Method and electronic device of managing user's resume based on dynamic interview based on artificial intelligence model

Abstract

A technology for managing a user's resume is provided. In one embodiment of the present disclosure, a method performed by an electronic device may be provided. The method may include the steps of: acquiring user information including at least some of the user's demographic information, educational background information, and career information; executing a dynamic interview with the user based on an interview scenario set by an artificial intelligence model based on the user information; generating a customized resume of the user based on the results of the dynamic interview; and transmitting the customized resume to the user's user terminal.

Description

Technology Field

[0001] The present disclosure relates to a technology for managing a user's resume. Background Technology

[0002] At the current level of technology, there is a lack of capabilities for dynamic career management. Generally, users manage their career-related information by manually recording it or updating it whenever necessary. This approach makes it difficult for users to manage their career data systematically and, in particular, increases the likelihood of missing or inaccurate information over time. Furthermore, existing career management systems are limited to simply storing data and cannot dynamically respond to changes or advancements in a user's career.

[0003] Such technical limitations pose a significant problem when users wish to change jobs. Users must write their resumes to find a new position, but this process consumes a considerable amount of time and effort. In particular, writing a resume is more complex because it is not merely a matter of listing information, but requires effectively expressing one's career history and capabilities. Consequently, users may experience stress during the resume writing process or end up failing to adequately reflect their competence.

[0004] Furthermore, interview opportunities are limited, and there is a lack of even the chance to practice. An interview is not merely a process of answering questions, but a crucial step where users must effectively convey their skills and experience. However, current systems fail to provide an environment where users can prepare for or practice interviews. As a result, users may fail to fully demonstrate their capabilities or miss opportunities due to insufficient preparation. These issues are exacerbating the difficulties users face during the job change process. The problem to be solved

[0005] The technical problem to be solved by the present disclosure is to provide a technology for efficiently managing a resume based on a user's history.

[0006] Another technical problem to be solved by the present disclosure is to provide a technology that allows a user to easily create and update their resume.

[0007] Another technical problem to be solved by the present disclosure is to provide the user with a realistic mock interview environment.

[0008] Another technical problem to be solved by the present disclosure is to provide a technology that identifies areas for improvement in users during an interview.

[0009] Another technical problem to be solved by the present disclosure is to provide a technology for analyzing user habits that are not easily revealed quantitatively (e.g., clarity of pronunciation).

[0010] Another technical problem to be solved by the present disclosure is to provide a technology that recommends a suitable candidate company to a user and recommends a suitable user to a candidate company.

[0011] Another technical problem to be solved by the present disclosure is to provide a technology that dynamically adjusts the difficulty of a mock interview according to the user's proficiency in answering as the mock interview progresses.

[0012] Another technical problem to be solved by the present disclosure is to provide a technology that objectively evaluates a company's recruitment competitiveness and reliability.

[0013] Another technical problem to be solved by the present disclosure is to provide technology that supports the establishment of corporate recruitment strategies.

[0014] Another technical problem to be solved through the present disclosure is to provide technology that provides continuous utility to users and companies seeking employment or a change of job.

[0015] The technical problems of the present disclosure are not to be interpreted as being limited to those mentioned above, and other unmentioned technical problems will be clearly understood by a person skilled in the art to which the present disclosure pertains from the description in the specification. means of solving the problem

[0016] In one embodiment of the present disclosure, a method performed by an electronic device may be provided. The method may include the steps of: acquiring user information including at least some of the user’s demographic information, educational background information, and career information; executing a dynamic interview with the user based on an interview scenario set by an artificial intelligence model based on the user information; generating a customized resume of the user based on the results of the dynamic interview; and transmitting the customized resume to the user’s user terminal.

[0017] In one embodiment of the present disclosure, the method may further include the steps of calculating a user's job suitability score based on the result of the dynamic interview and transmitting the job suitability score to an external device. The job suitability score may be calculated by the artificial intelligence model based on the user information and the user response information of the dynamic interview.

[0018] In one embodiment of the present disclosure, the step of acquiring user information may include acquiring essential items that are required to be input and optional items that are required to be input. The step of executing a dynamic interview with the user may include, based on a determination that the optional items have been acquired, a step of including a list of questions regarding the optional items in the interview scenario.

[0019] In one embodiment of the present disclosure, the selection item may include a career pivot item that includes content indicating that the user desires a career pivot. The step of conducting a dynamic interview with the user may include a step of increasing the number of questions regarding the career information in the interview scenario based on a determination that the career pivot item has been obtained.

[0020] In one embodiment of the present disclosure, the selection item may include the user's personality type item. The step of conducting a dynamic interview with the user may include, based on a determination that the personality type item has been obtained, the step of including a list of questions linked to the career information and the personality type item in the interview scenario.

[0021] In one embodiment of the present disclosure, the artificial intelligence model may be a model that dynamically designs the interview scenario based on the user information and the user response information of the dynamic interview. The step of executing the dynamic interview with the user may include the step of executing the dynamic interview by generating additional questions by decreasing the weight of the user information and increasing the weight of the user response information based on the progress according to the interview scenario.

[0022] In one embodiment of the present disclosure, the step of conducting a dynamic interview with the user may include selecting a question prompt from a preset pool of question prompts based on the user information and user response information of the dynamic interview, and tuning the question prompt based on the user information and the user response information.

[0023] In one embodiment of the present disclosure, the step of tuning the question prompt may include the step of extracting unstructured information of the user based on the user answer information, and the step of tuning the question prompt based on the user information and the unstructured information. The unstructured information may include at least some of the clarity of the user's pronunciation, structural completeness of sentences, vocabulary diversity, logical consistency, speed of speech, intonation, rhythm, clarity of pronunciation, confidence, ability to control emotions, expression of empathy, and adaptability of the answer.

[0024] In one embodiment of the present disclosure, the step of generating the customized resume may include the step of obtaining a draft of the user's resume and the step of generating the customized resume by updating the draft of the resume based on the result of the dynamic interview.

[0025] In one embodiment of the present disclosure, the interview scenario may include at least some of a first list of questions of a type for forming rapport with the user, a second list of questions of a type for verifying the user's information, a third list of questions of a type regarding career information, a fourth list of questions of a type regarding the next career desired by the user, and a fifth list of questions of a type regarding demographic information.

[0026] In one embodiment of the present disclosure, the customized resume may be provided. The customized resume may be created based on a template of a candidate company selected by the user. Additionally, the customized resume may be configured to be editable based on the user's modification input. Furthermore, the customized resume may include a feedback report for improving the user's responses to a dynamic interview.

[0027] In one embodiment of the present disclosure, the job fit score may include at least some of quantitative performance items, qualitative performance items, leadership items, communication ability items, crisis management ability items, collaboration items, growth mindset items, organizational culture fit items, career edge items, and personality items.

[0028] In one embodiment of the present disclosure, the step of calculating the job suitability score may include: generating a scoring rubric including evaluation criteria for each competency item based on a competency model predefined for the job; comparing user responses obtained from the dynamic interview with the evaluation criteria of the scoring rubric to calculate scores for each competency item; and applying pre-set weights to the scores for each competency item to calculate the job suitability score.

[0029] In one embodiment of the present disclosure, the artificial intelligence model may be pre-trained using a training dataset that includes job posting information, successful resume information, and mock interview script information grouped by a plurality of job functions.

[0030] In one embodiment of the present disclosure, the method may further include the steps of identifying a recommended job posting among a plurality of job postings obtained from an external device based on the contents of the customized resume, wherein the suitability with the user exceeds a preset threshold, and transmitting the recommended job posting to the user terminal.

[0031] In one embodiment of the present disclosure, the step of executing the dynamic interview may include: obtaining user response information of a user; determining the user’s proficiency level for at least one of a plurality of predefined job competencies based on the user response information; and generating a subsequent question having a difficulty level corresponding to the proficiency level and transmitting it to the user terminal.

[0032] In one embodiment of the present disclosure, the step of generating the customized resume may include the step of performing natural language processing on user response text, which is the result of the dynamic interview, to extract performance information in a STAR structure including Situation, Task, Action, and Result, and the step of adding the performance information to the customized resume.

