KR102255704B1 - System for providing legal service and method thereof - Google Patents

Abstract

Provided are a system for providing a legal service related to a job of a company and a method thereof. According to some embodiments of the present invention, a legal service providing system comprises: an analysis platform that derives laws related to a specific standard job by analyzing legal information and standard job information using an artificial intelligence model; and a service platform that provides legal services for a target job based on information derived by the analysis platform. For example, the service platform may provide legal information related to a target job and accordingly, a person in charge of a company's job can be provided with useful legal information that he needs to know when performing his job without help of a legal expert.

Description

Legal service provision system and its method {SYSTEM FOR PROVIDING LEGAL SERVICE AND METHOD THEREOF}

The present disclosure relates to a legal service providing system and method thereof. More specifically, it relates to a system for providing legal services related to the job of an enterprise and a method performed in the system.

Companies must establish in-house rules in consideration of job-related laws, and the company's job managers must familiarize themselves with and comply with the company rules when performing their duties. This is because, in case of violation of the relevant laws, corresponding economic or administrative sanctions may be imposed on not only the company but also the person in charge of the job.

In relation to this, some large corporations are establishing and maintaining job systems and internal rules based on job-related laws. However, most companies still have not established internal rules in consideration of job-related laws, and job systems are not in accordance with standards or are not clearly classified.

Companies such as the ones above need help from outside experts in order to familiarize themselves with job-related laws, but since this requires a considerable cost, it is inevitably a burden for companies. However, it can be said that it is also very difficult for a job manager without legal knowledge to directly search or find out job-related laws.

Therefore, there is a need for a system capable of providing legal information related to a job or comprehensive legal services to the above companies or job managers.

Korean Patent Publication No. 10-2015-0026240 (published on Mar 11, 2015)

A technical problem to be solved through some embodiments of the present disclosure is to provide a system capable of providing legal services related to a job and a method performed in the system.

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

In order to solve the above technical problem, the legal service providing system according to some embodiments of the present disclosure includes an analysis platform for deriving a law related to a specific standard job by analyzing legal information and standard job information using an artificial intelligence model, and A service platform that provides legal services for a target job based on the information derived by the analysis platform, wherein the analysis platform includes: a legal interpreter for interpreting the legal information, a job interpreter for analyzing the standard job information, and the By analyzing the relationship between the standard job and the law based on the analysis result of the legal interpreter and the analysis result of the job interpreter, a relationship analyzer for deriving a law related to the specific standard job may be included.

In some embodiments, the standard job information may be information collected from a national job competency standard site.

In some embodiments, the legal interpreter interprets the legal information through a Named Entity Recognition (NER) model, but the NER model includes a Bidirectional Encoder Representations from Transformers (BERT) model and a Bi-LSTM-CRF (Bidirectional Long Short- Term Memory with a Conditional Random Field Layer) model.

In some embodiments, the legal interpreter extracts a legal entity set from the legal information through a Named Entity Recognition (NER) model, and the job interpreter extracts a standard job entity set from the standard job information through the NER model. , The relationship analyzer may derive a law related to the specific standard job based on the similarity between the extracted legal entity set and the extracted standard job entity set.

In some embodiments, further comprising a dictionary interpreter for configuring a legal dictionary using the extracted legal entity set, and configuring a standard job dictionary using the extracted standard job entity set, wherein the relationship analyzer comprises: A law related to the specific standard job may be derived based on the degree of similarity between the configured legal dictionary and the configured standard job dictionary.

In some embodiments, the relationship analyzer, in determining the relationship between the specific standard job and the specific law, the specific standard job and the specific standard job based on a relationship between the specific law and a higher job or a lower job of the specific standard job. The relationship between the specific laws can be determined.

In some embodiments, the relationship analyzer, in analyzing the relationship between the specific standard job and the specific law, the specific standard job based on a relationship between the specific law and a higher job or a lower job of the specific standard job It is possible to determine the relationship between the and the specific law.

In some embodiments, the service platform provides visualized information on the target job and related laws based on the information derived by the analysis platform, but the space formed by the first axis, the second axis, and the third axis The visualized information is generated by arranging a plurality of entities for the target job, the related law, and the related standard job of the target job, and the first axis, the second axis, and the third axis are each target job. It can have correspondence with information, legal information and standard job information.

In some embodiments, the service platform may determine a standard job corresponding to the target job and provide legal information related to the determined standard job as legal information related to the target job.

The legal service providing method according to some embodiments of the present disclosure for solving the above-described technical problem is, in the legal service providing method performed by at least one computing device, legal information and standard duties using an artificial intelligence model. Deriving a law related to a specific standard job by analyzing information, and providing a legal service for a target job based on the derived result, the step of deriving a law related to the specific standard job, the artificial Analyzing the legal information using an intelligent model, analyzing the standard job information using the artificial intelligence model, and the specific standard job based on the analysis result of the legal information and the analysis result of the standard job information It may include the step of deriving a law related to the.

The computer program according to some embodiments of the present disclosure for solving the above-described technical problem is combined with a computing device to derive laws related to a specific standard job by analyzing legal information and standard job information using an artificial intelligence model. It may be stored in a computer-readable recording medium to execute the step of performing the step of performing the step of performing the step of providing a legal service for a target job based on the result of the derivation. At this time, the step of deriving the law related to the specific standard job includes interpreting the legal information using the artificial intelligence model, interpreting the standard job information using the artificial intelligence model, and the legal information It may include the step of deriving a law related to the specific standard job based on the analysis result and the analysis result of the standard job information.

