KR102217122B1 - Method and apparatus of matching tutor based on application - Google Patents

Abstract

Disclosed are an application-based private lesson matching method, and a device thereof. A method for providing application-based private lesson matching based on artificial intelligence comprises the steps of: obtaining student information of a student account logged in an application installed in a student terminal; obtaining private lesson waiting applications from tutor accounts logged in the application installed in tutor terminals and including pieces of tutor information pre-stored in the tutor accounts that have applied for private lesson waiting in a preliminary private lesson waiting list; obtaining a private lesson waiting list view request from the student terminal; creating each student-customized tutor information based on the student information and each tutor information of the preliminary private lesson waiting list, and including each student-customized tutor information in the private lesson waiting list; transmitting the private lesson waiting list to the student terminal; obtaining a tutor account selected by the student account; transmitting a private lesson matching acceptance request to a tutor terminal logged in the selected tutor account; obtaining, by the selected tutor account, the acceptance of the private lesson matching from the tutor terminal that is logged in; and transmitting the acceptance of the private lesson matching to the student terminal. Each piece of student-customized tutor information includes a unit time-specific private lesson fee of each tutor account customized to the student account, the unit time-specific private lesson fee of each tutor account customized to the student account is determined based on inference made by artificial intelligence, and the inference by the artificial intelligence is based on the student information and each piece of tutor information. According to the present invention, a student account user can receive fee conditions of each tutor account in a more reasonable manner.

Description

Application-based tutoring matching method and device {METHOD AND APPARATUS OF MATCHING TUTOR BASED ON APPLICATION}

The following embodiments relate to a matching technique between a student (student) account and a tutor (teacher) account using a tutor matching application.

As a background technology related to the embodiments, Korean Patent Publication No. KR 10-1898103 B1 discloses an online group tutoring providing method and system for connecting a group tutoring instructor and a group tutoring student online. Specifically, the prior literature includes an instructor terminal that transmits a lecture opening request including the input lecture information to an extracurricular class management server when lecture information including a subject name, lecture difficulty, and lecture size is input from an instructor; When the course requirements including the course name, lecture difficulty, lecture size, and course progress are entered from the learner, the course application including the entered course requirements is transmitted to the tutoring class management server, and matching the course requirements from the tutoring class management server. When a recommended lecture message including lecture information is received, a student terminal for receiving a selection for matching lecture information from a learner and transmitting a request for registration of a course for the selected lecture information to the tutoring management server; And when a lecture opening request is received, the lecture information included in the lecture opening request is stored, and when a lecture application is received, the stored lecture information matching the course requirements included in the lecture registration is inquired, and It discloses a system including a tutoring class management server that transmits a recommended lecture message including the lecture information inquired in response, and when a lecture registration request is received, manages student information about the students registered in the lecture.

Through this, the preceding literature is automatically matched when an instructor who wants to do group tutoring applies for his or her lecture online, and students who want to tutor can be introduced, and when students who want to tutor apply for tutoring they want, it is automatically matched. Discuss how you can be introduced to tutoring.

In addition, Korean Patent Application Publication No. KR 10-1578054 B1 discloses an apparatus and method for supporting online instructor-student matching based on tutoring conditions. Specifically, in the preceding literature, if the tutor and the student upload each other's desired tutoring conditions online, the optimal tutoring partner can be matched through comparison between the tutoring conditions, thereby promoting the convenience of students and instructors. , The tutor does not completely match the tutoring conditions uploaded by the student, but automatically selects an instructor that can match the student through a change of tutoring conditions, thereby increasing the probability of matching tutoring counterparts between students and instructors.

However, prior literature does not disclose a method for variably calculating the tutor's price according to the student's requirements. For example, prior literature does not disclose a method of automatically calculating tutoring costs so that the more the students' requirements are met, the higher the tutor's price is. Furthermore, prior literature does not disclose a method of calculating the incentive to be provided to the tutor when the position between the student and the tutor is far enough to cause a time difference in online tutoring.

Accordingly, in matching and conducting tutoring online, it is required to implement a technique capable of variably calculating the tutor's price according to the student's requirements. For example, the implementation of a technology that automatically calculates tutoring costs is required so that the higher the tutor's cost is, the higher the tutor's requirements are met. Furthermore, in the online tutoring process, when the position between the student and the tutor is far enough to cause a time difference, implementation of a technique for calculating the incentive to be provided to the tutor is required.

Republic of Korea Patent Publication KR 10-1898103 B1 Korean patent publication KR 10-1578054 B1 Korean patent publication KR 10-1771797 B1 Korean patent publication KR 10-1923704 B1

The embodiments are intended to provide a method of calculating tutoring cost per unit time of a tutor account based on an input condition requested by a student account user in the tutoring match between a student account and a tutor account.

Embodiments are the student account user (student), regardless of where they live, more opportunities to be matched with a desired tutoring teacher; A tutor account user (a tutoring teacher) tries to provide a way to obtain an opportunity to earn more income through incentives, as the time difference between the matching students increases.

Embodiments are intended to provide a method of setting not to calculate the cost according to the incentive per unit time of the tutor account when the tutor account requests that the incentive not be applied.

Further, the embodiments are intended to provide an application-based tutoring matching method and apparatus for solving the problems mentioned in the background art and the problems of the relevant technical field revealed in the present specification.

An application-based tutor matching method according to an embodiment includes providing tutor matching based on artificial intelligence, and acquiring student information of a student account logged into an application installed in a student terminal; Obtaining tutoring requests from tutor accounts logged in to applications installed in tutor terminals, and including pre-stored tutor information of tutor accounts that have requested tutoring in a tutoring standby list; Obtaining a request to view the tutoring waiting list from the student terminal; Generating information for each personalized tutor based on the student information and information on each tutor in the tutor waiting list, and including information on the tutor tailored for each student in the tutor waiting list; Transmitting a waiting list for tutoring to the student terminal; Obtaining a tutor account selected by the student account; Transmitting a tutor matching acceptance request to a tutor terminal in which the selected tutor account is logged in; Acquiring whether to accept tutor matching from the tutor terminal to which the selected tutor account is logged in; And transmitting whether or not to accept the tutoring match to the student terminal, wherein each of the student-customized tutor information includes a tutoring cost per unit time of each tutor account tailored to the student account, and each tutor tailored to the student account The tutoring cost per unit time of the tutor account is determined based on the inference of the artificial intelligence, and the inference of the artificial intelligence may be made based on the student information and each of the tutor information.

