KR20220043354A - Interview training providing device and the control method thereof - Google Patents

Abstract

The present invention relates to an interview training providing method for entering a company, comprising: a step of acquiring a new user's behavior tendency data and condition data related to work processing ability; a step of providing one or more company candidates matching the behavior tendency data and the condition data to a company proper for the new user by using a machine learning model; a step of acquiring priority on the provided one or more companies from the new user; a step of generating an interview training curriculum based on the priority by using the machine learning model; and a step of providing the interview training based on the generated interview training curriculum. The present invention aims to provide an interview training providing method for entering a company, which is capable of providing an interview training for a job seeker to enter a desired company based on the behavior tendency and work processing ability of the job seeker by using a machine learning model.

Description

INTERVIEW TRAINING PROVIDING DEVICE AND THE CONTROL METHOD THEREOF

The present invention relates to an apparatus for providing interview training, and more particularly, to an apparatus for providing interview training that provides interview training suitable for a user by using a machine learning model, and a method for controlling the same.

Recently, as the types of interviews for employee recruitment are diversified by industry and competition for employment intensifies, interest in mock interview services and interview coaching services that allow you to practice interview in advance and identify deficiencies is rapidly increasing.

However, the existing interview coaching service has a problem in that it cannot recommend companies that match the job seeker after accurately identifying the job processing ability or behavioral tendency of the job seeker.

In addition, job seekers applied only to large corporations or well-known companies without knowing which company it would be advantageous to apply for, given their current abilities and inclinations. In addition, if the job seeker was rejected by the company he applied for, he or she applied again to the same company and was rejected without knowing what kind of difference his competitor had in abilities or inclinations.

Therefore, it recommends companies that are currently suitable for the job seeker by judging the job seeker's behavioral propensity and work processing ability, and determines the behavioral disposition and work processing ability of other job seekers who have passed a specific company and the priority selected by the job seeker among the recommended companies. Considering that, it is necessary to provide interview training to get accepted into the desired company.

An object of the present invention for solving the above problems is, using a machine learning model, based on the job seeker's behavioral disposition and work processing ability, an interview training providing device for providing interview training for a job seeker to pass the desired company and The purpose of this is to provide a control method.

The method of providing interview training for a company entry of the present invention for solving the above-described problem includes: acquiring behavioral tendency data of a new user and condition data related to work processing ability; providing at least one company candidate matching the behavioral tendency data and the condition data as a company suitable for the new user by using a machine learning model; obtaining a priority for the provided at least one company from the new user; generating an interview training curriculum based on the priority by using the machine learning model; and providing interview training based on the generated interview training curriculum.

In addition, the learning data for the machine learning model of the present invention includes behavioral propensity data for at least one existing user who received interview training before the new user, condition data related to work processing ability, and training results of the interview training. It may include at least one of data and specific company acceptance result data.

In addition, the step of providing the interview training of the present invention includes: acquiring intermediate training result data of the new user during the interview training; and modifying the interview training curriculum based on the intermediate training result data.

In addition, the intermediate training result data of the present invention may include at least one of analysis information of the new user's brain wave signal, voice signal analysis information, and gesture analysis information measured during the interview training.

According to the embodiments disclosed in the present invention, by using a machine learning model, it is possible to provide interview training for a job seeker to pass a desired company based on the job seeker's behavioral disposition and work processing ability.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a view showing an example of providing interview training according to an embodiment of the present invention.
2 is a block diagram illustrating an apparatus for providing interview training according to an embodiment of the present invention.
3 is a flowchart illustrating a method for providing interview training according to an embodiment of the present invention.
4 is a flowchart illustrating a method for providing interview training according to an embodiment of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

The word "exemplary" is used herein in the sense of "used as an illustration or illustration." Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

Also, as used herein, the term “unit” refers to a hardware element such as software, FPGA, or ASIC, and “unit” performs certain roles. However, "part" is not meant to be limited to software or hardware. A “unit” may be configured to reside on an addressable storage medium and may be configured to refresh one or more processors. Thus, by way of example, “part” refers to elements such as software elements, object-oriented software elements, class elements and task elements, and processes, functions, properties, procedures, subroutines, and programs. It includes segments of code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functionality provided within elements and “parts” may be combined into a smaller number of elements and “parts” or further separated into additional elements and “parts”.

