KR20190142287A - Method for recommending related problem based on meta data - Google Patents

Abstract

The present invention relates to a method for recommending a related problem based on metadata, capable of analyzing an image type question and providing a learner with explanation information searched based on an analysis result. According to one embodiment of the present invention, the method for recommending a related problem based on metadata comprises the following steps of: receiving an image from a learner terminal; performing an optical character recognition (OCR) process for the received image; performing the OCR process to recognize a search request problem from the image; dividing characters included in the recognized search request problem into each component unit; extracting a keyword from a formula included in the recognized search request problem; filtering stored problems to be searched based on the character divided into each component unit and the extracted keyword; determining a similarity of the filtered problem to be searched; extracting a problem with a similarity equal to or greater than a preset similarity based on the determined similarity, wherein the extracted problem and the problem to be searched include metadata including section category information, difficulty information, and solution information of the problem, which are parameter information for measuring a correlation degree; comparing at least one parameter information of first metadata of the extracted problem and second metadata of the problem to be searched in the metadata to determine the correlation degree between the extracted problem and the problem to be searched; extracting a related problem with the relation degree equal to or greater than a preset correlation degree with respect to the search request problem among the problems to be searched based on a determination resource about the correlation degree; and transmitting the extracted related problem to the learner terminal.

Description

How to recommend related issues based on metadata {METHOD FOR RECOMMENDING RELATED PROBLEM BASED ON META DATA}

The present invention relates to a method for recommending related problems based on metadata. More specifically, the present invention relates to a metadata-based related problem recommendation method using OCR processing results for question images.

When a learner registers a question in a Q & A bulletin board on the web, an online service is used in which an answerer registers an answer to the question and the registered answer is provided back to the learner. In order to provide the answerer with a question registered in such an online service, keyword-based classification and retrieval techniques of text constituting the question are generally used. Such a keyword-based classification and search technique, in particular, in the case of a question composed of a text-based general-purpose keyword, there is an advantage that can provide a high number of answers and accurate answers by allowing the registered questions to be exposed to a large number of respondents.

On the other hand, keyword-based classification and retrieval techniques are not effective in the case where it is difficult for the learner to clearly express the question contents in text, such as a problem in which a question includes an equation or an image. This is because, in the case of a problem including an equation or an image in addition to the text, registration of the question is not easy and keyword-based classification and search are difficult. As a result, the question is not retrieved by the answerer by keyword search, which causes a problem that the learner cannot be provided with the answer.

Nevertheless, there is no method for easily registering, effectively classifying, and searching a question about a problem including an equation or an image. In particular, there is no learning service capable of analyzing questions in the form of an image and immediately providing matching commentary information.

Korean Patent Publication No. 2012-0131480

The technical problem to be solved by the present invention is to provide a method and apparatus for analyzing a question in the image format, and providing the learner with the commentary information retrieved based on the analysis result.

Specifically, the technical problem to be solved by the present invention is to perform an Optical Character Recognition (OCR) process for the image provided from the learner terminal, to search for problems identical or similar to the questions included in the image. To provide a way. In addition, the present invention provides a method of extracting commentary information matching the searched problem and providing the same to the learner terminal.

Another technical problem to be solved by the present invention is to provide a method and apparatus for constructing a problem and commentary information corresponding thereto as a database, and providing a learning service using the constructed database.

In detail, another technical problem to be solved by the present invention is to provide a method of storing a question as a new problem when a problem matching the learner's question does not exist in the database. The present invention also provides a method of receiving commentary information on a new problem from an answerer terminal and transmitting the commentary information to the learner's terminal.

Another technical problem to be solved by the present invention is to provide a method and apparatus for providing a Q & A messaging interface between the learner terminal and the answerer terminal.

Another technical problem to be solved by the present invention is to provide a learning service providing method and apparatus based on the learning service access position of the learner terminal.

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

The learning service providing method for solving the technical problem, the step of receiving an image from the learner terminal, performing the OCR process for the received image and the OCR process, the search request problem from the image Searching for a problem having a similarity or higher than a predetermined similarity criterion with the search request problem among the identifying and pre-stored search target problems; and if the problem having the similarity is extracted as a result of the search, the extracted problem And transmitting corresponding commentary information to the learner's terminal.

The apparatus for providing a learning service for solving the technical problem may include at least one processor and a network interface communicating with a learner terminal, a memory for loading a computer program executed by the processor, the computer program, a search target problem, and the And storage for storing commentary information matching the search target problem, wherein the computer program includes an operation of receiving an image from the learner terminal, an operation of performing an OCR process on the received image, and performing the OCR process. Accordingly, an operation of searching for a problem having a similarity with the search request problem and a preset similarity criterion among the operation for identifying a search request problem from the image and the search target problem, and a result of the search, If the problem with the extracted degree of similarity, and may include the operation to be transmitted to the learner terminal the description information corresponding to the extracted problem.

According to an embodiment of the present invention, the learner can easily register a question in the form of an image file, and can receive an answer to the registered question. In particular, according to an embodiment of the present invention, since the apparatus for providing a learning service can identify a learner's question including not only text, but also equations, figures, drawings, and photographs, the learner asks questions about various subjects. And commentary information can be provided.

In addition, according to another embodiment of the present invention, since the messaging interface between the learner terminal and the answerer terminal is provided, there is an effect that the learner can be asked in real time and the answer is provided.

In addition, according to another embodiment of the present invention, the learner can be recommended not only the question asked, but also various problems extracted from the learning service providing apparatus based on the question asked. Accordingly, the learning experience of the learner can be maximized.

In addition, according to another embodiment of the present invention, since the differential learning service is provided according to the learning service access position of the learner terminal, there is an advantage that the learner's learning immersion is increased at a specific access position.

1 is a block diagram of a learning service providing system according to an embodiment of the present invention.
2 is a block diagram of an apparatus for providing learning service according to another embodiment of the present invention.
3 is a flow chart of a learning service providing method according to another embodiment of the present invention.
4 is a flowchart illustrating a problem searching method of a learning service providing apparatus according to still another exemplary embodiment of the present invention.
FIG. 5 is an exemplary diagram for describing problem and commentary information stored in a learning service providing apparatus, which is referred to in some embodiments of the present disclosure.
6 and 7 are exemplary diagrams for explaining the components of the problem and the weights referred to in some embodiments of the present invention.
8 is a flowchart illustrating a method of providing commentary information to a learner terminal according to another embodiment of the present invention.
9 is an illustration of a messaging interface between a learner terminal and an answerer terminal, referred to in some embodiments of the present invention.
10 is an exemplary diagram for explaining a recommendation problem referred to in some embodiments of the present invention.
11 is an illustration of learner question based learning content, referred to in some embodiments of the invention.
12 is a block diagram of a system for providing a learning service based on learner ratings according to another embodiment of the present invention.
13 is a flowchart illustrating a method for providing a learner grade based learning service according to another embodiment of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Advantages and features of the present invention, and methods for achieving them will be 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 embodiments may make the posting of the present invention complete and the general knowledge in the technical field to which the present invention belongs. It is provided to fully convey the scope of the invention to those skilled in the art, and the present invention is defined only by the scope of the claims. Like reference numerals refer to like elements throughout.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used in a sense that can be commonly understood by those skilled in the art. In addition, the terms defined in the commonly used dictionaries are not ideally or excessively interpreted unless they are specifically defined clearly. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase.