[0033] In one embodiment of the present disclosure, the step of executing the dynamic interview by generating additional questions by decreasing the weight of the user information and increasing the weight of the user answer information may include the step of setting the weight of the user information higher than the weight of the user answer information at the start of the dynamic interview, and the step of decreasing the weight of the user information by applying a predefined damping function to the weight of the user information and increasing the weight of the user answer information by the amount by which the weight of the user information has decreased whenever the questions and answers of the dynamic interview are repeated a predetermined number of times.

[0034] In one embodiment of the present disclosure, the step of tuning the question prompt may include: calculating a plurality of scores for each of a plurality of parameters regarding the unstructured information; determining the user's answer proficiency level by combining the plurality of scores; and changing the difficulty of the question prompt according to the answer proficiency level. The step of changing the difficulty may include generating a follow-up question requiring deep thinking when the answer proficiency level is relatively high, and generating an open-ended question to elicit an answer when the answer proficiency level is relatively low.

[0035] According to one embodiment of the present disclosure, an electronic device may be provided. The electronic device may include a communication interface configured to enable communication with a network, a processor configured to execute a computer program comprising one or more instructions, and a memory in which said computer program is stored. Herein, said computer program may include one or more instructions for executing any one of the methods described above. Effects of the invention

[0036] According to the present disclosure, a resume based on a user's history can be efficiently managed.

[0037] According to the present disclosure, a user's resume can be easily created and updated.

[0038] According to the present disclosure, a realistic mock interview environment can be provided to the user.

[0039] According to the present disclosure, areas for improvement in the user can be identified during an interview.

[0040] According to the present disclosure, user habits that are not easily revealed quantitatively (e.g., clarity of pronunciation, etc.) can be analyzed.

[0041] According to the present disclosure, a candidate company suitable for the user can be recommended to the user, and a user suitable for the candidate company can be recommended to the candidate company.

[0042] According to the present disclosure, the difficulty of the mock interview can be dynamically adjusted according to the user's proficiency in answering as the mock interview progresses.

[0043] According to the present disclosure, the recruitment competitiveness and reliability of a company can be objectively evaluated.

[0044] According to the present disclosure, it is possible to support the establishment of a company's recruitment strategy.

[0045] According to the present disclosure, continuous utility can be provided to users and companies seeking employment or changing jobs.

[0046] The effects according to the present disclosure are not to be interpreted as being limited to the effects mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art to which the present disclosure pertains from the description in the specification. Brief explanation of the drawing

[0047] FIG. 1 illustrates an environment in which a management device according to one embodiment of the present disclosure can be applied. FIG. 2 illustrates an electronic device capable of implementing the management device described with reference to FIG. 1. FIG. 3 illustrates a flowchart showing a method according to one embodiment of the present disclosure. Figure 4 illustrates a flowchart showing the detailed operation of the user information acquisition operation described with reference to Figure 3. Figure 5 illustrates a flowchart showing the detailed operation of the execution operation of the dynamic interview described with reference to Figure 3. Figure 6 illustrates a flowchart showing the detailed operation of the execution operation of the dynamic interview described with reference to Figure 3. Figure 7 illustrates a flowchart showing the detailed operation of the tuning operation of the question prompt described with reference to Figure 6. Figure 8 illustrates a flowchart showing the detailed operation of the tuning operation of the question prompt described with reference to Figure 7. Figure 9 illustrates a flowchart showing the detailed operation of the difficulty change operation of the question prompt described with reference to Figure 8. Figure 10 illustrates a flowchart showing the detailed operation of the execution operation of the dynamic interview described with reference to Figure 3. Figure 11 illustrates a flowchart showing the detailed operation of the execution operation of the dynamic interview described with reference to Figure 10. FIG. 12 illustrates a flowchart showing the detailed operation of the execution operation of the dynamic interview described with reference to FIG. 3. FIG. 13 illustrates a flowchart showing the detailed operation of the resume creation operation described with reference to FIG. 3. FIG. 14 illustrates a flowchart showing the detailed operation of the resume creation operation described with reference to FIG. 3. FIG. 15 illustrates a flowchart showing a method according to one embodiment of the present disclosure. FIG. 16 illustrates a flowchart showing the detailed operation of the calculation of the job suitability score described with reference to FIG. 15. FIG. 17 illustrates a flowchart showing a method according to one embodiment of the present disclosure. FIG. 18 illustrates a flowchart showing a method according to one embodiment of the present disclosure. FIG. 19 illustrates a customized resume that may be referenced in various embodiments of the present disclosure. Specific details for implementing the invention

[0048] The various embodiments described in this disclosure are illustrative for the purpose of clearly explaining the technical concept of this disclosure and are not intended to limit it to specific embodiments. The technical concept of this disclosure includes various modifications, equivalents, alternatives, and embodiments selectively combined from all or part of each embodiment described in this disclosure. Furthermore, the scope of the technical concept of this disclosure is not limited to the various embodiments presented below or the specific descriptions thereof.

[0049] Terms used in this disclosure, including technical or scientific terms, may have the meaning generally understood by a person skilled in the art to which this disclosure pertains, unless otherwise defined. Since such terms may be referred to by various other terms depending on technological development, changes, conventions, and the preferences of skilled persons, the scope of the technical concept of this disclosure is not limited by the meaning of the terms used below.

[0050] Expressions used in this disclosure, such as “comprising,” “may compose,” “possessing,” “possessing,” “having,” and “possessing,” mean that the subject feature (e.g., function, operation, or component, etc.) exists and do not exclude the existence of other additional features. That is, such expressions should be interpreted as open-ended terms implying the possibility of including other embodiments.

[0051] Singular expressions used in this disclosure may include the meaning of the plural form unless otherwise indicated by the context, and this applies likewise to singular expressions described in the claims.

[0052] Ordinal expressions used in this disclosure, such as "first," "second," or "first," "second," etc., are used to distinguish one object from another when referring to a plurality of objects of the same kind, unless otherwise indicated in the context, and do not limit the order or importance of the objects.

[0053] Expressions used in the present disclosure, such as “A, B and C”, “A, B or C”, “at least one of A, B and C”, and “at least one of A, B or C”, may mean each of the listed items or all possible combinations of the listed items. For example, “at least one of A or B” may refer to (i) at least one A, (ii) at least one B, and (iii) at least one A and at least one B.

[0054] The expression “based on” as used in this disclosure is used to describe one or more factors affecting an act or action of a decision or judgment described in the phrase or sentence containing this expression, and this expression does not exclude additional factors affecting said act or action of a decision or judgment.

[0055] As used in the present disclosure, the expression that a certain component (e.g., a first component) is "connected" or "connected" to another component (e.g., a second component) may mean that the certain component is not only directly connected or connected to the other component, but is also connected or connected through a new other component (e.g., a third component).

[0056] As used in this disclosure, the expression "configured to" may have meanings such as "set to," "capable of," "modified to," "made to," or "capable of." This expression is not limited to the meaning of "specifically designed in hardware." For example, a processor configured to perform a specific operation may mean a generic-purpose processor capable of performing that specific operation by executing software, or a special-purpose computer structured through programming to perform that specific operation.

[0057] The expressions “part,” “module,” “model,” etc. used in this disclosure refer to software or hardware components such as FPGAs (field-programmable gate arrays) and ASICs (application-specific integrated circuits). Here, “part,” “module,” “model,” etc. may be configured to reside in an addressable storage medium and may be configured to operate one or more processors. Accordingly, “part,” “module,” “model,” etc. may include components such as software components, object-oriented software components, class components, and task components, as well as processors, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Additionally, functions provided through “part,” “module,” “model,” etc. may be combined into smaller units of “part,” “module,” or “model.”

[0058] As used in this disclosure, the term "artificial intelligence (AI)" may refer to a technology that mimics human learning ability, reasoning ability, perceptual ability, etc., and implements them on a computer. Such artificial intelligence may include concepts such as machine learning or symbolic logic.

[0059] The term "machine learning (ML)" as used in this disclosure may refer to a process of training an artificial intelligence model using experience in processing data. As a specific example, machine learning may be a technology that enables an artificial intelligence model to self-classify or learn the features of input data. An artificial intelligence model can analyze input data as a machine learning algorithm, learn based on the results of the analysis, and make judgments or predictions regarding new inputs based on the results of the learning. Unlike the examples provided, any technology that learns to mimic the cognitive and judgmental functions of the human brain (e.g., linguistic understanding, visual understanding, reasoning, prediction, knowledge representation, motion control, etc.) may be understood as falling under the category of machine learning. Examples of machine learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Such machine learning may be performed on the device itself where the artificial intelligence model is executed, or through a separate server or system.