Also, in this specification, all “units” may be controlled by at least one processor, and at least one processor may perform operations performed by the “units” of the present disclosure.

Embodiments of the present specification may be described in terms of a function or a block performing a function. Blocks, which may be referred to as 'parts' or 'modules', etc. in the present disclosure include logic gates, integrated circuits, microprocessors, microcontrollers, memories, passive electronic components, active electronic components, optical components, hardwired circuits, and the like. It may be physically implemented by analog or digital circuitry, such as, and optionally driven by firmware and software.

Embodiments of the present specification may be implemented using at least one software program running on at least one hardware device and may perform a network management function to control an element.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

In the present invention, the 'behavioral tendency data' may be data related to the personality of the user (or job seeker). For example, the behavioral tendency data may include “cheerful”, “cheerful”, “active”, “passive”, “cautious”, and “easy to get excited”, and the like.

In the present invention, 'condition data related to task processing ability' is data for determining whether a user has the ability to process a specific task, and may include language ability, mathematical thinking ability, and the like.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a view showing an example of providing interview training according to an embodiment of the present invention.

1 , the interview training providing apparatuses 120 and 150 may provide interview training to the user 110 . In this case, the interview training providing device 120 may output image data for the interview training to the user 110 through the display, and the user 110's eyes or gestures through the photographing device 130 such as a camera. can be filmed. Also, the interview training providing apparatus 120 may obtain the user's 110 voice through the sound device 130 such as a microphone and analyze the acquired voice.

In addition, the interview training providing apparatus 120 may acquire an EEG that is changed during training from the user 110 while providing the interview training through an EEG measuring device (not shown).

According to an embodiment of the present invention, information related to the user 110 can be obtained through the photographing device 130 and the sound device 130 like the interview training providing device 120 , and the interview training providing device 150 . As such, it may be a VR device including all of a display function, a shooting function, a sound acquisition function, and the like. In this case, the interview training providing apparatus 150 may measure the gesture of the user 110 through the photographing device 130 such as a camera.

Accordingly, the user 110 is provided with the interview training while facing the display of the interview training providing device 120 as shown in FIG. Information related to the user 110 may be input.

In addition, according to another embodiment of the present invention, as shown in (b) of FIG. 1, the user 110 receives interview training while wearing the interview training providing device 150 on his/her head, and the user ( 110) and related information may be input.

2 is a block diagram illustrating an apparatus 200 for providing interview training according to an embodiment of the present invention. The interview training providing apparatus 200 may include an input unit 210 , a memory 22 , a processor 230 , and an output unit 240 .

The interview training providing apparatus 200 of FIG. 2 may correspond to the interview training providing apparatus 120 and 150 of FIG. 1 .

Also, the input unit 210 may receive the voice signal, eye movement, and gesture signal of the user 110 from the photographing apparatus 130 and the sound apparatus 140 or an element having a photographing function and a sound acquisition function. Also, the input unit 210 may acquire a changed EEG during interview training of the user 110 through an EEG measuring device (not shown). In addition, the input unit 210 may obtain other user's specific company pass result data.

In the present invention, the input unit 210 may obtain various information from the outside of the apparatus 200 for providing interview training through a communication unit (not shown), and the communication unit communicates with various types of external devices according to various types of communication methods. communication can be performed. The communication unit may include at least one of a Wi-Fi chip, a Bluetooth chip, a wireless communication chip, and an NFC chip.

The Wi-Fi chip and the Bluetooth chip may perform communication using a WiFi method and a Bluetooth method, respectively. In the case of using a Wi-Fi chip or a Bluetooth chip, various types of connection information such as an SSID and a session key are first transmitted and received, and various types of information can be transmitted/received after communication connection using this. The wireless communication chip refers to a chip that performs communication according to various communication standards such as IEEE, ZigBee, 3rd Generation (3G), 3rd Generation Partnership Project (3GPP), and Long Term Evolution (LTE). The NFC chip refers to a chip operating in an NFC (Near Field Communication) method using a 13.56 MHz band among various RF-ID frequency bands such as 135 kHz, 13.56 MHz, 433 MHz, 860 to 960 MHz, and 2.45 GHz.