According to some embodiments of the present disclosure described above, a law related to a standard job is derived using an artificial intelligence model, and a legal service for the job of a company may be provided based on this. Accordingly, the person in charge of the job of the company can easily receive legal information related to the job without the help of a legal expert. Furthermore, companies can reduce the cost of consulting legal experts and establish in-house rules in consideration of job-related laws at a relatively low cost.

In addition, an entity set is extracted from various legal information and standard job information through the Named Entity Recognition (NER) model, which is one of the artificial intelligence models, and the relevance between the standard job and the law is determined based on the similarity between the entity sets. Can be. Accordingly, the relationship between the standard job and the law can be accurately determined.

In addition, the relationship between the standard job and the law may be determined based on the prior similarity between the standard job and the law. Accordingly, the relationship between the standard job and the law can be determined more accurately.

In addition, legal information related to the job of the company may be provided based on the standard job. Accordingly, consistent and accurate legal information can be provided even if the job name is different between companies.

In addition, legal information related to the job may be provided as visualized information along with the related standard job. Accordingly, information can be delivered more effectively.

The effects according to the technical idea of the present disclosure are not limited to the above-mentioned effects, and other effects that are not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

1 illustrates an exemplary service providing environment of a legal service providing system according to some embodiments of the present disclosure.
2 is an exemplary block diagram illustrating a legal service providing system according to some embodiments of the present disclosure.
3 is an exemplary flowchart illustrating a method of analyzing a relationship between standard job information and legal information according to some embodiments of the present disclosure.
4 is an exemplary diagram illustrating a process of extracting a legal entity set from legal information according to some embodiments of the present disclosure.
5 illustrates a structure of a Named Entity Recognition (NER) model according to some embodiments of the present disclosure.
6 and 7 illustrate an entity classification scheme that may be referred to in some embodiments of the present disclosure.
8 illustrates standard job information that can be referenced in some embodiments of the present disclosure.
9 is an exemplary flowchart showing a detailed process of the step S300 of deriving a relationship shown in FIG. 3.
10 and 11 are exemplary diagrams for explaining a method of pre-configuring a job according to some embodiments of the present disclosure.
12 and 13 are exemplary diagrams for explaining a method of determining a relationship between a law and a standard job according to some embodiments of the present disclosure.
14 is an exemplary flowchart illustrating a method of providing legal services for a target job according to some embodiments of the present disclosure.
15 to 19 are exemplary diagrams for explaining a method of visualizing legal information related to a target job according to some embodiments of the present disclosure.
20 illustrates an exemplary computing device capable of implementing a service providing system or components thereof according to some embodiments of the present disclosure.

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Advantages and features of the present disclosure, and a method of achieving them will be apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the technical idea of the present disclosure is not limited to the following embodiments, but may be implemented in various different forms, and only the following embodiments complete the technical idea of the present disclosure, and in the technical field to which the present disclosure pertains. It is provided to completely inform the scope of the present disclosure to those of ordinary skill in the art, and the technical idea of the present disclosure is only defined by the scope of the claims.

In adding reference numerals to elements of each drawing, it should be noted that the same elements are assigned the same numerals as possible, even if they are indicated on different drawings. In addition, in describing the present disclosure, when it is determined that a detailed description of a related known configuration or function may obscure the subject matter of the present disclosure, a detailed description thereof will be omitted.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used with meanings that can be commonly understood by those of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in a commonly used dictionary are not interpreted ideally or excessively unless explicitly defined specifically. The terms used in the present specification are for describing exemplary embodiments and are not intended to limit the present disclosure. In this specification, the singular form also includes the plural form unless specifically stated in the phrase.

In addition, in describing the constituent elements of the present disclosure, terms such as first, second, A, B, (a), (b) may be used. These terms are only for distinguishing the component from other components, and the nature, order, or order of the component is not limited by the term. When a component is described as being "connected", "coupled" or "connected" to another component, the component may be directly connected or connected to that other component, but another component between each component It will be understood that elements may be “connected”, “coupled” or “connected”.

As used in this disclosure, "comprises" and/or "comprising" refers to the referenced components, steps, actions, and/or elements of one or more other elements, steps, actions and/or elements. It does not exclude presence or addition.

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

1 illustrates an exemplary service providing environment of a legal service providing system according to some embodiments of the present disclosure.

As shown in FIG. 1, the legal service providing system 1 may provide various legal services for a job to the service use device 100. For example, the legal service providing system 1 may provide legal information related to a target job or may provide information of a legal expert who can consult (or advise) on legal issues related to the target job. However, it is not limited thereto. Here, the target job may refer to a job that is a job of a service user and is a subject of legal service consultation (e.g. an actual job of a company). Hereinafter, for convenience of description, "legal service providing system 1" will be abbreviated as "service providing system 1".

The service providing system 1 according to the embodiments may include an analysis platform 20 and a service platform 20. However, this is only a preferred embodiment for achieving the object of the present disclosure, and of course, some components may be added or deleted as necessary. The analysis platform 20 may mean a platform that collects standard job information and legal information and analyzes the relationship between them in order to provide legal services, and the service platform 10 is information analyzed by the analysis platform 20. It can mean a platform that provides legal services for a target job based on. The detailed configuration and operation principle of each of the platforms 10 and 20 will be described in detail later with reference to the accompanying drawings in FIG. 2.

