KR102596842B1 - The Method And The Computer-Readable Medium To Generate Information-Hierarchical-Tree For Each Issue Containing Regulatory Information, And The Computing System Performing That Same - Google Patents

Abstract

The present invention relates to a method for generating an issue-specific information hierarchical tree containing regulatory information, a computer-readable recording medium, and a computing system for performing the same. More specifically, the present invention provides a method for generating an issue-specific information hierarchical tree containing regulatory information, a computer-readable recording medium, and a computing system for performing the same. More specifically, the present invention includes recording information in one or more nodes based on a user's input, and , creates an initial tree by determining the relationship with other nodes for each of the nodes, receives basic regulatory information about the node selected by the user in the initial tree from the user, and receives the basic regulatory information and a plurality of laws stored in the database. An issue-specific information hierarchical tree containing regulatory information, which can create an issue-specific information hierarchical tree by deriving relevant regulatory information based on the similarity score and regulatory score between each clause and adding the derived relevant regulatory information to the initial tree. It relates to a method of generating a computer-readable recording medium and a computing system that performs the same.

Description

The Method And The Computer-Readable Medium To Generate Information-Hierarchical-Tree For Each Issue Containing Regulatory Information, And The Computing System Performs That Same}

The present invention relates to a method for generating an issue-specific information hierarchical tree containing regulatory information, a computer-readable recording medium, and a computing system for performing the same. More specifically, the present invention provides a method for generating an issue-specific information hierarchical tree containing regulatory information, a computer-readable recording medium, and a computing system for performing the same. More specifically, the present invention includes recording information in one or more nodes based on a user's input, and , creates an initial tree by determining the relationship with other nodes for each of the nodes, receives basic regulatory information about the node selected by the user in the initial tree from the user, and receives the basic regulatory information and a plurality of laws stored in the database. An issue-specific information hierarchical tree containing regulatory information, which can create an issue-specific information hierarchical tree by deriving relevant regulatory information based on the similarity score and regulatory score between each clause and adding the derived relevant regulatory information to the initial tree. It relates to a method of generating a computer-readable recording medium and a computing system that performs the same.

A tree is a non-linear hierarchical data structure in which nodes are connected like tree branches. It is also a recursive data structure in which there are subtrees within the tree and other subtrees within those subtrees.

In the tree, a node is a basic element constituting the tree and has a key or value and a pointer to a lower node. A node that has child nodes is called a parent node, and the child nodes of the parent node are called child nodes. In a tree structure, all nodes except the top node can be child nodes that have a parent node, and the top node without a parent is called the root node. Additionally, a node without child nodes is called an external node, a terminal node, or a leaf node.

Meanwhile, the government introduced the regulatory registration system to provide the contents of regulations in an easy-to-understand manner to policy users such as citizens and businesses and at the same time use it as a means of quality management of regulatory reform. The regulatory registration system refers to a system that informs the public of regulations registered by the Regulatory Reform Committee through internet websites, etc. in the form of regulation registration cards. Since 2015, registered regulations have been posted to the Regulatory Information Portal and the National Law Information Center. It is released in real time to provide information about regulations to the public.

However, the legal provisions provided in the aforementioned regulatory information portal or national legal information center are structured to open lower-level laws within higher-level law folders in a classification system listed by legal field and relevant ministry, and are simply listed without relation to the content of the legal field. Therefore, when a business or government tries to find regulatory information on a new industrial field and does not have sufficient relevant knowledge or information, it is not only difficult to select the specific field needed for the user among the numerous fields already classified, but especially for small and medium-sized businesses. When a start-up company provides convergence services and multiple fields and relevant ministries overlap, there is a problem that it is difficult to simultaneously check scattered information on the platform. Therefore, there is a need for a technology that effectively finds the regulatory legal provisions desired by the user and provides them to the user in a hierarchical tree structure classified by category so that the user can intuitively understand the regulatory legal provisions desired.

The present invention relates to a method for generating an issue-specific information hierarchical tree containing regulatory information, a computer-readable recording medium, and a computing system for performing the same. More specifically, the present invention provides a method for generating an issue-specific information hierarchical tree containing regulatory information, a computer-readable recording medium, and a computing system for performing the same. More specifically, the present invention includes recording information in one or more nodes based on a user's input, and , creates an initial tree by determining the relationship with other nodes for each of the nodes, receives basic regulatory information about the node selected by the user in the initial tree from the user, and receives the basic regulatory information and a plurality of laws stored in the database. An issue-specific information hierarchical tree containing regulatory information, which can create an issue-specific information hierarchical tree by deriving relevant regulatory information based on the similarity score and regulatory score between each clause and adding the derived relevant regulatory information to the initial tree. The purpose is to provide a method for generating a computer-readable recording medium, and a computing system for performing the same.

In order to solve the above problems, an embodiment of the present invention is a method of generating an issue-specific information hierarchical tree including regulatory information, which is performed on a computing system including one or more processors and one or more memories, and includes a plurality of A tree creation step of creating an information hierarchical tree with nodes, wherein the node in the information hierarchical tree is connected to at least one other node as a parent or child concept, and the information recorded in the node and the information recorded in the node and the other nodes are connected. A tree creation step in which relationships are determined according to user input; And with respect to the node selected by the user, a relevant regulatory law deriving step of deriving one or more related regulations for the node, wherein the relevant regulatory law deriving step includes receiving basic regulatory information for the corresponding node from the user. Basic regulatory information reception stage; And based on the similarity score, which is quantified information about the similarity between each of a plurality of legal provisions stored in the database and the basic regulatory information, and the regulation score, which is quantified information about the regulatory attributes of the relevant legal provisions, the first related regulation Provides a method of generating an information hierarchy tree for each issue, including a first related regulatory information derivation step of deriving information.

In one embodiment of the present invention, the tree creation step includes a root node input step of inputting information to be recorded in the highest node based on the user's input; And it may include a sub-node input step of inputting information to be recorded in one or more nodes connected to sub-concepts of the top-level node.

In one embodiment of the present invention, the sub-node input step includes inputting information recorded in a specific node into a detailed classification model; and inputting each of a preset number of a plurality of detailed items derived from the detailed classification model into each of one or more nodes connected to a sub-concept of the specific node.

In one embodiment of the present invention, the relevant regulatory law derivation step further includes a similarity score derivation step of deriving the similarity score, and the similarity score derivation step includes the basic regulatory information in a first inference model based on an artificial neural network. and a keyword matching score derivation step of deriving a keyword matching score by inputting each of a plurality of legal provisions stored in the database; and a cosine similarity score derivation step of deriving a cosine similarity score between each of a plurality of legal provisions stored in the database and the basic regulatory information.

In one embodiment of the present invention, the keyword matching score deriving step includes extracting keywords from basic regulatory information input from the user; Converting each of the extracted plurality of keywords into a vector having a dimension of 1 or more, and then deriving the converted plurality of vectors into one keyword vector; and entering a legal provision and a keyword vector into a first inference model based on an artificial neural network, thereby deriving a keyword matching score indicating the similarity between the input legal provision and the keyword vector.

In one embodiment of the present invention, the cosine similarity score deriving step includes deriving a basic regulation vector through an artificial neural network-based word embedding model for basic regulation information input from the user; and deriving a cosine similarity score based on the cosine similarity derived between each of all legal provisions stored in the database and the basic regulation vector.

In one embodiment of the present invention, the relevant regulatory law derivation step further includes a regulation score derivation step of deriving the regulation score, and the regulation score derivation step includes each of a plurality of legal provisions stored in the database in the regulatory classification model. By entering the regulation score corresponding to each legal provision, the regulatory classification model can be learned with the regulation card information of the legal provisions with regulation card information among the plurality of legal provisions stored in the database.

In one embodiment of the present invention, the relevant regulatory law derivation step determines legal provisions and corresponding legal information above a preset regulatory score as relevant regulatory information, and then provides the determined relevant regulatory information to the user in a layered form. .

In another embodiment of the present invention, the relevant regulatory law derivation step includes legal provisions and corresponding legal information greater than or equal to the standard regulatory score calculated based on the basic regulatory information and the regulatory score of the legal provisions having a preset number of higher similarity scores. After determining the relevant regulatory information, the determined related regulatory information can be provided to the user in a layered form.