The memory 220 of the present invention is a local storage medium capable of storing data acquired by the input unit 210 and data processed by the processor 230 . In addition, the memory 220 may store company information, such as the work ability of the previous successful candidates of the at least one company in order to recommend the at least one company to the user 110 .

If necessary, the processor 230 may generate an interview training curriculum using data stored in the memory 220 . In addition, the memory 220 of the present invention may store instructions for operating the processor 230 .

In addition, the memory 220 of the present invention should remain data even if the power supplied to the interview training providing device 200 is cut off, and may be provided as a writable non-volatile memory (Writable Rom) to reflect the changes. there is. That is, the memory 220 may be provided with either a flash memory, an EPROM, or an EEPROM. In the present invention, it is described that all instruction information is stored in one memory 220 for convenience of explanation, but the present invention is not limited thereto, and the interview training providing apparatus 200 may include a plurality of memories.

The processor 230 of the present invention may generate the interview training curriculum through data acquired through the input unit 210 or data stored in the memory 220 .

According to an embodiment of the present invention, when the user 110 uses the interview training apparatus 200 as a new user, the processor 230 is based on the behavioral tendency data of the user 110 and condition data related to work processing ability. Accordingly, at least one company candidate may be provided as a company suitable for the new user. In this case, the processor 130 may provide the behavioral tendency data of the user 110 and at least one company candidate matching the condition data as a company suitable for the user 110 by using the machine learning model.

In addition, the processor 230 may generate an interview training curriculum for the user 110 using a machine learning model based on the priority for at least one company obtained from the user 110 . When the interview training curriculum is generated, the processor 230 may provide interview training to the user 110 through the output unit 240 based on the generated interview training curriculum.

The output unit 240 of the present invention is an element for providing interview training to the user 110, and may include a display (not shown) for outputting an image, a sound device such as a speaker or a headset for outputting sound. can Accordingly, the interview training providing apparatus 200 may provide interview training according to the interview training curriculum generated by the processor 230 to the user 110 through the output unit 240 .

3 is a flowchart illustrating a method for providing interview training according to an embodiment of the present invention.

Each step of the control method of the apparatus 100 for providing interview training according to the present invention may be performed by various types of electronic devices including an input unit 210 , a memory 220 , a processor 230 , and an output unit 240 . there is.

Hereinafter, a process in which the processor 230 provides interview training according to the present invention will be described in detail with reference to FIG. 3 .

At least some or all of the embodiments described for the apparatus for providing interview training 200 are applicable to the control method of the apparatus for providing interview training 200, and on the contrary, the implementation described for the control method of the apparatus for providing interview training 200 Examples are applicable to at least some or all of the embodiments of the interview training providing apparatus 200 . In addition, the control method of the interview training providing apparatus 200 according to the disclosed embodiments is not limited to being performed by the interview training providing apparatus 200 disclosed herein, and is performed by various types of electronic devices. can be

First, the processor 230 may obtain the behavioral tendency data of the new user 110 and the condition data related to the work processing capability through the input unit 210 [S310].

According to an embodiment of the present invention, the behavioral propensity data of the user 110 may be obtained through questionnaire survey or consultation before providing interview training.

In addition, according to an embodiment of the present invention, the processor 130 may include a Myers-Briggs Type Indicator (MBTI), an Eisenck personality test, a California personality test, and a Minnesota Multiphasic Personality Inventory (MMPI), including various psychological or personality tests. can be obtained through

In addition, according to an embodiment of the present invention, the processor 130 provides a preliminary interview situation to the user 110 through the interview training providing device 200, the user's EEG signal, voice signal, and by analyzing at least one of gestures to obtain behavioral tendency data.

Accordingly, the obtained behavioral tendency data of the user 110 may represent a general personality of a person, including "cheerful", "cheerful", "active", "passive", "cautious" and "easy to get excited", etc., The MBTI test results may indicate complex and specific personality types such as ESTJs (extroverted energy, sensory perception, thinking-type judgment, judgment-type lifestyle).

According to an embodiment of the present invention, the processor 230 may acquire condition data related to work processing ability through a language ability test, a mathematical thinking ability test, etc. before providing interview training.

In addition, the processor 230 may acquire condition data related to work processing ability through official test scores such as foreign language ability test scores and college scholastic ability test scores.

