback and transmits them to the terminal.

[1836] 3. Recommendations for suitable jobs and companies

[1837] Step 1:

[1838] The user uses the terminal to input data such as work history, skill set, and interests.

[1839] Step 2:

[1840] The terminal sends the input data to the server.

[1841] Step 3:

[1842] The emotion engine recognizes the emotion the user is feeling when inputting and transmits the emotion data to the terminal.

[1843] Step 4:

[1844] The device transmits the emotion data to the server.

[1845] Step 5:

[1846] The server analyzes the input data and emotional data and recommends the most suitable jobs and companies.

[1847] Step 6:

[1848] The server sends the recommendation results to the terminal.

[1849] Step 7:

[1850] The terminal displays the recommendation results to the user.

[1851] Example 2

[1852] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1853] Conventional job-hunting support systems provide support for resume creation and interview practice without considering the user's emotions, which means that they are unable to provide optimal support tailored to the user's individual situation and emotions. As a result, users often receive suggestions and support that do not match their emotional state, which can cause their job hunting to stall or result in unsatisfactory results.

[1854] 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.

[1855] In this invention, the server includes means for a user to input information about their work history, skill set, and desired industry and occupation, means for transmitting the input information to the server, means for recognizing the user's emotions using an emotion engine and generating data, means for the server to generate resume text using a generative AI model based on the input information and emotion recognition data, means for transmitting the resume text generated by the server to a terminal, and means for the terminal to display the resume text to the user. This supports job hunting while taking the user's emotions into consideration, enabling effective resume creation, interview preparation, and recommendations for suitable occupations and companies.

[1856] "User" refers to an individual who uses the system to search for a new job.

[1857] "Terminal" means an electronic device through which a user inputs information and receives responses from a system. Examples include computers, smartphones, and tablets.

[1858] "Server" refers to a computer system that receives data sent from a user, processes it, and sends the generated results to a terminal.

[1859] "Work history" refers to information indicating the job content and work history of the user.

[1860] "Skill set" refers to information that lists the skills, knowledge, qualifications, etc. that a user possesses.

[1861] "Desired industry and occupation" refers to information about the industry and occupation to which the user wishes to transfer.

[1862] An "emotion engine" refers to software or algorithms that recognize a user's emotional state from their input or behavior and generate emotional data.

[1863] "Emotion recognition data" refers to information generated by the emotion engine regarding the user's emotional state.

[1864] A "generative AI model" refers to an artificial intelligence algorithm that generates appropriate sentences and answers based on user input information and emotion recognition data.

[1865] "Resume Text" refers to the textual content of the resume created by the generative AI model using user input and emotion recognition data.

[1866] "Interview Questions" refers to questions used in practice interviews that are generated by the server based on the user's profile data.

[1867] "Feedback" refers to a response that evaluates the user's practice interview answers and includes suggestions for improvement and advice.

[1868] "Recommendation results" refer to information about the most suitable jobs and companies for a user, generated by the server based on the user's input data and emotion recognition data.

[1869] The present invention relates to a system that supports users in their job hunting activities, and in particular, a system that recognizes the user's emotions by combining an emotion engine and a generative AI model, and then creates a resume, supports interviews, and recommends the most suitable jobs and companies based on those emotions. A specific embodiment of the present invention is described below.

[1870] Users input information about their work history, skill set, and desired industry or occupation into the device. The device temporarily stores this input information, then activates an emotion engine to recognize the user's emotions and generate emotion data. This emotion data and the input information are then sent to the server.

[1871] The server analyzes the received input information and emotional data, and generates resume text using a generative AI model (e.g., GPT-4 or an equivalent natural language processing algorithm). This generated resume text is then sent back to the device, where it is displayed to the user.

[1872] As a specific example, if a user inputs information such as "Five years of experience as a software engineer. Main skills are Python and Java. Interested in AI" into the device and the emotion engine recognizes this as "confident," the server will generate a sentence based on the received data: "I have worked as a software engineer for five years and have successfully completed multiple projects using mainly Python and Java. I am particularly interested in AI-related technologies and am aiming to further grow in that field." and send this to the device. The user can review and edit the displayed resume text.