According to an embodiment, the application-based tutor matching method includes: when the selected tutor account accepts the tutor matching acceptance request, setting the selected tutor account as a tutor account matching the student account; Obtaining a tutoring start notification from a tutor terminal to which the matched tutor account is logged in; Transmitting a tutoring start approval request to the student's terminal; Obtaining permission to start tutoring from the student terminal; Recording the start of tutoring; Obtaining an end of tutoring notification from the student terminal; Transmitting a tutoring termination approval request to a tutor terminal to which the matched tutor account is logged in; Obtaining permission to terminate tutoring from a tutor terminal to which the matched tutor account is logged in; Recording the end of the tutoring; Calculating an accumulated tutoring time for a predefined period based on the tutoring start record and the tutoring end record; The accumulative tutoring time may further include determining a first cost to be paid by the student account by applying the tutoring cost per unit time of the matched tutor account according to the student account.

According to an embodiment, an application-based tutoring matching method includes: obtaining a hardness of a student account from the student information; Obtaining the hardness of each tutor account from the information on each tutor account; Generating each student-tutor time difference based on the hardness of the student account and the hardness of each tutor account; Generating an incentive per unit time of each tutor account based on the time difference between each student and the tutor; Including incentives per unit time of each tutor account in the tutor customized tutor information; The method may further include determining a second cost to be paid by the student account by applying an incentive per unit time of the matched tutor account to the accumulated tutoring time.

According to an embodiment, an application-based tutor matching method includes: obtaining a request for non-applying an incentive for a tutor account; Including, in the tutor waiting list, an indication of non-incentive application in student-customized tutor information corresponding to the tutor account for which the incentive is not applied; If the matched tutor account is a tutor account for which the incentive has not been applied, the step of setting not to calculate the second cost may be further included.

An application-based tutoring matching device according to an embodiment acquires student information of a student account logged in to an application installed in a student terminal, and requests for tutoring waiting from tutor accounts logged in to an application installed in tutor terminals. In addition, the tutor information stored in advance of the tutor accounts that have applied for tutoring and waiting for tutoring are included in the tutoring standby list, obtain a request to view the tutoring standby list from the student terminal, and each of the student information and the tutoring standby list Based on the tutor information, each student-customized tutor information is generated, each student-customized tutor information is included in a tutoring waiting list, and a tutoring waiting list is transmitted to the student terminal, and the tutor account selected by the student account is selected. Acquiring and transmitting a tutoring matching acceptance request to the tutor terminal in which the selected tutor account is logged in, acquiring whether to accept tutoring matching from the tutor terminal in which the selected tutor account is logged in, and transmitting whether to accept the tutoring matching to the student terminal Including a processor, wherein each of the tutor customized tutor information includes a tutoring cost per unit time of each tutor account tailored to the student account, and the tutoring cost per unit time of each tutor account tailored to the student account is inferred by the artificial intelligence Is determined based on, and the inference of the artificial intelligence may be made based on the student information and the respective tutor information.

Embodiments may provide a method of calculating tutoring cost per unit time of a tutor account based on an input condition requested by a student account user in the tutoring match between a student account and a tutor account. Through this, when selecting a tutor account, the student account user can more reasonably receive the price condition of each tutor account according to his or her requirements.

Embodiments are the student account user (student), regardless of where they live, more opportunities to be matched with a desired tutoring teacher; A tutor account user (a tutoring teacher) can provide a way to obtain an opportunity to earn more income through incentives as the time difference between the matching students increases. Through this, tutor matching and progress that maximizes temporal and spatial constraints, which are the advantages of online, can be actively performed.

Embodiments may provide a method of not calculating a cost according to an incentive per unit time of a tutor account when a tutor account requests that the incentive not be applied. Through this, tutor accounts, which are judged to be relatively inferior in terms of class quality compared to other tutor accounts, do not receive incentives despite the time difference with the student account, thereby providing tutoring services relatively cheaper than other tutors. By providing it, it is possible to have a plan to secure competitiveness.

On the other hand, the effects according to the embodiments are not limited to those mentioned above, and other effects that are not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

1 is an exemplary diagram of a configuration of a system according to an embodiment.
2 is a diagram for explaining a tutor matching operation according to an embodiment.
3 is a diagram for describing an operation of determining an extracurricular cost according to an embodiment.
4 is a flowchart illustrating an incentive application operation according to an embodiment.
5 is a flowchart illustrating an operation of not applying an incentive according to an embodiment.
6 are diagrams for explaining learning of an artificial neural network according to an embodiment.
7 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the rights of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents, or substitutes to the embodiments are included in the scope of the rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only, and may be changed in various forms and implemented. Accordingly, the embodiments are not limited to a specific disclosure form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea.

Although terms such as first or second may be used to describe various components, these terms should be interpreted only for the purpose of distinguishing one component from other components. For example, a first component may be referred to as a second component, and similarly, a second component may be referred to as a first component.

When a component is referred to as being "connected" to another component, it is to be understood that it may be directly connected or connected to the other component, but other components may exist in the middle.

The terms used in the examples are used for illustrative purposes only and should not be interpreted as limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc., as shown in the figure It can be used to easily describe the correlation between a component and other components. Spatially relative terms should be understood as terms including different directions of components during use or operation in addition to the directions shown in the drawings. For example, if a component shown in a drawing is turned over, a component described as "below" or "beneath" of another component will be placed "above" the other component. I can. Accordingly, the exemplary term “below” may include both directions below and above. Components may be oriented in other directions, and thus spatially relative terms may be interpreted according to orientation.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms as defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this application. Does not.

In addition, in the description with reference to the accompanying drawings, the same reference numerals are assigned to the same components regardless of the reference numerals, and redundant descriptions thereof will be omitted. In describing the embodiments, when it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the embodiments, the detailed description thereof will be omitted.

1 is an exemplary diagram of a configuration of a system according to an embodiment.

The system according to an embodiment may include a server 100 and user terminals 111-113. The server 100 and the user terminals 111-113 may be connected to a network to exchange data through wired or wireless communication.

The server 100 communicates with the user terminals 111-113; You can perform actions for online tutoring, such as matching tutoring, providing an online tutoring environment, and determining tutoring costs. The server 100 may be its own server owned by a person or organization that provides services using the server 100; May be a cloud server; It may be a peer-to-peer (p2p) set of distributed nodes. The server 100 includes arithmetic functions of an ordinary computer; Save/reference function; Input/output function; And it may be configured to perform all or part of the control function. The server 100 may include at least one artificial neural network that performs an inference function. The server 100 may be configured to communicate with the user terminals 111 to 113 via wired or wireless communication.