The service providing system 1 or the platforms 10 and 20 constituting the same may be implemented as one or more computing devices. For example, each of the service platform 10 and the analysis platform 20 may be implemented as one or more computing devices. Alternatively, the service platform 10 and the analysis platform 20 may be implemented in the form of different logic in one or more computing devices. In this way, a method of implementing the service providing system 1 may be designed in various ways.

The computing device may be a notebook, a desktop, a laptop, etc., but is not limited thereto, and may include all types of devices equipped with a computing function and a communication function. However, in an environment in which legal services are provided to a plurality of service use devices 100, the service providing system 1 may be preferably implemented as a high-performance server-class computing device. Referring to FIG. 20 for an example of a computing device.

Next, the service use device 100 may refer to a device of a service user who receives legal services from the service providing system 1. Here, the service user may be, for example, a company or a job manager of a company, but is not limited thereto. The service use device 100 provides the company's target job information to the service providing system 1, can request legal services for the target job, and receive legal services for the target job from the service providing system 1 have. For example, the service use device 100 may receive legal information related to a target job or information of a legal expert.

The service use device 100 may be implemented as various types of computing devices such as a notebook, a desktop, a laptop, and a smart phone.

As shown, the service providing system 1 and the service using device 100 may communicate through a network. Here, the network is implemented as all kinds of wired/wireless networks such as a local area network (LAN), a wide area network (WAN), a mobile radio communication network, and a Wibro (Wireless Broadband Internet). Can be.

So far, a service providing system 1 and a service providing environment according to some embodiments of the present disclosure have been schematically described with reference to FIG. 1. As described above, legal services for a target job may be provided through the service providing system 1. Accordingly, the company's job manager can easily receive legal information about his or her job without the help of a legal expert.

Hereinafter, a detailed configuration and operation principle of the service providing system 1 will be described in detail with reference to the accompanying drawings in FIG. 2.

2 is an exemplary block diagram illustrating a service providing system 1 according to some embodiments of the present disclosure. In particular, FIG. 2 shows an example of the data flow of the service providing system 1 together.

As shown in FIG. 2, the service providing system 1 may include an analysis platform 20 and a service platform 10. In addition, each of the analysis platform 20 and the service platform 10 may include one or more components. However, this is only a preferred embodiment for achieving the object of the present disclosure, and of course, some components may be added or deleted as necessary. In addition, it should be noted that each of the components of the service providing system 1 shown in FIG. 2 represents functional elements that are functionally divided, and a plurality of components may be implemented in a form in which they are integrated with each other in an actual physical environment. .

In addition, in an actual physical environment, each of the components may be implemented in a form that is separated into a plurality of detailed functional elements. For example, a first function of the service server 11 may be implemented in a first computing device, and a second function may be implemented in a second computing device. Alternatively, a specific function of the service server 11 may be implemented by a plurality of computing devices. Hereinafter, each component of the service providing system 1 will be described.

First, the analysis platform 20 according to the embodiments may include various information DBs 25 to 27, a legal interpreter 22, a job interpreter 23, a dictionary interpreter 24, and a relationship analyzer 21. .

Various information DBs (25 to 27) may include, for example, legal information DB 25, job information DB 26, and dictionary information DB 27, and information collected from the data source 30 is stored. Can be. However, it is not limited thereto.

Legal information collected from the legal site 31 may be stored in the legal information DB 25. Here, the legal information may include, for example, laws, treaties, orders, rules, guidelines, precedents, etc., but is not limited thereto. In addition, the legal information may be collected from the legal site 31 through crawling of a legal information collector (not shown), but is not limited thereto, and any method of collecting legal information may be used. .

Next, the job information DB 26 may store job information collected from a standard job site 32 (e.g. NCS national job competency standard site). Here, the job information may include a variety of job-related information such as job system, job description (e.g. job description, etc.), job difficulty, as well as job-related documents (e.g. report sample, etc.). In addition, the job information may be collected from the standard job site 32 through crawling of a job information collector (not shown), but is not limited thereto, and any method of collecting job information may be used.

In addition, the job information DB 26 may also store target job information received from the service use device 100. The target job information may be job information customized according to the characteristics of a company, for example, but is not limited thereto. Some or all of the target job information may be the same as the standard job information.

Next, dictionary information collected from the portal/wiki site 33 may be stored in the dictionary information DB 27. Here, the dictionary information may include, for example, definitions for terms (e.g. legal terms, job terms), and explanatory information such as example sentences, similar words, and opposite words, but is not limited thereto. In addition, the dictionary information may be collected by the portal/wiki site 33 through crawling by a dictionary information collector (not shown), but is not limited thereto, and the method of collecting the dictionary information may be any method.

Next, the legal interpreter 22 may interpret the legal information stored in the legal information DB 25 using an artificial intelligence model. For example, the legal interpreter 22 may extract a legal entity set by analyzing legal information through a natural language processing model (e.g. NER model). The legal entity set may include one or more legal entities, for example entities related to an actor (i.e. actor) of the law, an entity related to an act (i.e., an act) of the law, and the name of the law. It may include entities and entities representing key keywords of other laws. However, it is not limited thereto. In addition, the legal interpreter 22 may interpret the relationship between the extracted legal entities. For example, the legal interpreter 22 may determine a correspondence relationship between an actor-related entity and an act-related entity, or may define a type of relationship.