Also, the processor 230 may request and obtain the company career of the user 110 from the user 110 . If the user 110 has a previous company experience, when working for a company in a similar industry, the business processing ability may be high and may be advantageous in an interview, so the processor 230 may recommend a company suitable for the user 110 . However, since the user 110 may want to work in another industry, if the user 110 requests, data related to the previous company career may not be considered in the matching process for company recommendation S320, which will be described later.

Next, the processor 230 may provide at least one company candidate matching the behavioral tendency data and the condition data as a company suitable for the user 110 by using the machine learning model [S320].

Learning data for a machine learning model according to embodiments of the present invention may be generated based on various machine learning models.

As an example, the deep neural network (DNN) of the present invention may include a system or network that constructs one or more layers in one or more computers and performs a determination based on a plurality of data.

The deep neural network may be implemented as a set of layers including a convolutional pooling layer, a locally-connected layer, and a fully-connected layer.

The convolutional pooling layer or local connection layer may be configured to extract features in an image.

The fully connected layer may determine a correlation between features of an image.

As another example, the overall structure of the deep neural network of the present invention may be formed in a form in which a local access layer is connected to a convolutional pooling layer, and a fully connected layer is formed in the local access layer. The deep neural network may include various judgment criteria (ie, parameters), and may add new judgment criteria (ie, parameters) through input image analysis.

A deep neural network according to embodiments of the present invention is a structure called a convolutional neural network suitable for image analysis, and a feature extraction layer that learns by itself the feature with the greatest discriminative power from given image data. ) and a prediction layer that learns a predictive model to obtain the highest predictive performance based on the extracted features may have an integrated structure.

The feature extraction layer spatially integrates a convolution layer that creates a feature map by applying a plurality of filters to each region of the image and a feature map that is invariant to changes in position or rotation. It can be formed in a structure that alternately repeats the pooling layer several times to allow the extraction of . Through this, various level features can be extracted from low-level features such as points, lines, and planes to complex and meaningful high-level features.

The convolution layer obtains a feature map by taking a nonlinear activation function on the dot product of the filter and the local receptive field for each patch of the input image. Compared to , CNNs are characterized by using filters with sparse connectivity and shared weights. Such a connection structure reduces the number of parameters to be learned, makes learning through the backpropagation algorithm efficient, and consequently improves prediction performance.

The integration layer (Pooling Layer or Sub-sampling Layer) generates a new feature map by using local information of the feature map obtained from the previous convolutional layer. In general, the newly created feature map by the integration layer is reduced to a smaller size than the original feature map. Representative integration methods include Max Pooling, which selects the maximum value of the corresponding region in the feature map, and the corresponding feature map in the feature map. There is an average pooling method that obtains the average value of an area. In general, the feature map of the integrated layer may be less affected by the position of an arbitrary structure or pattern existing in the input image than the feature map of the previous layer. That is, the integration layer can extract features that are more robust to regional changes such as noise or distortion in the input image or previous feature map, and these features can play an important role in classification performance. Another role of the integration layer is to reflect the features of a wider area as you go up to the upper learning layer in the deep structure. More and more abstract features can be generated that reflect the features of the entire image.

As such, the features finally extracted through the iteration of the convolutional layer and the integration layer are fully connected to a classification model such as a multi-layer perception (MLP) or a support vector machine (SVM). -connected layer) and can be used for classification model training and prediction.

Also, according to an embodiment of the present invention, training data for machine learning may be generated based on the U-Net-dhSgement model. Here, the U-Net-dhSgement model is based on an end-to-end fully connected convolutional network (FCN), and contracts an expansive path and a symmetric path. It could be a model that created a U-shaped architecture with a skip connection for each level by setting it to .

According to an embodiment of the present invention, the learning data for the machine learning model includes behavioral propensity data for at least one existing user who has been provided with interview training before the user 110, condition data related to work processing ability, and interview training. It may include at least one of the training result data and the specific company pass result data.

According to an embodiment of the present invention, information on at least one company to be recommended to the user 110 may be obtained from employment information sites such as Sarain (registered trademark) and Job Korea (registered trademark). Here, the information on the company may include at least one of a list of companies currently being newly hired, preferential treatment, application period, and application conditions. In addition, the information about the company may include condition data related to behavioral tendencies and work processing ability of people who have passed a specific company.