The user terminals 111-113 may be desktop computers, notebook computers, tablets, smart phones, and the like. The user terminals 111-113 include arithmetic functions of a typical computer; Save/reference function; Input/output function; And it may be configured to perform all or part of the control function. The user terminals 111-113 may be configured to communicate with the server 100 through wired or wireless communication.

An application developed or distributed by a person or organization providing services using the server 100 may be installed on the user terminals 111-113. The application includes a student application for students or parents; There may be applications for tutors for tutors or tutors. The student application may include functions such as tutor matching for online tutoring, online tutoring environment provision, and tutoring fee payment; The tutor application may include functions such as tutor matching for online tutoring, online tutoring environment provision, and tutoring expenses. Student account 121 can be logged into the student application installed in the student terminal 111; Each tutor account (122, 123) may be logged into the tutor application installed in each tutor terminal (112, 113). Since the server 100 and the user terminals 111-113 can communicate via wired or wireless, the accounts 121-123 logged into the applications installed in the user terminals 111-113 communicate data with the server 100 You can give and receive.

For example, as shown in Fig. 1, the student terminal 111 may be a desktop computer; The first tutor terminal 112 may be a notebook computer; The second tutor terminal 113 may be a smartphone. The student terminal 111 may have a student application installed, and the tutor terminals 112 and 113 may have a tutor application installed. Student account 121 can be logged into the student application installed in the student terminal 111; The first tutor account 122 can be logged into the tutor application installed in the first tutor terminal 112; The second tutor account 123 may be logged in to the tutor application installed in the second tutor terminal 113. Since the server 100 and the user terminals 111-113 can communicate via wired or wireless, the accounts 121-123 logged into the applications installed in the user terminals 111-113 communicate data with the server 100 You can give and receive. The server 100 communicates with the user terminals 111-113; You can perform actions for online tutoring, such as matching tutoring, providing an online tutoring environment, and determining tutoring costs.

On the other hand, for convenience of explanation, only one student account 121 and two tutor accounts 122 and 123 are shown in FIG. 1, but the number of student accounts and the number of tutor accounts may vary. Thus, the number of student terminals and the number of tutor terminals may vary. Furthermore, as long as the processing capacity of the server 100 allows, there is no particular limitation on the limit of each number.

Hereinafter, embodiments will be described based on the operation of the server 100, and the embodiments are not limited by the subject or aspect of communication, and various application examples may be employed. Hereinafter, an embodiment related to a tutor matching operation will be described with reference to FIG. 2. Referring to Fig. 3, an embodiment related to the operation of determining the extracurricular cost is described. Referring to Fig. 4, an embodiment related to the incentive application operation is described. Referring to Fig. 5, an embodiment related to an operation of not applying an incentive will be described. Referring to FIG. 6, an embodiment related to a learning operation of an artificial neural network will be described.

2 is a diagram for explaining a tutor matching operation according to an embodiment.

First, the server 100 may acquire Student information of the Student account 121 logged in to the application installed in the Student terminal 111 (201). Student information may include the student's name, age, grade, academic level, past test grades, subject to which the student wants to take classes, the test to prepare for, and where to live. Students or parents may input Student information of the Student account 121 through the Student terminal 111. For example, a user of the Student account 121 may input Student information through the Student terminal 111. The Student terminal 111 may transmit Student information to the server 100.

Next, the server 100 may obtain tutor waiting applications from the tutor accounts 122 and 123 logged in to the application installed in the tutor terminals 112 and 113 (202). A tutoring teacher or a tutoring company may input a tutoring waiting request to seek students to receive tutoring through the tutor terminals 112 and 113. For example, a user of the first and second tutor accounts 122 and 123 may input an application for waiting for tutoring through the first and second tutor terminals 112 and 123 to find a student to receive tutoring. The tutor terminals 112 and 113 may transmit a tutoring standby request to the server 100.

Subsequently, the server 100 may include pre-stored tutor information of tutor accounts that have applied for tutoring waiting in the tutoring standby list (203). The pre-stored tutor information may include the tutor's name, age, academic background, past tutoring experience, past tutoring achievements, tutors available for classes, exams available for class, available hours and days of class, and location where you live. The server 100 may store information on tutor accounts that have applied for tutoring in the tutoring standby list. For example, the server 100 may store tutor information corresponding to the pre-stored first and second tutor accounts 122 and 123 in the tutor standby list.

In the following order, the server 100 may obtain a request to view the tutoring waiting list from the student terminal 111 (204). The student's terminal 111 receives input from the student or parent, the subject the student wants to take, the exam he/she prepares, the region where he lives, the time and day of the week he/she wants to receive online tutoring, and the range of extracurricular expenses per hour when he or she is willing to pay. I can. The student terminal 111 may transmit, to the server 100, a request to view a waiting list for tutoring to show tutor accounts meeting an input condition.

Next, the server 100 may generate information for each customized tutor based on the student information and each tutor information in the tutoring standby list (205). To this end, the server 100 may classify the tutor accounts 122 and 123 that meet the input condition of the request to view the tutor wait list from the tutor standby preliminary list. Thereafter, the server 100 generates information for each student-customized tutor based on the tutor information of each of the classified tutor accounts 122 and 123 and the student information of the student account 121 requesting to view the tutoring list. I can.

For example, the server 100 may classify the first tutor account 122 and the second tutor account 123 as meeting the input condition of the student account 121 in the tutoring standby list. Thereafter, the server 100 may generate tutor information tailored to each student based on the tutor information of the respective tutor accounts 122 and 123 and the student information of the student account 121.

The tutor information tailored to each student is the tutor information of each tutor account that meets the conditions entered by the student account among the tutor accounts in the tutor's waiting list-the tutor's name, age, education, past tutoring experience, past tutoring. You can include performance, subjects available for tutoring, exams available for instruction, where you live-and tailored information tailored to your Student account.

Customized information tailored to the Student account may include extracurricular costs per unit time of the tutor account tailored to the Student account, the time difference between the student and the tutor, and incentives per hour of the tutor account. The unit time may be, for example, 1 hour. The tutoring cost per unit time of the tutor account tailored to the student account may be the tutoring cost per unit time of each tutor account 122 and 123 calculated according to the student account 121. Specifically, the tutoring cost per unit time of tutor accounts tailored to the student account 121 may increase as the student account 121 meets the requested input condition. Through this, in selecting a tutor account, the user of the student account 121 can more reasonably receive the price condition of the tutor account that meets his or her needs.