In the present specification, a problem is designed to measure a learner's learning achievement and increase a learning level, and may be a work including at least one of letters, figures, graphs, drawings, photographs, and equations. Further, in the present specification, the problem may be documented and printed on paper, but embodiments of the present invention are not limited thereto, and the problem may mean data that can be stored in the form of individual files.

The learning service providing method according to an embodiment of the present invention may include, as an embodiment, a related problem recommendation method based on metadata. The learning service providing apparatus according to the embodiment of the present invention may include, as an embodiment, a related problem recommendation apparatus based on metadata.

Accordingly, in the present specification, the metadata-based related problem recommendation method and the metadata-based related problem recommendation device may be referred to as a learning service providing method and a learning service providing device, respectively.

1 is a block diagram of a learning service providing system according to an embodiment of the present invention.

Referring to FIG. 1, the learning service providing system may include a learner terminal 50, a learning service providing apparatus 100, and an answerer terminal 200. The illustrated learner terminal 50, the learning service providing apparatus 100, and the answerer terminal 200 are computing devices capable of communicating with each other through a network. In particular, in FIG. 1, the problem 10 captured by the learner terminal 50 is illustrated as an example of including a character and a mathematical expression.

The learner terminal 50 may obtain an image of the problem 10 and transmit it to the learning service providing apparatus 100. In FIG. 1, as a method of acquiring an image of the problem 10 by the learner terminal 50, the case where the problem 10 is photographed through a camera equipped with the learner terminal 50 is illustrated as an example. The embodiment is not limited to this. As another example, the learner terminal 50 may download an image including the problem 10 from a web page on the Internet, or generate a content input from the learner as a problem through an input unit (not shown). Image may be obtained.

According to an embodiment of the present invention, the learner terminal 50 may perform a preprocessing operation on the acquired image before transmitting the image of the problem 10 to the learning service providing apparatus 100. have. As an example of the preprocessing work performed by the learner terminal 50, the learner terminal 50 may automatically correct a direction of an image in which the problem 10 is photographed. For example, according to the photographing angle of the problem 10, such as when the learner terminal 50 photographs the problem 10 upside down, the problem 10 may be included in the image while being inclined or inverted. In this case, the learner terminal 50 may correct the direction of the captured image. In detail, the learner terminal 50 may correct and store the image by rotating the direction of the image so that the direction of the problem 10 coincides with the direction displayed by the learner terminal 50.

As another example of the preprocessing operation on the image, the learner terminal 50 may identify only the problem 10 portion on the image and store only the problem 10 portion. That is, the image photographed through the learner terminal 50 may include the area of the problem 10 and the background area. The learner terminal 50 identifies only the area of the problem 10 on the image, and the problem. Areas other than (10) may be treated as noise and filtered. This may be transmitted to the learning service providing apparatus 100.

As another example, the learner terminal 50 identifies the position of the problem 10 on the image, and when the problem 10 is photographed to be biased to a specific position on the image, the problem 10 is positioned at an intermediate position of the image. The image may be corrected so that the image may be corrected in consideration of the size of the problem 10 on the image.

In order to perform the illustrated preprocessing task, the learner terminal 50 may store a preprocessing program for the acquired image in advance.

According to another embodiment of the present invention, the above-described preprocessing job may be performed by the learning service providing apparatus 100 after the image acquired by the learner terminal 50 is transmitted to the learning service providing apparatus 100. have.

The learning service providing apparatus 100 may perform OCR processing on an image including the problem 10 received from the learner terminal 50. According to an embodiment of the present invention, as the OCR process is performed, the learning service providing apparatus 100 may identify the problem 10 on the image. For example, when the problem 10 is a math problem including an equation, the learning service providing apparatus 100 identifies letters, equations, etc. constituting the problem 10 included in the image through OCR processing on the image. Then, the image of the area where the problem 10 is displayed can be converted into the identified characters, mathematical expressions, and the like. The learning service providing apparatus 100 may build a database of problems by storing the converted characters and equations as one problem unit.

According to another exemplary embodiment of the present invention, when the learning service providing apparatus 100 receives an image of a problem from the learner terminal 50, the learning service providing apparatus 100 may search whether a problem that is the same as or similar to that of the received image exists in the constructed database. Can be. To this end, the learning service providing apparatus 100 may receive the learner terminal 100 and store at least one problem in advance before an image including the problem is OCRed. In detail, the learning service providing apparatus 100 may collect and store the at least one problem from a terminal of another learner or by crawling on the web. Alternatively, the learning service providing apparatus 100 may directly receive and store the at least one problem from an administrator of the learning service providing apparatus 100.

According to another embodiment of the present invention, the learning service providing apparatus 100 may store not only the at least one problem but also commentary information on the problem in advance. The learning service providing apparatus 100 may store problem and commentary information, respectively, and store a matching relationship with both. To this end, the database built in the learning service providing apparatus 100 may be a relational database, but the embodiment of the present invention is not limited thereto, and the database built in the learning service providing apparatus 100 may be a graph database. (Graph Database). In this case, each of the stored problems and their explanation information may be configured as nodes, and the similarity or recommendation relations between the problems and the matching relation between each problem and the description information may be configured as edges. .

Meanwhile, commentary information on a problem may be received from the answerer terminal 200.

The answerer terminal 200 may receive a problem from the learning service providing apparatus 100 and receive commentary information on the problem from the answerer. The answerer terminal 200 may transmit commentary information input from the answerer to the learning service providing apparatus 100.

According to another embodiment of the present invention, the answerer terminal 200 receives a question about a problem from the learner terminal 50 through a Q & A messaging interface provided by the learning service providing apparatus 100, Commentary information may be generated based on the answer of the answerer and provided to the learner terminal 50.

The learner terminal 50 may be any one device of a smartphone, a tablet PC, and a camera having a communication module, but is not limited thereto. The learner terminal 50 may include a desktop PC, a laptop PC, a PDA VR (Virtual Reality) imaging device, and an AR ( Augmented Reality) may be any one of a computing device such as an imaging device. The answerer terminal 200 may also be any one of the computing devices illustrated with respect to the learner terminal 50.

The learning service providing apparatus 100 may be either a mobile or a stationary computing device. In particular, according to an embodiment of the present invention, by identifying the problem by analyzing the image received from the learner terminal 50, in response to the retrieval information collected from the answerer terminal 200, and retrieves the search results from the learner terminal ( It may be a server device provided to 50).

In addition to the above-described configuration, the learning service providing system of FIG. 1 may further include a relay server for relaying data communication between components, one or more other learner terminals, and one or more other answerer terminals.

Next, the function and operation of the learning service providing apparatus 100 will be described in more detail with reference to FIG. 2. 2 is a block diagram of a learning service providing apparatus 100 according to another embodiment of the present invention.

