WO2024029708A1

WO2024029708A1 - Method of generating math problem code provision model based on artificial intelligence, and method and device for generating math problem by using same model

Info

Publication number: WO2024029708A1
Application number: PCT/KR2023/007317
Authority: WO
Inventors: 이재윤; 유병용; 이용현
Original assignee: 주식회사 프로키언
Priority date: 2022-08-03
Filing date: 2023-05-26
Publication date: 2024-02-08

Abstract

An embodiment of the present invention provides a method of generating a math problem code provision model, the method comprising the steps of: generating a training data set through a preprocessing process including an operation of converting, into an embedding vector, math problem data to be learned, a code block required for generating the math problem data to be learned, and annotation data for the code block; and generating an artificial intelligence model that is trained with the training data set to receive the text of a particular math problem and output at least one of a problem code block for the math problem and a main code block group including multiple problem code blocks for the math problem.

Description

Artificial intelligence-based mathematical problem code provision model generation method and mathematical problem generation method and device using the model

The present invention provides a method for generating an artificial intelligence model trained to learn mathematical problems and output problem code blocks to automatically generate various mathematical problems that can be derived or derived from the fingerprint of a specific mathematical problem, and the artificial intelligence model This is a method and device for generating mathematical problems using.

Recently, as the computing performance of personal portable terminals has improved, various types of online services that easily recognize various mathematical problems by taking pictures and provide answers in real time are being commercialized. However, this not only infringes on the efforts of the author who creates the problem, but also has the potential to degenerate into a form of students learning mathematics only copying the answer without directly solving the problem.

In order to prevent this, technology is needed that can generate various math problems in real time, solve this abusing problem, and provide customized problems to learners in real time.

The present invention proposes a method for generating various mathematical problems using computer program codes. In other words, by setting the program code for creating each math problem, you can easily create a specific math problem through that code, and by changing the settings of a few variables, you can quickly create other problems of the same type as the math problem. can do. However, the method of creating math problems with program codes requires not only mathematical knowledge but also an understanding of programming languages, and because mathematical concepts are expressed in codes, detailed settings are required, so a method to improve productivity is needed.

The present invention is intended to solve the above-mentioned problems, and uses an artificial intelligence model trained to learn mathematical problems and output problem code blocks to automatically generate various mathematical problems that can be derived or derived from the fingerprint of a specific mathematical problem. One technical task is to provide a method and device for generating mathematical problems using the corresponding artificial intelligence model.

The technical problems achieved by the present invention are not limited to the above-described technical problems, and other technical problems of the present invention can be derived from the following description.

As a technical means for solving the above-described technical problem, the embodiment according to the first aspect of the present invention includes learning target math problem data, code blocks required to generate the learning target math problem data, and annotation data for the code blocks. Generating a training data set by performing a preprocessing process including converting to an embedding vector; and, through learning of the training data set, receiving a fingerprint of a specific mathematical problem as an input, a problem code block for the mathematical problem, and Disclosed is a method for generating a model for providing a mathematical problem code, including generating an artificial intelligence model trained to output at least one of the main code block clusters including a plurality of problem code blocks for the mathematical problem.

In addition, as a technical means for solving the above-described technical problem, an embodiment according to the second aspect of the present invention includes at least one processor, a model generating program that provides a mathematical problem code that is electrically connected to the processor, and is performed in the processor. It provides a model generation device that provides a mathematical problem code containing memory to be stored. The memory is trained by performing a preprocessing process in which the processor converts learning target math problem data, code blocks required to generate the learning target math problem data, and annotation data for the code blocks into an embedding vector. Generate a data set, and through learning of the training data set, receive a fingerprint of a specific mathematical problem and select a problem code block for the mathematical problem and a cluster of main code blocks including a plurality of problem code blocks for the mathematical problem. Discloses code that causes an artificial intelligence model to be trained to output at least one.

In addition, as a technical means for solving the above-described technical problem, an embodiment according to the third aspect of the present invention includes the steps of obtaining a fingerprint of a math problem and converting the fingerprint into an embedding vector, and preprocessing the math problem data to be studied. , through learning of a training data set containing code blocks necessary for generating the learning target math problem data and annotation data for the code blocks, a fingerprint of a specific math problem is input, and a problem code block for the math problem and the above A problem code block for the math problem based on the fingerprint converted to an embedding vector using an artificial intelligence model trained to output at least one of the main code block clusters including a plurality of problem code blocks for the math problem, and Disclosing a method for generating a mathematical problem comprising the steps of obtaining at least one of the main code block clusters including a plurality of problem code blocks for the mathematical problem, and generating a new mathematical problem using the obtained code block. do.

In addition, as a technical means for solving the above-described technical problem, an embodiment according to the fourth aspect of the present invention includes at least one processor, electrically connected to the processor, and storing a mathematical problem generation program executed by the processor. Disclosed is a mathematical problem providing device including a memory. The memory includes a code block necessary for the processor to obtain a fingerprint for a math problem, convert the fingerprint into an embedding vector, preprocessed math problem data to be learned, and generate the math problem data to be learned, and the code block. Through learning of a training data set containing annotation data, a fingerprint of a specific mathematical problem is input and a problem code block for the mathematical problem and a cluster of major code blocks including a plurality of problem code blocks for the mathematical problem are selected. A main code block cluster including a problem code block for the math problem and a plurality of problem code blocks for the math problem based on the fingerprint converted to an embedding vector using an artificial intelligence model trained to output at least one Obtain at least one of the following, and store a code that causes a new mathematical problem to be created using the obtained code block.