Accordingly, the processor 230 uses the learned machine learning model to provide (recommend) at least one company candidate matching the behavioral propensity data and condition data of the user 110 as a company suitable for the user 110 . can

Next, the processor 230 may obtain a priority for the at least one company candidate provided from the user 110 through the input unit 210 [S330].

In the present invention, the priority may refer to a ranking of companies that the user 110 prefers and ultimately wants to join.

According to an embodiment of the present invention, when the interview training providing apparatus 200 provides at least one company candidate to the user 110 , the priority may be input by the user 110 through voice and EEG signals. Also, the user 110 may input a priority through an electronic input device (not shown) such as a mouse and a keyboard.

According to an embodiment of the present invention, the priority is prioritized in order of the company having a high matching rate based on the behavioral tendency data and condition data of the user 110 , separately from the input by the user 110 , by the processor 230 . The ranking may also provide at least one company candidate. In this case, when the priority provided by the processor 230 matches the priority preferred by the user 110, the user 110 inputs a signal indicating that the provided priority is accepted, and the user 110 does not match the preferred priority. In this case, you can enter the priority again.

Next, using the machine learning model, based on the obtained priorities, it is possible to generate an interview training curriculum [S340].

The personalities of preferred employees and the ability to handle their work may vary from company to company. Therefore, by using behavioral tendency data of the user 110, condition data related to work processing ability, behavioral tendency data for other users before the user 110, condition data, training result data, and specific company acceptance result data, etc. It is necessary to create an interview training curriculum to increase the probability of passing the interview of a company located at a high priority among the priorities.

Accordingly, the processor 230 may generate the interview training curriculum using the machine learning model learned through the learning data based on the priority of the user 110 .

According to an embodiment of the present invention, when the companies that the user 110 has given a high priority (eg, top 3) generally conduct an interview that presses the psychology of the job seeker in the interview, the processor 230 can generate interview training including tail biting questions and difficult-to-answer questions as an interview training curriculum.

According to an embodiment of the present invention, when the companies that the user 110 has given a high priority generally require a lot of self-introduction in an interview, the processor 230 provides an interview training curriculum for self PR (public relation) practice. can create

In addition, according to an embodiment of the present invention, when the companies that the user 110 has given a high priority place importance on language ability, such as conversing in a foreign language in an interview, an interview training curriculum for improving foreign language ability can be created. there is.

In addition, according to an embodiment of the present invention, when the companies that the user 110 has given a high priority place importance on a neat posture in the interview, the processor 230 is interview training for gaze processing, such as looking straight at the interviewer. You can create a curriculum or create an interview training curriculum for preventing behaviors that are likely to make a bad impression on the user 110 . Here, the behavior likely to give a bad impression may include an act of crossing legs during an interview, an act of shaking legs, an act of frequently touching the head, and the like. In this case, the processor 230 may observe the gaze processing or gesture of the user 110 with the photographing device 130 .

Next, the processor 230 may provide interview training based on the generated interview training curriculum [S350].

According to an embodiment of the present invention, when providing interview training, the processor 230 may change a situation variable in an interview situation in order to maximize the training effect. Here, the situation variable may include environment variables that may occur in the actual interview room. For example, the situation variable may include the size of the interview room, the number of interviewers, the interviewer's age or gender, and a response to the user's 110 response. In addition, since training must be conducted in response to the interviewer's questions, there are various questions, and the form or method of various interview questions, such as a sudden pressure interview, may become a situational variable.

According to an embodiment of the present invention, the processor 230 may set a one-person interview, a three-person interview, or a multi-person interview in which the number of interviewers among the situational variables of the interview, the age or gender of the interviewer and the expression of the interviewer You can also select a situation variable for the atmosphere for the reaction.

In particular, the processor 230 has the type or form of a question received from the interviewer, and data on various questions such as a personality evaluation-related question for evaluating personality or a question for job-related evaluation of a specific occupational group is stored, , the selected evaluation questions may be presented at random. Furthermore, a specific type of question can be stored and selected to enable preparation for a specific type of interview, such as a pressure interview.

4 is a flowchart illustrating a method for providing interview training according to an embodiment of the present invention. The step of FIG. 4 may be an example of S350 of FIG. 3 .

The processor 230 may acquire intermediate training result data of the user 110 during interview training [S410].