[0060] As used in this disclosure, the term "artificial intelligence model" may refer to a model constructed by extracting and analyzing features from given data and modeling the correlations between the data. The process of constructing the correlations of an artificial intelligence model may be referred to as machine learning. An artificial intelligence model may be composed of one or more neural network layers. Each neural network layer has one or more weights and performs neural network operations through operations between the result of a previous layer's operation and one or more weights. The one or more weights possessed by a neural network layer may be optimized based on the learning results of the artificial intelligence model. As a specific example, one or more weights may be updated during the learning process so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized. Artificial intelligence models may include, for example, CNN (Convolutional Neural Network), DNN (Deep Neural Network), RNN (Recurrent Neural Network), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), BRDNN (Bidirectional Recurrent Deep Neural Network), or Deep Q-Networks, but are not limited thereto.

[0061] Embodiments of the present disclosure will be described below with reference to the attached drawings. In the attached drawings, identical or corresponding components are given the same reference numerals. Furthermore, in the description of the embodiments below, the description of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0062] FIG. 1 illustrates an environment (100) to which a management device (110) according to one embodiment of the present disclosure can be applied.

[0063] Referring to FIG. 1, the environment (100) may include a management device (110), a user terminal (120), and an external device (130). The components of the environment (100) may communicate with each other through a network. For example, the network may be a Local Area Network (LAN), a Wide Area Network (WAN), the Internet, a cellular network, a satellite network, Bluetooth, and Wi-Fi. However, the present disclosure is not limited thereto.

[0064] The management device (110) can perform the function of managing a user's resume using a dynamic interview based on an artificial intelligence model. The management device (110) can obtain user information from a user terminal (120). The user information may include at least some of the user's demographic information, educational background information, and career information. Based on the obtained user information, the management device (110) can set up an interview scenario through an artificial intelligence model.

[0065] The management device (110) can execute a dynamic interview with a user based on a set interview scenario. The management device (110) can generate a customized resume for the user based on the results of the dynamic interview. The management device (110) can transmit the generated customized resume to a user terminal (120). Additionally, the management device (110) can calculate a job suitability score for the user based on the results of the dynamic interview and transmit the calculated score to an external device (130).

[0066] In one embodiment of the present disclosure, the management device (110) may be implemented as one or more servers. For example, the management device (110) may be a virtual server operating in a cloud computing environment. The management device (110) may be configured as a system in which a plurality of servers operate in a distributed processing manner for large-scale data processing.

[0067] A user terminal (120) may be a device that provides an interface for a user to interact with a management device (110). The user may input user information, including their demographic information, educational background information, career information, etc., through the user terminal (120). The user terminal (120) may transmit the input user information to the management device (110) via a network.

[0068] The user terminal (120) can provide an environment in which the user can participate in a dynamic interview executed by the management device (110). The user can check interview questions and input answers through the input and output devices of the user terminal (120). The user terminal (120) can receive a customized resume transmitted from the management device (110) and display or save it on the screen.

[0069] For example, the user terminal (120) may be a smartphone, tablet computer, personal computer, laptop, wearable device, smart TV, kiosk, and game console. However, the present disclosure is not limited thereto.

[0070] The external device (130) may be an external system or server capable of exchanging data with the management device (110). The external device (130) may provide the management device (110) with data necessary for training an artificial intelligence model. For example, the external device (130) may be a database server storing job posting information, information on successful resumes, mock interview script information, etc.

[0071] In one embodiment of the present disclosure, an external device (130) may receive results processed from a management device (110). For example, the external device (130) may be a recruitment management system server of a company. The management device (110) may transmit a customized resume generated with the user's consent or a calculated job suitability score to the external device (130).

[0072] In one embodiment of the present disclosure, the external device (130) may be a recruitment platform server that provides a plurality of job postings. The management device (110) can identify recommended job postings that have a high degree of suitability to the user among the plurality of job postings obtained from the external device (130) based on the content of the generated customized resume. The management device (110) can transmit the identified recommended job postings to the user terminal (120).

[0073] FIG. 2 illustrates an electronic device (200) capable of implementing the management device (110) described with reference to FIG. 1.

[0074] Referring to FIG. 2, an electronic device (200) capable of implementing the management device (110) described with reference to FIG. 1 may be illustrated. The electronic device (200) may be configured to perform a method of managing a user's resume through a dynamic interview based on an artificial intelligence model. The electronic device (200) may include a processor (210), memory (220), and a communication interface (230).

[0075] The processor (210) can control the overall operation of the electronic device (200). The processor (210) can perform operations according to embodiments of the present disclosure by executing one or more instructions stored in memory (220). The processor (210) may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or an Application Processor (AP).

[0076] The processor (210) can manage dynamic interviews by executing an artificial intelligence model stored in memory (220). For example, the processor (210) can set up interview scenarios based on user information. The processor (210) can dynamically generate follow-up questions based on the user's answers. The processor (210) can perform operations to generate a customized resume by analyzing the interview results. Additionally, the processor (210) can perform the function of calculating a job suitability score by combining the user's answer information and history information.

[0077] In one embodiment of the present disclosure, the processor (210) may include specialized hardware for accelerating the computation of an artificial intelligence model. For example, the processor (210) may include an artificial intelligence accelerator such as a Neural Processing Unit (NPU) or a Tensor Processing Unit (TPU). The processor (210) can efficiently perform large-scale data processing and complex model computations. This configuration can contribute to conducting real-time dynamic interviews and rapid resume generation.

[0078] The memory (220) can store data and instructions necessary for the operation of the processor (210). The memory (220) can store an operating system (OS), an application program, and a computer program including one or more instructions for performing a method according to an embodiment of the present disclosure. The memory (220) may include both volatile memory and non-volatile memory.

[0079] The memory (220) can function as a storage medium for storing artificial intelligence models and related data. Specifically, the memory (220) can store a training data set including job posting information, successful resume information, and mock interview script information grouped by multiple job roles. Additionally, the memory (220) can store user information obtained from the user, question and answer data generated during the dynamic interview process, and finally, a customized resume and job suitability score generated.

[0080] For example, the memory (220) may be Random Access Memory (RAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, and Hard Disk Drive (HDD). However, the present disclosure is not limited thereto.

[0081] The communication interface (230) can support the electronic device (200) to communicate with other devices through a network. The communication interface (230) can support wired or wireless communication methods. The communication interface (230) can be responsible for transmitting and receiving data with the user terminal (120) and the external device (130).

[0082] The communication interface (230) can receive user information including the user's demographic information, educational background information, and career information from the user terminal (120). The communication interface (230) can transmit questions generated during the dynamic interview to the user terminal (120). The communication interface (230) can perform the role of receiving the user's answers. After the interview is completed, the communication interface (230) can transmit the generated customized resume to the user terminal (120). The communication interface (230) can transmit the calculated job suitability score to an external device (130).

[0083] For example, the communication interface (230) may support communication methods including Bluetooth, Wi-Fi, Near Field Communication (NFC), Zigbee, cellular communication, Wide Area Network (WAN), Local Area Network (LAN), and Ethernet. However, the present disclosure is not limited thereto.

[0084] The operation of the method to be described with reference to the drawings below may be performed by an electronic device (200). In other words, the operation of the method may be implemented by one or more instructions executed by a processor (210) of the electronic device (200). All operations included in this method may be performed by a single physical computing device, but the first operation of the method may be performed by the first computing device and the second operation of the method may be performed by the second computing device.

[0085] Hereinafter, the explanation will continue assuming that the operation of the above-described method is performed by the management device (110) illustrated in FIG. 1. For the convenience of explanation, the subject of the operation included in the method may be omitted, but unless otherwise indicated by the context, it should be interpreted that the operation is performed by the management device (110).

[0086] FIG. 3 illustrates a flowchart showing a method (S300) according to one embodiment of the present disclosure.

[0087] Referring to FIG. 3, the method (S300) can be performed by a management device (110).