In one embodiment of the present invention, the step of deriving relevant regulatory laws includes receiving additional regulatory information from a user; and a similarity score between each of a plurality of legal provisions stored in a database and the additional regulatory information; and a regulatory score of the corresponding legal provision; may further include deriving second related regulatory information.

In order to solve the above problems, an embodiment of the present invention is a computing system that includes one or more processors and one or more memories and performs a method of generating an information hierarchy tree for each issue including regulatory information, and includes a plurality of A tree creation step of creating an information hierarchical tree with nodes, wherein the node in the information hierarchical tree is connected to at least one other node as a parent or child concept, and the information recorded in the node and the information recorded in the node and the other nodes are connected. A tree creation step in which relationships are determined according to user input; And with respect to the node selected by the user, a relevant regulatory law derivation step of deriving one or more related regulations for the node; and the relevant regulatory law derivation step includes receiving basic regulatory information for the corresponding node from the user. Basic regulatory information reception stage; And based on the similarity score, which is quantified information about the similarity between each of a plurality of legal provisions stored in the database and the basic regulatory information, and the regulation score, which is quantified information about the regulatory attributes of the relevant legal provisions, the first related regulation A computing system including a first related regulatory information deriving step of deriving information is provided.

In order to solve the above problems, an embodiment of the present invention provides a method for generating an information hierarchical tree for each issue including regulatory information, which is performed on a computing system including one or more processors and one or more memories. A computer-readable recording medium, the computer-readable recording medium storing instructions for causing the computing system to perform the following steps, wherein the following steps include: creating an information hierarchical tree having a plurality of nodes; In the tree creation step, the node in the information hierarchical tree is connected to at least one other node as a parent or child concept, and the information recorded in the node and its relationship with other nodes are determined according to the user's input. Tree creation step; And with respect to the node selected by the user, a relevant regulatory law deriving step of deriving one or more related regulations for the node, wherein the relevant regulatory law deriving step includes receiving basic regulatory information for the corresponding node from the user. Basic regulatory information reception stage; And based on the similarity score, which is quantified information about the similarity between each of a plurality of legal provisions stored in the database and the basic regulatory information, and the regulation score, which is quantified information about the regulatory attributes of the relevant legal provisions, the first related regulation A computer-readable recording medium comprising a first relevant regulatory information deriving step of deriving information is provided.

According to one embodiment of the present invention, rather than a list classified by field of law or relevant ministry, regulatory legal provisions classified by subject based on the actual legal provisions are provided in a tree form with a hierarchical structure, so that the search term entered by the user Alternatively, it can have the effect of providing legal provisions substantially related to the search statement.

According to one embodiment of the present invention, laws that are not classified as regulations are derived by deriving regulatory legal provisions based on regulatory scores derived through a learned regulatory classification model without providing information on regulatory legal provisions designated by the government as is. This can have the effect of making it easier to find shadow regulation provisions that are recognized as regulations.

According to one embodiment of the present invention, when a company, etc., is reviewing a new business or preparing to start a business, it is possible to easily find regulatory provisions related to the business to be conducted.

According to one embodiment of the present invention, government ministries, legislative bodies, and national research institutes reviewing regulations can easily find legal provisions related to a specific field, which has the effect of easily monitoring legal provisions that require legislation or revision. can be demonstrated.

According to one embodiment of the present invention, users can enter search terms in various formats, allowing users to easily search for regulatory provisions in the field they are looking for even if they do not have sufficient legal knowledge such as legal terms.

Figure 1 schematically shows the execution steps of a method for deriving regulatory legal provisions according to an embodiment of the present invention and the configuration of a computing system that performs the same.
Figure 2 schematically shows the process of deriving a list of related regulatory laws in a computing system according to an embodiment of the present invention.
Figure 3 schematically shows the process of learning a regulatory classification model according to an embodiment of the present invention.
Figure 4 schematically shows the process of deriving a regulatory score for each legal provision through a learned regulatory classification model according to an embodiment of the present invention.
Figure 5 schematically shows the execution steps of the similarity score derivation step according to an embodiment of the present invention.
Figures 6 and 7 schematically show the execution process of the keyword matching score derivation step according to several embodiments of the present invention.
Figure 8 schematically shows the process of performing the cosine similarity score derivation step according to an embodiment of the present invention.
Figure 9 schematically shows the process of deriving candidate legal provisions according to an embodiment of the present invention.
Figure 10 schematically shows the process of deriving a similarity score according to an embodiment of the present invention.
Figure 11 schematically shows the process of determining regulatory legal provisions according to an embodiment of the present invention.
Figure 12 schematically shows a list of regulatory laws provided to users according to an embodiment of the present invention.
Figure 13 schematically shows the execution steps of a method for generating an information hierarchical tree for each issue according to an embodiment of the present invention and the configuration of a computing system that performs this.
Figure 14 schematically shows the process of performing the tree creation step of generating an initial tree according to user input according to an embodiment of the present invention.
Figure 15 schematically shows the execution process of the subnode input step according to an embodiment of the present invention.
Figure 16 schematically shows an information hierarchical tree including a second child node created according to an embodiment of the present invention.
Figures 17 and 18 schematically show the execution steps and execution process of the relevant regulatory law derivation step according to an embodiment of the present invention.
Figures 19 and 20 schematically show information hierarchy trees according to some embodiments of the present invention.
Figure 21 exemplarily shows the internal configuration of a computing device according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS Various embodiments and/or aspects are now disclosed with reference to the drawings. In the following description, for purposes of explanation, numerous specific details are set forth to facilitate a general understanding of one or more aspects. However, it will also be appreciated by those skilled in the art that this aspect(s) may be practiced without these specific details. The following description and accompanying drawings set forth in detail certain example aspects of one or more aspects. However, these aspects are illustrative and some of the various methods in the principles of the various aspects may be utilized, and the written description is intended to encompass all such aspects and their equivalents.

Additionally, terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, a first component may be named a second component, and similarly, the second component may also be named a first component without departing from the scope of the present invention. The term and/or includes any of a plurality of related stated items or a combination of a plurality of related stated items.

In addition, in the embodiments of the present invention, unless otherwise defined, all terms used herein, including technical or scientific terms, are generally understood by those skilled in the art to which the present invention pertains. It has the same meaning as Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless clearly defined in the embodiments of the present invention, have an ideal or excessively formal meaning. It is not interpreted as

Meanwhile, the "legal provisions" mentioned below are a concept that includes all laws, enforcement decrees, enforcement regulations, and administrative rules, and according to one embodiment of the present invention, precedents, administrative guidance, supervisory administration, and oral instructions are further included. It can be included.

1. How to derive regulatory provisions related to the entered query

Figure 1 schematically shows the performance steps of a method for deriving regulatory legal provisions according to an embodiment of the present invention and the configuration of a computing system 1000 that performs this, and Figure 2 shows a computing system 1000 according to an embodiment of the present invention. The process of deriving a list of related regulatory laws from the system 1000 is schematically shown.

As shown in FIG. 1, a method of deriving regulatory legal provisions related to an input query, performed in a computing system 1000 including one or more processors and one or more memories, includes regulatory card information among a plurality of legal provisions. A regulatory score derivation step (S100) that uses the regulatory classification model 2100 learned with the regulation card information of the existing legal provisions to derive a regulatory score indicating how related each of the plurality of legal provisions stored in the database 2000 is to the regulation. ); A similarity score derivation step (S200) of deriving a similarity score according to the similarity between a query input from a user and each of the plurality of legal provisions using one or more artificial neural network-based inference models; and a related regulatory law provision step (S300) in which regulatory legal provisions related to the query are classified and sorted according to preset standards based on the regulatory score and the similarity score and provided to the user.

Schematically, Figure 1 (a) shows the execution steps of the method for deriving regulatory legal provisions, and Figure 1 (b) shows the configuration of the computing system 1000 that performs the method for deriving the regulatory legal provisions. It shows.