The learning service providing apparatus 100 may load a computer program performed by the processor 101, the network interface 102 communicating with the one or more processors 101, the learner terminal 50, and the answerer terminal 200. Memory 103, and storage 104 for storing computer programs.

The processor 101 controls the overall operation of each component of the learning service providing apparatus 100. The processor 101 may be a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), an application processor (AP), an application processor (AP), or any form well known in the art. It may be configured to include a processor. In addition, the processor 101 may perform an operation on at least one application or program for executing a method according to embodiments of the present invention. The learning service providing apparatus 100 may include one or more processors.

The network interface 102 supports wired / wireless internet communication of the learning service providing apparatus 100. In addition, the network interface 102 may support various communication methods in addition to the Internet, which is a public communication network. To this end, the network interface 102 may comprise a communication module well known in the art.

In particular, the network interface 102 may provide various user interfaces (UIs) to the learner terminal 50 and the answerer terminal 200 during the learning service providing process of the learning service providing apparatus 100 according to an exemplary embodiment of the present invention. Can be.

The memory 103 stores various data, commands and / or information. Memory 103 may load one or more programs 105 from storage 104 to execute methods in accordance with embodiments of the present invention. The memory 103 may be composed of volatile memory capable of reading and writing and having a high read or write speed. As one example, the memory 103 may be provided with any one of RAM, DRAM, or SRAM.

Storage 104 may non-temporarily store the one or more programs 105 and Q & A data 107. In FIG. 2, a learning service providing software (hereinafter referred to as learning service providing software) 105 is illustrated as an example of the one or more programs 105.

Storage 104 is well known in the art, such as non-volatile memory, hard disks, removable disks, or the like to which the present invention pertains, such as Read Only Memory (ROM), Eraseable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, and the like. It may comprise any known type of computer readable recording medium.

According to another embodiment of the present invention, the learning service providing software 105 may be installed in the learner terminal 50 and the answerer terminal 200 as well as the learning service providing apparatus 100. That is, the learning service providing software 105 may be an agent that performs various functions by using data transmitted and received between the installed devices.

The Q & A data 107 may be generated by the learner terminal 50 or generated by the learner's input and received by the learning service providing apparatus 100 or by the learning service providing apparatus 100 crawled on the web. It may include data about. In addition, the Q & A data 107 may include commentary information received by the learning service providing apparatus 100 crawled on the web or directly input or commentary information received from the answerer terminal 200. The Q & A data 107 may be stored in the form of a database illustrated in the description of FIG. 1.

In addition, although not shown, the learning service providing apparatus 100 may include an output unit for outputting a UI according to an exemplary embodiment of the present invention, for example, a display unit, and an input for directly receiving problem and commentary information. It may further include wealth.

Hereinafter, the functions and operations of the learning service providing apparatus 100 according to an exemplary embodiment of the present invention will be described in detail with reference to FIGS. 1 and 2.

3 is a flow chart of a learning service providing method according to another embodiment of the present invention. Each of the following steps is performed by the learning service providing apparatus 100, and in particular, each of the following steps is performed by the processor 101 performing an operation according to the learning service providing software 105.

Referring to FIG. 3, the learning service providing apparatus 100 may receive an image of a problem (S10). As described above, the image of the problem may be an image obtained from the learner terminal 50, or may be received by the learning service providing apparatus 100 by performing a web crawl.

The learning service providing apparatus 100 may perform OCR processing on the received image (S20). At this time, the algorithm used in the OCR processing may be used algorithms well known in the technical field to which the present invention belongs, but according to an embodiment of the present invention, a machine learning algorithm is applied as an algorithm for the OCR processing and included in a problem. It is possible to improve the recognition accuracy of the generated characters and expressions. For example, as a machine learning algorithm, a convolutional neural network (CNN) model may be used.

Hereinafter, an operation of performing OCR processing by the learning service providing apparatus 100 using the composite product neural network model according to an embodiment of the present invention will be described.

In the general OCR processing method, a segmentation process of identifying a type of a character on an image and recognizing the character into component units such as a phoneme was essential.

In contrast, according to an exemplary embodiment of the present invention, the apparatus 100 for providing a learning service may not perform a recognition process on a type of a character and a separation recognition on a component unit of a character by using a composite product neural network model. In more detail, the apparatus 100 for providing a learning service may extract feature information for each pixel by analyzing all pixels on the image without prior recognition of the type of the character and without separate recognition from the received image. Next, the learning service providing apparatus 100 may predict the type of the character on the image from the feature information of the extracted pixel based on the information learned in advance through the composite product neural network model. In this case, the probabilistic prediction technique may be applied to the prediction, and the higher the learning amount through the multiplicative neural network model, the higher the accuracy of the probabilistic prediction.

 In particular, according to an embodiment of the present invention, the learning service providing apparatus 100 may recognize not only letters but also equations by using a learning result through a composite product neural network model. The formula may include numbers and arithmetic symbols. In detail, the learning service providing apparatus 100 may predict a character or a mathematical expression that each pixel configures, based on the extracted feature information. The apparatus 100 for providing learning services may cluster a plurality of pixels constituting any one of the predicted letters or equations, and identify at least one of letters and equations based on the plurality of clustered pixels.

When the problem on the image is a problem composed only of characters, the learning service providing apparatus 100 recognizes each character even in the case of a Korean problem, an English problem, or a problem in which Korean and English are mixed, thereby causing problems on the image. Can be identified. In addition, when the problem on the image is a problem composed of a letter and a formula, for example, a math problem, the learning service providing apparatus 100 may identify the problem on the image by recognizing the letter and the formula, respectively.

Next, the learning service providing apparatus 100 may search for a problem stored in advance on the basis of the problem identified as a result of the OCR process (S30). As described above, the learning service providing apparatus 100 may previously store the Q & A data 107 constructed as a database constructed in the storage 104. The learning service providing apparatus 100 may search the prestored Q & A data 107 to determine whether a problem that is the same as or similar to the problem identified as the OCR processing result is extracted (S40). In addition, in operation S40, the learning service providing apparatus 100 may extract a problem that is the same as or similar to the identified problem as a result of the search of the Q & A data 107. In this case, the learning service providing apparatus 100 may determine a problem having a similarity between the identified problem and the preset reference as the same or similar problem. Description of the similarity determination method will be described later with reference to FIG. 4.

As a result of the determination in step S40, when the same or similar problem is extracted, the learning service providing apparatus 100 may extract commentary information on the extracted problem and transmit it to the learner terminal 50 (S50). . To this end, the learning service providing apparatus 100 may match and store a problem and commentary information about the problem on the Q & A data 107.

If the same or similar problem is not extracted as a result of the determination in step S40, the learning service providing apparatus 100 uses the Q & A data 107 as the problem identified as a result of the OCR process performed in step S20. Can be stored (S60). That is, if the same or similar problem is not extracted in step S40, the identified problem is treated as a new problem that does not exist in the Q & A data 107, and the Q & A data 107 is stored in the storage 104. Are stored in. According to another embodiment, the learning service providing apparatus 100 may store a received image and a problem identified as a result of the OCR process.