[0088] In step S310, the management device (110) may obtain user information. The user information may include at least some of the user's demographic information, educational background information, and career information. The management device (110) may receive user information through a user interface provided to the user terminal (120). For example, the demographic information may include the user's name, contact information, email address, residential address, date of birth, gender, nationality, military service information, etc. However, the present disclosure is not limited thereto. The educational background information may include the name of the school graduated from, major field, degree obtained, period of study, GPA information, etc. The career information may include the name of the company where the user worked, job duties, position, period of employment, details of projects performed, achievements, and technology stack information.

[0089] In step S320, the management device (110) can execute a dynamic interview with the user based on an interview scenario set by an artificial intelligence model based on user information. The management device (110) can analyze the acquired user information to configure an interview scenario suitable for the user's career level and job characteristics. The artificial intelligence model can conduct the interview by generating questions related to job experience listed in the user's career information.

[0090] In one embodiment of the present disclosure, the artificial intelligence model may be a model that dynamically designs an interview scenario based on user information and user response information of a dynamic interview.

[0091] In one embodiment of the present disclosure, the interview scenario may include at least some of a first list of questions of a type for building rapport with the user, a second list of questions of a type for verifying user information, a third list of questions of a type regarding career information, a fourth list of questions of a type regarding the next career desired by the user, and a fifth list of questions of a type regarding demographic information.

[0092] In one embodiment of the present disclosure, an artificial intelligence model may be pre-trained using a training dataset that includes job posting information, successful resume information, and mock interview script information grouped by a plurality of job functions.

[0093] In step S330, the management device (110) can generate a user's customized resume based on the results of the dynamic interview. The management device (110) can analyze the user's voice or text responses obtained during the dynamic interview process. The management device (110) can extract career details, achievements, and competencies from the analyzed responses. The management device (110) can reorganize the extracted career details, achievements, and competencies into a structured resume format.

[0094] In one embodiment of the present disclosure, a customized resume may be created based on a template of a candidate company selected by the user.

[0095] In one embodiment of the present disclosure, a customized resume may be configured to be editable based on the user's modification input.

[0096] In one embodiment of the present disclosure, the customized resume may include a feedback report for improving the user's response to a dynamic interview.

[0097] In step S340, the management device (110) can transmit the customized resume to the user's user terminal (120). The management device (110) can convert the generated customized resume into a file format that the user can view or edit. The management device (110) can transmit the converted resume file to the user terminal (120) via the communication interface (230). The user can view the customized resume received on the user terminal (120). The user can modify the customized resume as needed. The user can submit the customized resume to an external device (130).

[0098] FIG. 4 illustrates a flowchart showing the detailed operation (S400) of the user information acquisition operation (S310) described with reference to FIG. 3.

[0099] Referring to FIG. 4, the detailed operation (S400) may be a detailed operation of the user information acquisition operation (S310) described with reference to FIG. 3. The detailed operation (S400) may be performed by a management device (110).

[0100] In step S410, the management device (110) may obtain mandatory items that are required to be entered and optional items that are required to be entered. The management device (110) may receive information from the user through the user terminal (120). Mandatory items may be information that is basically required to generate the user's resume. For example, mandatory items may be demographic information such as the user's name, contact information, and email address; educational information such as school name, major, and year of graduation; and basic career information such as company name, job title, and length of employment. However, the present disclosure is not limited thereto. Optional items may be information that can be additionally entered for the user's customized interview and resume generation.

[0101] In one embodiment of the present disclosure, the management device (110) may include a list of questions regarding the selected item in the interview scenario based on a determination that the selected item has been obtained. When a user inputs the selected item, the artificial intelligence model may adjust the depth and direction of the interview by reflecting the information. For example, if the user inputs specific project experience or possessed skills as selected items, the interview scenario may be dynamically changed to include in-depth questions regarding the specific role or technical skills of the project. However, the present disclosure is not limited thereto.

[0102] In one embodiment of the present disclosure, the selection items may include a career pivot item that includes the content that the user desires a career pivot. In this case, the management device (110) may increase the number of questions regarding career information in the interview scenario based on the determination that the career pivot item has been obtained. If the user desires to transition to a new field different from their existing job, the artificial intelligence model may add questions to determine how the user's existing capabilities can contribute to the new field. Through this, the management device (110) can collect information necessary to generate a customized resume that persuasively presents the career transition.

[0103] In one embodiment of the present disclosure, the selection items may include a user's personality type item. In this case, the management device (110) may include a list of questions linking career information and the personality type item in the interview scenario based on the determination that the personality type item has been obtained. For example, the user's personality type item may be the Myers-Briggs Type Indicator (MBTI), Enneagram, Big Five personality traits, DISC behavioral type, Holland Codes, StrengthsFinder, or Workplace Big 5. However, the present disclosure is not limited thereto. The management device (110) may generate questions that explore the association between the user's personality type and past work performance. For example, if a user with an extroverted personality has experience performing marketing duties, questions regarding successful cases utilizing interpersonal skills may be included in the interview scenario.

[0104] FIG. 5 illustrates a flowchart showing the detailed operation (S500) of the execution operation (S320) of the dynamic interview described with reference to FIG. 3.

[0105] Referring to FIG. 5, the detailed operation (S500) of the execution operation (S320) of the dynamic interview can be performed by the management device (110).

[0106] In step S510, the management device (110) can obtain user response information. The management device (110) can receive the user's response from the user terminal (120). The user response information may include voice data, text data, or video data. For example, the management device (110) can receive the user's voice response input through the microphone of the user terminal (120). The management device (110) can convert the voice response into text data using Speech-to-Text (STT) technology.

[0107] In step S520, the management device (110) can determine the user's proficiency level for at least one of a plurality of predefined job competencies based on user response information. The management device (110) can analyze the content, vocabulary used, sentence structure, and logical flow of the user's response using an artificial intelligence model. Based on the analysis results, the artificial intelligence model can evaluate the user's level of understanding and experience regarding a specific job competency. Based on the evaluation results, the artificial intelligence model can determine the proficiency level. For example, job competencies may be problem-solving ability, communication ability, leadership, technical expertise, project management ability, data analysis ability, creative thinking ability, customer service ability, collaboration ability, and crisis management ability. However, the present disclosure is not limited thereto.

[0108] In step S530, the management device (110) can generate follow-up questions with a difficulty level corresponding to the proficiency level and transmit them to the user terminal (120). The management device (110) can dynamically adjust the difficulty of the interview according to the determined proficiency level. For example, if the user's proficiency level is determined to be high, the management device (110) can generate in-depth questions requiring deeper thinking or specific success stories. On the other hand, if the user's proficiency level is determined to be low, the management device (110) can generate open-ended questions that verify basic concepts or elicit answers. This can provide a customized interview experience optimized for the individual user's competency level.

[0109] FIG. 6 illustrates a flowchart showing the detailed operation (S600) of the execution operation (S320) of the dynamic interview described with reference to FIG. 3.

[0110] Referring to FIG. 6, the detailed operation (S600) of the execution operation (S320) of the dynamic interview can be performed by the management device (110).

[0111] In step S610, the management device (110) can select a question prompt from a pre-set pool of question prompts based on user information and user response information from a dynamic interview. The management device (110) can prioritize selecting question prompts related to the job or industry sector specified in the user's career information. Additionally, the management device (110) can analyze the user's response to a previous question. Based on the analysis results, the management device (110) can select a prompt suitable for a subsequent question. For example, the pool of question prompts may include behavior-based question prompts, situation-based question prompts, technical competency assessment question prompts, problem-solving ability assessment question prompts, leadership competency assessment question prompts, teamwork and collaboration ability assessment question prompts, creativity and innovation competency assessment question prompts, and professional ethics-related question prompts. However, the present disclosure is not limited thereto.

[0112] In step S620, the management device (110) can tune the question prompts based on user information and user answer information. The management device (110) can insert specific information, such as specific project names, skills used, or achievements listed in the user's resume, into the selected general question prompts. Through this, the management device (110) can generate personalized questions. The management device (110) can construct the next question more deeply by utilizing keywords or concepts mentioned in the user's previous answers. Through this, the management device (110) can ensure that the flow of the interview continues naturally. Additionally, the management device (110) can evaluate the user's capabilities from various angles.