According to the problem-solving means of the present invention described above, a main code block cluster including a problem code block for the mathematical problem and a plurality of problem code blocks for the mathematical problem is generated from the fingerprint of a specific mathematical problem through an artificial intelligence model. It can be easily derived.

In addition, according to the present invention, understanding of programming language is achieved by receiving a math problem fingerprint from a user who wants to create a math problem, outputting a problem code block for the fingerprint, and creating a math problem based on the problem code block. Even without , users can easily create math problems through mathematical knowledge.

Figure 1 is a block diagram showing the configuration of a mathematical problem code providing model generating device according to an embodiment of the present invention.

FIG. 2 is a diagram illustrating an example of training an artificial intelligence model using the model generating device for providing mathematical problem codes shown in FIG. 1.

Figures 3 and 4 are diagrams showing examples of problem code blocks and comments.

Figures 5 to 7 are diagrams showing examples of training data sets.

Figure 8 is an operation flowchart explaining a method for generating a model for providing mathematical problem codes according to another embodiment of the present invention.

FIG. 9 is a flowchart showing detailed steps included in the steps of the method for generating a mathematical problem code providing model shown in FIG. 8.

Figure 10 is a diagram illustrating a mathematical problem generating device connected to communication with a user terminal according to another embodiment of the present invention.

FIG. 11 is a block diagram showing the configuration of the mathematical problem generating device shown in FIG. 10.

FIG. 12 is a diagram illustrating an example of generating a math problem using the math problem generating device shown in FIG. 11.

Figure 13 is an operation flowchart explaining a method for generating a math problem according to another embodiment of the present invention.

Hereinafter, the present invention will be described in detail with reference to the attached drawings. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In addition, the attached drawings are only intended to facilitate understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the attached drawings. All terms, including technical and scientific terms, used herein should be interpreted as meanings commonly understood by those skilled in the art in the technical field to which the present invention pertains. Terms defined in the dictionary should be interpreted as having additional meanings consistent with the related technical literature and currently disclosed content, and should not be interpreted in a very ideal or limited sense unless otherwise defined.

In order to clearly explain the present invention in the drawings, parts not related to the description are omitted, and the size, shape, and shape of each component shown in the drawings may be modified in various ways. Throughout the specification, identical/similar parts are given identical/similar reference numerals.

The suffixes “module” and “part” for components used in the following description are given or used interchangeably only for the ease of preparing the specification, and do not have distinct meanings or roles in themselves. Additionally, in describing the embodiments disclosed in this specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed descriptions are omitted.

Throughout the specification, when a part is said to be “connected (connected, contacted, or combined)” with another part, this means not only when it is “directly connected (connected, contacted, or combined),” but also when it is “connected (connected, contacted, or combined)” with another member in between. It also includes cases where they are “indirectly connected (connected, contacted, or combined).” Additionally, when a part is said to "include (equip or provide)" a certain component, this does not exclude other components, unless specifically stated to the contrary, but rather "includes (provides or provides)" other components. It means that you can.

Terms representing ordinal numbers, such as first, second, etc., used in this specification are used only for the purpose of distinguishing one component from another component and do not limit the order or relationship of the components. For example, the first component of the present invention may be named a second component, and similarly, the second component may also be named a first component. As used herein, singular forms of expression should be construed to also include plural forms of expression, unless the contrary is clearly indicated.

The communication module described below may include a device including hardware and software necessary to transmit and receive signals such as control signals or data signals through wired or wireless connections with other network devices. Additionally, the memory may store at least one of information and data input to the communication module, information and data required for functions performed by the processor, and data generated according to execution of the processor. Memory should be interpreted as a general term for non-volatile storage devices that retain stored information even when power is not supplied, and volatile storage devices that require power to maintain stored information. Memory may include magnetic storage media or flash storage media in addition to volatile storage devices that require power to maintain stored information, but the scope of the present invention is not limited thereto. A processor may include various types of devices that control and process data. A processor may refer to a data processing device built into hardware that has physically structured circuitry to perform functions expressed by codes or instructions included in a program. In one example, the processor may include a microprocessor, central processing unit (CPU), processor core, multiprocessor, application-specific integrated circuit (ASIC), or field programmable gate (FPGA). array), etc., but the scope of the present invention is not limited thereto.

Figure 1 is a block diagram showing the configuration of a mathematical problem code providing model generating device 100 according to an embodiment of the present invention, and Figure 2 shows an artificial intelligence model trained by the mathematical problem code providing model generating device 100. This is a drawing showing an example of what to do.

Referring to FIGS. 1 and 2 together, the mathematical problem code providing model generating device 100 includes at least one processor 130 and a memory 140, and further includes a communication module 110 and a database 120. can do.

The communication module 110 may transmit and receive information with an external device or server to transmit and receive data necessary for generating a model for providing a mathematical problem code.