The tutoring cost per unit time of each tutor account 122 and 123 tailored to the student account 121 may be determined based on an inference of an artificial intelligence (artificial neural network) learned in advance. The inference of artificial intelligence (artificial neural network) may be made based on student information and each tutor information. The learning method of artificial intelligence (artificial neural network) will be described later with reference to FIG. 6.

Subsequently, the server 100 may include information about each student tailored tutor in the tutor waiting list (206). The tutor waiting list may be a list-type data structure. Each element of the tutor waiting list may be a student-customized tutor information object.

In the following order, the server 100 may transmit the tutoring waiting list to the student terminal 111 (207). The tutor waiting list may be displayed in the form of a UI on the student terminal 111. For example, a tutor waiting list may be displayed on a map. Specifically, student-customized tutor information corresponding to each tutor account may be displayed in an area where each tutor lives. As long as the user of the student account 121 can easily grasp each student customized tutor information through the tutor waiting list, there is no particular limitation on how the tutor waiting list is displayed on the student terminal 111.

Next, the server 100 may acquire the tutor account selected by the student account 121 (208). To this end, the student terminal 111 may receive a selection of a tutor account of the student account 121. For example, the user of the student account 121 may select a student-customized tutor information object corresponding to the first tutor account 122 from among the student-customized tutor information objects displayed on the student terminal 111. The student terminal 111 may transmit to the server 100 that the student account 121 has selected the first tutor account 122.

In the following order, the server 100 may transmit a tutor matching acceptance request to the tutor terminal to which the selected tutor account is logged in (209). For example, the server 100 may transmit a tutor matching acceptance request to the first tutor terminal 112 to which the first tutor account 122, which is the selected tutor account, is logged in. Through this, the tutor matching acceptance request may include tutor matching acceptance or tutor matching rejection.

Next, the server 100 may obtain whether to accept tutor matching from the tutor terminal to which the selected tutor account is logged in (210). To this end, the tutor terminal to which the selected tutor account is logged in may receive an input to accept or reject tutor matching of the selected tutor account. For example, the first tutor terminal 112 may receive an input of a tutor matching acceptance or tutor matching rejection of a user of the first tutor account 122. When the user of the first tutor terminal 112 accepts the tutor matching, the first tutor terminal 112 may transmit the tutor matching acceptance to the server 100. When the user of the first tutor terminal 112 rejects the tutor matching, the first tutor terminal 112 may transmit the tutor matching rejection to the server 100.

In the following order, the server 100 may transmit whether or not to accept the tutoring match to the student terminal 111 (211). Through this, the student using the student terminal 103 may check whether or not the tutor selected by him/her is matched.

Through the above, tutoring matching between the student account 121 and the selected account 122 may be performed. Particularly, based on the input condition requested by the user of the student account 121, the tutoring cost per unit time of the tutor account may be calculated. Through this, the user of the student account 121 may receive tutoring costs per unit time of tutor accounts tailored to the student account 121. Through this, the user of the student account 121 can more reasonably receive the price condition of each tutor account when selecting a tutor account.

When the selected account 122 accepts tutor matching, the server 100 may set the selected tutor account 122 as a tutor account matched with the student account 121. Thereafter, the server 100 may provide an online tutoring environment for the matched tutor account 122 and student account 121 and perform an operation of determining tutoring cost. In addition, the server 100 records the number of times of acceptance of the selected account 122, and the tutor account having a high number of matching acceptances may apply benefits.

On the other hand, when the selected account 122 rejects the tutor matching, the server 100 may retransmit the tutor waiting list except for the selected account 122 to the student account 121. The server 100 may record the number of rejections of the selected account 122, and a penalty may be applied to a tutor account with a high number of rejections of matching.

3 is a diagram for describing an operation of determining an extracurricular cost according to an embodiment.

First, the server 100 may obtain tutor matching acceptance (301). Specifically, the server 100 may transmit a tutor matching acceptance request to the first tutor terminal 112 to which the tutor account 122 selected by the student account 121 is logged in. The first tutor terminal 112 may receive a tutor matching acceptance input from the selected tutor account 122 user from among tutor matching acceptance or tutor matching rejection. The first tutor terminal 112 may transmit the received tutor matching acceptance to the server 100.

Next, when the selected tutor account accepts the tutor matching acceptance request, the server 100 may set the selected tutor account 122 as the tutor account 122 matched with the student account (302). For example, the server 100 may set the selected account 122 as the tutor account 122 matched with the student account 121.

In the following order, the server 100 may obtain a tutoring start notification from the first tutor terminal 122 to which the matched tutor account 122 is logged in (303). To this end, the first tutor terminal 112 may obtain an input indicating that the tutoring has started from the matched tutor account 122 user. The first tutor terminal 112 having obtained the input may transmit a tutoring start notification to the server 100.

Next, the server 100 may transmit a tutoring start approval request to the student terminal 111 (304).

In the following order, the server 100 may obtain permission to start tutoring from the student terminal 111 (305). To this end, the student terminal 111 may receive a tutoring start approval from a user of the student account 121. The student terminal 111 receiving the input of the tutoring start approval may transmit the tutoring start approval to the server 100.

Next, the server 100 may record the start of tutoring (306). After recording the start of the tutoring, the server 100 may provide an environment for online tutoring of the tutor account 122 matched with the student account 121. Specifically, the server 100 displays a picture or video of a user of the tutor account 122 matched with the user of the student account 121 on the student terminal 111 and the first tutor terminal 112; Display a UI corresponding to a textbook, a blackboard, or a writing implement; An online space that displays handwriting, highlight display, problem solving, and the like of the student account 121 and the matched tutor account 122 may be created in real time. In addition, the server 100 may count the time elapsed after recording the start of the tutoring.

In the following order, the server 100 may obtain a tutoring end notification from the student terminal 111 (307). To this end, the student terminal 111 may obtain an input that the tutoring has ended from the user of the student account 121. The student terminal 111 having obtained the input may transmit a tutoring end notification to the server 100.

Next, the server 100 may transmit a tutoring end approval request to the first tutor terminal 112 to which the matched tutor account is logged in (308).

In the following order, the server 100 may obtain the tutor termination approval from the first tutor terminal 112 to which the matched tutor account is logged in (309). To this end, the first tutor terminal 112 may receive a tutoring end approval from a matched tutor account 122 user. The first tutor terminal 112 receiving the tutor termination approval may transmit the tutor termination approval to the server 100.