[0113] In one embodiment of the present disclosure, step S620 may include a step of dynamically adjusting the difficulty of a question prompt according to the user's answer level. For example, if the user demonstrates high proficiency regarding a specific competency, the management device (110) may tune the question prompt to present a more complex scenario. The management device (110) may tune the question prompt to require strategic thinking. Conversely, if the user has difficulty answering, the management device (110) may tune the question prompt to induce the user to explain a concept. The management device (110) may tune the question prompt to require a simpler example.

[0114] FIG. 7 illustrates a flowchart showing the detailed operation (S700) of the tuning operation (S620) of the question prompt described with reference to FIG. 6.

[0115] Referring to FIG. 7, the detailed operation (S700) of the tuning operation (S620) of the question prompt can be performed by the management device (110).

[0116] In step S710, the management device (110) may perform a step of extracting unstructured information of the user based on user response information. The management device (110) may analyze unstructured data from the user's voice, text, or video response. The unstructured information may be used to identify the user's communication ability, logical thinking ability, and emotional state. The management device (110) may evaluate the unstructured information quantitatively or qualitatively using Natural Language Processing (NLP) technology and voice analysis technology.

[0117] In one embodiment of the present disclosure, unstructured information may include at least some of the following: clarity of the user's pronunciation, structural completeness of the sentence, vocabulary diversity, logical consistency, speed of speech, intonation, rhythm, clarity of pronunciation, confidence, emotional regulation ability, expression of empathy, and adaptability of the response. For example, unstructured information may be extracted through speech recognition technology, text analytics technology, sentiment analysis technology, image processing technology, biosignal analysis technology, behavioral pattern analysis technology, and machine learning-based models. However, the present disclosure is not limited thereto.

[0118] In step S720, the management device (110) may perform a step of tuning the question prompt based on user information and unstructured information. The management device (110) may combine the unstructured information extracted in step S710 with previously acquired user information. Based on the combined information, the management device (110) may dynamically adjust the difficulty, type, or focus of the next question. For example, if it is determined that the user's answer lacks logical consistency, the management device (110) may tune the prompt to a question that reconfirms the content of the previous answer or requires specific examples. Conversely, if the user demonstrates a high level of vocabulary diversity and confidence, the management device (110) may adjust the prompt to a question that asks for more in-depth expertise.

[0119] In one embodiment of the present disclosure, the step of tuning a question prompt may include tuning the question prompt based on user information and unstructured information. The management device (110) may comprehensively consider the user's career information and unstructured information such as the adaptability of the answer. Based on comprehensive consideration, the management device (110) may tune the prompt to generate a situation-based question that evaluates how flexibly the user responds to a specific situation.

[0120] FIG. 8 illustrates a flowchart showing the detailed operation (S800) of the tuning operation (S720) of the question prompt described with reference to FIG. 7.

[0121] Referring to FIG. 8, the detailed operation (S800) of the tuning operation (S720) of the question prompt can be performed by the management device (110).

[0122] In step S810, the management device (110) may calculate multiple scores for each of multiple parameters regarding unstructured information. The management device (110) may assign scores according to pre-set criteria to quantitatively evaluate unstructured information extracted from the user's response. For example, the unstructured information may be the clarity of the user's pronunciation, structural completeness of the sentence, vocabulary diversity, logical consistency, speed of speech, intonation, rhythm, clarity of pronunciation, confidence, emotional control ability, expression of empathy, and adaptability of the response. However, the present disclosure is not limited thereto. The management device (110) may evaluate the clarity of pronunciation or the speed of speech using speech recognition technology. Additionally, the management device (110) may calculate scores for each parameter by analyzing sentence structure and logical consistency through natural language processing technology.

[0123] In step S820, the management device (110) can determine the user's answer proficiency level by combining multiple scores. The management device (110) can calculate a total score by applying a preset weight to each parameter score calculated in step S810. The management device (110) can map the calculated total score to a specific proficiency level according to a preset interval. For example, the proficiency level can be divided into several stages, such as beginner, intermediate, advanced, expert, etc.

[0124] In step S830, the management device (110) can change the difficulty of the question prompt according to the answer proficiency level. The management device (110) can adjust the complexity or depth of the next question to match the user's answer proficiency level determined in step S820. For example, if the answer proficiency level is relatively high, the management device (110) can generate follow-up questions that require deep thinking. Additionally, if the answer proficiency level is relatively low, the management device (110) can generate open-ended questions to elicit answers. Through this, the management device (110) can provide a customized interview experience to the user. Furthermore, the management device (110) can more accurately assess the user's capabilities.

[0125] FIG. 9 illustrates a flowchart showing the detailed operation (S900) of the difficulty change operation (S830) of the question prompt described with reference to FIG. 8.

[0126] Referring to FIG. 9, the detailed operation (S900) of the difficulty change operation (S830) of the question prompt described with reference to FIG. 8 can be performed by the management device (110).

[0127] In step S910, the management device (110) can generate follow-up questions requiring in-depth thinking when the answer proficiency level is relatively high. When the answer proficiency level is relatively low, the management device (110) can generate open-ended questions to elicit an answer. For example, the management device (110) can determine that the proficiency level is high based on the user's answer being logical and containing specific achievements. The management device (110) can generate questions requiring an analysis of the pros and cons of the strategies used to achieve those achievements. The management device (110) can determine that the proficiency level is low based on the user's answer being abstract or lacking structural completeness. The management device (110) can generate open-ended questions that elicit specific examples of the answer or request an explanation of the experience step by step.

[0128] FIG. 10 illustrates a flowchart showing the detailed operation (S1000) of the execution operation (S320) of the dynamic interview described with reference to FIG. 3.

[0129] Referring to FIG. 10, the detailed operation (S1000) of the execution operation (S320) of a dynamic interview according to one embodiment of the present disclosure can be performed by a management device (110).

[0130] In step S1000, the management device (110) can perform detailed operations. The detailed operations (S1000) may represent a process in which an artificial intelligence model dynamically adjusts the weights of information that form the basis for question generation while conducting a dynamic interview. The management device (110) can generate questions by assigning high weights to static user information provided by the user at the beginning of the interview. As the interview progresses, the management device (110) can add depth to the interview by assigning higher weights to dynamic information obtained in real-time from the user's answers.

[0131] In step S1010, the management device (110) can generate additional questions by decreasing the weight of user information and increasing the weight of user answer information based on the progress according to the interview scenario. The management device (110) can execute a dynamic interview through the generated additional questions. For example, at the start of the interview, the weight of user information can be set to 0.9 and the weight of user answer information to 0.1 to generate basic questions such as verifying resume content. Subsequently, as user answers accumulate, the management device (110) can generate in-depth questions regarding specific project experience or problem-solving ability by gradually decreasing the weight of user information and increasing the weight of user answer information.

[0132] In one embodiment of the present disclosure, the artificial intelligence model may be a model that dynamically designs an interview scenario based on user information and user response information from a dynamic interview. The step of conducting a dynamic interview with a user may include the step of conducting the dynamic interview by generating additional questions by decreasing the weight of user information and increasing the weight of user response information based on the progress according to the interview scenario. The weight adjustment mechanism may induce the interview to more accurately identify the user's actual capabilities and experiences.

[0133] FIG. 11 illustrates a flowchart showing the detailed operation (S1100) of the execution operation (S1010) of the dynamic interview described with reference to FIG. 10.

[0134] Referring to FIG. 11, the detailed operation (S1100) of the execution operation (S1010) of the dynamic interview can be performed by the management device (110).

[0135] In step S1110, the management device (110) may set the weight of user information higher than the weight of user answer information at the start of the dynamic interview. The management device (110) may generate questions based on static resume information provided by the user during the initial stage of the interview. For example, the weight of user information may be set to 0.7, and the weight of user answer information may be set to 0.3. These weight settings may contribute to setting the initial direction of the interview to align with the user's existing career and educational information.

[0136] In step S1120, the management device (110) can apply a predefined damping function to the weight of user information to decrease it whenever the questions and answers of the dynamic interview are repeated a preset number of times. Additionally, the management device (110) can increase the weight of user answer information by the amount that the weight of user information has decreased. As the interview progresses, the management device (110) can obtain new information from the user's answers. The management device (110) can dynamically adjust the weights to generate follow-up questions based on the newly obtained information. For example, whenever the questions and answers are repeated three times, the weight of user information can be decreased by 0.1 and the weight of user answer information can be increased by 0.1.