The database 120 may be a place where data necessary for creating a model for providing mathematical problem codes is stored. The database 120 may be built in a portion of the memory 140 or may be implemented as separate hardware.

The processor 130 performs operations according to the code stored in the memory 140.

The memory 140 is electrically connected to the processor 130 and stores a model generation program that provides mathematical problem codes performed by the processor 130. The memory 140 stores code that, when executed through the processor 130, causes the processor 130 to perform the following functions and procedures.

The memory 140 contains the learning target math problem data 210, the code block 220 required to generate the learning target math problem data 210, and the annotation data 230 for the code block. An operation of converting data into an embedding vector. A training data set 300 is generated by performing a preprocessing process including.

The code block 220 includes at least one of a variable setting code block, a variable condition code block, a result condition code block, and a result generation code block. The variable setting code block is a code block that sets the variables of the math problem data to be studied. A variable condition code block is a code block that sets the conditions of a variable. The result condition code block is a code block that sets conditions for the correct and incorrect answers of the math problem data to be studied. The result generation code block is a code block that generates one correct answer that satisfies the conditions of the correct answer and one or more incorrect answers that satisfy the conditions of the incorrect answer, based on the variables created through the variable setting code block and the variable condition setting code block. The annotation 230 includes a description of at least one block among a variable setting code block, a variable condition code block, a result condition code block, and a result generation code block.

The memory 140 stores a code that causes code similarity to be calculated by comparing a specific code block included in the training data set 300 with a different code block. The memory 140 stores a code that causes a code block cluster to be created by clustering a specific code block and a code block different from the specific code block based on code similarity. If the code similarity is greater than a preset value, the memory 140 stores a code that causes a code block cluster to be generated by clustering a specific code block and a code block different from the specific code block.

Code similarity includes at least one of variable setting code similarity, variable condition code similarity, result condition code similarity, and result generation code similarity. Variable setting code similarity is a similarity derived by comparing a variable setting code block included in a specific code block with a variable setting code block included in a code block different from the specific code block. Variable condition code similarity is a similarity derived by comparing a variable condition code block included in a specific code block with a variable condition code block included in a code block different from the specific code block. Resulting condition code similarity is the similarity derived by comparing a result condition code block included in a specific code block with a result condition code block included in a code block different from the specific code block. Result generation code similarity is a similarity derived by comparing a result generation code block included in a specific code block with a result generation code block included in a code block different from the specific code block.

The memory 140 stores a code that causes the code block cluster to match the math problem data to be learned included in the training data set 300 corresponding to the code block cluster and the annotations included in the training data set 300. The training data set 300 may be a data table formed by matching math problem data to be learned converted into an embedding vector, code blocks, annotations, and code block clusters.

The code block cluster includes at least one of a variable setting code cluster, a variable condition code cluster, a result condition code cluster, and a result generation code cluster. Variable setting code clustering clusters variable setting code blocks included in a specific code block included in the training data set 300 and variable setting code blocks included in a code block different from the specific code block, based on variable setting code similarity. It is a cluster of codes. The variable condition code cluster is a code cluster that clusters variable condition code blocks included in a specific code block and variable condition code blocks included in a code block different from the specific code block, based on variable condition code similarity. The result condition code cluster is a code cluster that clusters result condition code blocks included in a specific code block and result condition code blocks included in a code block different from the specific code block, based on result condition code similarity. The result generation code cluster is a code cluster that clusters result generation code blocks included in a specific code block and result generation code blocks included in a code block different from the specific code block, based on result generation code similarity.

The memory 140 receives the fingerprint of a specific math problem through learning of the training data set 300, and contains a main code block cluster containing a problem code block for a specific math problem and a plurality of problem code blocks for a specific math problem. A code that causes the artificial intelligence model 400 to be trained to output at least one of the following is stored.

The problem code block includes the variable setting code block that sets the variables of the math problem, the variable condition code block that sets the conditions for the variables, the result condition code block that sets the conditions for the correct and incorrect answers in the math problem, and the variable setting code block and variable conditions. Based on the variables created through the code block, it includes at least one of a result generation code block that generates one correct answer that satisfies the conditions of the correct answer and one or more incorrect answers that satisfy the conditions of the incorrect answer.

The artificial intelligence model 400 learns the code block position relationship between the variable setting code block, variable condition code block, result condition code block, and result generation code block included in the code block of the training data set. The artificial intelligence model 400 calculates fingerprint similarity by comparing the fingerprint of a specific math problem with the learning target math problem data included in the training data set 300 and at least one of the annotations included in the training data set 300. .

The artificial intelligence model 400 selects a code block cluster corresponding to the learning target math problem data included in the training data set 300 or the annotation included in the training data set 300, based on fingerprint similarity. If the fingerprint similarity is more than a preset value, the artificial intelligence model 400 selects a code block cluster corresponding to the learning target math problem data included in the training data set 300 or the annotation included in the training data set 300. . The artificial intelligence model 400 is an artificial intelligence trained to output code blocks that satisfy preset criteria among code blocks included in the code block cluster as problem code blocks for mathematical problems, or to output the code block cluster as the main code block cluster. It is an intelligence model. The artificial intelligence model may be a supervised learning model. More specifically, the artificial intelligence model may be an artificial intelligence model based on the Omikuji algorithm.