[1873] Furthermore, when a user starts an interview practice, they can issue a command to start the simulation from their device, which causes the emotion engine to recognize and update the user's emotions in real time and send them to the server along with the user's profile data. The server then generates appropriate interview questions based on the user's profile data and emotion data and sends them to the device. When the user answers the questions, the answers and emotion data are sent back to the server, which analyzes them to generate feedback and the next question, which are then sent to the device.

[1874] For example, if a question such as "How did you choose your data analysis method in past projects?" is generated for a user hoping to become a data scientist, and the user answers "Based on the project requirements, I chose a data cleaning method using Python's Pandas," and the emotion engine recognizes this as "nervous," the server will generate feedback such as "Your answer is a little vague. Please briefly describe the specific dataset, the analysis tools used, and the results." In this way, the next question can be generated in a more appropriate format based on the emotion data.

[1875] Furthermore, the server recommends the most suitable jobs and companies for each individual based on the user's input data and emotional data. Specifically, it analyzes work history, skill set, interests, and emotional data to provide appropriate job information. The recommendation results are sent to the terminal and displayed to the user.

[1876] For example, if a user enters "I have skills in Python and Java and am interested in AI-related jobs" and the emotion engine recognizes this as "full of expectations," the server will generate recommendation results such as "AI software engineer jobs" and "data scientist jobs" and send them to the device.

[1877] As described above, the job change support system of the present invention can effectively create a resume, prepare for an interview, and find the most suitable job type and company while taking into consideration the user's feelings, which allows the user's job change search to proceed more smoothly and achieve a highly satisfying result.

[1878] Prompt Sentence Examples

[1879] 1. Example resume prompts:

[1880] "A user confidently enters his background as a software engineer. Use a natural language processing algorithm to generate a resume based on his experience."

[1881] 2. Example prompts for interview support:

[1882] "A user is nervous about attending a practice interview. Generate appropriate interview questions based on his past work and provide feedback."

[1883] 3. Example job recommendation prompts:

[1884] "Users are excited and interested in AI-related jobs. Please recommend suitable jobs and companies for users with Python and Java skill sets."

[1885] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1886] Step 1: User enters information using the terminal

[1887] The user enters information about their work history, skill set, and desired industry and job type into an input form on the device. The entered information is temporarily stored on the device. The input data includes specific details such as "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI." This input data is used for processing in the next step.

[1888] Step 2: Emotion recognition by the emotion engine

[1889] The device activates the emotion engine, which analyzes the user's input data and recognizes emotions. The emotion engine uses input speed and data from activated sensors to quantify the user's emotions. This emotion recognition data indicates a specific emotional state, such as "confident." This data is then sent to the server in the next step.

[1890] Step 3: The device sends the data to the server

[1891] The device sends the user's input information and emotion recognition data to the server. By sending the input data and emotion recognition data together, the server can process the data comprehensively. For example, data such as "5 years of experience as a software engineer. Main skills are Python and Java. Interested in AI" and "Full of confidence" are sent.

[1892] Step 4: Analyze data on the server and use generative AI models

[1893] The server analyzes the input data and emotion recognition data it receives and uses a generative AI model to generate resume sentences. Based on the input data and emotion recognition data, a natural language processing algorithm such as GPT-4 runs and generates appropriate sentences. For example, the server might generate a sentence like, "I have worked as a software engineer for five years, primarily using Python and Java, and have successfully completed multiple projects. I am particularly interested in AI-related technologies and hope to further my growth in this field."

[1894] Step 5: Send the server-generated text to the terminal

[1895] The server sends the generated resume text to the device. The generated text is converted into a format that the device can display and sent so that the user can understand it. For example, a text like "I have worked as a software engineer for five years and have successfully completed multiple projects using mainly Python and Java. I am particularly interested in AI-related technologies and hope to further grow in that field." is sent to the device.

[1896] Step 6: The terminal displays the generated text to the user.

[1897] The device displays the resume text received from the server to the user. The user can review the generated text and enter corrections or additional information as needed. For example, if the user wants to add specific project experience related to AI, they can edit that section. This correction information is sent back to the server and used for the next data processing.