Specifically, the regulatory score derivation unit 1100 of the computing system 1000 performs a regulatory score derivation step (S100) of deriving a regulatory score for each of all legal provisions stored in the database 2000. The database 2000 stores a plurality of legal provisions and information corresponding to each legal provision. Information corresponding to the above legal provisions includes the title of the law, the original text of the legal provisions, information on the upper law of the relevant law, relevant department, date of promulgation, date of enforcement, and regulatory information. It is desirable that the above regulatory information corresponds to the information included in the regulatory registration card released by the Regulatory Reform Committee.

The regulatory score derivation unit 1100 derives a regulatory score for each of all legal provisions stored in the database 2000 using a regulatory classification model 2100 that has learned legal provisions containing regulatory information. According to one embodiment of the present invention, the regulatory classification model 2100 is stored in the database 2000. Additional explanation of the regulatory score derivation step (S100) will be described later with reference to FIGS. 3 and 4, and the computing device provides query information (hereinafter referred to as query) input from the user after the regulatory score derivation step (S100). Based on this, the similarity score derivation step (S200) and the related regulatory law provision step (S300) are performed to derive regulatory law provisions related to the query information entered by the user.

As shown in FIG. 2, the user can input a search word or search statement through his or her terminal, and the terminal generates a query based on the input search word or search statement, and sends the generated query to the computing system ( 1000). Or, as another embodiment of the present invention, the terminal may transmit the input search word or search sentence to the computing system 1000, and generate the search word or search sentence received from the computing system 1000 as query information. . The computing system 1000 that has received this may derive regulatory law provisions related to the data included in the query, generate a list of regulatory laws, and provide the generated list to the user.

It is desirable for the user to enter legal provisions in the field he or she desires as the search word or search statement. For example, if the user wants to know the regulatory laws regarding clinical trials, the user enters ‘Drug clinical trial plan approval procedures and standards’ as a search term, or ‘Article 24 of the Rules on the Safety of Pharmaceuticals, etc.’ Alternatively, you can enter the full text of Article 24 of the Rules on the Safety of Medicines, etc. as a search query.

Meanwhile, according to another embodiment of the present invention, the user can input the sentence 'Please tell me the regulatory laws related to clinical trials in the bio healthcare field' as a search sentence, and according to another embodiment of the present invention, Users can enter only key keywords as search terms, such as 'bio healthcare, clinical trials, and regulations'.

The similarity score deriving unit 1200, which has received a query containing data related to such a search term or search statement, uses one or more artificial neural network-based inference models to determine the similarity between the query input from the user and each of the plurality of legal provisions. A similarity score derivation step (S200) is performed to derive a similarity score according to the present invention. Briefly, the similarity score derivation step (S200) includes a keyword matching score derivation step (S210) and a cosine similarity score derivation step (S220), and the similarity score derivation unit 1200 calculates the keyword matching score and cosine derived through this. Based on the similarity score, the similarity score between the input query and the legal provisions is derived. A more detailed description of the similarity score derivation step (S200) will be described later in the description of FIGS. 5 to 10.

When a regulatory score is derived for each legal provision stored in the database (2000) and a similarity score between the input query and each of the legal provisions is derived, the related regulation law provision unit 1300 determines the score based on the similarity score and the regulatory score. A related regulatory law provision step (S300) is performed to derive regulatory legal provisions related to the entered query.

More specifically, relevant regulatory laws refer to regulatory legal provisions related to the query entered by the user, and the relevant regulatory laws include regulatory laws designated by government agencies, etc. In addition, even if they are legal provisions that are not designated as regulatory laws, they are in fact regulatory provisions. It is characterized by the ability to further include legal provisions corresponding to so-called 'shadow regulations' that play a role. In other words, the present invention applies regulatory laws and shadow regulations designated by the government in the current law in the field desired by the user based on the regulatory score and similarity score derived through the above-described regulatory score derivation step (S100) and similarity score derivation step (S200). The corresponding legal provisions can be derived, and through this, users can easily find regulatory legal provisions related to the field they wish to proceed or review even if they do not have sufficient legal knowledge.

In addition, the relevant regulatory law provision step (S300) provides a list of regulatory laws classified by topic based on the actual legal provisions for the derived related regulatory laws, thereby providing legal provisions substantially related to the search word or search statement entered by the user. It can be effective in providing legal provisions in a way that is structured so that lower-level laws are opened within a higher-level law folder in a classification system that simply lists legal provisions by relevant ministries in conventional technology or related platforms, or in a category composed of topics related to real life. Compared to the method of providing a classification system based on , working-level staff can effectively provide more practical and necessary legal provisions.

Below, the above-described regulatory score derivation step (S100), similarity score derivation step (S200), and related regulatory law provision step (S300) will be described in more detail along with related drawings.

Figure 3 schematically shows the process of learning the regulatory classification model 2100 according to an embodiment of the present invention.

Specifically, the database 2000 stores a plurality of legal provisions and legal information for each of the plurality of legal provisions. The legal information, as described above in the description of Figure 1, includes the title of the law, the original text of the law, information on the higher law of the law, relevant department, date of promulgation, date of enforcement, and regulatory information, and in the [Background of the invention] in this specification As described in [Technology that becomes this], the regulatory information may be stored in the form of a regulatory registration card. The regulatory registration card includes information such as regulatory office name, regulatory summary, promulgation date, enforcement date, sunset information, name of law, main search words, related laws, and name of relevant ministry. In the case of legal provisions other than regulatory laws designated by the government, There is no regulatory information in legal provisions.

As shown in FIG. 3, the regulatory classification model 2100 performs a process of learning about legal provisions for which the regulatory information (regulation card information in FIG. 3) exists among all legal provisions stored in the database 2000. Additionally, the regulatory legal model learns not only the aforementioned regulatory information, but also the entire original text of the legal provisions in which the regulatory information exists. Through this, the regulatory classification model 2100 analyzes the original text of the input legal provision and derives a regulatory score indicating how related the input legal provision is to the regulation.

In other words, even if it is a legal provision that was not classified as a regulatory legal provision from the beginning, in the case of a legal provision in which the rules derived from the learned regulatory legal provisions appear at a certain level or higher, a regulatory score higher than a certain value is performed in the regulatory score derivation step (S100) of the present invention. It is derived through this method, and the technical feature is that the relevant legal provisions are classified as regulatory legal provisions accordingly.

An example of a rule derived from the above regulatory legal provisions is "If it contains words with mandatory or necessary force, such as 'regulation', 'obligation', or 'must', more than a preset number, it is classified as a regulatory legal provision." There may be etc. The process of deriving the regulatory score through the regulatory classification model 2100 learned as above will be described in the explanation of FIG. 4.

Figure 4 schematically shows the process of deriving a regulatory score for each legal provision through the learned regulatory classification model 2100 according to an embodiment of the present invention.

Schematically, Figure 4(a) shows the process of deriving the regulatory score of the corresponding legal provision by inputting an unclassified legal provision into the regulatory classification model 2100, and Figure 4(b) shows the process of deriving the regulatory score of the regulatory score list. One embodiment is shown.

Specifically, with reference to the contents of FIG. 3, as shown in (a) of FIG. 4, in the regulatory score derivation step (S100), when a legal provision is entered into the regulatory classification model (2100), the regulation for the corresponding legal provision is Derive a score. Preferably, the regulatory score derivation step (S100) may be performed on all legal provisions stored in the database 2000 before receiving a query from the user. The regulatory score can be expressed as a real number between 0 and 1, as shown in (b) of FIG. 4, and the closer it is to 1, the closer it is to 1, meaning that the relevant legal provision is closely related to regulation.

As an embodiment of the present invention, the relevant regulatory law provision unit 1300 converts legal provisions with regulatory scores greater than or equal to a preset standard into regulatory laws based on the regulatory score derived through the regulatory score derivation step (S100). You can judge. For example, a legal provision with a regulatory score of 0.800 or higher can be judged as a regulatory law. In other words, the regulatory score derivation step (S100) of the present invention uses a regulatory classification model (2100) that has learned regulatory laws designated by the government, etc., to determine regulatory laws in the field where the relevant laws are actually applied even though they have not been designated as regulatory laws by the government, etc. It can have the effect of deriving regulatory laws corresponding to shadow regulations that can act as.