The legal information interpreted by the legal interpreter 22 may be utilized by the relationship analyzer 21 in the future, and may be stored in the legal information DB 25 and/or the service information DB 12. Detailed operations of the legal interpreter 22 will be described in more detail later with reference to FIGS. 3 to 7, and the like.

Next, the job interpreter 23 may analyze job information stored in the job information DB 26 through an artificial intelligence model. For example, the job interpreter 23 may extract a standard job entity set by analyzing standard job information through a natural language processing model (e.g. NER model). A standard job entity set may contain one or more standard job entities, for example entities related to the actors (i.e. actors) of the job, entities related to the acts of the job (i.e. actions), entities related to the name of the job, and It may include entities that represent key keywords of other jobs. However, it is not limited thereto. As another example, the job interpreter 23 may extract a target job entity set by analyzing the target job information.

The job information analyzed by the job interpreter 23 may be utilized by the relationship analyzer 21 in the future, and may be stored in the job information DB 26 and/or the service information DB 12. The detailed operation of the job interpreter 23 will be described in more detail later with reference to FIGS. 3 to 8, and the like.

Next, the dictionary interpreter 24 may interpret the dictionary information stored in the dictionary information DB 27 based on the entities extracted by the legal interpreter 22 and/or the job interpreter 23. For example, the dictionary interpreter 24 may generate a dictionary for a specific law based on the previously collected dictionary information, or may generate a dictionary for a standard job (or a target job). The dictionary for the legal and/or standard job (or target job) may be utilized by the relationship analyzer 21, and may be stored in the dictionary information DB 27 and/or the service information DB 12. Detailed operations of the pre-analyzer 24 will be described in more detail later with reference to FIGS. 3, 9 to 11, and the like.

Next, the relationship analyzer 21 may derive a relationship between the job and the law by analyzing information provided from the legal interpreter 22, the job interpreter 23, and the dictionary interpreter 24. For example, the relationship analyzer 21 may analyze a relationship between a standard job and a law based on a legal entity set, a standard job entity set, a legal dictionary, a standard job dictionary, and the like. As another example, the relationship analyzer 21 may analyze the relationship between a target job and a law based on a legal entity set, a target job entity set, a legal dictionary, a target job dictionary, and the like.

The analysis result of the relationship analyzer 21 may be stored in the service information DB 12 and/or another DB. Detailed operations of the relationship analyzer 21 will be described in more detail later with reference to FIGS. 3, 12, and 13.

Hereinafter, the service platform 10 will be described.

The service platform 10 according to embodiments may include a service information DB 12, a service server 11, and a management server 13.

The service information DB 12 may store various pieces of information for providing legal services. For example, the service information DB 12 may store analysis results of the analysis platform 20, legal expert information, member information, and the like. However, the present invention is not limited thereto, and various types of information used in the analysis platform 20 may be further stored in the service information DB 12.

Next, the service server 11 may provide legal services for a job (or company). Here, the legal service may include, for example, a legal information provision service, a legal expert matching service, a legal information inquiry service, a job-related consulting service, and various information subscription services, but is not limited thereto.

As a specific example, the service server 11 may receive target job information from the service use device 100 or a service user, and may provide legal information related to the target job. In this case, the service server 11 may also provide the legal information as described above in a visualized form. In connection with this embodiment, it will be described in more detail with reference to FIGS. 14 to 19 later.

As another example, the service server 11 may provide information on a legal expert related to a target job. The service server 11 may determine a legal expert suitable for consulting a target job based on the history of the legal expert and the degree of relevance to the target job, and provide information of the determined legal expert. At this time, the above relevance is the number (or ratio) of lawsuits related to the target job, the value (or cost) of the lawsuit, the increase or decrease in the number of lawsuits, the increase or decrease in the value of the lawsuit, the success rate of the lawsuit, the increase or decrease in the victory rate, and legal experts. It can be calculated by comprehensively considering the size of the company to which the company belongs, the number (or ratio) of lawsuits related to the target job of the affiliated company, the winning rate, and the trend of their increase or decrease. In addition, the more lawsuit information related to the target job in the recent history information of the legal expert (e.g. the number of related lawsuits recently increased or increased), the higher the degree of relevance can be calculated. According to the present embodiment, it is possible for a person in charge of a job of a company to easily receive information of a legal expert related to the job.

As another example, the service server 11 may provide an inquiry service for legal information. Specifically, the service server 11 may provide a function of inquiring legal information related to a job or a function of inquiring a job related to specific legal information. For example, the service server 11 may receive (input) a job keyword or a legal keyword, and provide an inquiry result for the received keyword. Alternatively, the service server 11 receives (inputs) natural language (eg sentences, etc.) related to the job or law, extracts keywords from the natural language received through the natural language processing model (eg extracts entities through NER, etc.), Search results for extracted keywords can be provided. According to the present embodiment, it is possible for a person in charge of a job of a company to easily search for job-related legal information.