According to another embodiment, the learning service providing apparatus 100 may generate metadata about the identified problem when the identified problem is stored. In addition, the apparatus 100 for providing a learning service may perform an indexing operation for improving a search speed in a later search for the identified problem. For example, indexing may be performed based on special keywords or symbols that make up the identified problem. That is, when a mathematical symbol or keyword which is a criterion for classifying the identified problem is identified, the learning service providing apparatus 100 may automatically assign a preset key value to the identified problem.

According to another exemplary embodiment of the present disclosure, the learning service providing apparatus 100 may determine whether to store the identified problem based on a predetermined quality criterion in step S60.

For example, the learning service providing apparatus 100 may identify a degree of noise included in a received image, a resolution, or the like, and store the identified problem when it is equal to or more than a preset quality standard.

As another example, the learning service providing apparatus 100 may identify when the quality of the Hangul, an equation, a figure, a graph, an image, etc., which are components included in the identified problem, is greater than or equal to a preset quality standard, as a result of the OCR processing of the received image. You can also save the problem. In this case, whether to store the identified problem may be determined by the administrator of the learning service providing apparatus 100 in a policy manner. That is, when the required component set in advance in the learning service providing apparatus 100 by the administrator exists, the learning service providing apparatus 100 identifies when the OCR processing result for the required component is equal to or greater than a preset quality standard. You can also save the problem.

As another example, the learning service providing apparatus 100 may determine whether the component of the problem identified as a result of the OCR processing is equal to or greater than a preset quality standard. Accordingly, the learning service providing apparatus 100 according to the embodiment of the present invention determines the quality score for each component of the problem identified as a result of the OCR process, and only if the total score summed is equal to or greater than a preset score, the identified problem You can also save it.

In the above-described embodiment, the preset quality criterion may be determined by the manager of the learning service providing apparatus 100.

According to the embodiment, the learning service providing apparatus 100 may not be stored in the storage 104 when the quality of the received image is low or the quality of the OCR processing result is low. As a result, it is possible to save the storage 104 space of the learning service providing apparatus 100 and to minimize unnecessary search targets.

4 is a flowchart illustrating a problem searching method of the apparatus 100 for providing learning services according to still another exemplary embodiment of the present invention. In particular, the problem search method of the learning service providing apparatus 100, which is performed between the step of searching for a problem based on the result of the OCR process described above with reference to FIG. 3 and the problem extracting step S40 based on the similarity criteria. Will be described with reference to each step of FIG.

Referring to FIG. 4, the learning service providing apparatus 100 may separate the identified characters into component units as a result of the OCR process (S31). For example, when the identified character is Korean, the learning service providing apparatus 100 may identify it, and may separate the Korean characters identified by consonants and vowels which are components of the character.

In addition, the learning service providing apparatus 100 may extract a keyword in the identified equation as a result of the OCR process (S32). For example, when the identified formula includes a specific formula symbol such as an integral symbol, the learning service providing apparatus 100 may identify it.

In operation S31 and S32, the learning service providing apparatus 100 may separately identify components of the identified text in units of components, such as a text, an equation, an image, a figure, and a graph.

The learning service providing apparatus 100 may filter a problem previously stored in the learning service providing apparatus 100 based on the components of the separated character and the extracted keywords (S33). To this end, the learning service providing apparatus 100 may store the Q & A data 107 in the storage 104 as described above with reference to FIG. 2. In addition, the Q & A data 107 may include a problem that is the same as or similar to at least some of the components of the separated character and the extracted keywords.

Hereinafter, for convenience of explanation, the problem identified by the learning service providing apparatus 100 is referred to as a search request problem, and among the problems previously included in the Q & A data 107, the same or similar problem as at least part of the problem is described. This problem will be referred to as a search target problem. The filtering step S33 will be described in detail.

In detail, the learning service providing apparatus 100 may separate a character included in the search request problem for each component, and extract a keyword in a formula. In addition, the apparatus 100 for providing a learning service may perform hashing on a search request problem based on components of separated characters and keywords in an extracted formula. When the hashing of the search request problem is performed, the learning service providing apparatus 100 may filter the search target problem that is indexed and stored in advance based on this. That is, the learning service providing apparatus 100 filters at least some of the same or similar problems as the components of the characters included in the search request problem and the extracted keywords in the formula among the problems previously included in the Q & A data 107. can do. In this case, at least one search target problem may be filtered.

Next, the learning service providing apparatus 100 may separate each filtered search target problem by category of the component (S34). For example, the learning service providing apparatus 100 may be divided into a character part and a mathematical part among the components of the search target problem.

The learning service providing apparatus 100 may assign a similarity weight to the separated character part (S35). To this end, the learning service providing apparatus 100 may perform similarity measurement on the character parts separated in step S34, based on the components of the text of the search request problem in step S31. In detail, the apparatus 100 for providing a learning service may include each component of the separated characters included in the search request problem, a distance between each component, an arrangement order of each component, a word in which each component is combined, and a whole. Based on at least one of the lengths of the text area, the text part of the search target problem may be analyzed and a similarity weight may be given to the analyzed text part.

The case where the character portion of the search request problem is "Get x value" and the character part of the search target problem is "x value is" will be described as an example. The learning service providing apparatus 100 may identify an English letter “x” and a Korean “value”, “a”, “gu”, “ha”, “shi”, and “o” of a search request problem. At this time, the apparatus 100 for providing a learning service analyzes a character part of a search target problem based on the existence of an English letter “x” and a Korean “value” of a search request problem, a distance between them, an arrangement order, and the like. You can weight the problem. In the above example, the learning service providing apparatus 100 may also identify components of each word in the case of Hangul, and may analyze the text part of the search target problem based on this.

The learning service providing apparatus 100 may assign a weight to each problem filtered in the filtering step S33, and determine the similarity level of each filtered search target problem using the assigned weight ( S36). The learning service providing apparatus 100 may determine whether a problem above a preset similarity criterion is extracted based on the similarity (S40). That is, the learning service providing apparatus 100 may determine whether there is a problem above the similarity criterion based on the similarity determined in step S36. Referring to FIGS. 3 and 4, if a problem equal to or greater than the similarity criterion exists, the learning service providing apparatus 100 may extract it and transmit commentary information on the extracted problem to the learner terminal 50.

FIG. 5 is an exemplary diagram for describing problem and commentary information stored in the learning service providing apparatus 100, which is referred to in some embodiments of the present disclosure. Hereinafter, with reference to FIG. 5, the relationship between a problem and commentary information is demonstrated in detail. In Fig. 5, the Q & A data 107 is shown as an example, and in particular, the case where the problem and the commentary information about it are matched in advance is shown as an example.

The Q & A data 107 includes a problem 10, a problem 11, and a problem 12, and includes commentary information 500, commentary information 501, and commentary information 502 matched with each other. Can be. As illustrated in FIGS. 3 and 4, the apparatus 100 for providing learning services may identify a received image as a search request problem as a result of OCR processing on the received image. In this case, in operation S36 of the learning service providing apparatus 100, a search target problem having a similarity or higher than a preset similarity criterion may be extracted from the Q & A data 107. For example, when the problem 10 of FIG. 5 is an extracted problem, the learning service providing apparatus 100 may extract commentary information 501 and transmit the commentary information 501 to the learner terminal 50.