[0137] The decay function may be, for example, a linear decay function, an exponential decay function, a step function, a sigmoid function, a logarithmic decay function, a cosine decay function, or a Gaussian decay function. However, the present disclosure is not limited thereto. Through such a weighting adjustment mechanism, the interview can gradually transition into in-depth content based on the user's answers.

[0138] FIG. 12 illustrates a flowchart showing the detailed operation (S1200) of the execution operation (S320) of the dynamic interview described with reference to FIG. 3.

[0139] Referring to FIG. 12, the detailed operation (S1200) of the execution operation (S320) of the dynamic interview can be performed by the management device (110).

[0140] In step S1210, the management device (110) may include a list of questions regarding the selected item in the interview scenario based on the determination that the selected item has been obtained. The management device (110) may check whether there is an item provided selectively among the information entered by the user. If the selected item exists, the management device (110) may analyze the content of the selected item to personalize the flow of the interview. For example, if the user enters a specific project experience as a selected item, the management device (110) may generate specific questions regarding the goals of the project entered as a selected item, the user's role, and performance, and add them to the interview scenario.

[0141] In one embodiment of the present disclosure, the step of acquiring user information may include acquiring mandatory items that are required to be entered and optional items that are required to be entered. The step of conducting a dynamic interview with a user may include, based on a determination that optional items have been acquired, a step of including a list of questions regarding optional items in an interview scenario. Mandatory items may be used to identify the user's basic identity and career details. Optional items may be utilized to identify the user's in-depth competencies or characteristics.

[0142] In one embodiment of the present disclosure, the selection item may include a career pivot item that includes the content that the user desires a career pivot. The step of conducting a dynamic interview with the user may include a step of increasing the number of questions regarding career information in the interview scenario based on the determination that a career pivot item has been obtained. For example, if the user desires to transition to a new job different from the existing job, the management device (110) may intensively generate questions to determine how the user's existing experience can contribute to the new job or to what extent the user has knowledge of the new field and willingness to learn.

[0143] In one embodiment of the present disclosure, the selection items may include a user's personality type item. The step of conducting a dynamic interview with the user may include, based on a determination that a personality type item has been obtained, a step of including a list of questions linked to career information and a personality type item in the interview scenario. For example, if the user has entered their personality type as a specific type of the Myers-Briggs Type Indicator (MBTI), the management device (110) may generate questions about how the strengths of the entered personality type were demonstrated in past projects or what efforts were made to compensate for weaknesses.

[0144] In one embodiment of the present disclosure, step S1210 may include the step of analyzing the content of a selection item entered by a user using natural language processing technology and dynamically generating highly relevant questions based on the analyzed keywords or context. The management device (110) may analyze selection items such as a portfolio link or a personal blog address submitted by the user. The management device (110) may identify the user's expertise and interests based on the analysis results. The management device (110) may integrate customized questions into an interview scenario to verify the identified expertise and interests.

[0145] FIG. 13 illustrates a flowchart showing the detailed operation (S1300) of the resume creation operation (S330) described with reference to FIG. 3.

[0146] Referring to FIG. 13, FIG. 13 may illustrate a flowchart showing a detailed operation (S1300) of the resume creation operation (S330) described with reference to FIG. 3. The detailed operation (S1300) may be performed by a management device (110).

[0147] In step S1310, the management device (110) can perform natural language processing on the user response text, which is the result of a dynamic interview, to extract performance information in a STAR structure including Situation, Task, Action, and Result. The management device (110) can analyze the context related to a specific project or work experience in the user's response using a Natural Language Processing (NLP) model. For example, the management device (110) can identify the project name, duration, role, etc., through Named Entity Recognition (NER) technology. Additionally, the management device (110) can classify information corresponding to each element of the STAR by identifying the structure of the subject, action, object, etc., within the sentence through Semantic Role Labeling (SRL) technology.

[0148] In step S1320, the management device (110) can add performance information to the customized resume. The management device (110) can systematically organize and insert the extracted STAR-structured performance information into the career description section or performance section of the resume. The management device (110) can improve readability by summarizing each performance item in the form of bullet points. Through this, the user's experience can be described with a focus on specific and quantitative performance, thereby strengthening the professionalism of the customized resume.

[0149] In one embodiment of the present disclosure, the step of generating a customized resume may perform natural language processing on user response text, which is the result of a dynamic interview. Through this, it may include the step of extracting performance information in a STAR structure including Situation, Task, Action, and Result. Additionally, the step of generating a customized resume may include the step of adding performance information to the customized resume.

[0150] In one embodiment of the present disclosure, step S1310 may further include the step of identifying and quantifying numerical data related to Key Performance Indicators (KPIs) in user response text. For example, the management device (110) may detect expressions such as “sales increased by 20 percent” or “costs were reduced by 10 million won.” The management device (110) may extract such numerical information and explicitly include it in a Result item.

[0151] In one embodiment of the present disclosure, step S1320 may include a step of determining priority by associating a plurality of extracted performance information with the user's career goals or the required competencies of the job being applied for. The management device (110) can increase the attention of the hiring manager by placing performance information with high priority first in the customized resume.

[0152] FIG. 14 illustrates a flowchart showing the detailed operation (S1400) of the resume creation operation (S330) described with reference to FIG. 3.

[0153] Referring to FIG. 14, the detailed operation (S1400) of the resume creation operation (S330) can be performed by the management device (110).

[0154] In step S1410, the management device (110) may obtain a draft of the user's resume. The management device (110) may receive a draft resume file uploaded from the user terminal (120). Alternatively, the management device (110) may obtain the draft resume by accessing the user's resume information stored in an external device (130). The draft resume may be provided in various file formats. For example, the draft resume may be a word processor document (e.g., .docx), a Portable Document Format (PDF) file, a text file (e.g., .txt), a Markdown file, a HyperText Markup Language (HTML) file, JavaScript Object Notation (JSON) data, or profile data from an external recruitment platform. However, the present disclosure is not limited thereto.

[0155] In step S1420, the management device (110) can generate a customized resume by updating the resume draft based on the results of the dynamic interview. The management device (110) can analyze the user's response text, voice data, unstructured information, etc. collected during the dynamic interview process. The management device (110) can identify parts that need reinforcement by comparing the analyzed results with the content of the resume draft. Subsequently, the management device (110) can update the resume draft by adding information such as new achievements, competencies, and experiences derived from the dynamic interview to the identified parts, or by modifying the existing content.

[0156] According to the present disclosure, the management device (110) can start work based on a resume previously created by the user. This reduces the burden of creating a completely new resume. The management device (110) can utilize in-depth information obtained through dynamic interviews to enrich the content of the existing resume draft and perform updates in a way that more effectively highlights the user's strengths.

[0157] In one embodiment of the present disclosure, step S1420 may include mapping the user's core competencies and achievements extracted from a dynamic interview to relevant items in a resume draft, reinforcing existing content or adding new content based on the mapped information, and optimizing sentence structure and expression to improve the consistency and readability of the overall content. For example, the management device (110) may automatically add achievement information extracted from the interview answers in a STAR structure to the 'career skills' item of the resume draft. Additionally, the management device (110) may update the 'core competencies' item by reflecting specific examples related to leadership or problem-solving ability revealed in the user's answers.

[0158] FIG. 15 illustrates a flowchart showing a method (S1500) according to one embodiment of the present disclosure.

[0159] Referring to FIG. 15, a method (S1500) according to one embodiment of the present disclosure can be performed by a management device (110).

[0160] In step S1510, the management device (110) can calculate the user's job suitability score based on the results of the dynamic interview. The management device (110) can comprehensively analyze the user's response text, voice data, unstructured information, etc. obtained from the dynamic interview. Based on the analysis results, the management device (110) can evaluate the user's capabilities, experience, tendencies, etc. from various angles. The management device (110) can generate a quantified score based on the evaluation results.