Figure 5 schematically shows the execution steps of the similarity score derivation step (S200) according to an embodiment of the present invention.

As shown in Figure 5, the similarity score derivation step (S200) performed by the similarity score derivation unit 1200 includes a keyword matching score derivation step (S210) of deriving a keyword matching score; A cosine similarity score derivation step (S220) of deriving a cosine similarity score; and a relevance score derivation step (S230) for deriving a relevance score indicating how substantially the input query is related to the candidate legal provisions derived through the similarity score derivation step (S200) and the keyword matching score derivation step (S210); Includes.

As an embodiment of the present invention, in FIG. 5, the keyword matching score derivation step (S210) is shown as preceding the cosine similarity score derivation step (S220). However, according to another embodiment of the present invention, the cosine similarity score derivation step (S220) is shown in FIG. Step (S220) may precede the keyword matching score derivation step (S210), and this corresponds to a matter that can be changed in design as much as desired by the inventor of the present invention.

Therefore, the similarity score derivation unit 1200 derives the keyword matching score, the cosine similarity score, and the relevance score to calculate a similarity score between the query input from the user and each of the plurality of legal provisions stored in the database 2000. Perform the similarity score derivation step (S200).

Figures 6 and 7 schematically show the execution process of the keyword matching score derivation step (S210) according to several embodiments of the present invention.

As shown in Figures 6 and 7, the similarity score derivation step (S200) is a query input from a user in the first inference model 2300 based on an artificial neural network and a plurality of legal provisions stored in the database 2000. It includes a keyword matching score derivation step (S210) in which each keyword is entered to derive a keyword matching score.

Additionally, as shown in FIG. 7, the keyword matching score derivation step (S210) includes extracting keywords from a query input from the user; Converting each of the extracted plurality of keywords into a vector having a dimension of 1 or more, and then deriving the converted plurality of vectors into one keyword vector; and entering a legal provision and a keyword vector into the first inference model 2300 based on an artificial neural network, thereby deriving a keyword matching score indicating the similarity between the input legal provision and the keyword vector.

Specifically, Figure 6 schematically shows the process of deriving a keyword matching score as an embodiment of the present invention. As shown in (a) of FIG. 6, the similarity score deriving unit 1200 extracts a plurality of keywords (A , B, C, D) are derived. Figures 6 and 7 show an example of deriving four keywords (A, B, C, D) from one query, but the number of keywords actually derived in the present invention is not limited to those shown in Figures 6 and 7. No.

Afterwards, as shown in (b) of FIG. 6, the similarity score deriving unit 1200 calculates the keyword set {A, B, C, D} of the query entered by the user and each of the legal provisions stored in the database 2000. The keyword matching score is derived by inputting it into the first inference model (2300) based on artificial intelligence.

The keyword matching score determines how many keywords (A, B, C, D) belonging to the keyword set {A, B, C, D} appear in the legal provisions input to the first inference model 2300, or the number of keywords (A, B, C, D) belonging to the keyword set {A, B, C, D}. It is a score that quantifies the occurrence of noun phrases related to keywords (A, B, C, D) belonging to the keyword set {A, B, C, D}. The higher the keyword matching score, the more keywords in the query are included in the legal provisions. Alternatively, it can be understood as a legal provision that appears with high frequency.

Meanwhile, the keyword matching score is another embodiment of the present invention, as shown in FIG. 7, rather than directly inputting the keyword set {A, B, C, D} into the first inference model 2300, After converting the keyword set {A, B, C, D} into a keyword vector, each of the keyword vector and the legal provisions stored in the database (2000) is input into the first inference model (2300) to derive a keyword matching score. You can.

More specifically, the similarity score deriving unit 1200 derives a keyword set {A, B, C, D} through the process shown in (a) of FIG. 6 and then converts the words into a vector with a dimension of 1 or more. By entering each keyword belonging to the keyword set into the conversion matrix, vectors (V _A , V _B , V _C , V _D ) for each keyword can be derived. According to various embodiments of the present invention, the function performed by the matrix may be performed by a model or algorithm that converts text into a corresponding vector, such as W2V. When a vector for each of the keywords (V _A , V _B , V _C , V _D ) is derived, the similarity score deriving unit 1200 converts them into one keyword vector, as shown in (a) of FIG. 7. Derived as (V _A+B+C+D ). For example, the corresponding keyword vector can be derived by adding up the vectors for each of the keywords (V _A , V _B , V _C , and V _D ).

When the keyword vector (V _A+B+C+D ) is derived, the similarity score derivation unit 1200 combines the derived keyword vector (V _A+B+C+D ) and the legal provisions with the first inference model (2300). ), you can derive the keyword matching score between the relevant legal provisions and the query.

Figure 8 schematically shows the process of performing the cosine similarity score derivation step (S220) according to an embodiment of the present invention.

As shown in FIG. 8, the cosine similarity score derivation step (S220) includes deriving a query vector for the query input from the user through an artificial neural network-based word embedding model (2400); and deriving a cosine similarity score based on the cosine similarity derived between each of all legal provisions stored in the database 2000 and the query vector.

Schematically, Figure 8(a) shows the process of deriving a query vector (V query) by inputting a query input from a user into the word embedding model 2400, and Figure 8(b) shows the process of deriving a query vector (V _query ) from the database 2000. Shows the process of deriving a legal vector (V _law ) for each legal provision by inputting each of a plurality of legal provisions stored in the word embedding model 2400, and Figure 8 (c) shows the query vector (V The process of deriving the cosine similarity score between _query ) and the law vector (V _law ) is shown.

Specifically, the similarity score deriving unit 1200 inputs each of the query input from the user and the legal provisions stored in the database 2000 into the word embedding model 2400 shown in FIG. 8 to generate a query vector (V _query ) and a plurality of After deriving a legal vector (V _law ), cosine similarity is derived between the query vector (V _query ) and the law vector (V _law ) to obtain a cosine similarity score for each of the query and the legal provisions stored in the database (2000). Derive . The word embedding model 2400 performs a similar function to the matrix in the keyword matching score derivation step (S210), but preferably corresponds to a different model or algorithm. That is, both the keyword vector (V _A+B+C+D ) and the query vector (V _query ) mean vectors for queries, but they correspond to different vectors.

V _law1 , V _law2 , V _law3 , V _law4 , V _law5 , and V _law6 shown in FIG. 8 are LAW#1, LAW#2, LAW#3, LAW#4, and LAW#, respectively, with reference to FIG. 2. 5, and LAW #6 correspond to a vector with a dimension of 1 or more output by inputting it into the word embedding model (2400), and the process of obtaining the legal vector (V _law ) is based on all legal provisions stored in the database (2000). It is desirable to carry out

In other words, the similarity score deriving unit 1200 derives a query vector (V _query ) and a plurality of law vectors (V _law ) through the word embedding model 2400, as shown in (c) of FIG. 8. As described above, the cosine similarity between each of the query vector (V _query ) and the plurality of law vectors (V _law ) is calculated, and then the cosine similarity score is derived based on the calculated cosine similarity.

As described above, the similarity score deriving unit 1200 derives a similarity score according to the similarity between the query input from the user and the legal provisions based on different scores (keyword matching score and cosine similarity score) derived in different ways. Through this, the similarity score can be derived more accurately than deriving the similarity score using a single method.

Figure 9 schematically shows a process for deriving candidate legal provisions according to an embodiment of the present invention, and Figure 10 schematically shows a process for deriving a similarity score according to an embodiment of the present invention.

As shown in Figures 9 and 10, the similarity score derivation step (S200) is a second inference model based on an artificial neural network based on legal provisions and input queries derived based on the keyword matching score and the cosine similarity score. It further includes a relevance score derivation step (S230) of entering into (2500) to derive a relevance score indicating the correlation between the derived legal provisions and the input query, wherein the keyword matching score, the cosine similarity score, and the Based on the relevance score, a similarity score is derived for each of the plurality of legal provisions.

Specifically, according to one embodiment of the present invention, the similarity score deriving unit 1200 may derive the similarity score of the relevant legal provision based on the keyword matching score and the cosine similarity score. Meanwhile, preferably, as another embodiment of the present invention, a similarity score for each of the plurality of legal provisions can be derived based on the keyword matching score, the cosine similarity score, and the relevance score.