3 and 4 are diagrams illustrating an example of a code block 220 and annotation 230.

Referring to FIGS. 2 to 4 together, the code block 220 includes at least one of a variable setting code block 221, a variable condition code block 222, a result generation code block 223, and a result condition code block 224. Includes more. The annotation 230 includes the variable setting annotation 231 for the variable setting code block 221, the variable condition annotation 232 for the variable condition code block 222, and the result generation annotation for the answer generation code block 223 ( (not shown) and a correct answer condition annotation (not shown) for the result condition code block 224.

The problem code block 220 is a code block that generates a math problem. More specifically, the problem code block 220 is a code block that generates various math problems in which variables, incorrect answers, and correct answers are changed in the same math problem type as a specific math problem. For example, in a math problem type such as 'If a=x and a+b=y, find the value of b', the variable setting code block 221 is It may be a code block in which the overall variables, x-value range, and y-value range are set. More specifically, the variable setting code block 221 may be a code block that is set so that the x value range is between 2 and 10, and the y value range is between 1 and 7. The variable condition code block 222 may be a code block in which variable conditions are set, such as the condition that the x value and the y value must be prime or that they must not be 0. The resulting condition code block 224 may be a code block in which conditions such as a condition that the value of b must not be 0, a condition that it must be a positive number, etc. are set. The result generation code block 223 is one or more that satisfies the correct answer and incorrect answer conditions set by the correct answer generation code block 223 based on the variables created through the variable setting code block 221 and the variable condition code block 222. This is a code block set up to generate incorrect and correct answers.

Figures 5 to 7 are diagrams showing an example of the training data set 300. Specifically, Figure 5 is a diagram showing a training data set in the form of a data table in which mathematical problems, annotations, and code blocks are matched to each other, and Figure 6 is a diagram showing another type of training data set in which mathematical problems and annotations are matched. FIG. 7 is a diagram showing an example of converting the training data set shown in FIG. 6 into an embedding vector. The training data set shown in FIGS. 5 and 6 is a diagram showing a training data set that has not been converted to an embedding vector.

Referring to FIG. 5, the training data set 300 includes learning target math problem data 510, a variable setting code block included in the code block 530 for the learning target math problem data 510, a variable condition code block, This is a data table that matches the annotations 520 for each of the answer condition code block and the answer generation code block.

Referring to Figures 6 and 7, the training data set 300 includes math problem data to be learned 610, code blocks (not shown) for the math problem data to be learned 610, and annotation data 620 for the code blocks. ) may be a data table that matches each other. If the data included in the data table shown in FIG. 6 is converted into an embedding vector, it may appear as shown in FIG. 7.

FIG. 8 is an operation flowchart explaining a method for generating a mathematical problem code providing model according to another embodiment of the present invention, and FIG. 9 is a flowchart showing detailed steps included in the steps of the method for generating a mathematical problem code providing model. Hereinafter, with reference to FIGS. 8 and 9, a method for generating a model for providing a mathematical problem code will be described. Each step of the method for generating a mathematical problem code providing model to be described below may be performed by the mathematical problem code providing model generating apparatus 100 previously described with reference to FIGS. 1 to 7 . Accordingly, the contents of the embodiments of the present invention previously described with reference to FIGS. 1 to 7 can be equally applied to the embodiments described below, and any content that overlaps with the description described above will be omitted below. The steps described below do not necessarily have to be performed in order, the order of the steps may be set in various ways, and the steps may be performed almost simultaneously.

Referring to FIG. 8, the method of generating a model for providing a mathematical problem code includes a training data set creation step (S1100) and an artificial intelligence model training step (S1200).

The training data set generation step (S1100) performs a preprocessing process including converting the learning target math problem data, the code blocks required to generate the learning target math problem data, and the annotation data for the code blocks into an embedding vector to generate training data. This is the step of creating a set.

In the artificial intelligence model training step (S1200), based on the training data set, at least one of the main code block clusters including a problem code block for the mathematics problem and a plurality of problem code blocks for the mathematics problem is extracted from the fingerprint of a specific mathematics problem. This is the step of creating an artificial intelligence model that has been trained to output.

The artificial intelligence model calculates fingerprint similarity by comparing the fingerprint of the math problem with at least one of the target math problem data included in the training data set and annotations included in the training data set. The artificial intelligence model selects a cluster of code blocks corresponding to the math problem data to be learned or annotations included in the training data set based on fingerprint similarity. The artificial intelligence model is an artificial intelligence model that is trained to output code blocks that meet preset criteria among code blocks included in a code block cluster as problem code blocks for math problems, or output code block clusters as main code block clusters. .

Referring to FIG. 9, the training data set generation step (S1100) includes a code similarity derivation step (S1110), a code block cluster generation step (S1120), and a data matching step (S1130).

The code similarity derivation step (S1210) is a step of calculating code similarity by comparing a specific code block included in the training data set with a code block different from the specific code block included in the training data set. The code block cluster generation step (S1120) is a step of generating a code block cluster by clustering a specific code block and a code block different from the specific code block based on code similarity. The data matching step (S1130) is a step of matching the code block cluster with the learning target math problem data included in the training data set corresponding to the code block cluster and the annotations included in the training data set.