For example, the learning service providing apparatus 100 may transmit, to the learner terminal 50, commentary information on a problem having the highest similarity when there are a plurality of search target problems having a similarity equal to or greater than a preset similarity criterion.

As another example, when the problem to be searched for having a degree of similarity equal to or greater than a preset similarity criterion is a problem 10 or a problem 11, the learning service providing apparatus 100 includes both the commentary information 501 and the commentary information 502. It may also transmit to the terminal 50.

In the above examples, the learning service providing apparatus 100 may transmit not only the commentary information but also the extracted problem to the learner terminal 50. This is because the search request problem and the extracted problem identified as a result of OCR processing of the received image may be similar but not identical. In this case, the learner terminal 50 may receive a problem similar to the problem on the image transmitted first, but a similar problem and explanation information thereof. Accordingly, the learner can indirectly learn the search target problem by learning the received problem and the commentary information.

Next, the problem search method of the learning service providing apparatus 100, which is referred to in the description of FIG. 4, will be described in more detail with reference to FIGS. 6 and 7.

6 and 7 are exemplary diagrams for explaining the components of the problem and the weights referred to in some embodiments of the present invention. In particular, in FIG. 6, it is assumed that the Q & A data 107 includes a problem 10, a problem 11, a problem 12, and the problem 10 is a search request problem.

Referring to FIG. 6, the learning service providing apparatus 100 may separately identify the problem 10 in component units. Accordingly, the learning service providing apparatus 100 may separately identify the equation 610 and the character 620 of the problem 10. 4 and 6, the learning service providing apparatus 100 filters the problem 11 and the problem 12 among the Q & A data 107 (S33), the problem 11, and the problem 12. ) May be divided into an expression 611 and a character 621, and an expression 612 and a character 622, respectively.

The learning service providing apparatus 100 may determine the similarity between the equation 610 of the search request problem 10 and the equation 611 and the equation 612 of the filtered problems 11 and 12. To this end, the learning service providing apparatus 100 may extract a keyword of the equation 610, and analyze the equation 611 and the equation 621 based on the keyword. As a result of the analysis, the learning service providing apparatus 100 may assign similarity weights to the equations 611 and 621. The weight given to the formula is hereinafter referred to as first weight.

The learning service providing apparatus 100 may determine the similarity between the text 620 of the search request problem 10 and the text 621 and the text 622 of the filtered problems 11 and 12. To this end, the learning service providing apparatus 100 separates the characters 620 into component units, and includes components of the separated characters, distances between components, words in which components are combined, arrangement order of components, and entire characters. Based on at least one of the lengths of the regions, the characters 621 and 622 may be analyzed. As a result of the analysis, the learning service providing apparatus 100 may assign the similarity weight to the character 621 and the character 622. The weight given to the character is hereinafter referred to as second weight.

The learning service providing apparatus 100 may determine the similarity of the problem 11 and the problem 12 based on the first weight and the second weight.

In FIG. 7, a problem 710 in which the graph 630 is further included in the problem 10 of FIG. 6 is illustrated as a search request problem. Referring to FIG. 7, the apparatus 100 for providing a learning service may separately identify a search request problem 710 by an equation 610, a character 620, and a graph 630. In particular, in FIG. 7, the case in which the problem 710 includes the graph 630 is illustrated, but embodiments of the present invention are not limited thereto, and the problem 710 may include a figure or an image. In addition, it is assumed that at least some of the equation 611, the character 720, and the graph 730 may be included in one filtered problem, and may be included in different problems.

4 and 7, the apparatus 100 for providing a learning service may be included in the Q & A data 107 based on the equation 610, the character 620, and the graph 630 of the problem 710, respectively. Filtered filtered problems can be analyzed.

For example, the learning service providing apparatus 100 may include a formula 610 and a formula 611 of the problem 710, a character 620 and a character 720 of the problem 710, and a graph 630 of the problem 710. ) And the graph 730 may be compared, and similarity weights may be assigned to the equation 611, the character 720, and the graph 730, respectively.

In this case, the learning service providing apparatus 100 may determine the problem based on the importance of the first weight, which is a weight for a formula, a second weight, which is a weight for a character, and a weight for a graph (hereinafter, referred to as a third weight). Similarity can be determined. For example, the importance of weights may be preset in the learning service providing apparatus 100 in the order of the first weight, the second weight, and the third weight. That is, the weight may be added to the first weight, the second weight, and the third weight again. To this end, a specific factor is multiplied by the first weight, the second weight, and the third weight, such that the superiority relationship between the first, second, and third weights may be predetermined.

According to another embodiment of the present invention, the learning service providing apparatus 100 separately identifies the equation 610, the character 620, and the graph 630 which are the components of the problem 710, and then each component is a problem. The area occupied at 710 may be analyzed and the importance between the first, second, and third weights determined. For example, when the graph 630 occupies the largest area, the learning service providing apparatus 100 may determine the third weight as the most important factor in determining the similarity with respect to the filtered problem. Alternatively, when the horizontal length of the character 620 is the longest, the learning service providing apparatus 100 may determine the second weight as the most important factor in determining the similarity with respect to the filtered problem. In other words, the learning service providing apparatus 100 may dynamically determine the importance of the weight of each component based on the OCR processing result for the problem 710.

According to another embodiment of the present invention, the learning service providing apparatus 100 may assign an additional weight to a value having the highest similarity weight. For example, equations 610 and 611 of the problem 710, letters 620 and 720 of the problem 710, graphs 630 and 730 of the problem 710, respectively. As a result of the comparison, the similarity between the equations 610 and 611 is the highest, the similarity between the graphs 630 and 730 is second highest, and the similarities between the letters 620 and 720 are the lowest. In this case, the magnitude of the similarity weight may be determined in the order of the first weight, the third weight, and the second weight. In this case, the learning service providing autonomous 100 may, for example, multiply only the first weight by one or more factors, so that the first weight is most reflected in the similarity determination.

So far, referring to FIGS. 3 to 7, based on the similarity, when a search target problem that is the same or similar to the search request problem is extracted, transmitting commentary information to the learner terminal 50 (S50) or the similarity degree. When the problem above the criterion is not extracted, the step S60 of storing the identified search request problem is mainly described.

Next, after step S60 of FIG. 3, when the learner terminal 50 does not acquire commentary information on a search request problem, an embodiment performed by the learning service providing apparatus 100 is illustrated in FIG. 8 and FIG. It demonstrates with reference to FIG.

However, in the embodiment described herein, the learning service after the step S50 of FIG. 3 as well as the case where the learner terminal 50 does not acquire commentary information on the search request problem after the step S60 of FIG. 3. It may be performed by the providing device 100. For example, even if the learner terminal 50 obtains commentary information on a search request problem, when the learner requests additional commentary, the present embodiment may be performed after step S50. Alternatively, even when the learner terminal 50 obtains commentary information on a search request problem and a similar problem, and the learner requests explanation of the search request problem.