As another example, the service server 11 may provide a subscription service for job information or legal information. As a more specific example, the service server 11 may provide a subscriber with information on amendments to laws related to a specific job, recent precedents, recent dispute cases, legal expert information, and the like. As another example, the service server 11 may provide update information related to a standard job to a subscription applicant. As another example, the service server 11 may provide the subscriber with legal information related to a target job in which legal service requests are increasing, or update information of a standard job related thereto. The service server 11 may provide subscription information as exemplified periodically or aperiodically to the subscriber. Such a subscription service may be provided only to paid members. In this case, a differentiated service according to whether or not to be charged may be provided, and the service provider's profit model may be improved.

In the previous example, the service server 11 may provide subscription information to non-subscribers. For example, the service server 11 creates and provides customized subscription information (eg recent dispute cases related to target job information, legal amendment information, etc.) based on the legal service usage history of non-subscribers (eg target job information, search keywords, etc.) can do. In this case, an effect of inducing non-subscribers to subscribe to the subscription service can be achieved.

Meanwhile, the service providing interface of the service server 11 (or the service using interface of the service using device 100) may be variously designed. For example, the service server 11 may receive or provide legal services such as a messenger (e.g. chatbot interface), web, e-mail, and app (e.g. mobile app). However, it is not limited thereto.

Next, the management server 13 may provide various management functions related to the service platform 10 or the service information DB 12. For example, the management server 13 may provide a function of management (eg addition, deletion, modification, etc.) for information (eg the relationship between job information and legal information) stored in the service information DB 12. A management interface for convenience can also be provided.

Each component of FIG. 2 may mean software or hardware such as a Field Programmable Gate Array (FPGA) or an Application-Specific Integrated Circuit (ASIC). However, the components are not limited to software or hardware, and may be configured to be in an addressable storage medium, or may be configured to execute one or more processors. The functions provided in the components may be implemented by more subdivided components, or may be implemented as one component that performs a specific function by combining a plurality of components.

So far, the service providing system 1 according to some embodiments of the present disclosure has been described with reference to FIG. 2. According to the above, a law related to a standard job is derived using an artificial intelligence model, and legal services for the job of a company can be provided based on this. Accordingly, the person in charge of the job of the company can easily receive legal information related to the job without the help of a legal expert. Furthermore, companies can reduce the cost of consulting legal experts and establish in-house rules in consideration of job-related laws at a relatively low cost.

Hereinafter, a method of providing a legal service according to some embodiments of the present disclosure will be described with reference to the accompanying drawings of FIG. 3.

Each step of the method for providing legal services to be described later may be performed by a computing device. In other words, each step of the method may be implemented with one or more instructions executed by the processor of the computing device. All of the steps included in the method may be performed by one physical computing device, but may be distributed and performed by a plurality of physical computing devices. For example, first steps of the method may be performed by a first computing device, and second steps of the method may be performed by a second computing device. In the following, description will be continued on the assumption that the method is performed by the service providing system 1 illustrated in FIG. 1 or 2. Accordingly, when a subject of a specific operation is omitted in the following description, it may be understood that it may be performed by the illustrated system 1.

The legal service provision method according to the embodiments may be conceptually divided into a method of analyzing a relationship between standard job information and legal information and a method of providing legal services for a target job based on the relationship analysis result. Therefore, in order to provide convenience for understanding, the above methods will be divided and described.

3 is an exemplary flowchart illustrating a method of analyzing a relationship between standard job information and legal information according to some embodiments of the present disclosure. However, this is only a preferred embodiment for achieving the object of the present disclosure, and of course, some steps may be added or deleted as necessary.

As shown in FIG. 3, the relationship analysis method may be started in step S100 of collecting legal information and standard job information. For example, the service provision system 1 may collect various legal information and standard job information by crawling legal sites and standard job sites (e.g. NCS National Competency Standards site).

In some embodiments, the service providing system 1 may further collect dictionary information for various terms. For example, the service providing system 1 may collect dictionary information on legal terms and/or job terms by crawling portal sites and/or wiki sites. As mentioned above, the dictionary information may include explanatory information such as definitions for terms, example sentences, similar words, and opposite words. The collected prior information can be used to more accurately determine the relationship between legal and standard jobs.

In step S200, the service providing system 1 may interpret legal information and standard job information collected through the natural language processing model. In more detail, the service providing system 1 may extract a legal entity set and a standard job entity set from legal information and standard job information collected through a Named Entity Recognition (NER) model. In order to provide a more convenient understanding, a description will be made with reference to the example shown in FIG. 4.

As illustrated in FIG. 4, the service providing system 1 may extract various legal entities 42 and 43 by applying the NER model 40 to the information 41 of a specific law (eg the industrial product quality management method). . In this case, the NER model 40 may be a deep learning-based model, a dictionary-based model, or other known models.

In some embodiments, the NER model 40 may be based on a Bidirectional Long Short-Term Memory with a Conditional Random Field Layer (Bi-LSTM-CRF) model (e.g. HighWay-LSTM, etc.). Those skilled in the art will be able to clearly understand the operation method of the Bi-LSTM-CRF model, and a description thereof will be omitted. Refer to FIG. 5 for an example of the structure of the Bi-LSTM-CRF model.

In some embodiments, the NER model 40 may be based on a combination of a Bidirectional Encoder Representations from Transformers (BERT) model and a Bi-LSTM-CRF model. Specifically, the BERT model performs a word embedding function that converts a word constituting legal information into an embedding vector, and the Bi-LSTM-CRF model receives an embedding vector and outputs an NER result. have. In this case, due to the improvement in the performance of the NER model 40, the extraction accuracy of the legal entity may be improved.