Figure 10 is a diagram showing a mathematical problem generating device 1000 connected to communication with a user terminal 1100 according to another embodiment of the present invention.

Referring to FIG. 10, the math problem generating device 1000 is interconnected with the user terminal 1100 through a wired or wireless communication network. A communication network is any wired network such as a Local Area Network (LAN), Wide Area Network (WAN), or Value Added Network (VAN), a mobile radio communication network, or a satellite communication network. It can be implemented as any type of wireless network. The math problem generating device 1000 may be formed as a cloud computing server such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). The user terminal 1100 is, for example, a laptop equipped with a web browser, a desktop, a laptop, a wireless communication device or smartphone that ensures portability and mobility, and a tablet including a touchpad. It can refer to all types of handheld-based wireless communication devices such as PCs, etc.

The user terminal 1100 includes a communication module, an input/output module, at least one processor, and a memory. The communication module of the user terminal 1100 may transmit and receive information with the math problem generating device 1000. The input/output module of the user terminal 1100 may receive information or data transmitted to the user terminal 1100 from the outside, or may output information or data held by the user terminal 1100 to the outside. For example, the input/output module of the user terminal 1100 may include a display, a touch pad, a speaker, and a microphone. The processor of the user terminal 1100 is electrically connected to the memory and operates by executing the code stored in the memory. The memory of the user terminal 1100 is electrically connected to the processor and stores at least one code executed by the processor. The memory of the user terminal 1100 stores code that, when executed through the processor, causes the processor to perform the following functions and procedures.

A code that causes a fingerprint for a math problem to be transmitted to the math problem generating device 1000 is stored in the memory of the user terminal 1100 . The memory of the user terminal 1100 receives a problem code block for a math problem or a main code block cluster including a plurality of problem code blocks for a math problem from the math problem generating device 1000 and causes it to be output to the input/output module. The code is saved. The memory of the user terminal 1100 stores a code that causes a problem code block for a math problem or a selection input for a specific problem code block among a plurality of problem code blocks for a math problem to be transmitted to the math problem generating device 1000. do. A code that causes the user terminal 1100 to receive a math problem generated through a problem code block or a specific problem code block corresponding to a selection input from the math problem generating device 1000 is stored in the memory of the user terminal 1100 .

FIG. 11 is a block diagram showing the configuration of the math problem generating device 1000, and FIG. 12 is a diagram showing an example of generating a math problem by the math problem generating device 1000.

Referring to FIGS. 10 to 12 together, the math problem generating device 1000 includes at least one processor 1030 and a memory 1040, and may further include a communication module 1010 and a database 1020. .

The communication module 1010 can transmit and receive data necessary for creating math problems by exchanging information with an external device or server.

The database 1020 may be a place where data necessary for creating math problems is stored. The database 1020 may be built in a portion of the memory 1040 or may be implemented as separate hardware.

The processor 1030 performs operations according to the code stored in the memory 1040. The memory 1040 is electrically connected to the processor 1030 and stores a math problem generation program performed by the processor 1040. The memory 1040 stores code that, when executed through the processor 1030, causes the processor 1040 to perform the following functions and procedures.

The memory 1040 stores a code that causes the fingerprint 1200 for a math problem to be obtained and the fingerprint to be converted into an embedding vector. More specifically, a code that causes a fingerprint 1200 for a math problem to be received from the user terminal 1100 is stored in the memory 1040 . Memory 1040 stores a code that causes the fingerprint to be converted into an embedding vector.

The memory 1040 includes a problem code block 1400 for a math problem and a plurality of problem code blocks for a math problem based on a fingerprint converted to an embedding vector using the math problem code provision model 1300. Code that causes at least one of the code block clusters to be obtained is stored. Here, the math problem code provision model 1300 learns a specific training data set including preprocessed math problem data to be learned, code blocks required to generate the math problem data to be learned, and annotation data for the code blocks. It is trained to receive a fingerprint of a math problem as an input and output at least one of a problem code block for the math problem and a major code block cluster containing a plurality of problem code blocks for the math problem. In addition, the math problem code providing model 1300 calculates fingerprint similarity by comparing the fingerprint of the math problem with at least one of the learning target math problem data included in the training data set and the annotations included in the training data set, and calculates the fingerprint similarity. Based on this, a code block cluster corresponding to the learning target math problem data included in the training data set or annotations included in the training data set is selected, and a preset standard is applied among the code blocks included in the code block cluster. It can be trained to output a satisfying code block as a problem code block for the math problem, or to output the code block cluster as the main code block cluster.

The problem code block is a variable setting code block that sets the variables of a math problem, a variable condition code block that sets conditions for variables, and a result condition code block and variable setting code block that sets conditions for correct and incorrect answers in a math problem. It includes at least one of a result generation code block that generates one correct answer that satisfies the conditions of the correct answer and one or more incorrect answers that satisfy the conditions of the incorrect answer based on the variables created through the condition code block. The math problem code provision model 1300 is based on a training data set that preprocesses the math problem data to be learned, the code blocks needed to generate the math problem data to be learned, and the annotation data for the code blocks, and is based on a training data set that preprocesses the math problem data to be learned, and the annotation data for the code blocks required to generate the math problem data to be learned. It is an artificial intelligence model trained to output at least one of the problem code blocks for math problems and major code block clusters that include multiple problem code blocks for specific math problems.