As shown in (a) of FIG. 9, the similarity score deriving unit 1200 calculates an intermediate score for each legal provision based on the keyword matching score and cosine similarity score for all legal provisions stored in the database 2000. is derived, and as shown in (b) of Figure 9, all the legal provisions are listed in order of the intermediate score.

Afterwards, among all the above legal provisions, some are selected as candidate legal provisions according to pre-established conditions. In Figure 9, an embodiment of selecting legal provisions with the top 100 intermediate scores as candidate legal provisions is shown. However, the configuration of selecting candidate legal provisions of the present invention is not limited to the above-described embodiment and can be selected through various embodiments. A number of candidate legal provisions can be selected.

In this way, each of the plurality of legal provisions selected as candidate legal provisions and the query input from the user are entered into the artificial intelligence-based second inference model 2500 to derive a correlation score between the query and each candidate legal provision. The relevance score derivation step (S230) is performed by the similarity score derivation unit 1200. In a preferred embodiment, the relevance score may be derived based on self-supervised learning using a hard negative mixing method.

The similarity score derivation unit 1200 derives a similarity score different from the input query and the legal provisions based on the keyword matching score, cosine similarity score, and relevance score derived through the above-described process. Thereafter, referring to the explanation of FIG. 1, the relevant regulatory law provision unit 1300 of the computing system 1000 derives regulatory law provisions related to the query entered by the user based on the regulatory score and the similarity score, and , provided to the user.

Figure 11 schematically shows the process of determining regulatory law provisions according to an embodiment of the present invention, and Figure 12 schematically shows a list of regulatory laws provided to users according to an embodiment of the present invention.

As shown in Figures 11 and 12, as an embodiment of the present invention, the relevant regulatory law provision step (S300) classifies regulatory legal provisions related to the query based on the similarity score, and preset regulations After determining the legal provisions higher than the score as regulatory legal provisions, the determined regulatory legal provisions are provided to the user in the form of a list arranged in order according to preset conditions.

Meanwhile, in another embodiment of the present invention, the relevant regulatory law providing step (S300) classifies regulatory legal provisions related to the query based on the similarity score, and classifies the regulatory legal provisions related to the query based on the similarity score, After determining the legal provisions above the standard regulatory score calculated based on the regulatory score as regulatory legal provisions, the determined regulatory legal provisions are provided to the user in the form of a list arranged in order according to preset conditions.

Specifically, as shown in FIG. 11, the relevant regulatory law provision unit 1300 determines whether the legal provision is a relevant regulatory legal provision based on the similarity score and regulatory score for the legal provision, The relevant regulatory law provision step (S300) is performed in which a plurality of legal provisions determined to be relevant regulatory legal provisions are provided in the form of a list.

An example of the relevant regulatory provisions provided in the form of the list is shown in FIG. 12. As shown in Figure 12, with reference to the explanation of Figure 3, the list includes the title of the law, the original text of the law, information on the higher law of the law, relevant department, date of publication, date of enforcement, and regulation information, for convenience of explanation. For this purpose, Figure 12 shows only the title of the law, the text of the legal provision, the similarity score of the relevant legal provision, and the regulatory score of the relevant legal provision. The similarity score can be expressed as a real number between 0 and 1, similar to the regulation score described above in the description of FIG. 4, and the closer it is to 1, the closer the legal provision is to the entered query.

The relevant regulatory law provision step (S300) may generate the list in order of similarity scores for queries entered by the user. At this time, only legal provisions that are higher than a preset regulatory score can be included in the list, or after calculating the standard regulatory score according to a specific rule, only legal provisions that are higher than the standard regulatory score can be included in the list.

As an embodiment of the present invention, the standard regulatory score is based on the regulatory scores of legal provisions with a preset number of higher similarity scores when listing a plurality of legal provisions based only on the pure similarity score, regardless of the regulatory score. can be derived. For example, when a plurality of legal provisions are listed based on the similarity score, the average value of the regulatory scores of the top 50 legal provisions can be determined as the standard regulatory score.

The present invention provides users with a list of related regulatory laws derived through the above-described embodiments, allowing users to easily search for regulatory laws and regulations in the field they are looking for by simply entering a search word or search statement. It can be provided in a structured way to open lower-level laws within a higher-level law folder in a classification system that simply lists legal provisions by relevant ministries in conventional technology or related platforms, or provides a classification system based on categories made up of topics related to real life. Compared to the provision method, working-level staff can provide more practical and necessary legal provisions more effectively.

In the description of FIGS. 1 to 12 above, related regulatory information related to the basic regulatory information (the above-mentioned 'query') input from the user in 2. Method of generating an information hierarchical tree for each issue including regulatory information of the present invention. The method of deriving the (aforementioned ‘relevant regulatory provisions’) is described. Hereinafter , the present invention for providing related regulatory information in the form of a tree with a hierarchical structure rather than simply in the form of a list will be described.

2. How to create an information hierarchy tree for each issue including regulatory information

As described above, the method of generating an issue-specific information hierarchical tree including regulatory information of the present invention has a technical feature of providing related regulatory information in the form of a tree with a hierarchical structure. Roughly speaking, relevant regulatory information can be derived based on the method for deriving regulatory legal provisions in the above-described 1. Method for deriving regulatory legal provisions related to the entered query . Below, we will describe how to create an information hierarchy tree before deriving relevant regulatory information and input relevant regulatory information into the created information hierarchy tree.

Meanwhile, the superordinate and subconcepts described below include not only the semantic superordinate and subconcepts of the word itself, but also superordinate and subconcepts under the legal classification system. For example, referring to Figure 16, the words 'manufacturing/registration/licensing' do not correspond to the sub-concept of pharmaceutical in a typical semantic concept. However, in the domestic legal system, legal provisions related to pharmaceuticals include legal provisions related to ‘manufacturing, registration, and licensing’ and also include legal provisions related to ‘drug safety,’ so the ‘manufacturing, registration, and licensing’ categories and the ‘drugs’ The 'Safety' category can be entered as a sub-concept of the 'Medicine' node.

Figure 13 schematically shows the execution steps of a method for generating an information hierarchical tree for each issue according to an embodiment of the present invention and the configuration of a computing system 10000 that performs this.

As shown in FIG. 13, a method of generating an information hierarchical tree for each issue including regulatory information, performed in a computing system 10000 including one or more processors and one or more memories, is an information hierarchical tree having a plurality of nodes. In the tree creation step (S1000), the node in the information hierarchical tree is connected to at least one other node as a parent or child concept, and the information recorded in the node and the relationship with other nodes are determined by the user. Tree generation step (S1000), which is determined according to the input; And with respect to the node selected by the user, a relevant regulation law derivation step (S2000) of deriving one or more related regulations for the node; A basic regulatory information reception step of receiving basic regulatory information; And based on the similarity score, which is numerical information about the similarity between each of a plurality of legal provisions stored in the database (2000) and the basic regulatory information, and the regulation score, which is numerical information about the regulatory attributes of the relevant legal provisions, 1. It includes a first related regulatory information derivation step of deriving related regulatory information.

Schematically, Figure 13 (a) shows the execution steps of a method for generating an information hierarchical tree for each issue including regulatory information, and Figure 13 (b) shows a computing system performing the method for generating the information hierarchical tree. The configuration of (10000) is shown.

Specifically, the computing system 10000 shown in FIG. 13 includes the computing system 1000 shown in FIG. 1. More specifically, the computing system 10000 includes a tree creation unit 12000 that performs a tree creation step (S1000); And a relevant regulatory law derivation unit 11000 that performs the relevant regulatory law derivation step (S2000), and according to an embodiment of the present invention, performs the method of deriving the regulatory law provisions shown in FIG. The computing system 1000 may be included in the relevant regulatory law derivation unit 11000, or the functions required when the method of deriving the regulatory law provisions is performed may be performed by the relevant regulation law derivation unit 11000. .

The tree creation step (S1000) creates an information hierarchical tree with a plurality of nodes. The node in the information hierarchical tree is connected to at least one other node as a higher or lower level concept. That is, the tree creation unit 12000 records specific information in each node based on the received user input and determines the relationship between different nodes.