[0161] In one embodiment of the present disclosure, a job suitability score may be calculated by an artificial intelligence model based on user information and user response information from a dynamic interview. The artificial intelligence model may evaluate the consistency, logic, depth, etc., between the information initially entered by the user and the interview responses. The artificial intelligence model may calculate a score based on the evaluation results. The artificial intelligence model may extract specific keywords, competency indicators, performance examples, etc., from the user's responses. The artificial intelligence model may reflect the extracted information in the score calculation.

[0162] In one embodiment of the present disclosure, the job fit score may include at least some of quantitative performance items, qualitative performance items, leadership items, communication skills items, crisis management skills items, collaboration items, growth mindset items, organizational culture fit items, career edge items, and personality items. For example, the items constituting the job fit score may be quantitative performance items, qualitative performance items, leadership items, communication skills items, crisis management skills items, collaboration items, growth mindset items, organizational culture fit items, career edge items, personality items, problem-solving skills items, and creativity items. However, the present disclosure is not limited thereto.

[0163] In step S1520, the management device (110) can transmit the job suitability score to the external device (130). The management device (110) can transmit the calculated job suitability score to the external device (130) based on the user's explicit consent. For example, the external device (130) may be a recruitment platform server, a corporate human resources (HR) management system, a server of a career development consulting agency, a server of a headhunting firm, a learning management system of an educational institution, an employment support system of a government agency, or an in-house talent management platform. However, the present disclosure is not limited thereto. The external device (130) can utilize the transmitted job suitability score to evaluate candidates in the recruitment process. Additionally, the external device (130) can utilize the job suitability score to recommend suitable jobs to the user. The management device (110) can securely transmit the job suitability score to the database of the external device (130) via an Application Programming Interface (API).

[0164] FIG. 16 illustrates a flowchart showing the detailed operation (S1600) of the operation (S1510) of calculating the job suitability score described with reference to FIG. 15.

[0165] Referring to FIG. 16, the detailed operation (S1600) of the calculation operation (S1510) of the job suitability score can be performed by the management device (110).

[0166] In step S1610, the management device (110) may generate a scoring rubric containing evaluation criteria for each competency item based on a predefined competency model for a job. The management device (110) may use a competency model that systematically defines the knowledge, skills, and behavioral characteristics required to successfully perform a specific job. The management device (110) may construct the scoring rubric by setting specific evaluation criteria for each competency item included in the competency model. For example, the scoring rubric may define evaluation grades such as 'Excellent', 'Good', 'Average', 'Poor', 'Very Poor', 'Needs Improvement', and 'Better than Expected' for a competency item called 'Problem Solving Ability'. However, the present disclosure is not limited thereto. Additionally, the scoring rubric may ensure objectivity in the evaluation by including specific behavioral indicators corresponding to each grade.

[0167] In step S1620, the management device (110) can calculate scores for each competency item by comparing user responses obtained from a dynamic interview with evaluation criteria of a scoring rubric. The management device (110) can analyze the user's response text using Natural Language Processing (NLP) technology. The management device (110) can extract keywords, phrases, semantic patterns, etc., associated with evaluation criteria of the scoring rubric from the analyzed text. Based on the extracted information, the management device (110) can determine the user's proficiency level for each competency item. The management device (110) can convert the determined level into a quantitative score.

[0168] In step S1630, the management device (110) can calculate a job suitability score by applying a preset weight to the score for each competency item. The management device (110) can set the relative importance of each competency item differently depending on the characteristics of the target job. The management device (110) can assign differential weights to the score for each competency item according to the set importance. For example, in the case of a research and development job requiring technical expertise, a high weight can be applied to the relevant technical competency item. The management device (110) can calculate a final job suitability score by combining the scores of all competency items to which weights have been applied.

[0169] FIG. 17 illustrates a flowchart showing a method (S1700) according to one embodiment of the present disclosure.

[0170] Referring to FIG. 17, the method (S1700) can be performed by a management device (110).

[0171] In step S1710, the management device (110) can identify recommended job postings among multiple job postings obtained from an external device that exceed a preset threshold for suitability with the user, based on the content of the customized resume. The management device (110) can analyze the user's career, skills, education, and certification information listed in the generated customized resume. Additionally, the management device (110) can analyze multiple job posting data received from the external device (130). The management device (110) can calculate the semantic similarity between the core keywords of the resume and the job requirements of the job posting using Natural Language Processing (NLP) technology. Suitability can be calculated through cosine similarity, Jaccard similarity, or a machine learning-based classification model.

[0172] In one embodiment of the present disclosure, a step of identifying recommended job postings among a plurality of job postings obtained from an external device (130) based on the content of a customized resume, wherein the suitability with the user exceeds a preset threshold, may be performed. The management device (110) may extract information such as the user's career goals, desired salary, and preferred work location from the resume. The management device (110) may perform a filtering process by comparing the extracted information with the conditions of the job postings. Through this, the management device (110) can increase the accuracy of the recommendation by selecting only job postings that meet the user's requirements.

[0173] In one embodiment of the present disclosure, step S1710 may include a step of analyzing the user's skill stack, project experience, Key Performance Indicator (KPI), and career goals included in the customized resume. Additionally, step S1710 may include a step of analyzing the job description, qualifications, preferred qualifications, etc. of a job posting received from an external device (130) using natural language processing technology. The management device (110) may map the analyzed resume information and job posting information into a vector space. The management device (110) may calculate the suitability by calculating the similarity between the two vectors.

[0174] In step S1720, the management device (110) can transmit recommended job postings to the user terminal. The management device (110) can organize the identified recommended job postings into a list. The management device (110) can transmit the organized list to an application or web page of the user terminal (120) via a network. The transmitted list may include the company name, job title, main duties, qualification requirements, and a calculated suitability score. The user can view the recommended job postings through the user terminal (120) and decide whether to apply.

[0175] In one embodiment of the present disclosure, the management device (110) can transmit a list of recommended job postings to a user terminal (120) in the form of a push notification, email, or message. The user can receive job information suitable for them in real time. Through this, the user can reduce the time and effort required to search for job information.

[0176] FIG. 18 illustrates a flowchart showing a method (S1800) according to one embodiment of the present disclosure.

[0177] Referring to FIG. 18, the method (S1800) can be performed by a management device (110).

[0178] The method (S300) of FIG. 3 described above may include a series of operations to obtain user information from a user who wishes to seek employment or change jobs, and to generate and transmit a customized resume based on the execution of a dynamic interview and the results thereof. On the other hand, the method (S1800) of FIG. 18 may include a series of operations to obtain company information from a company that wishes to hire, and to generate and transmit a customized report based on the execution of a dynamic interview and the results thereof. That is, the company may execute the method (S1800) through an external device (130) shown in FIG. 1.

[0179] In step S1810, the management device (110) can acquire corporate information. The corporate information may include various information about the company, such as salary levels by job, benefits, recruitment processes, job structure, company size, company growth rate, organizational culture, and history of new hires or departures. The management device (110) can receive corporate information through a user interface provided to an external device (130).

[0180] In step S1820, the management device (110) can execute a dynamic interview with a company based on an interview scenario set by an artificial intelligence model based on company information. The management device (110) can analyze the acquired company information to configure an interview scenario suitable for the characteristics of the company. The artificial intelligence model can conduct the interview by generating questions corresponding to the company information.

[0181] In one embodiment of the present disclosure, a first artificial intelligence model targeting dynamic interviews of a company may be a model trained based on different training data than a second artificial intelligence model targeting dynamic interviews of a user. For example, the first artificial intelligence model may be pre-trained using a training data set including recruitment information grouped by multiple job functions, salary levels, benefits, recruitment processes, job structures, company size, company growth rate, organizational culture, and history of employees joining or leaving the company, and the second artificial intelligence model may be pre-trained using a training data set including job posting information grouped by multiple job functions, information on successful resumes, and mock interview script information.

[0182] In one embodiment of the present disclosure, a first artificial intelligence model targeting a dynamic interview of an enterprise may have a question prompt pool different from a second artificial intelligence model targeting a dynamic interview of a user.

[0183] In one embodiment of the present disclosure, the first artificial intelligence model targeting dynamic interviews of a company and the second artificial intelligence model targeting dynamic interviews of a user design interview scenarios and conduct dynamic interviews in a similar manner, except that the training data set or question prompt pool is different, so the technical concept described with reference to FIGS. 3 to 15 can be likewise applied to FIG. 18.