In some embodiments, the service providing system 1 may classify the extracted legal entities according to a predetermined criterion. 6 shows an exemplary classification system of legal entities, and the service providing system 1 may classify the extracted legal entities according to the classification system as illustrated. Specifically, the service providing system 1 may classify the extracted legal entity using the entity classification result (e.g. person, organization, location, misc, etc.) of the NER model. Specifically, when the entity classification result is "person" or "organization", the service providing system 1 may classify legal entities as actor entities (e.g. workers, users, etc.). Other legal entities may be classified as act entities.

Further, in some embodiments, the service providing system 1 may hierarchically classify (organize) the legal entity based on the hierarchical information of the law from which the legal entity is extracted. For example, referring to FIG. 7, the service providing system 1 classifies the first legal entity (eg worker) extracted from the first law (eg the Labor Standards Act) as a higher entity, and 2 The second legal entity (eg production manager) extracted from the law may be classified as a sub-entity of the first legal entity. The classified parent entity may be designated as a representative entity when visualized information is generated later. In this regard, reference will be made to the descriptions of FIGS. 18 and 19.

Meanwhile, the service providing system 1 may extract a standard job entity set from standard job information in the same manner as described above. That is, various examples related to the legal entity set (e.g. NER model-based extraction, actor/act entity classification, hierarchical classification, etc.) can also be applied to the standard job entity set. As mentioned above, the standard job information may include various job information obtained from the NCS National Competency Standards site. For example, as illustrated in FIG. 8, various job information such as a specific standard job and its substandard jobs, a job description for each standard job, and job difficulty may be obtained from the NCS national job competency standard site. In order to exclude redundant descriptions, a description of the detailed process of extracting the standard job entity set will be omitted.

This will be described with reference to FIG. 3 again.

In step S300, the service providing system 1 may analyze the extracted legal entity set and the standard job entity set to derive a relationship between the standard job and the law. The detailed process of this step may be designed in various ways, which may vary according to embodiments. Hereinafter, various embodiments related to this step will be described.

In the first embodiment, the relationship between the legal entity and the standard job may be determined based on the similarity between the legal entity set and the standard job entity set. For example, the relationship between the first law (eg the Industrial Product Quality Management Act) and the first standard job (eg quality management) is based on the similarity between the entity set of the first law and the entity set of the first standard job (hereinafter, “entity set similarity”). It can be judged on the basis of. For example, when the entity set similarity between the first law and the first standard job is greater than or equal to the reference value, it may be determined that there is a relationship between the first law and the first standard job.

In the above-described embodiments, a method of calculating the degree of similarity between entity sets may be various. As an example, the entity set similarity may be calculated based on the number (or weight) of the same or similar entities. Here, whether it is a similar entity (ie, entity similarity) may be determined by various keyword similarity calculation techniques. For example, it may be determined based on a similarity between vectors (e.g. cosine similarity, etc.), a relationship predicted through a Neural Tensor Network (NTN) model, and the like. In addition, the entity similarity may be calculated by further considering a difference in the hierarchy of the two entities (e.g. see FIG. 7). For example, when two entities are located in the same hierarchy, the entity similarity may be calculated higher, and in the opposite case, may be calculated lower. As another example, the entity set similarity may be calculated based on the similarity between actor entity sets and the similarity between act entity sets. That is, the entity set similarity may be calculated based on the similarity between the legal actor entity set and the standard job actor entity set and the similarity between the legal act entity set and the standard job act entity set (e.g. sum of weights, etc.). The degree of similarity between actors and/or act entity sets may be calculated in the same or similar manner as in the above example. As another example, the entity set similarity may be calculated based on a combination of the above-described examples.

In the second embodiment, the relationship between the law and the standard job may be determined based (more) on the degree of similarity between the dictionary between the law and the standard job (hereinafter, “priority similarity”). This embodiment will be described in more detail with reference to FIGS. 9 to 12.

As illustrated in FIG. 9, the service providing system 1 may configure a dictionary for each of the legal and standard jobs (S310 and S320), and further derive a relationship between the legal and standard jobs based on the dictionary similarity ( S330). For reference, steps S310 and S320 may be performed by the dictionary interpreter 24, and step S330 may be performed by the relationship analyzer 21.

An example of configuring a standard job dictionary is shown in FIG. 10. As illustrated in FIG. 10, the dictionary 55 of the standard job (A) may be configured based on the previously collected dictionary information, and in detail, the similar words 51, definitions 52, and the like of the standard job (A) The dictionary 55 of the standard job A may be configured based on explanatory information such as definitions 53 and 54 of the entities a and b extracted from the information of the standard job A.

In some embodiments, the NER model may be applied to construct a standard job dictionary (e.g. 55). For example, the NER model may be applied to extract keywords such as similar words (e.g. 51) from collected dictionary information (e.g. example sentences, etc.). Alternatively, the NER model may be applied to extract only keywords from dictionary information in the form of sentences (e.g. definitions 52, 53, 54, etc.). In this case, the standard job dictionary (e.g. 55) can be configured in the same form as a keyword set.