After the rough structure of the information hierarchical tree is completed, the user inputs basic regulatory information into the computing system 10000, and the relevant regulatory law derivation unit 11000 derives relevant regulatory information related to the basic regulatory information. Perform the relevant regulatory law derivation step (S2000).

In the database 2000 of the computing system 10000, as described above in the description of FIGS. 1 to 12, a regulatory classification model 2100 used in the regulatory score derivation step (S100); and the cosine similarity score derivation step The noun extractor 2200, the first inference model 2300, the word embedding model 2400, and the second inference model 2500 used in (S220) are stored, and additionally, in the subnode input step described later. The detailed classification model (2600) used is stored.

Below, a more detailed description of the tree creation step (S1000) and the related regulatory law derivation step (S2000) will be described.

Figure 14 schematically shows the process of performing the tree creation step (S1000) of generating an initial tree according to user input according to an embodiment of the present invention.

As shown in FIG. 14, the tree creation step (S1000) includes a root node input step of inputting information to be recorded in the highest node based on the user's input; and a sub-node input step of inputting information to be recorded in one or more nodes connected to sub-concepts of the top-level node.

Schematically, Figure 14 (a) shows the process of the tree creation unit 12000 generating a primary information hierarchical tree based on user input, and Figure 14 (b) shows the tree creation unit 12000 Shows an example of a primary information hierarchical tree generated by .

Specifically, the primary information hierarchical tree includes a root node and a first lower node, as shown in (b) of FIG. 14. The initial node described throughout the specification corresponds to the primary information hierarchical tree. The root node is the highest node of the generated information hierarchical tree and refers to a node without a parent node, and the first child node refers to a node connected to a child concept of the root node. As an embodiment of the present invention, the number of first sub-nodes may be determined by the user, and information input to the root node and the first sub-node may also be determined by the user.

In other words, the tree creation unit 12000 can input the structure of the primary information hierarchical tree and information to be recorded in each node included in the primary information hierarchical tree according to user input. According to one embodiment of the present invention, after creating the structures of both the root node and the first subnode, information to be recorded in each of the primary information hierarchical trees can be input, and according to another embodiment of the present invention, , the information to be recorded in the root node can be input first and then the structure of the first sub-node can be created. According to another embodiment of the present invention, after the primary information hierarchical tree is completed through user input, additional Structure of the primary information hierarchical tree by receiving user input; Alternatively, information recorded in nodes included in the primary information hierarchical tree may be modified or deleted. The user input includes various inputs that can be performed on the user terminal, such as selection input, text input, and drag input.

As shown in (b) of FIG. 14, as an example of the root node input step performed by the tree creation unit 12000, the process is performed to find regulatory provisions related to the business to be conducted when reviewing a new business or preparing to start a business in a company, etc. Keywords related to the business you wish to pursue can be entered into the root node, or government ministries, legislative bodies, and national research institutes reviewing regulations can enter keywords from the specific field into the root node to find legal provisions related to a specific field. You can enter it.

When information is input to the root node, the tree generator 12000 receives sub-concepts of the information input to the root node through user input and inputs the received sub-concepts into the first sub-node. Perform. Meanwhile, as an embodiment of the present invention, the sub-node input step may be performed by the user based on user input directly as described above. However, as another embodiment of the present invention, the sub-node input step may be performed by the computing It can be performed on its own inside the system 10000. A description of the other embodiments will be provided later in the description of FIG. 15 .

Additionally, the subnode input step may be performed repeatedly. More specifically, after the primary information hierarchical tree is completed through the root node input step and the subnode input step, the tree creation unit 12000 additionally receives user input to create a secondary information hierarchical tree including a second subnode. An information hierarchical tree can be created.

In other words, the upper node of the node where the information input in the lower node input step is recorded is not limited to the top node, and is one or more nodes connected to the lower concept of the node selected by the user on the information hierarchy tree through the lower node input step. Information to be recorded can be entered. An example of the secondary information hierarchical tree is shown in FIG. 16.

Figure 15 schematically shows the execution process of the subnode input step according to an embodiment of the present invention.

As shown in FIG. 15, the sub-node input step includes inputting information recorded in a specific node into a detailed classification model 2600; and inputting each of a preset number of detailed items derived from the detailed classification model 2600 into each of one or more nodes connected to a sub-concept of the specific node.

Specifically, with reference to FIG. 13, the detailed classification model 2600 may correspond to a discriminative model stored in the database 2000, but is not limited thereto and may be stored outside the computing system 10000. It may correspond to a generative AI model such as Chat GPT/Bing/Bard. That is, the detailed classification model 2600 can be implemented using various known technologies.

When the tree generator 12000 inputs the information recorded in the root node through the root node input step into the detailed classification model 2600, the detailed classification model 2600 corresponds to a sub-concept of the input information. More than one keyword or noun (phrase) can be derived. Afterwards, the tree creation unit 12000 can perform a sub-node input step using the derived sub-concept. Referring to FIG. 14, when the user selects the root node, the detailed classification model 2600 includes sub-concepts related to bio-healthcare entered into the root node, such as 'medical device', 'medicine', 'digital healthcare', and ' 'Bio' can be output, and referring to FIG. 16, when the user selects the 'drug' node of the first sub-node, the detailed classification model 2600 receives the word 'drug' as input and selects 'manufacturing/registration'. ·You can derive sub-concepts such as ‘license’, ‘drug safety’, ‘clinical trial’, and ‘import/sale/advertisement’.

More specifically, the detailed classification model 2600 learns the legal information of the legal provisions stored in the database 2000 and then creates legal provisions with similar content to the information of the input upper node based on the original content of the legal provisions. By deriving the keywords or key words of the relevant legal provisions, it can be derived as a sub-concept of the information of the upper node.

As described above, information to be recorded in nodes other than the root node can be directly input by user input or can be input by the computing system 10000 of the present invention. Meanwhile, as an embodiment of the present invention, the order of keywords or words derived as lower-level concepts may be determined based on the similarity with information input to the parent node of the corresponding node.

Figures 17 and 18 schematically show the execution steps and execution process of the relevant regulatory law derivation step (S2000) according to an embodiment of the present invention.

As shown in Figures 17 and 18, the relevant regulatory law derivation step (S2000) includes a basic regulatory information reception step of receiving basic regulatory information for the corresponding node from the user; And based on the similarity score, which is numerical information about the similarity between each of a plurality of legal provisions stored in the database (2000) and the basic regulatory information, and the regulation score, which is numerical information about the regulatory attributes of the relevant legal provisions, 1. It includes a first related regulatory information derivation step of deriving related regulatory information.

In addition, the relevant regulatory law derivation step (S2000) includes a similarity score derivation step (S200) and a regulatory score derivation step (S100) for deriving related regulatory information, and the similarity score derivation step (S200) includes a keyword matching score. It includes a derivation step (S210), a cosine similarity score derivation step (S220), and a correlation score derivation step (S230).

According to one embodiment of the present invention, the relevant regulatory law derivation step (S2000) determines legal provisions and corresponding legal information greater than or equal to a preset regulatory score as relevant regulatory information, and then provides the determined relevant regulatory information to the user in a layered form. to provide. Meanwhile, according to another embodiment of the present invention, the relevant regulatory law derivation step (S2000) is a standard regulatory score calculated based on the basic regulatory information and the regulatory score of the legal provisions having a preset number of higher similarity scores. After determining legal provisions and relevant legal information as related regulatory information, the determined related regulatory information is provided to users in a layered form.

Specifically, as shown in (a) of FIGS. 17 and 18, the user may select (S2100) one of a plurality of nodes included in the information hierarchical tree created in the tree creation step (S1000). Afterwards, the user inputs basic regulatory information for the selected node, and the relevant regulatory law derivation unit 11000 performs a basic regulatory information reception step of receiving the information (S2200). For example, referring to FIG. 16, when the user selects the 'drug safety' node, the user can input legal provisions related to 'drug safety' as basic regulatory information.