The memory 1040 stores a specific problem code block selected among a plurality of problem code blocks included in the main code block cluster and a code that causes a math problem to be generated based on at least one of the problem code blocks. More specifically, in the memory 1040, when a main code block cluster is obtained by the mathematical problem code provision model 1300, a list is generated in which a plurality of problem code blocks included in the main code block cluster are displayed and selected from the list. Upon obtaining a selection input for a specific problem code block, code that causes the math problem to be generated (1500) based on the specific problem code block is stored.

The memory 1040 stores a code that, when the problem code block 1400 is obtained by the mathematical problem code provision model 1300, generates a mathematical problem 1500 based on the problem code block. More specifically, the memory 1040 stores a code that causes code blocks included in the problem code block 1400 or a specific problem code block to be arranged in order using an artificial intelligence model. The memory 1040 stores a problem code block 1400 in which code blocks are arranged in order or a code that causes a math problem to be generated 1500 based on a specific problem code block. The artificial intelligence model is based on the code block position relationship between the variable setting code block, variable condition code block, result condition code block, and result generation code block included in the code block required to generate the math problem data to be studied, and the problem code to be analyzed. It is an artificial intelligence model trained to place the variable setting code block, variable condition code block, answer condition code block, and answer generation code block included in the block in order. The artificial intelligence model may be a model that uses the Extreme Multi-classification algorithm.

Figure 13 is an operation flowchart explaining a method for generating a math problem according to another embodiment of the present invention. Below, with reference to FIG. 13, a method for generating a math problem will be described. Each step of the math problem generation method to be described below may be performed by the math problem generation device 1000 previously described with reference to FIGS. 10 to 12 . Therefore, the contents of the embodiments of the present invention previously described with reference to FIGS. 10 to 12 can be equally applied to the embodiments to be described below, and any content that overlaps with the description described above will be omitted below. The steps described below do not necessarily have to be performed in order, the order of the steps may be set in various ways, and the steps may be performed almost simultaneously.

Referring to FIG. 13, the math problem generation method is a method in which each step is performed by a processor, and includes a vector conversion step (S2100), a code provision step (S2200), and a math problem creation step (S2300).

The vector conversion step (S2100) is a step of obtaining a fingerprint for a math problem and converting the fingerprint into an embedding vector.

The code provision step (S2200) uses a math problem code provision model to select a problem code block for a math problem based on a fingerprint converted to an embedding vector and a cluster of main code blocks including a plurality of problem code blocks for a math problem. This is the step of acquiring at least one thing. Here, the math problem code provision model is designed to provide a specific math problem through learning of a training data set that includes preprocessed math problem data to be learned, code blocks needed to generate the math problem data to be learned, and annotation data for the code blocks. It is trained to receive a fingerprint as an input and output at least one of the main code block clusters including a problem code block for a math problem and a plurality of problem code blocks for a math problem. In addition, the fingerprint similarity is calculated by comparing the fingerprint of the mathematical problem in the mathematical problem code providing model with at least one of the learning target mathematical problem data included in the training data set and the annotations included in the training data set, and the fingerprint similarity is based on Select a code block cluster that corresponds to the learning target math problem data included in the training data set or annotations included in the training data set, and code that satisfies preset criteria among the code blocks included in the code block cluster. It can be trained to output blocks as problem code blocks for math problems, or to output clusters of code blocks as clusters of major code blocks.

The problem code block includes the variable setting code block that sets the variables of the math problem, the variable condition code block that sets the conditions for the variables, the result condition code block that sets the conditions for the correct and incorrect answers in the math problem, and the variable setting code block and variable conditions. Based on the variables created through the code block, it includes at least one of a result generation code block that generates one correct answer that satisfies the conditions of the correct answer and one or more incorrect answers that satisfy the conditions of the incorrect answer. The math problem code provision model is based on a training data set that preprocesses the math problem data to be learned, the code blocks needed to generate the math problem data to be learned, and the annotation data for the code blocks, and is based on a training data set that preprocesses the math problem data to be learned, and the annotation data for the code blocks, from the fingerprint of the specific math problem. It is an artificial intelligence model trained to output at least one of the major code block clusters, including problem code blocks for and multiple problem code blocks for specific math problems.

The math problem generation step (S2300) is a step of generating the math problem based on at least one of a specific problem code block and a problem code block selected among a plurality of problem code blocks included in the main code block cluster. More specifically, in the mathematical problem generation step (S2300), when a major code block cluster is obtained by a mathematical problem code provision model, a list displaying a plurality of problem code blocks included in the major code block cluster is generated and a specific problem code block from the list is generated. When a selection input for a problem code block is obtained, generating the math problem based on a specific problem code block, and when a problem code block is obtained by a math problem code provision model, generating a math problem based on the problem code block. am.