Meanwhile, in some embodiments, the standard job dictionary may be corrected using the NTN model. The NTN model is a model for predicting the degree of relationship between two entities, and those skilled in the art will be able to clearly understand the structure and operation principle of the NTN model, and thus a description thereof will be omitted. Specifically, as shown in FIG. 11, the first entity (E1; or keyword) belonging to the dictionary 61 of the standard job (A) through the NTN model 60 and the entity set 62 of the standard job (A). The degree of relationship between the second entity E2 belonging to) may be predicted. And, when the predicted relevance is less than the reference value, the first entity E1 is removed from the dictionary 61 of the standard job A, so that the corrected dictionary 63 may be configured. In this case, the problem that terms not related to the standard job are included in the standard job dictionary (e.g. 61) can be prevented in advance. For example, since the standard job dictionary (e.g. 55) is constructed based on general information collected from portals/wiki sites, etc., words or example sentences that are not related to the standard job may be included. For example, when a specific entity (i.e., standard job term) belonging to the standard job entity set (eg 62) is used in different meanings according to the context or field (eg polyphonic, Chinese), the standard job dictionary (eg 61) Dictionary information associated with other meanings may be included. According to the present embodiment, this problem can be solved.

Meanwhile, the legal dictionary may be configured in the same or similar manner as the standard job dictionary. In order to exclude redundant descriptions, detailed descriptions thereof will be omitted.

When the legal dictionary and the standard job dictionary are constructed, the relationship between the legal and standard jobs can be judged based on (more) the similarity between the legal dictionary and the standard job dictionary. For example, as illustrated in FIG. 12, the relationship between the law (A) and the standard job (A) is similarity between the entity set 72 of the law (A) and the entity set 71 of the standard job (A) (75) It can be determined based on the similarity 76 between the dictionary 74 of the law (A) and the dictionary 73 of the standard job (A). In this case, a larger weight may be assigned to the entity set similarity 75. Also, the prior similarity 76 may be calculated in the same or similar manner as the entity set similarity 75.

In the third embodiment, the relationship between the law and the standard job may be determined in consideration of (more) the relationship between the legal and the standard job and the upper and/or lower job. For example, as illustrated in FIG. 13, when determining the relationship between the law (A) and the standard job (A), the relationship 81 between the standard job (B) and the law (A), which is a higher job, and a lower job, At least one of the degree of association 82 between the standard job (C) and the law (A) may be further considered. In this case, the degree of relevance between the standard job (B, C) and the law (A) may be calculated in the same or similar manner as the above-described relevance determination method.

In the fourth embodiment, the relationship between the legal and the standard job may be determined based on (more) the relationship between the legal actor entity set and the job act entity set and/or the relationship between the legal act entity set and the job actor entity set. . In this case, the relationship between the actor entity set and the act entity set may be calculated using the NTN model. For example, the relationship between the actor entity set and the act entity set may be calculated by synthesizing the relationship between the actor entity and the act entity predicted through the NTN model.

In the fifth embodiment, the relationship between the law and standard duties can be derived based on various combinations of the first to fourth embodiments described above. Hereinafter, various combinations of the first to fourth embodiments described above will be further described.

For example, by synthesizing the degree of similarity between the entity set according to the first embodiment, the prior similarity according to the second embodiment, and the relationship between the upper/lower job and the law according to the third embodiment, the relationship between the law and the standard job can be determined. I can. For example, a relationship between a law and a standard job may be determined based on a weighted sum of the entity set similarity, the prior similarity, and the relationship. In this case, the weights assigned to each similarity (or relevance) may be the same or different. For example, the highest weight may be assigned to the similarity of the entity set, and the lowest weight may be assigned to the relationship.

As another example, when the entity set similarity according to the first embodiment is greater than or equal to the reference value, it may be determined that there is a relationship between the law and the standard job. And, if it is less than the reference value, the relationship between the law and the standard job may be determined again based on the prior similarity according to the second embodiment. If the degree of similarity in advance is less than the reference value, it may be determined that there is no relevance between the law and the standard job, or relevance may be determined again based on the degree of relevance according to the third embodiment.

As another example, the relationship between the law and the standard job is determined based on the entity set similarity according to the first embodiment, and verification may be performed on the determination result based on other metrics (eg prior similarity, relevance, etc.). have. For example, verification may be performed on the determined relevance using the prior similarity according to the second embodiment. For example, when the prior similarity is less than the reference value, the relationship between the law and the standard job may be denied, and the relationship may be determined again through other metrics (e.g., relevance, etc.).

As another example, a relationship between a law and a standard job may be determined based on the entity set similarity according to the first embodiment and the entity set relationship according to the fourth embodiment. In this case, the entity set similarity is based on, for example, whether the legal actor (act) entity and the job actor (act) entity match, and the similarity between the vector for the legal actor (act) entity and the vector for the job actor (act) entity. Can be calculated by In addition, the entity set relationship may be calculated based on, for example, a first relationship between a legal actor entity and a job act entity predicted through the NTN model and a second relationship between the job actor entity and the legal act entity.

Meanwhile, the service providing system 1 may derive laws related to each standard job by repeatedly performing the above-described steps S100 to S300 for various laws and standard jobs.

For reference, each step of the method shown in FIG. 3 may be performed by the analysis platform 20 among the components of the service providing system 1. In more detail, step S100 may be performed by a legal information collector (not shown) and a standard job information collector (not shown) of the analysis platform 20, and step S200 is a legal interpreter 22 and a job interpreter 23 It may be performed by, step S300 may be performed by the relationship analyzer 21. In addition, pre-configuration for the law and the target job may be performed by the dictionary interpreter 24.