That is, the relevant regulation law derivation unit 11000, which receives the node selected by the user and the basic regulatory information input by the user, refers to the description of FIGS. 5 to 10 described above, and generates the basic regulatory information and database. (2000), derive the similarity score and regulatory score between each of the plurality of legal provisions stored. The similarity score corresponds to quantified information about the similarity between two different legal provisions, and the regulation score corresponds to quantified information about the regulatory attributes of the input legal provisions.

The relevant regulatory law derivation unit 11000 performs a first related regulatory information reception step of deriving first related regulatory information related to the basic regulatory information input based on the derived similarity score and regulatory score (S2300), First, relevant regulatory information may be included in the aforementioned regulatory legal provisions.

Among the plurality of derived regulatory law provisions, the first relevant regulatory information can be derived by the relevant regulatory law derivation unit 11000. More specifically, the relevant regulatory law derivation unit 11000 derives legal provisions having a similarity score higher than the basic regulatory information and a preset standard for a plurality of regulatory legal provisions derived according to the input basic regulatory information (S2400 ), then, from among the derived legal provisions, a preset number of legal provisions with a regulatory score higher than the preset standard are derived (S2500), and these are determined as the first relevant regulatory information.

More specifically, as an embodiment of the present invention, the relevant regulatory law derivation unit 11000 selects the legal provisions with a higher similarity score with respect to the basic regulatory information input from the user among a plurality of legal provisions stored in the database 2000. Derived, a preset number of legal provisions with a preset regulatory score or higher among the legal provisions with the higher similarity score and the corresponding legal information can be determined as the first relevant regulatory information.

Meanwhile, in another embodiment of the present invention, the relevant regulatory law derivation unit 11000 derives legal provisions with a higher similarity score for basic regulatory information input from the user among a plurality of legal provisions stored in the database 2000. , the average value of the regulatory scores of the legal provisions with the higher similarity score can be derived, and the legal provisions and the corresponding legal information with the regulatory score higher than the average value can be determined as the first relevant regulatory information.

After the first related regulatory information is determined, the related regulatory law derivation unit 11000 adds the first related regulatory information as a sub-concept of the node selected by the user, referring to (a) of FIG. 18. The first related regulatory information can be added as a lower node and the related regulatory information can be provided to the user in a hierarchical information hierarchical tree. An example of the information hierarchical tree is shown in FIGS. 19 and 20.

Figure 19 shows information including the title and enforcement date of the legal provisions derived from the first related regulatory information, and Figure 20 shows the regulatory summary and related laws and regulations for each derived legal provision.

That is, as shown in Figure 19, according to one embodiment of the present invention, the three legal provisions derived in the first relevant regulatory information derivation step can be added as child nodes of the 'clinical trial' node selected by the user. . Meanwhile, according to another embodiment of the present invention, as shown in FIG. 20, more legal information may be described for the first related regulatory information added as a child node. 19 and 20 are examples of the present invention. In the actual present invention, less or more first related regulatory information may be added as lower nodes.

Additionally, the relevant regulatory law derivation step (S2000) includes receiving additional regulatory information from the user; and a similarity score between each of a plurality of legal provisions stored in the database (2000) and the additional regulatory information; and a step of deriving second related regulatory information based on the regulatory score of the relevant legal provision.

Specifically, as shown in FIGS. 19 and 20, after the tertiary information hierarchical tree from which the first related regulatory information is derived is created, the user selects any one of the nodes included in the tertiary information hierarchical tree, If additional regulatory information for the selected node is input, the related regulation law provision unit 1300 can derive second related regulatory information.

Similar to the process of deriving the first relevant regulatory information, the relevant regulatory law provision unit 1300 derives a similarity score between the additional regulatory information and each of the plurality of legal provisions stored in the database 2000, and The legal provisions and relevant legal information derived based on the derived regulatory score and the similarity score are determined as the second relevant regulatory information. The determined second related regulatory information, like the first related regulatory information, may be added as a child node of the node selected by the user and then provided to the user in the form of a hierarchical information hierarchy tree.

FIG. 21 exemplarily shows the internal configuration of a computing device 11000 according to an embodiment of the present invention.

The computing system 1000 mentioned in the description of FIG. 1 and the computing system 10000 mentioned in the description of FIG. 13 may include components of the computing device 11000 shown in FIG. 21, which will be described later.

As shown in FIG. 21, the computing device 11000 includes at least one processor 11100, a memory 11200, a peripheral interface 11300, and an input/output subsystem ( It may include at least an I/O subsystem (11400), a power circuit (11500), and a communication circuit (11600).

Specifically, the memory 11200 may be, for example, high-speed random access memory, magnetic disk, SRAM, DRAM, ROM, flash memory, or non-volatile memory. may include. The memory 11200 may include software modules, instruction sets, or other various data necessary for the operation of the computing device 11000.

At this time, access to the memory 11200 from other components such as the processor 11100 or the peripheral device interface 11300 may be controlled by the processor 11100. The processor 11100 may be composed of a single processor or a plurality of processors, and may include GPU and TPU type processors to improve calculation processing speed.

The peripheral device interface 11300 may couple input and/or output peripheral devices of the computing device 11000 to the processor 11100 and the memory 11200. The processor 11100 may execute a software module or set of instructions stored in the memory 11200 to perform various functions for the computing device 11000 and process data.

The input/output subsystem 11400 can couple various input/output peripheral devices to the peripheral device interface 11300. For example, the input/output subsystem 11400 may include a controller for coupling peripheral devices such as a monitor, keyboard, mouse, printer, or, if necessary, a touch screen or sensor to the peripheral device interface 11300. there is. According to another aspect, the input/output peripheral devices may be coupled to the peripheral interface 11300 without going through the input/output subsystem 11400.

The power circuit 11500 may supply power to all or part of the components of the terminal. For example, the power circuit 11500 may include a power management system, one or more power sources such as batteries or alternating current (AC), a charging system, a power failure detection circuit, a power converter or inverter, a power status indicator, or It may include any other components for power generation, management, and distribution.

The communication circuit 11600 may enable communication with another computing device using at least one external port. Alternatively, as described above, if necessary, the communication circuit 11600 may include an RF circuit to transmit and receive RF signals, also known as electromagnetic signals, to enable communication with other computing devices.

This embodiment of FIG. 21 is only an example of the computing device 11000, and the computing device 11000 may omit some components shown in FIG. 21 or further include additional components not shown in FIG. 21. , may have a configuration or arrangement that combines two or more components. For example, a computing device for a communication terminal in a mobile environment may further include a touch screen or a sensor in addition to the components shown in FIG. 21, and the communication circuit 1160 may include various communication methods (Wi-Fi, Circuits for RF communication (3G, LTE, 5G, 6G, Bluetooth, NFC, Zigbee, etc.) may be included. Components that can be included in the computing device 11000 may be implemented as hardware, software, or a combination of both hardware and software, including an integrated circuit specialized for one or more signal processing or applications.

Methods according to embodiments of the present invention may be implemented in the form of program instructions that can be executed through various computing devices and recorded on a computer-readable medium. In particular, the program according to this embodiment may be composed of a PC-based program or a mobile terminal-specific application. The application to which the present invention is applied can be installed on a user terminal through a file provided by a file distribution system. As an example, the file distribution system may include a file transmission unit (not shown) that transmits the file according to a request from the user terminal.

The device described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, devices and components described in embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), etc. , may be implemented using one or more general-purpose or special-purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used by any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be stored or executed in a standardized manner on a networked computing device. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

According to one embodiment of the present invention, rather than a list classified by field of law or relevant ministry, regulatory legal provisions classified by subject based on the actual legal provisions are provided in a tree form with a hierarchical structure, so that the search term entered by the user Alternatively, it can have the effect of providing legal provisions substantially related to the search statement.

According to one embodiment of the present invention, laws that are not classified as regulations are derived by deriving regulatory legal provisions based on regulatory scores derived through a learned regulatory classification model without providing information on regulatory legal provisions designated by the government as is. This can have the effect of making it easier to find shadow regulation provisions that are recognized as regulations.

According to one embodiment of the present invention, when a company, etc., is reviewing a new business or preparing to start a business, it is possible to easily find regulatory provisions related to the business to be conducted.