[0184] In step S1830, the management device (110) can generate a customized report for the user based on the results of the dynamic interview. The management device (110) can analyze the voice or text responses of the company representative obtained during the dynamic interview process. The management device (110) can extract various information about the company from the analyzed responses. The management device (110) can reconstruct the extracted information about the company into a structured report format.

[0185] In one embodiment of the present disclosure, the customized report may include indicators regarding comparison with other companies in the same industry group (e.g., comparison of averages, etc.).

[0186] In one embodiment of the present disclosure, a customized report may be created based on a template selected by the user.

[0187] In one embodiment of the present disclosure, a customized report may be configured to be editable based on user modification input.

[0188] In one embodiment of the present disclosure, the customized report may include comments to improve recruitment strategies and branding strategies.

[0189] In step S1840, the management device (110) can transmit the customized report to the external device (130). The management device (110) can convert the generated customized report into a file format that can be viewed or edited by a corporate representative. The management device (110) can transmit the converted report file to the external device (130) via the communication interface (230). The corporate representative can check the customized report received from the external device (130).

[0190] FIG. 19 illustrates a customized resume (1900) that may be referenced in various embodiments of the present disclosure.

[0191] Referring to FIG. 19, a customized resume (1900) may be referenced in various embodiments of the present disclosure. The customized resume (1900) may be an electronic document generated by a management device (110). The management device (110) may generate a customized resume (1900) that effectively represents the user's career and capabilities based on the results of a dynamic interview. The generated customized resume (1900) is transmitted to the user's user terminal (120) so that the user can view and utilize it.

[0192] The management device (110) can deeply analyze the user's answer information obtained during the dynamic interview process. The management device (110) can extract information related to the user's specific performance from the analyzed answer information. For example, the management device (110) can use Natural Language Processing (NLP) technology to extract performance information in a STAR structure including Situation, Task, Action, and Result from the user's answer text. The extracted performance information can be systematically organized and included in a customized resume (1900).

[0193] A customized resume (1900) may be composed of multiple sections. For example, a customized resume (1900) may include a demographic information section, a core competency section, a skills and professional field section, an education section, and a career section. The demographic information section may include basic personal information such as the user's name, contact details, and address, and the core competency section may summarize and describe the user's major strengths and job competencies identified through dynamic interviews. The career section may describe the user's previous career based on the STAR structure, focusing on specific achievements.

[0194] In one embodiment of the present disclosure, a customized resume (1900) can be generated optimized for a specific company or job that the user intends to apply for. The user may select one of a plurality of candidate companies, and the management device (110) may generate a customized resume (1900) based on a resume template of the candidate company selected by the user. Through this, the customized resume (1900) can be provided in a form that matches the talent profile or required competencies of the company being applied to.

[0195] In one embodiment of the present disclosure, the customized resume (1900) may be configured to be editable based on the user's modification input. The user may review the contents of the customized resume (1900) received via the user terminal (120) and directly modify or supplement the necessary parts. Additionally, the customized resume (1900) may include a feedback report to improve the user's answers in a dynamic interview. The feedback report may help the user recognize areas where their answers were lacking and provide better answers in future interviews.

[0196] In one embodiment of the present disclosure, the management device (110) may additionally utilize the contents of the generated customized resume (1900). The management device (110) may analyze the suitability of a user among a plurality of job postings obtained from an external device (130) based on the contents of the customized resume (1900). The management device (110) may identify recommended job postings whose suitability exceeds a preset threshold, and the identified recommended job postings may be transmitted to the user's user terminal (120).

[0197] Although the operations included in the method of the present disclosure have been described in a sequential order according to the flowchart in which the method is illustrated, the operations included in the method of the present disclosure may be executed in any other combination. Furthermore, detailed changes or modifications may be made to the operations included in the method of the present disclosure, and the fact that such operations are included in the flowchart does not imply that they are essential for executing the method of the present disclosure. In one embodiment, at least some of the operations included in the method of the present disclosure may be performed in parallel, iteratively, or heuristically. Additionally, in another embodiment, at least some of the operations included in the method of the present disclosure may be omitted.

[0198] Various embodiments of the present disclosure may be implemented as software on a machine-readable storage medium (MRSM). Additionally, software may be inferred from the various embodiments of the present disclosure by a person skilled in the art to which the present disclosure pertains. For example, the software may be a computer program containing instructions that can be read by a computing device. In one embodiment, the processor of the computing device may execute a called instruction to cause the components of the computing device to perform a function corresponding to that instruction. The storage medium may refer to any type of recording medium in which information that can be read by a device is stored. The storage medium may include, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, or optical information storage device. In one embodiment, the storage medium may be implemented in a distributed form in a networked computer system, etc. Here, the software may be stored in a distributed manner in a computer system, etc. Additionally, in another embodiment, the storage medium may be a non-transitory storage medium. Non-transient storage media refer to media that exist regardless of whether information is stored semi-permanently or temporarily, and do not include signals that are transmitted transiently.

[0199] A method according to one embodiment of the present disclosure may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., downloadable app) may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0200] Although the technical concept according to the present disclosure has been described by various embodiments above, the technical concept according to the present disclosure includes various substitutions, modifications, and changes that can be made within the scope of understanding by a person skilled in the art to which the present disclosure pertains. Furthermore, such substitutions, modifications, and changes should be interpreted as being included within the scope of the claims.

Claims

Claim 1 A method performed by an electronic device comprising: acquiring user information including at least some of the user’s demographic information, educational background information, and career information; executing a dynamic interview with the user based on an interview scenario set by an artificial intelligence model based on the user information; generating a customized resume of the user based on the result of the dynamic interview; and transmitting the customized resume to the user’s terminal, wherein the artificial intelligence model is a model that dynamically designs the interview scenario based on the user information and user response information of the dynamic interview, and the step of executing a dynamic interview with the user includes the step of executing the dynamic interview by generating additional questions by decreasing the weight of the user information and increasing the weight of the user response information based on the progress according to the interview scenario. Claim 2 A method according to claim 1, further comprising: a step of calculating a job suitability score of the user based on the result of the dynamic interview; and a step of transmitting the job suitability score to an external device, wherein the job suitability score is calculated by the artificial intelligence model based on the user information and the user response information of the dynamic interview. Claim 3 A method according to claim 1, wherein the step of acquiring user information includes the step of acquiring mandatory items that are required to be entered and optional items that are required to be entered, and the step of conducting a dynamic interview with the user includes the step of including a list of questions regarding the optional items in the interview scenario based on a determination that the optional items have been acquired. Claim 4 In paragraph 3, the selection item includes a career pivot item that includes content indicating that the user desires a career pivot, and the step of conducting a dynamic interview with the user includes the step of increasing the number of questions regarding the career information in the interview scenario based on a determination that the career pivot item has been obtained. Claim 5 A method according to paragraph 3, wherein the selection item includes a personality type item of the user, and the step of conducting a dynamic interview with the user includes, based on a determination that the personality type item has been obtained, the step of including a list of questions linked to the career information and the personality type item in the interview scenario. Claim 6 delete Claim 7 A method according to claim 1, wherein the step of conducting a dynamic interview with the user comprises: selecting a question prompt from a preset pool of question prompts based on the user information and the user answer information of the dynamic interview; and tuning the question prompts based on the user information and the user answer information. Claim 8 A method according to claim 7, wherein the step of tuning the question prompt comprises: a step of extracting unstructured information of the user based on the user answer information; and a step of tuning the question prompt based on the user information and the unstructured information, wherein the unstructured information includes at least some of the clarity of the user's pronunciation, structural completeness of sentences, vocabulary diversity, logical consistency, speed of speech, intonation, rhythm, clarity of pronunciation, confidence, emotional control ability, expression of empathy, and adaptability of the answer. Claim 9 A method according to claim 1, wherein the step of generating the customized resume comprises: the step of obtaining a draft of the user’s resume; and the step of generating the customized resume by updating the draft of the resume based on the result of the dynamic interview. Claim 10 An electronic device comprising: a communication interface configured to enable communication with a network; a processor configured to execute a computer program comprising one or more instructions; and a memory in which said computer program is stored, wherein said computer program comprises one or more instructions for executing any one of the methods of claims 1 to 5 and claims 7 to 9.

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