Next, the server 100 may record the end of the tutoring (310). After recording the end of the tutoring, the server 100 may store the result of the online tutoring performed by the tutor account 122 matched with the student account 121 and clear the online space for online tutoring. The result of the online tutoring process may be viewed later by the student account 121 or the matched tutor account 122. In addition, the server 100 may stop the counted time after recording the start of the tutoring.

Subsequently, the server 100 may calculate an accumulated tutoring time for a predefined period based on the tutoring start record and the tutoring end record (311). The predefined period may be, for example, 1 month. During a predefined period, the server 100 may calculate the accumulated tutoring time reflecting the most recently ended tutoring by adding the counted time from the tutoring start record to the tutoring end record with the accumulated tutoring time during that time.

In the following order, the server 100 may determine the first cost to be paid by the Student account by applying the tutoring cost per unit time of the tutor account 122 matched to the student account 121 to the accumulated tutoring time. (312). The first cost may be an extracurricular cost for a predefined period of time, for example one month. After the pre-defined period ends, the server 100 is the accumulated tutoring time for a predefined period between the student account 121 and the matched tutor account 122, and the matched tutor according to the student account 121 By multiplying the tutoring cost per unit time of the account 122, the tutoring cost for a predefined period may be determined.

Through the above, the server 100 uses the tutoring start input of the matched tutor account 122 as a starting point, obtains a tutoring start notification and tutoring approval, and performs a tutoring start record; By using the tutoring end input of the student account 121 as a starting point, acquiring tutoring end notification and tutoring end approval, and performing tutoring end recording; Reasonable cost decisions can be made. That is, when the operation until the server 100 records the start of tutoring does not start from the input of the matched tutor account 122 but from the input of the student account 121, the student account 121 starts tutoring. It is possible to deliberately enter late, and in this case, the accumulated tutoring time for a predefined period is reduced, so that the matched tutor account 122 suffers a loss. In addition, when the operation until the server 100 records the end of the tutoring does not start from the input of the student terminal 111 but from the input of the matched tutor account 122, the matched tutor account 122 The end may be intentionally entered late, and in this case, the cumulative extracurricular time for a predefined period increases, so that the student account 121 suffers a loss. Therefore, in order to reasonably calculate the tutoring cost without incurring unreasonable damage to both the student account 121 and the matched tutor account 122, the server 100 is based on the tutoring start input received by the matched tutor terminal 112. The start of tutoring is recorded as and the end of tutoring is recorded based on the tutoring end input received by the student terminal 111.

4 is a flowchart illustrating an incentive application operation according to an embodiment.

First, the server 100 may generate each student-tutor time difference based on the hardness of the student account and each tutor account (410). To this end, the server 100 may obtain the hardness of the student account from the student's living location among the student information. In addition, the server 100 may obtain the hardness of each tutor account from the location where the tutor lives from among the information of each tutor account included in the tutor waiting list. Thereafter, the server 100 may generate each student-tutor time difference based on the hardness of the student account and the hardness of each tutor account.

For example, a student account 121 user may live in New York, a first tutor account 122 user may live in Texas, and a second tutor account 123 user may live in California ( California). The server 100 may obtain the hardness of the student account from the location where the student lives among the student information of the student account 121, and the first tutor account from the location where the tutor lives in the tutor information of the first tutor account 122 The hardness of the tutor account can be obtained, and the hardness of the tutor account can be obtained from the location where the tutor lives in the tutor information of the second tutor account 123. Thereafter, the server 100 may generate “student-first tutor time difference: 1 hour” and “student-second tutor time difference: 3 hours” based on the hardness of the student account and the hardness of each tutor account. .

Next, the server 100 may generate an incentive per unit time of each tutor account based on the time difference between each student and the tutor (420). The incentive per unit time may be made as a ratio (%) to the tutoring cost per unit time of a tutor account tailored to the student account.

In this case, in the first parallax section, as the student-tutor parallax increases, the incentive per unit time may increase faster than linearly increasing. For example, since the time difference between the student and the first tutor is 1 hour, the incentive per unit time of the first tutor account 122 may be 6%, and the time difference between the student and the first tutor is 3 hours, so the first tutor account 122 The incentive per unit time of) may be a value greater than 3 times 6%, for example, 20%.

In the second parallax section, as the student-tutor parallax increases, the incentive per unit time may increase more slowly than linearly increasing. For example, assuming that there is a third tutor account, a fourth tutor account, and a fifth tutor account; The time difference between the student and the third tutor may be 9 hours, the incentive per unit time of the third tutor account may be 35%, the time difference between the student and the fourth tutor may be 10 hours, and the incentive per unit time of the fourth tutor account may be 40% In addition, the time difference between the student and the fourth tutor is 11 hours, and the incentive per unit time of the fourth tutor account may be less than 45%, for example, 43%.

In the third parallax section, as the student-tutor parallax increases, the incentive per unit time may decrease faster than linearly decreasing. For example, assuming that there is a 6th tutor account, a 7th tutor account, and an 8th tutor account; The time difference between the student and the 6th tutor is 13 hours, the incentive per unit time of the third tutor account can be 43%, the time difference between the student and the 7th tutor is 14 hours, and the incentive per unit time of the 7th tutor account can be 40%. In addition, the time difference between the student and the fourth tutor is 15 hours, and the incentive per unit time of the fourth tutor account may be less than 37%, for example, 35%.

In the fourth parallax section, as the student-tutor parallax increases, the incentive per unit time may decrease more slowly than linearly decreasing. For example, assuming that there is a ninth tutor account, a tenth tutor account, and an eleventh tutor account; The time difference between the student and the ninth tutor may be 21 hours, the incentive per unit time of the ninth tutor account may be 20%, the time difference between the student and the tenth tutor may be 22 hours, and the incentive per unit time of the 10th tutor account may be 12% In addition, the time difference between the student and the eleventh tutor is 23 hours, and the incentive per unit time of the eleventh tutor account may be greater than 4%, for example, 6%.

The first parallax section may be greater than 0 hours and less than 6 hours; The second parallax section may be greater than 6 hours and less than 12 hours; The third parallax section may be greater than 12 hours and less than 18 hours; The fourth parallax section may be greater than 18 hours and less than 24 hours. When the time difference between the student and the tutor is 0 hours, the incentive per unit time of the tutor account may be 0%.