According to one embodiment of the present invention, government ministries, legislative bodies, and national research institutes reviewing regulations can easily find legal provisions related to a specific field, which has the effect of easily monitoring legal provisions that require legislation or revision. can be demonstrated.

According to one embodiment of the present invention, users can enter search terms in various formats, allowing users to easily search for regulatory provisions in the field they are looking for even if they do not have sufficient legal knowledge such as legal terms.

As described above, although the embodiments have been described with limited examples and drawings, various modifications and variations can be made by those skilled in the art from the above description. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent. Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (12)

A method of generating an issue-specific information hierarchy tree including regulatory information, performed on a computing system including one or more processors and one or more memories,
A tree creation step of creating an information hierarchical tree with a plurality of nodes, wherein the node in the information hierarchical tree is connected to at least one other node as a parent or child, and information recorded in the node and other nodes A tree creation step in which the relationship with is determined according to the user's input; and
For the node selected by the user, it includes a related regulation law derivation step of deriving one or more related regulations for the node,
The relevant regulatory law derivation step is,
A basic regulatory information receiving step of receiving basic regulatory information for a corresponding node from a user, wherein the basic regulatory information corresponds to legal provisions related to keywords included in the node selected by the user; and
Based on the similarity score, which is quantified information about the similarity between each of a plurality of legal provisions stored in the database and the basic regulatory information, and the regulation score, which is quantified information about the regulatory attributes of the relevant legal provisions, first related regulatory information Includes a first relevant regulatory information derivation step of deriving,
The tree creation step is,
A root node input step of inputting information to be recorded in the highest node based on the user's input; and
A sub-node input step of inputting information to be recorded in one or more nodes connected to sub-concepts of the top-level node,
The subnode input step is,
Inputting information recorded in a specific node into a detailed classification model; and
Including a step of inputting each of a preset number of detailed items derived from the detailed classification model into each of one or more nodes connected to a sub-concept of the specific node,
The detailed classification model learns the legal information of the legal provisions stored in the database, then derives legal provisions with similar content to the information of the input upper node based on the original content of the legal provisions, and identifies the keyword or core of the legal provision. A method of creating an issue-specific information hierarchy tree by extracting words and deriving them as subconcepts of the information of the upper node.
delete delete In claim 1,
The relevant regulatory law derivation step further includes a similarity score derivation step of deriving the similarity score,
The similarity score derivation step is,
A keyword matching score derivation step of deriving a keyword matching score by inputting each of the basic regulatory information and a plurality of legal provisions stored in the database into a first inference model based on an artificial neural network; and
A method of generating an information hierarchical tree for each issue, including a cosine similarity score deriving step of deriving a cosine similarity score between each of a plurality of legal provisions stored in the database and the basic regulatory information.
In claim 4,
The keyword matching score derivation step is,
Extracting keywords from basic regulatory information input from the user;
Converting each of the extracted plurality of keywords into a vector having a dimension of 1 or more, and then deriving the converted plurality of vectors into one keyword vector; and
A method of generating an information hierarchical tree for each issue, including the step of inputting legal provisions and keyword vectors into a first inference model based on an artificial neural network, thereby deriving a keyword matching score indicating the similarity between the input legal provisions and the keyword vectors. .
In claim 4,
The cosine similarity score derivation step is,
Deriving a basic regulation vector for the basic regulation information input from the user through an artificial neural network-based word embedding model; and
A method of generating an information hierarchical tree for each issue, comprising: deriving a cosine similarity score based on cosine similarity derived between each of all legal provisions stored in the database and the basic regulation vector.
In claim 1,
The relevant regulatory law derivation step further includes a regulatory score derivation step of deriving the regulatory score,
The regulatory score derivation step is,
Enter each of the plurality of legal provisions stored in the database into the regulatory classification model to derive the regulatory score corresponding to each legal provision,
The regulatory classification model is a method of generating an information hierarchical tree for each issue, learned with regulation card information of legal provisions with regulatory card information among a plurality of legal provisions stored in the database.
In claim 1,
The relevant regulatory law derivation step is,
A method of creating an information hierarchy tree for each issue that determines legal provisions and relevant legal information above a preset regulatory score as relevant regulatory information and then provides the determined related regulatory information to users in a hierarchical form.
In claim 1,
The relevant regulatory law derivation step is,
After determining the basic regulatory information and the legal provisions above the standard regulatory score calculated based on the regulatory score of the legal provisions with a preset number of higher similarity scores and the corresponding legal information as relevant regulatory information, the determined related regulatory information is presented in a hierarchical form. A method of deriving regulatory legal provisions provided to users.
In claim 1,
The relevant regulatory law derivation step is,
Receiving additional regulatory information from the user; and
A similarity score between each of a plurality of legal provisions stored in the database and the additional regulatory information; A method of generating an information hierarchy tree for each issue, further comprising: deriving second relevant regulatory information based on the regulatory score of the relevant legal provision.
A computing system that includes one or more processors and one or more memories, and performs a method of generating an issue-specific information hierarchical tree including regulatory information,
A tree creation step of creating an information hierarchical tree with a plurality of nodes, wherein the node in the information hierarchical tree is connected to at least one other node as a parent or child, and information recorded in the node and other nodes A tree creation step in which the relationship with is determined according to the user's input; and
For the node selected by the user, perform a related regulation law derivation step of deriving one or more related regulations for the node,
The relevant regulatory law derivation step is,
A basic regulatory information receiving step of receiving basic regulatory information for a corresponding node from a user, wherein the basic regulatory information corresponds to legal provisions related to keywords included in the node selected by the user; and
Based on the similarity score, which is quantified information about the similarity between each of a plurality of legal provisions stored in the database and the basic regulatory information, and the regulation score, which is quantified information about the regulatory attributes of the relevant legal provisions, first related regulatory information Includes a first relevant regulatory information derivation step of deriving,
The tree creation step is,
A root node input step of inputting information to be recorded in the highest node based on the user's input; and
A sub-node input step of inputting information to be recorded in one or more nodes connected to sub-concepts of the top-level node,
The subnode input step is,
Inputting information recorded in a specific node into a detailed classification model; and
Including a step of inputting each of a preset number of detailed items derived from the detailed classification model into each of one or more nodes connected to a sub-concept of the specific node,
The detailed classification model learns the legal information of the legal provisions stored in the database, then derives legal provisions with similar content to the information of the input upper node based on the original content of the legal provisions, and identifies the keyword or core of the legal provision. A computing system that extracts words and derives them as subconcepts of the information of the upper node.
A computer-readable recording medium for implementing a method of generating an issue-specific information hierarchical tree including regulatory information, performed on a computing system including one or more processors and one or more memories, comprising:
The computer-readable recording medium stores instructions that cause the computing system to perform the following steps,
The steps below are:
A tree creation step of creating an information hierarchical tree with a plurality of nodes, wherein the node in the information hierarchical tree is connected to at least one other node as a parent or child, and information recorded in the node and other nodes A tree creation step in which the relationship with is determined according to the user's input; and
For the node selected by the user, it includes a related regulation law derivation step of deriving one or more related regulations for the node,
The relevant regulatory law derivation step is,
A basic regulatory information receiving step of receiving basic regulatory information for a corresponding node from a user, wherein the basic regulatory information corresponds to legal provisions related to keywords included in the node selected by the user; and
Based on the similarity score, which is quantified information about the similarity between each of a plurality of legal provisions stored in the database and the basic regulatory information, and the regulation score, which is quantified information about the regulatory attributes of the relevant legal provisions, first related regulatory information Includes a first relevant regulatory information derivation step of deriving,
The tree creation step is,
A root node input step of inputting information to be recorded in the highest node based on the user's input; and
A sub-node input step of inputting information to be recorded in one or more nodes connected to sub-concepts of the top-level node,
The subnode input step is,
Inputting information recorded in a specific node into a detailed classification model; and
Including a step of inputting each of a preset number of detailed items derived from the detailed classification model into each of one or more nodes connected to a sub-concept of the specific node,
The detailed classification model learns the legal information of the legal provisions stored in the database, then derives legal provisions with similar content to the information of the input upper node based on the original content of the legal provisions, and identifies the keyword or core of the legal provision. A computer-readable recording medium that extracts words and derives them as subconcepts of the information of the upper node.

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