Hereinafter, after step S60 of FIG. 3, a case in which the learner terminal 50 fails to obtain commentary information on a search request problem will be described as an example.

8 is a flowchart illustrating a method of providing commentary information to the learner terminal 50 according to another embodiment of the present invention. Referring to FIG. 8, the learning service providing apparatus 100 may receive a comment request message about a problem from a learner terminal (S61). The learner may operate the learner terminal 50 and transmit a comment request message about the problem to the learning service providing apparatus 100. To this end, the learning service providing apparatus 100 may provide a learner terminal 50 with a user interface for generating and transmitting the commentary request message.

The learning service providing apparatus 100 may transmit a commentary request message to at least one of the plurality of answerer terminals in response to the commentary request message received from the learner terminal 50 (S63). The narration request message received from the learner terminal 50 and the narration request message transmitted to the at least one terminal may be the same message, but embodiments of the present invention are not limited thereto. For example, the learning service providing apparatus 100 may generate a comment request message for transmitting to at least one terminal based on the comment request message received from the learner terminal 50. Alternatively, if the problem identified in step S60 is stored as a new problem, the learning service providing apparatus 100 automatically generates a comment request message for the new problem even if the comment request is not received from the learner terminal 50. In addition, the commentary request message which is automatically generated may be transmitted to at least one terminal among the plurality of answering terminal.

The answerer terminal 200 illustrated in FIG. 1 may be provided in plural, and the plurality of answerer terminals may be terminal devices registered in advance in the learning service providing apparatus 100. The learning service providing apparatus 100 may store contact information of a plurality of answerer terminals in advance.

The learning service providing apparatus 100 may receive commentary information on a problem from at least one answerer terminal (S65). Assume that at least one answerer terminal is the answerer terminal 200 of FIG. 1. The answerer terminal 200 may receive a commentary request message from the learning service providing apparatus 100, and may receive at least one of an image of a problem and a search request problem.

The answerer may input commentary information on at least one of the image and the search request problem for the received problem to the answerer terminal 200. In this case, the commentary information may be an image file format. Alternatively, the answerer terminal 200 may generate commentary information by receiving an input from the answerer through the commentary information generation interface provided through the answerer terminal 200.

When the commentary information is received from the answerer terminal 200, the learning service providing apparatus 100 may transmit the received commentary information to the learner terminal 50 (S67). In addition, the learning service providing apparatus 100 may match and store the received commentary information and a search request problem. In this case, the learning service providing apparatus 100 may determine whether to store the commentary information received according to the quality criteria described above in the description of step S60 of FIG. 3. The learning service providing apparatus 100 may match and store the received commentary information and the answerer information of the answerer terminal 200.

Next, the learning service providing apparatus 100 may determine whether a messaging interface generation request is received from the learner terminal 50 (S68). For example, when the learner has received commentary information through the learner terminal 50 in step S67, but there is an additional question or an inquiry about a related problem, the learner provides the learning service through the learner terminal 50. A request to create a messaging interface can be sent to 100.

In operation S68, as a result of the determination, the learning service providing apparatus 100 may generate a messaging interface between the learner terminal 50 and the answerer terminal 200 when a messaging interface generation request is received (S69). In this case, prior to generation of the messaging interface, the learning service providing apparatus 100 may transmit an invitation message for the messaging interface to the answerer terminal 200. The learning service providing apparatus 100 may generate a messaging interface between the learner terminal 50 and the answerer terminal 200 when a message corresponding to the invitation message is received from the answerer terminal 200. The apparatus 100 for providing a learning service may control a session between the learner terminal 50 and the answerer terminal 200 to be formed, and provide an instant messaging interface having the learner and the answerer as a participant.

9 is an illustration of a messaging interface between a learner terminal and an answerer terminal, referred to in some embodiments of the present invention. In FIG. 9, as an example of a messaging interface, a chat window 900 is shown with respondents and learners as participants.

Referring to FIG. 9, the learning service providing apparatus 100 may support a message exchange 910 for a problem and commentary information between a learner and an answerer through the chat window 900. To this end, message characters, symbols, and formulas can be used. In addition, the learning service providing apparatus 100 may search for and extract an image of a problem received from the learner terminal 50 or an identified search request problem and a problem having a preset similarity criterion. Problems above the preset similarity criterion may include the same problem as the search request problem and the similar problem.

The learning service providing apparatus 100 may analyze metadata of a problem identical to or similar to a search request problem. In addition, the learning service providing apparatus 100 may search for and extract a related problem for the extracted same problem or similar problem based on the analyzed metadata.

For example, the similar problem may be a problem in which some characters are different from the search request problem or a different number. In other words, when a mathematical problem is described as an example, the principle or formula required to solve the problem is the same, but the given constant value is different or the expression of the variable is different.

In the case of a similar problem, the similarity is higher than the preset similarity criterion, but may not be classified into a search request problem and the sameness area. For example, when the similarity set in advance in the learning service providing apparatus 100 is 90% or more, it is determined that the problem is the same as or similar to the search request problem. Assume In this case, the similar problem may be a problem in which equality of 90% or more and less than 95% is recognized.

For example, the related problem may be a problem in which a category such as a search target problem and a unit of mathematics is the same or a problem solving method is related. That is, when the search request problem is a problem for a set unit in a mathematical unit, the related problem is a case where an equation is calculated using a number obtained by obtaining an intersection of two sets.

In the related problem, although the similarity between the search request problem and the preset similarity criterion is low, the information on the metadata may be related. As illustrated above, it is assumed that the similarity preset in the learning service providing apparatus 100 requires 90% or more of identity. In this case, when the learning service providing apparatus 100 determines that a problem of 80% identity is found as a result of determining the similarity with the search request problem, the learning service providing apparatus 100 may determine that the searched problem is not the same as or similar to the search request problem.

In one embodiment, the learning service providing apparatus 100 may analyze metadata about the found problem. For example, when the problem is a math problem, the metadata may include unit information of the problem, keyword information for describing and classifying the problem, and the like. The learning service providing apparatus 100 may analyze metadata to determine a correlation between the searched problem and the search request problem. That is, the learning service providing apparatus 100 determines not only the case where the searched problem is the same or similar problem as the search request problem but also the relation with the search request problem through the metadata analysis of the searched problem. can do.

To this end, the learning service providing apparatus 100 may receive a preset relevance criterion in addition to the similarity criterion.

Here, the relevance criterion is the similarity in the metadata of the search request problem and the searched problem, not the criterion for determining the similarity between letters, numbers, and formulas constituting the search request problem and the searched problem when the search request problem is a mathematical problem. It may be a criterion for determining. In other words, the relevance criterion is a criterion for determining a relationship between a problem and a problem, and may be determined in a policy manner.