Through this, the smaller the absolute value of the parallax between the student account and the tutor account (the first parallax section and the fourth parallax section), the smaller the incentive per unit time is; As the absolute value of the time difference between the student account and the tutor account increases (the second time difference period and the third time difference period), the incentive per unit time may be relatively increased. In addition, as the absolute value of the disparity between the student account and the tutor account increases, in the section where the time difference experienced by the tutor account user is large (the first and fourth parallax sections), as the absolute value of the parallax increases, the unit It increases more than if the hourly incentive increases linearly; Even if the absolute value of the time difference between the student account and the tutor account increases, in the section where the time difference experienced by the tutor account user is small (the second time difference section and the third time difference section), as the absolute value of the time difference increases, incentives per unit time It can be set to increase less than in the case of increasing linearly. Through this, an incentive per unit time according to a time difference that is reasonable for the user of the tutor account may be set.

On the other hand, the specific calculation of the amount or ratio of the incentive per unit time according to the parallax may be performed by the pre-learned artificial intelligence (artificial neural network) of the server 100. The learning process of artificial intelligence (artificial neural network) will be described later with reference to FIG. 6.

Subsequently, the server 100 may include the incentive per unit time of each tutor account in each tutor customized tutor information (430). For example, the incentive per unit time of the first tutor account 122 may be 6% of the extracurricular cost per unit time of the first tutor account 122 tailored to the student account 121. The incentive per unit time of the second tutor account 123 may be 20% of the extracurricular cost per unit time of the second tutor account 123 tailored to the student account 121. The server 100 may include incentives per unit time of each tutor account 122 and 123 in a student-customized tutor information object corresponding to each tutor account 122 and 123 in the tutor waiting list.

In the following order, the server 100 may determine a second cost to be paid by the Student account by applying an incentive per unit time of the matched tutor account to the accumulated tutoring time for a predefined period (440). For example, the matched tutor account may be the first tutor account 122. The second cost can be an incentive for a predefined period of time, such as for one month. After the pre-defined period ends, the server 100 is the accumulated tutoring time for a predefined period between the student account 121 and the matched tutor account 122, and the matched tutor according to the student account 121 By multiplying by 6% of the extracurricular cost per unit time of the account 122, an incentive for a predefined period may be determined.

Through this, the tutor account user (teacher) can receive incentives if he/she needs to conduct online tutoring with a student account in an area where there is a time difference, so even in an uncomfortable time period (for example, the time difference between the student and the tutor is 3 hours). In the case of me, if the student wishes to take tutoring at 6 pm, the tutor must teach at 9 pm)) I am motivated to conduct online tutoring. In addition, if the student account user (student) has a tutor who wants to take a class, even if the tutor lives in a distant place where there is a time difference, they can take the class while providing incentives. Through this, students, regardless of where they live, have more opportunities to be matched with their desired tutors; Tutoring teachers, the more time lag between the matching students, the greater the opportunity to earn income. Through this, tutor matching and progress that maximizes temporal and spatial constraints, which are the advantages of online, can be actively performed. Through this, more tutors and students can be encouraged to use online tutoring services.

5 is a flowchart illustrating an operation of not applying an incentive according to an embodiment.

First, the server 100 may obtain a request for non-applying incentives of a tutor account (510). For example, even if the first tutor account 122 user and the second tutor account 123 user are matched with the student account 121 in a region with time difference through the first and second tutor terminals 112 and 113 , You can enter a request not to receive incentives based on the student-tutor time difference. The first and second tutor terminals 112 and 113 that have obtained the user's input may transmit a request for the first and second tutor accounts 122 and 123 to the server 100.

Next, the server 100 may include an indication of non-application of incentive incentives in student-customized tutor information corresponding to the tutor account that has requested non-application of incentives in the tutoring waiting list (520). For example, the server 100 applies incentives to the student-customized tutor information object corresponding to the first and second tutor accounts 122 and 123 in the tutor waiting list, and the first and second tutor accounts 122 and 123 You can include an indication that the account does not work. Accordingly, the student terminal 111 having acquired the tutoring waiting list may indicate that the first and second tutor accounts 122 and 123 are accounts that do not receive incentives. Through this, the user of the student terminal 111 can select a tutor account that does not receive incentives, and thus can be provided with an opportunity to select a tutor while relatively saving costs.

Subsequently, when the matched tutor account is a tutor account for which no incentive application has been requested, the server 100 may not apply the incentive per unit time of the matched tutor account (530). For example, the matched tutor account may be the first tutor account 122. The server 100 is, after the end of a predefined period, for example, one month, the accumulated tutoring time for a predefined period between the student account 121 and the matched tutor account 122, the student account 121 The first cost may be determined by multiplying the tutoring cost per unit time of the matched tutor account 122 according to ). On the other hand, if the matched tutor account 122 has not requested the incentive not to be applied, the server 100 is the unit of the matched tutor account 122 according to the student account 121 in the accumulated tutoring time for a predefined period. The second cost may have been determined by multiplying by 6% of the hourly extracurricular cost, but since the matched tutor account 122 requested that the incentive not be applied, the second cost may not be calculated by not applying the incentive per unit time. In this case, the student account 121 only needs to pay the first cost after a predefined period has passed, and the second cost is omitted.

As such, when the matched tutor account is a tutor account for which an incentive has not been applied, the server 100 may set not to calculate the cost according to the incentive per unit time of the tutor account, that is, the second cost, even if a predefined period has elapsed. . Through this, a tutor account that has relatively secured time or is judged to be less competitive in terms of class quality compared to other tutor accounts is set to not receive incentives despite the time difference with the student account. They can provide tutoring services at a relatively cheaper price than they do. Through this, a tutor account with a large amount of time or less class know-how may have a method of securing competitiveness, and an opportunity to receive a selection of a student account may be increased. Through this, more new tutors can be encouraged to participate in the online tutoring system.

6 is a diagram illustrating learning of an artificial neural network according to an embodiment.

The artificial neural network may be a component included in the server 100 and may be learned through the server 100 or a separate learning device.

The first artificial neural network is the student's information (the student's age, grade, academic level, past exam grades, the subject the student wants to take, the exam he is preparing for, where he lives, etc.), and the student requirements (the subject the student wants to take, preparation Exams to be taken, region where you live, time and day of the week you want to take online tutoring, and the range of tutoring costs per hour you are willing to pay), and tutor information (name, age, education, past tutoring experience, past tutoring achievements, tutoring) It is possible to print out the tutoring cost per unit time of the tutor account tailored to the student's account by inputting the subjects that the teacher can teach, the exams that can be taught, the hours and days of the class, and where they live.