In the present invention, the intermediate training result data may include information for evaluating whether the user 110 is following the provided interview training curriculum well. In this case, the processor 230 is the user 110 through at least one of the analysis information of the EEG signal of the user 110 measured during the interview training, the analysis information of the voice signal, and the analysis information of the gesture, the user 110 through the interview training curriculum well You can evaluate whether you are following.

Next, the processor 230 may revise the interview training curriculum based on the intermediate training result data [S420].

According to an embodiment of the present invention, the processor 230, based on the intermediate training result data, if the user 110 is not well following the provided interview training curriculum, the probability of passing the company located in a high priority is more In order to improve the interview training curriculum, the interview training curriculum can be modified to alleviate the difficulty of the interview related to the previously provided interview environment.

According to an embodiment of the present invention, the processor 230 is following the interview training curriculum provided by the user 110 well, based on the intermediate training result data, and the intermediate training result data of the user 110 and high priority By comparing the intermediate training result data of other users who have passed the company, if there is no significant difference between the two, the interview training curriculum can be modified to increase the interview difficulty related to the interview environment in order to increase the probability of acceptance.

According to one embodiment of the present invention, the processor 230 does not follow the interview training curriculum provided by the user 110 well, and the intermediate training result data of the user 110 and other users who have passed the company located in the high priority By comparing the intermediate training result data of the users, if the difference between the two is large, it is possible to request the user 110 to revise the priority with a warning message indicating that the probability of passing is remarkably low. In this case, when the modified priority is obtained from the user 110, the processor 230 generates a modified interview training curriculum using the machine learning model again, and then provides the user 110 with the modified interview training. there is.

Various embodiments of the present invention include one or more instructions stored in a storage medium (eg, memory) readable by a machine (eg, the apparatus 200 for providing interview training or a computer). It can be implemented as software that includes. For example, the processor (eg, the processor 230 ) of the device may call at least one of the one or more instructions stored from the storage medium and execute it. This makes it possible for the device to be operated to perform at least one function according to the called at least one instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, the 'non-transitory storage medium' is a tangible device and only means that it does not include a signal (eg, electromagnetic wave), and this term means that data is semi-permanently stored in the storage medium. and temporary storage. For example, the 'non-transitory storage medium' may include a buffer in which data is temporarily stored.

According to one embodiment, the method according to various embodiments disclosed herein may be provided as included in a computer program product. Computer program products may be traded between sellers and buyers as commodities. The computer program product is distributed in the form of a machine-readable storage medium (eg compact disc read only memory (CD-ROM)), or through an application store (eg Play Store™) or on two user devices. It can be distributed (eg downloaded or uploaded) directly or online between devices (eg smartphones). In the case of online distribution, at least a portion of the computer program product (eg, a downloadable app) is stored at least on a machine-readable storage medium, such as a memory of a manufacturer's server, a server of an application store, or a relay server. It may be temporarily stored or temporarily created. In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those of ordinary skill in the art to which the present invention pertains will appreciate the technical spirit or essential features of the present invention. It will be understood that the invention may be embodied in other specific forms without modification. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

110: user
120: interview training providing device
130: shooting device
140: sound device
150: device for providing interview training

Claims (4)

In the method of providing interview training for entering a company,
acquiring behavioral propensity data of a new user and condition data related to business processing ability;
providing at least one company candidate matching the behavioral tendency data and the condition data as a company suitable for the new user by using a machine learning model;
obtaining a priority for the provided at least one company from the new user;
generating an interview training curriculum using the machine learning model, based on the priority; and
Providing interview training based on the generated interview training curriculum; comprising, a method of providing interview training. The method of claim 1,
The learning data for the machine learning model is,
An interview comprising at least one of behavioral propensity data for at least one existing user who was provided with interview training before the new user, condition data related to work processing ability, training result data of the interview training, and specific company acceptance result data How to provide training. The method of claim 1,
The step of providing the interview training is,
acquiring intermediate training result data of the new user during the interview training; and
Based on the intermediate training result data, modifying the interview training curriculum; Containing, interview training providing method. 4. The method of claim 3,
The intermediate training result data includes at least one of analysis information of the new user's brain wave signal, voice signal analysis information, and gesture analysis information measured during the interview training, interview training providing method.

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