For example, a search request problem may have a quadratic equation as a unit category, a root formula as a solution method, and metadata at level 3 as a difficulty information, and the problem found may be a quadratic equation as a unit category, a root equation as a solution method, and Assume that you have level 3 metadata for factoring and difficulty information. In addition, it is assumed that the unit category, the information of the solving method, and the difficulty information are information having a digitized value as a parameter of each relevance criterion. In this case, the learning service providing apparatus 100 may determine a relevance by comparing the parameter value of the search request problem and the searched problem. The learning service providing apparatus 100 may determine that the searched problem is related to the search request problem when the difference between the parameter value of the search request problem and the searched problem is equal to or less than a preset value. In this case, the learning service providing apparatus 100 may compare a plurality of parameters to determine a relation between the searched problem and the search request problem.

The learning service providing apparatus 100 may determine the searched problem as a related problem when the relatedness equal to or greater than a preset relevance criterion is recognized.

The learning service providing apparatus 100 may search for and extract a related problem in step S30 of searching for a problem to be searched based on the identified problem of FIG. 3 and extracting a problem of similarity criteria. . The learning service providing apparatus 100 may extract a problem that is the same as or similar to a search request problem and then search and extract a related problem for the extracted problem, but embodiments of the present invention are not limited thereto. That is, the learning service providing apparatus 100 may search for and extract a related problem in a process separate from a search and extraction operation of a problem that is the same as or similar to a search request problem.

The learning service providing apparatus 100 may provide a related problem to at least one of the learner terminal 50 and the answerer terminal 200. For example, when a related problem is provided to the answerer terminal 200, the answerer may recommend the related problem 920 to the learner through the chat window 900. To this end, the learning service providing apparatus 100 may provide the answerer terminal 200 with an interface for selecting the provided related problem and providing the learner terminal 50 through the chat window 900. In addition, the learning service providing apparatus 100 may provide an interface for searching for a related problem to the answerer's terminal 200.

Meanwhile, according to another embodiment of the present invention, the search request is automatically performed by the learning service providing apparatus 100 by a request of the learner terminal 50 or by an operation on the answerer terminal 200 of the answerer. Although not the same as the problem, similar or related problems may be recommended. As such, as a problem that is the same as or similar to the search request problem or a related problem, a problem provided to the learner terminal 50 may be referred to as a recommendation problem, which is different from the problem requested by the learner terminal 50.

10 is an exemplary diagram for explaining a recommendation problem referred to in some embodiments of the present invention.

Referring to FIG. 10, the learning service providing apparatus 100 may classify a problem belonging to the Q & A data 107 of FIG. 2 according to at least one of a similarity criterion and a relevance criterion. Problem group 0, problem group 1, problem group 2, problem group 3, and problem group 10 in FIG. 10 are groups of problems classified according to relevance criteria, and problems belonging to each problem group are similar problems grouped according to similarity criteria. to be.

Problems A1 and B1 are the same problems as the search request problem or the search request problem. In FIG. 10, problem group 0 is a problem group corresponding to difficulty 1, problem group 1, problem group 2, and problem group 3 are groups of problems corresponding to difficulty 2, and problem group 10 is a problem corresponding to difficulty 3. The case of a group is illustrated. Difficulty 1, Difficulty 2, and Difficulty 3 can be further classified into detailed difficulty levels. That is, the problem belonging to the same difficulty level 2 may also have differential difficulty in the horizontal direction. For example, problem A1 and problem A2 may correspond to difficulty 2, but problem A2 may have a higher level of difficulty than problem A1.

Also, the problem group 1, the problem group 2, and the problem group 3 of FIG. 10 may be groups of problems corresponding to difficulty 2 having the same difficulty level, but may be a group having different unit categories on the metadata. That is, the vertical height of the problem group of FIG. 10 may represent a difference between unit categories.

In one embodiment, the learning service providing apparatus 100 may identify problem group 1 as a group of similar problems as a result of similarity determination when question A1 is requested to be searched, that is, when the learner asks question A1. The learning service providing apparatus 100 may extract a problem similar to the question asked by the learner from the identified problem group 1 and provide it to the learner terminal 50 as a recommendation problem. The problem belonging to the same problem group may be a similar problem having only a difference between a search request problem and a constant, and the learning service providing apparatus 100 may, for example, recommend problem A2 of problem group 1 as a recommendation problem.

In another embodiment, when the question A2 is requested to be searched, the learning service providing apparatus 100 identifies the problem group 2 as a group of similar problems as a result of the similarity determination, extracts a similar problem from the problem group 2, and learner terminal 50. Can serve as a recommendation problem. In addition, the learning service providing apparatus 100 may extract a problem equal to or greater than a predetermined relevance criterion based on the problem A2. In this case, the learning service providing apparatus 100 may first identify a problem group having adjacent difficulty based on the difficulty information on the metadata, and then extract and recommend a problem belonging to the identified problem group. In FIG. 10, problem group 0 and problem group 10 are illustrated as the identified problem group. Problem group 0 is a group in which problem B1 and a unit category are the same, but the difficulty is low, and a problem group for basic learning is included. Problem group 10 is a group in which problem B1 and unit category are the same but the difficulty level is high. have.

The learning service providing apparatus 100 may extract, for example, a problem a2 belonging to the problem group 0 and / or a problem D1 belonging to the problem group 10 as a related problem and provide the learner terminal 50 as a recommended problem.

In another embodiment, the learning service providing apparatus 100 may store a learner's question problem history 1000. In FIG. 10, the problem A1 and the problem B1 are stored in the question problem history is illustrated. The learning service providing apparatus 100 may identify a problem group having a same question problem and difficulty but different unit categories for the learner based on the question problem history 1000. That is, the apparatus 100 for providing a learning service may identify a problem group 3 and, for example, may provide a problem C1 belonging to the problem group 3 to the learner terminal 100 as a recommended problem.

In addition, the learning service providing apparatus 100 may identify a problem group having a different difficulty level from the question problem based on the question problem history 1000. For example, the learning service providing apparatus 100 may identify the problem group 0 and / or the problem group 10, and may recommend a problem belonging to the identified problem group to the learner.

11 is an illustration of learner question based learning content, referred to in some embodiments of the invention.

Referring to FIG. 11, the learning service providing apparatus 100 may generate learner-specific learning content 1100 based on the question problem history 1000 of FIG. 10. The learning service providing apparatus 100 may determine a learner's learning level based on the question problem history 1000. In addition, the learning service providing apparatus 100 may determine a learner's learning achievement for a predetermined period of time based on problems added to the question problem history 1000 for a preset period.

The learning service providing apparatus 100 may extract a search request problem, the same problem, a similar problem, and a related problem as a recommendation problem based on the question problem history 1000, and the learning content 1100 configured as the extracted problem. Can be generated. In FIG. 11, an example in which the learning content 1100 includes a problem 1110 determined to be the same problem as a search request problem or a search request problem in a first area, and a recommendation problem 1120 in a second area is illustrated. It became. The search request problem 1110 and the recommendation problem 1120 may be sequentially arranged in the learning content 1100 based on at least one parameter of metadata, but embodiments of the present invention are not limited thereto. The search request problem or the problem 1110 and the recommendation problem 1120 determined as the same problem may be arranged in the learning content 1100 in any order.

The learning service providing apparatus 100 may generate individual learning contents based on parameters on metadata such as a unit category, a difficulty level, a solving method, and the like. For example, the learning service providing apparatus 100 may generate learning content composed only of the same unit, learning content composed only of a problem of deep difficulty, and learning content composed only of problems to which a similar solving method is repeatedly applied.