The second artificial neural network may receive a student-tutor time difference and output an incentive per unit time of the tutor account.

Hereinafter, a process of learning an artificial neural network through a learning device will be described.

First, the learning device may acquire training data and a label (600).

For learning the first artificial neural network, the learning device acquires, as training data, each student-tutor information object including student information, student requirements, and tutor information of students and tutors whose tutor matching is actually performed. I can.

In addition, for training the first artificial neural network, the tutoring cost per unit time actually paid may be obtained as a label for each training data. In this case, as tutor information meets the student's requirement, the tutoring cost per unit time actually paid may be higher.

For learning the second artificial neural network, the learning device may acquire as training data the student-tutor disparity between the student and the tutor who have actually matched the tutor.

In addition, for training of the second artificial neural network, the learning device may acquire the actually paid incentive per unit time as a label for each training data.

Now, the learning device may generate an input of an artificial neural network from the training data (610).

The training device may use the training data as an input of the artificial neural network or may generate an input of the artificial neural network after undergoing known processing such as removing unnecessary information from each training data.

Next, the learning device may apply the input to the artificial neural network (620). The artificial neural network included in the server may be an artificial neural network that is learned according to supervised learning. The artificial neural network may be a convolutional neural network (CNN) or a recurrent neural network (RNN) structure suitable for training through supervised learning.

Subsequently, the learning device may obtain an output from the artificial neural network (630).

The output of the first artificial neural network may be an inference of a tutoring cost for a predefined period between a student and a tutor according to student information, student requirement conditions, and tutor information. Specifically, the artificial neural network includes a student's grade, academic level, exam preparation, time to take online tutoring, past exam grades, etc.; By analyzing the pattern according to the tutor's academic background, past tutoring experience, past tutoring achievements, and available subjects, the tutoring cost per unit time inferred to correspond to the state of students and tutors can be output.

The output of the second artificial neural network may be inference of an incentive for a predefined period between a student and a tutor according to a student-tutor parallax. Specifically, the artificial neural network analyzes patterns according to the case where the Student-Tutor parallax belongs to the first parallax section, the second parallax section, the third parallax section, and the fourth parallax section, You can output the incentive per unit time inferred to correspond to the time difference between students and tutors.

In this case, the characteristics of the output of the second artificial neural network may be divided into four parallax sections. In the first parallax section, as the Student-Tutor parallax increases, the incentive per unit time can increase faster than linearly increasing; In the second parallax section, as the Student-Tutor parallax increases, the incentive per unit time may increase more slowly than linearly increasing; In the third parallax section, as the Student-Tutor parallax increases, the incentive per unit time can decrease faster than linearly decreasing; In the fourth parallax section, as the student-tutor parallax increases, the incentive per unit time may decrease more slowly than linearly decreasing.

The first parallax section may be greater than 0 hours and less than 6 hours; The second parallax section may be greater than 6 hours and less than 12 hours; The third parallax section may be greater than 12 hours and less than 18 hours; The fourth parallax section may be greater than 18 hours and less than 24 hours. When the time difference between the student and the tutor is 0 hours, the incentive per unit time of the tutor account may be 0%.

Thereafter, the learning device may compare the output and the label (640 ). The process of comparing the output of the artificial neural network corresponding to the inference with the label corresponding to the correct answer may be performed by calculating a loss function. As the loss function, a known mean squared error (MSE), cross entropy error (CEE), etc. may be used. However, the present invention is not limited thereto, and loss functions used in various artificial neural network models may be used as long as a deviation, error, or difference between the output of the artificial neural network and the label can be measured.

Next, the learning device may optimize the artificial neural network based on the comparison value (650). By updating the weight of the nodes of the artificial neural network so that the comparison value of the learning device becomes smaller, the output of the artificial neural network corresponding to the inference and the label corresponding to the correct answer can be gradually matched. Through this, the artificial neural network It can be optimized to output an inference close to the correct answer. Specifically, the learning apparatus may optimize the artificial neural network by repeating the process of resetting the weight of the artificial neural network so that the loss function corresponding to the comparison value approaches the estimated value of the minimum value. For the optimization of artificial neural networks, known backpropagation algorithms, stochastic gradient descent, and the like can be used. However, the present invention is not limited thereto, and weight optimization algorithms used in various neural network models may be used.

The learning device can train an artificial neural network by repeating this process.

Through this, the learning device is based on the student information, the student requirements, and the tutor information; It is possible to train an artificial neural network that outputs the tutoring cost per unit time of a tutor account tailored to the student account. In this case, as tutor information matches the student's requirement condition, the tutoring cost per output unit time may be higher. The artificial neural network may be used to generate an extracurricular cost per unit time of a tutor account tailored to the student account described above with reference to FIGS. 2 and 3 and a first cost for a predetermined period of the matched tutor account.

In addition, the learning device is based on the student-tutor parallax; It is possible to train an artificial neural network that outputs the incentive per unit time of the tutor account. The artificial neural network may be used to generate the incentive per unit time of the tutor account described above with reference to FIG. 4 and the second cost for a predefined period of the matched tutor account.

7 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

The device 701 according to an embodiment includes a processor 702 and a memory 703. The processor 702 may include at least one of the devices described above with reference to FIGS. 1 to 6, or may perform at least one method described above with reference to FIGS. 1 to 6. Specifically, the device 701 may be the server 100, the student terminal 111, the tutor terminals 112 and 113, or an artificial neural network learning device. A person or organization using the device 701 may provide services related to some or all of the methods described above with reference to FIGS. 1 to 6.

The memory 703 may store information related to the above-described methods or a program in which methods described below are implemented. The memory 703 may be a volatile memory or a nonvolatile memory.

The processor 702 can execute a program and control the device 701. The code of a program executed by the processor 702 may be stored in the memory 703. The device 701 is connected to an external device (eg, a personal computer or a network) through an input/output device (not shown), and may exchange data through wired or wireless communication.

The device 701 may be used to train an artificial neural network or use the learned artificial neural network. The memory 703 may include a learning artificial neural network or a learned artificial neural network. The processor 702 may train or execute an artificial neural network algorithm stored in the memory 703. The apparatus 701 for training the artificial neural network and the apparatus 701 using the learned artificial neural network may be the same or may be separate.

The embodiments described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices, methods, and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions, such as one or more general purpose computers or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

As described above, although the embodiments have been described by the limited drawings, a person of ordinary skill in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments and claims and equivalents fall within the scope of the following claims.