The method for generating a mathematical problem code providing model and the method for generating a mathematical problem according to the embodiments of the present invention described so far are also available in the form of a recording medium containing instructions executable by a computer, such as a program module executed by a computer. It can be implemented. Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and non-volatile media, removable and non-removable media. Additionally, computer-readable media may include computer storage media. Computer storage media includes both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.

Those of ordinary skill in the technical field to which the present invention pertains will be able to understand that the present invention can be easily modified into other specific forms without changing the technical idea or essential features of the present invention based on the above description. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive. The scope of the present invention is indicated by the patent claims described below, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present invention. The scope of the present application is indicated by the claims described below rather than the detailed description above, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present application.

The form for carrying out the invention is the same as the best form for carrying out the invention described above.

The present invention is a technology for automatically generating various mathematical problems and can be used in the education industry, so it has industrial applicability.

[Explanation of symbols]

100: Model generation device providing mathematical problem code

110: communication module 120: database

130: Processor 140: Memory

210: Learning target math problem data 220: Code block

230: Annotation

300: Training data set

400: Artificial intelligence model

1000: Mathematics problem generation device

1010: Communication module 1020: Database

1030: Processor 1040: Memory

1100: User terminal

1200: Passage for math problems

1300: Math problem code provision model

1400: Problem code block

Claims

How each step is performed by a processor, comprising:

a) generating a training data set by performing a preprocessing process including converting learning target math problem data, code blocks required to generate the learning target math problem data, and annotation data for the code blocks into an embedding vector. ; and

b) Through learning of the training data set, a fingerprint of a specific mathematical problem is input and output at least one of a problem code block for the mathematical problem and a main code block cluster including a plurality of problem code blocks for the mathematical problem. A method of generating a model for providing a mathematical problem code, including the step of generating an artificial intelligence model trained to do so.
According to paragraph 1,

The problem code block is,

a variable setting code block that sets variables of the math problem;

a variable condition code block that sets conditions for the variable;

a result condition code block that sets conditions for correct and incorrect answers to the math problem; and

Among the result generation code blocks that generate one correct answer that satisfies the conditions of the correct answer and one or more incorrect answers that satisfy the conditions of the incorrect answer, using the variables created by the variable setting code block and the variable condition code block. Contains at least one block, and

The annotation includes a description of at least one block of the variable setting code block, the variable condition code block, the result condition code block, and the result generation code block.
According to paragraph 2,

In step a),

a-1) calculating code similarity by comparing a specific code block included in the training data set with a code block different from the specific code block included in the training data set;

a-2) generating a code block cluster by clustering the specific code block and the code blocks different from the specific code block based on the code similarity; and

a-3) a mathematical problem code providing model comprising the step of matching the code block cluster with learning target mathematical problem data included in the training data set corresponding to the code block cluster and annotations included in the training data set. How to create it.
According to paragraph 3,

The code similarity is,

Variable setting code similarity derived by comparing a variable setting code block included in the specific code block with a variable setting code block included in a code block different from the specific code block;

Variable condition code similarity derived by comparing a variable condition code block included in the specific code block with a variable condition code block included in a code block different from the specific code block;

Result condition code similarity derived by comparing a result condition code block included in the specific code block with a result condition code block included in a code block different from the specific code block; and

A mathematical problem code that includes at least one similarity among result generation code similarities derived by comparing a correct answer generation code block included in the specific code block and a result generation code block included in a code block different from the specific code block. How to create a serving model.
According to paragraph 4,

The code block cluster is,

a variable setting code cluster clustering variable setting code blocks included in the specific code block and variable setting code blocks included in a code block different from the specific code block, based on the variable setting code similarity;

a variable condition code cluster clustering variable condition code blocks included in the specific code block and variable condition code blocks included in a code block different from the specific code block, based on the variable condition code similarity;

a result condition code cluster that clusters result condition code blocks included in the specific code block and result condition code blocks included in code blocks different from the specific code block, based on the result condition code similarity; and

Based on the similarity of the result generation code, it includes at least one cluster among the result generation code clusters obtained by clustering the result generation code block included in the specific code block and the result generation code block included in the code block different from the specific code block. That is, how to create a model that provides math problem code.
According to clause 5,

The artificial intelligence model is,

Calculating fingerprint similarity by comparing the fingerprint of the math problem with at least one of the math problem data to be learned included in the training data set and annotations included in the training data set,

Based on the fingerprint similarity, select a code block cluster corresponding to the learning target math problem data included in the training data set or the annotation included in the training data set, and

Trained to output a code block that satisfies a preset standard among the code blocks included in the code block cluster as a problem code block for the math problem, or to output the code block cluster as the main code block cluster, Math problem code provided How to create a model.
at least one processor; and

It is electrically connected to the processor and includes a memory that stores a model generation program that provides mathematical problem codes performed by the processor,

The memory, the processor,

Generate a training data set by performing a preprocessing process including converting learning target math problem data, code blocks required to generate the learning target math problem data, and annotation data for the code blocks into an embedding vector,

Through learning of the training data set, a fingerprint of a specific math problem is input and trained to output at least one of a problem code block for the math problem and a main code block cluster including a plurality of problem code blocks for the math problem. A model generation device that provides mathematical problem codes, storing the code that causes it to generate an artificial intelligence model.
In clause 7,