The learning service providing apparatus 100 may transmit the generated learning content 1100 to the learner terminal 50.

12 is a block diagram of a system for providing a learning service based on learner ratings according to another embodiment of the present invention. In particular, as the example of the learner grade-based learning service providing system of FIG. 12, a learner authentication device 1200 is added to the learning service providing system of FIG. 1.

Referring to FIG. 12, the learner authentication device 1200 may detect that the learner terminal 50 is located in the preset area 1210. When the learner terminal 50 is connected to the learning service provided by the learning service providing apparatus 100, the learner authentication device 1200 may identify the unique information of the learner terminal 50 accessing the learning service in the area 1210. Can be identified. When the identified unique information is transmitted to the learning service providing apparatus 100, the learning service providing apparatus 100 may determine a learner grade of a learner who is a user of the learner terminal 50. The learning service providing apparatus 100 may provide the learner terminal 50 with a learning service corresponding to the determined learner grade.

For example, the unique information of the learner terminal 50 may be Mac address information, and the learner authentication device 1200 may provide an access point (AP) for providing Wi-Fi in a preset area 1210. Can be. The learning service providing apparatus 100 starts accessing the learning service in the area 1210 preset in the learner terminal 50 based on the ip information assigned to the AP and / or the MAC address information of the learner terminal 50. Can be determined.

The learning service providing apparatus 100 may provide different learning services for each learner grade. The learning service providing apparatus 100 may determine a learner grade based on at least one of a learning service type, a learning service billing amount, a service access location, and a learning service participation degree applied by the learner.

For example, the learning service providing apparatus 100 may provide a basic learning service when a learner having a basic level is connected to the learning service. When the basic learning service receives an image including a problem from the learner terminal 50, the basic learning service may be a service that provides description information on the identified search request problem by analyzing the OCR. In this case, the learner, which is the basic level, may be charged for the service whenever the commentary information is transmitted to the learner terminal 50 about the search request problem.

As another example, when the learner having a higher level than the basic level is connected to the learning service, the learning service providing apparatus 100 may provide a learning service for each level according to the grade of each learner. The graded learning service may be a service including the basic learning service described above and additionally providing a recommendation problem to the learner terminal 50. In addition, the learning service for each grade may include a service for periodically or irregularly providing the learning content 1100 illustrated in FIG. 11 to the learner terminal 50 according to the learner's grade. In this case, the learner higher level learner, the commentary information on the search request problem can be provided free of charge, the learning content 1100 may be provided for a fee for each content.

As another example, when the learner of the basic level or the learner whose learner grade is not determined is connected to the learning service, the learning service providing apparatus 100 may determine whether the access position of the learner terminal 50 is within the preset area 1210. Can be. When the access location of the learner terminal 50 is in the area 1210, the learning service providing apparatus 100 may determine a learner's grade as a higher grade learner. The learning service providing apparatus 100 may provide a learning service for each grade to the learner terminal 50 of the learner determined as the higher grade learner.

The contents and the charging policy of the basic learning service and the learning service for each grade are merely examples, and embodiments of the present invention are not limited thereto.

13 is a flowchart illustrating a method for providing a learner grade based learning service according to another embodiment of the present invention. Hereinafter, referring to FIG. 13, a method of providing a learner grade based learning service will be described once again. Each step is performed by the learning service providing apparatus 100.

Referring to FIG. 13, the learning service providing apparatus 100 may identify a learning service access position of the learner terminal 50 (S1301). To this end, the learning service providing system may include a learner authentication device 1200 as illustrated in FIG. 12. The learning service providing apparatus 100 may identify the connection location of the learner terminal 50 by receiving the unique information of the learner terminal 50 from the learner authentication device 1200.

The learning service providing apparatus 100 may determine whether the connection location of the learner terminal 50 is a preset area (S1303). For example, the preset area may be a place where learning services are provided and may be an educational facility such as a reading room, a school, or a school. The place information of the educational facility may be preset in the learning service providing apparatus 100.

As a result of the determination, when the access position of the learner terminal 50 is not a preset area, the learning service providing apparatus 100 determines whether the learner's grade of the learner terminal 50 connected to the learning service is a basic grade or a higher grade. can do. That is, the learning service providing apparatus 100 may determine whether the learner is a learning service target for each grade (S1305).

When the learner determines that the learner is a learner of the basic level, the learning service providing apparatus 100 may provide a basic learn service to the learner terminal 50 (S1307).

On the other hand, when it is determined in step S1303 that the access position of the learner terminal 50 is within a preset area, or when the learner's grade is determined to be a higher grade in step S1305, the learning service providing apparatus 100 may learner. According to the grade of the learner, the terminal 50 may provide a learning service for each grade (S1309).

The methods according to the embodiments of the present invention described above with reference to the accompanying drawings may be performed by execution of a computer program implemented in computer readable code. The computer program may be transmitted to and installed on the second computing device from the first computing device via a network such as the Internet, and thus may be used in the second computing device. The first computing device and the second computing device include both server devices, fixed computing devices such as desktop PCs, and mobile computing devices such as laptops, smartphones, and tablet PCs.

Although embodiments of the present invention have been described above with reference to the accompanying drawings, those skilled in the art to which the present invention pertains may implement the present invention in other specific forms without changing the technical spirit or essential features thereof. I can understand that. Therefore, it is to be understood that the embodiments described above are exemplary in all respects and not restrictive.

Claims (3)

The method performed by the metadata-based related problem recommendation device,
Receiving an image from a learner terminal;
Performing OCR processing on the received image;
As the OCR process is performed, identifying a search request problem from the image;
Separating the characters included in the identified search request problem into component units;
Extracting a keyword in a formula included in the identified search request problem;
Filtering a previously stored search target problem based on the characters separated by the component units and the extracted keywords;
Determining a similarity degree to the filtered search target problem;
Extracting a problem having a similarity based on a predetermined similarity criterion based on the determined similarity;
The extracted problem and the search target problem each have metadata including unit category information of the problem, difficulty information of the problem, and information on how to solve the problem, which are parameter information for relevance measurement. Comparing at least one parameter information on the first metadata of the extracted problem and at least one parameter information on the second metadata of the search subject problem to determine a relation between the extracted problem and the search subject problem;
Extracting a related problem having a relevance greater than or equal to a preset relevance criterion among the search request problems based on the determination result of the relevance; And
Transmitting the extracted related problem to the learner's terminal;
How to recommend related issues based on metadata. The method of claim 1,
The step of transmitting the extracted related problem to the learner's terminal,
Providing a problem having similarity above the preset similarity criterion and the related problem as a recommendation problem to the user terminal,
How to recommend related issues based on metadata. The method of claim 2,
Providing to the user terminal as a recommendation problem,
Classifying a problem group based on at least one of unit category information of the problem, difficulty information of the problem, and information on a method of solving the problem; And
Providing the classified problem group as a recommended problem to the user terminal;
How to recommend related issues based on metadata.

Images