Claims (5)

Obtaining Student information of a Student account logged in to an application installed on a Student terminal;
Obtaining tutoring requests from tutor accounts logged in to applications installed in tutor terminals, and including pre-stored tutor information of tutor accounts that have requested tutoring in a tutoring standby list;
Obtaining a request to view the tutoring waiting list from the student terminal;
Generating information for each personalized tutor based on the student information and information on each tutor in the tutor waiting list, and including information on the tutor tailored for each student in the tutor waiting list;
Transmitting a waiting list for tutoring to the student terminal;
Obtaining a tutor account selected by the student account;
Transmitting a tutor matching acceptance request to a tutor terminal in which the selected tutor account is logged in;
Acquiring whether to accept tutor matching from the tutor terminal to which the selected tutor account is logged in; And
Transmitting whether to accept the tutoring matching to the student terminal
Including,
Each of the student-customized tutor information includes extracurricular costs per unit time of each tutor account tailored to the student account,
The tutoring cost per unit time of each tutor account tailored to the student account is determined based on the inference of artificial intelligence,
The inference of the artificial intelligence is made based on the student information and each tutor information,
In the application-based tutoring matching method,
Obtaining a hardness of a Student account from the Student information;
Obtaining the hardness of each tutor account from the information on each tutor account;
Generating each student-tutor time difference based on the hardness of the student account and the hardness of each tutor account;
Generating incentives per unit time of each tutor account based on the respective student-tutor time difference; And
Including incentives per unit time of each tutor account in the tutor customized tutor information;
Including more,
The student-tutor time difference is divided into 4 time difference sections,
In the first parallax section, as the student-tutor parallax increases, the incentive per unit time of the tutor account increases faster than linearly increasing;
In the second parallax section, as the student-tutor parallax increases, the incentive per unit time of the tutor account increases more slowly than linearly increases;
In the third parallax section, as the student-tutor parallax increases, the incentive per unit time of the tutor account decreases faster than linearly decreases;
In the fourth parallax section, as the student-tutor parallax increases, the incentive per unit time of the tutor account decreases more slowly than linearly decreases;
The first parallax section is more than 0 hours and less than 6 hours;
The second parallax section is more than 6 hours and less than 12 hours;
The third parallax section is more than 12 hours and less than 18 hours;
The fourth parallax section is greater than 18 hours and less than 24 hours;
If the student-tutor time difference is 0 hours, there is no incentive per unit time of the tutor account;
Application-based tutoring matching method.
The method of claim 1,
Setting the selected tutor account as a tutor account matched with the student account when the selected tutor account accepts the tutor matching acceptance request;
Obtaining a tutoring start notification from a tutor terminal to which the matched tutor account is logged in;
Transmitting a tutoring start approval request to the student's terminal;
Obtaining permission to start tutoring from the student terminal;
Recording the start of tutoring;
Obtaining an end of tutoring notification from the student terminal;
Transmitting a tutoring termination approval request to a tutor terminal to which the matched tutor account is logged in;
Obtaining permission to terminate tutoring from a tutor terminal to which the matched tutor account is logged in;
Recording the end of the tutoring;
Calculating an accumulated tutoring time for a predefined period based on the tutoring start record and the tutoring end record;
Determining a first cost to be paid by the student account by applying the tutoring cost per unit time of the matched tutor account in accordance with the student account to the accumulated tutoring time
Further comprising,
Application-based tutoring matching method.
The method of claim 2,
Determining a second cost to be paid by the student account by applying an incentive per unit time of the matched tutor account to the accumulated tutoring time
Further comprising,
Application-based tutoring matching method.
The method of claim 3,
Obtaining a request for non-applying an incentive for a tutor account;
Including, in the tutor waiting list, an indication of non-incentive application in student-customized tutor information corresponding to the tutor account for which the incentive is not applied;
Setting not to calculate the second cost if the matched tutor account is a tutor account for which no incentive has been requested;
Further comprising,
Application-based tutoring matching method.
Acquires Student information of the Student account logged in to the application installed on the Student terminal,
Acquire tutoring requests from tutor accounts logged in to applications installed on tutor terminals, and include pre-stored tutor information of tutor accounts who have applied for tutoring in the tutoring standby list,
Obtaining a request to view the tutor waiting list from the student terminal,
On the basis of the student information and each tutor information in the tutor waiting list, each student tailored tutor information is generated, and each student tailored tutor information is included in the tutor waiting list,
Transmit a tutoring waiting list to the student terminal,
Acquire the tutor account selected by the student account,
The selected tutor account transmits a request to accept tutor matching to the logged-in tutor terminal,
Obtaining whether to accept tutor matching from the tutor terminal to which the selected tutor account is logged in,
Processor for transmitting whether to accept the tutoring matching to the student terminal
Including,
Each of the student-customized tutor information includes extracurricular costs per unit time of each tutor account tailored to the student account,
The tutoring cost per unit time of each tutor account tailored to the student account is determined based on the inference of artificial intelligence,
The inference of the artificial intelligence is made based on the student information and each tutor information,
In the application-based tutoring matching device,
The operation performed by the processor,
Obtaining the hardness of the Student account from the Student information,
Obtaining the hardness of each tutor account from the information of each tutor account,
Based on the hardness of the student account and the hardness of each tutor account, each student-tutor time difference is generated,
Generate incentives per unit time of each tutor account based on the time difference between each student and tutor,
Including incentives per unit time of each tutor account in each tutor customized tutor information
Includes actions,
The student-tutor time difference is divided into 4 time difference sections,
In the first parallax section, as the student-tutor parallax increases, the incentive per unit time of the tutor account increases faster than linearly increasing;
In the second parallax section, as the student-tutor parallax increases, the incentive per unit time of the tutor account increases more slowly than linearly increases;
In the third parallax section, as the student-tutor parallax increases, the incentive per unit time of the tutor account decreases faster than linearly decreases;
In the fourth parallax section, as the student-tutor parallax increases, the incentive per unit time of the tutor account decreases more slowly than linearly decreases;
The first parallax section is more than 0 hours and less than 6 hours;
The second parallax section is more than 6 hours and less than 12 hours;
The third parallax section is more than 12 hours and less than 18 hours;
The fourth parallax section is greater than 18 hours and less than 24 hours;
If the student-tutor time difference is 0 hours, there is no incentive per unit time of the tutor account;
Application-based tutoring matching device.

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