The problem code block is,

a variable setting code block that sets variables of the math problem;

a variable condition code block that sets conditions for the variable;

a result condition code block that sets conditions for correct and incorrect answers to the math problem; and

Among the result generation code blocks that generate one correct answer that satisfies the conditions of the correct answer and one or more incorrect answers that satisfy the conditions of the incorrect answer, using the variables created by the variable setting code block and the variable condition code block. Contains at least one block, and

The annotation includes a description of at least one block of the variable setting code block, the variable condition code block, the result condition code block, and the result generation code block.
According to clause 8,

The memory allows the processor to:

Calculate code similarity by comparing a specific code block included in the training data set with a code block different from the specific code block included in the training data set,

Based on the code similarity, the specific code block and the code blocks different from the specific code block are clustered to create a code block cluster, and

A mathematical problem code providing model generating device that stores a code that causes the code block cluster to match the learning target mathematical problem data included in the training data set corresponding to the code block cluster and annotations included in the training data set. .
In clause 7,

The artificial intelligence model is,

Calculating fingerprint similarity by comparing the fingerprint of the math problem with at least one of the math problem data to be learned included in the training data set and annotations included in the training data set,

Based on the fingerprint similarity, select a code block cluster corresponding to the learning target math problem data included in the training data set or the annotation included in the training data set,

Mathematics, which is trained to output code blocks that satisfy preset criteria among code blocks included in the code block cluster as problem code blocks for the math problem, or to output the code block cluster as the main code block cluster. Problem code provision model generator.
How each step is performed by a processor, comprising:

i) obtaining a fingerprint of a math problem and converting the fingerprint into an embedding vector;

ii) Through learning of a training data set including preprocessed learning target math problem data, code blocks required to generate the learning target math problem data, and annotation data for the code blocks, a fingerprint of a specific math problem is input and Based on the fingerprint converted to an embedding vector using an artificial intelligence model trained to output at least one of a problem code block for a math problem and a cluster of major code blocks including a plurality of problem code blocks for the math problem. Obtaining at least one of a problem code block for the math problem and a main code block cluster including a plurality of problem code blocks for the math problem; and

iii) A method of generating a mathematical problem, including the step of generating a new mathematical problem using the code block obtained in step ii) above.
According to clause 11,

The problem code block is,

a variable setting code block that sets variables of the math problem;

a variable condition code block that sets conditions for the variable;

a result condition code block that sets conditions for correct and incorrect answers to the math problem; and

Among the result generation code blocks that generate one correct answer that satisfies the conditions of the correct answer and one or more incorrect answers that satisfy the conditions of the incorrect answer, using the variables created by the variable setting code block and the variable condition code block. Contains at least one block, and

The annotation includes a description of at least one block of the variable setting code block, the variable condition code block, the result condition code block, and the result generation code block.
According to clause 11,

The artificial intelligence model is,

Calculating fingerprint similarity by comparing the fingerprint of the math problem with at least one of the math problem data to be learned included in the training data set and annotations included in the training data set,

Based on the fingerprint similarity, select a code block cluster corresponding to the learning target math problem data included in the training data set or the annotation included in the training data set, and

Trained to output a code block that satisfies a preset standard among the code blocks included in the code block cluster as a problem code block for the math problem, or to output the code block cluster as the main code block cluster, How to create math problems.
at least one processor; and

It is electrically connected to the processor and includes a memory that stores a math problem generation program executed by the processor,

The memory, the processor,

Obtaining a fingerprint for a math problem, converting the fingerprint into an embedding vector,

Through learning of a training data set including preprocessed math problem data to be learned, code blocks needed to generate the math problem data to be learned, and annotation data for the code blocks, a fingerprint of a specific math problem is input and the math problem is solved. The math problem based on the fingerprint converted to an embedding vector using an artificial intelligence model trained to output at least one of a major code block cluster including a problem code block for and a plurality of problem code blocks for the math problem. Obtain at least one of a cluster of major code blocks comprising a problem code block for a problem and a plurality of problem code blocks for the mathematical problem, and

A mathematical problem generation device that stores code that causes new mathematical problems to be created using the obtained code blocks.
According to clause 14,

The problem code block is,

a variable setting code block that sets variables of the math problem;

a variable condition code block that sets conditions for the variable;

a result condition code block that sets conditions for correct and incorrect answers to the math problem; and

Among the result generation code blocks that generate one correct answer that satisfies the conditions of the correct answer and one or more incorrect answers that satisfy the conditions of the incorrect answer, using the variables created by the variable setting code block and the variable condition code block. Contains at least one block, and

The annotation includes a description of at least one block of the variable setting code block, the variable condition code block, the result condition code block, and the result generation code block.
According to clause 14,

The artificial intelligence model is,

Calculating fingerprint similarity by comparing the fingerprint of the math problem with at least one of the math problem data to be learned included in the training data set and annotations included in the training data set,

Based on the fingerprint similarity, select a code block cluster corresponding to the learning target math problem data included in the training data set or the annotation included in the training data set, and

Trained to output a code block that satisfies a preset standard among the code blocks included in the code block cluster as a problem code block for the math problem, or to output the code block cluster as the main code block cluster, Math problem generation device.