[1898] The above is the specific flow of processing, from when a user inputs information about their work history, skill set, and desired industry and job type, to when emotions are recognized by the emotion engine, and when resume text is generated and displayed using a generative AI model on the server, users can easily create the optimal resume that takes their emotions into account.

[1899] (Application example 2)

[1900] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1901] Conventional job-change support systems do not take the user's emotions into consideration, which can result in suboptimal resumes and interview support. Furthermore, they are unable to properly address the user's nervousness and anxiety during interview practice, making it difficult to conduct effective practice. Furthermore, because they do not take the user's emotions into consideration when recommending optimal jobs and companies, the user may not be satisfied with the results. To address these issues, the present invention aims to provide a system that recognizes the user's emotions and effectively supports job-change activities based on those emotions.

[1902] 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.

[1903] In this invention, the server includes: means for a user to input information about their work history, skill set, and desired industry and occupation; means for transmitting the input information to the server; means for the server to generate resume text using a natural language processing algorithm based on the input information; means for the server to transmit the generated resume text to a terminal; means for the terminal to display the resume text to the user; emotion recognition means for recognizing the user's emotions; means for transmitting emotion data obtained by the emotion recognition means to the server; and means for the server to optimize resume creation and interview support based on the input information and the emotion data. This enables effective job change support that takes the user's emotions into consideration, allowing the user to obtain more satisfactory results.

[1904] "Work history" refers to the history of the jobs and positions held by the user in the past, and is information including the specific work content and period of time.

[1905] A "skill set" is a collection of specialized skills and knowledge that a user possesses, and refers to skills that are useful in the workplace in a particular job or industry.

[1906] The "target industry or occupation" refers to the industry or occupation the user wishes to work in in the future, and includes specific job content and type of industry.

[1907] A "natural language processing algorithm" is a computer program that analyzes input text data and performs appropriate document creation and information extraction.

[1908] "Emotion recognition means" refers to technology or devices for detecting emotions from a user's facial expressions, voice, actions, etc., and analyzing that data.

[1909] A "resume text" is a document that describes a user's career history, skills, etc. for use in job hunting, and is generated using a natural language processing algorithm.

[1910] "Interview Practice" is a simulation activity that allows users to improve their question-answering and communication skills in preparation for a real interview.

[1911] "Feedback" refers to evaluations and advice on the user's actions and answers, and is information indicating the next action or areas for improvement.

[1912] The "recommendation results" are a list of occupations and companies that the server suggests based on the user's aptitude and desires.

[1913] This invention will specifically explain a system that combines emotion recognition technology to help users effectively conduct job hunting. The system is configured as follows:

[1914] First, the user inputs information about their work history, skill set, and the industry and job type they are aiming for into their terminal. The terminal then sends this input information to the server. The server uses a natural language processing algorithm based on the information to generate resume text. The generated resume text is then sent from the server to the terminal and displayed to the user. At this time, emotion recognition means is used to analyze the user's emotions in real time. For example, if emotion data indicating that the user is "confident" is obtained when creating a resume, the server will generate resume text using positive expressions that correspond to that emotion.

[1915] Next, if the user wants to practice for an interview, they input instructions for the interview practice into the terminal. The terminal sends the user's profile data and emotional data to the server. The server generates appropriate int...

Claims

1. A way for users to input information about their work history, skill set, and desired industry or job type; means for transmitting the input information to a server; A means for the server to generate resume text using a natural language processing algorithm based on the input information; means for transmitting the resume text generated by the server to a terminal; means for displaying the resume text to the user on the terminal; A system including:

2. A means for a user to input an instruction to start an interview practice into a terminal; means for transmitting user profile data to a server; means for the server to generate interview questions based on the profile data and transmit the questions to the terminal; a means for a user to input answers to interview questions into said terminal, said terminal transmitting said answers to a server; means for the server to evaluate the answers and generate and send feedback to the terminal; means for the server to generate new interview questions and transmit them to the terminal; The system of claim 1 , comprising:

3. A means for the server to recommend the most suitable occupations and companies for the individual based on the input data of the user; a means for the server to transmit the recommendation results to a terminal and display them to a user; The system of claim 1 , comprising:

Images