So far, a method of analyzing a relationship between standard job information and legal information according to some embodiments of the present disclosure has been described with reference to FIGS. 3 to 13. As described above, complex legal information and standard job information can be accurately interpreted through the natural language processing model, and the relationship between the law and the standard job can be accurately determined through similarity between entity sets.

Hereinafter, a method of providing legal services for a target job will be described based on the results analyzed as described above.

14 is an exemplary flowchart illustrating a method of providing legal services for a target job according to some embodiments of the present disclosure. However, this is only a preferred embodiment for achieving the object of the present disclosure, and of course, some steps may be added or deleted as necessary.

As shown in FIG. 14, the method for providing legal services may begin in step S400 of obtaining information on a target job. For example, the service providing system 1 may obtain target job information from the service using device 100. The service providing device 1 may acquire target job information through various interfaces such as web, messenger, and email. In this case, the target job information may include one or more target jobs.

In step S500, the service providing system 1 may determine and provide legal information related to a target job based on a result of analyzing the relationship between the standard job information and the legal information. Alternatively, the service providing system 1 may provide information of a legal expert related to a target job. In this step, specific methods of determining legal information related to a target job may vary, and this may vary according to embodiments.

In some embodiments, the service providing system 1 may determine a standard job corresponding to the target job, and determine legal information related to the determined standard job as legal information related to the target job. When a corresponding standard job does not exist, the service providing system 1 may derive and provide legal information related to the target job through the same or similar analysis process as described with reference to FIGS. 3 to 13.

In some other embodiments, the service provision system 1 does not determine the correspondence relationship between the target job and the standard job, and uses the same or similar analysis process as described with reference to FIGS. 3 to 13 to determine the law related to the target job. Information can also be derived and provided.

For reference, each step of the method shown in FIG. 14 may be performed by the service platform 10 among the components of the service providing system 1.

So far, a method of providing legal services for a target job according to some embodiments of the present disclosure has been described with reference to FIG. 14. According to the above, it is possible for a person in charge of a job of a company to easily receive legal information related to a job without the help of a legal expert. Furthermore, companies can reduce the cost of consulting legal experts and establish in-house rules in consideration of job-related laws at a relatively low cost.

Meanwhile, according to some embodiments of the present disclosure, the service providing system 1 may provide legal information related to a target job in a visualized form. Let me explain.

15 to 19 are exemplary diagrams for explaining a method of visualizing legal information related to a target job according to some embodiments of the present disclosure.

First, as illustrated in FIG. 15, legal information related to a target job may be visualized in a graph form in a three-dimensional space formed by three axes. At this time, the X axis may correspond to legal information, the Y axis may correspond to standard job information, and the Z axis may correspond to target job information. However, the corresponding relationship may be changed. In addition, a portion that does not have a corresponding relationship with the target job on the Z-axis may have a corresponding relationship with the standard job (e.g. when the target job is not fully established or is not responded, replacing a part with the standard job).

In addition, the correspondence relationship between the X-axis and legal information may be determined based on a hierarchical system of laws. For example, as shown in FIG. 15, higher-level laws may be located farther from the origin on the X-axis, and lower-level laws may be located closer to the origin. As a more specific example, higher-level laws such as the Constitution may be located farthest from the origin, and may be located in the order of laws, treaties, orders, rules, and precedents toward the origin.

In addition, the correspondence relationship between the Y-axis and standard job information may be determined based on job difficulty. For example, as shown in FIG. 15, a job with higher difficulty may be located farther from the origin on the Y-axis, and a job with lower difficulty may be located closer to the origin. As a more specific example, the standard job may be mapped on the Y-axis based on the job difficulty obtained from the NCS site. In this case, the job performance of the high-ranking job manager is usually located far from the origin.

In addition, a correspondence relationship between the Z-axis and the target job information may also be determined based on job difficulty. For example, as shown in FIG. 15, a job with higher difficulty may be located farther from the origin on the Z-axis, and a job with lower difficulty may be located closer to the origin. The difficulty level of the target job may be obtained from the service user, or may be automatically determined by referring to the difficulty level of standard job information.

Hereinafter, with reference to FIGS. 16 to 19, a process of constructing visualized information on the 3D space illustrated in FIG. 15 will be described by way of example. As shown, the target job is “in-house wage management”, the related standard job is “wage management”, and the related law is “Labor Standards Act” as an example.

First, clustering is performed on entities related to the Labor Standards Act and wage management, and one or more clusters 91 and 92 generated as a result may be disposed in a specific area 90 of the XY plane. Here, it may be preferable that the specific area 90 be an area close to the X-axis position of the Labor Standards Act and the Y-axis position of the wage management.

For example, one or more clusters 91 and 92 may be constructed by converting legal entities related to the Labor Standards Act and standard job entities related to wage management into vectors (e.g. two-dimensional vectors), and clustering the transformed vectors. In order to convert entities into vectors, an embedding technique well known in the art, such as Word2Vec, may be used, and a clustering technique well known in the art may be used to cluster the transformed vector.

Next, as shown in Fig. 17, clustering is also performed on entities related to the Labor Standards Act and in-house wage management, and one or more clusters 111 and 112 generated as a result are a specific area 110 of the XZ plane. Can be placed on Clustering may be performed in the same or similar manner as described with reference to FIG. 16.

Next, the step of adjusting the cluster (e.g. 91, 92, 111, 112) may be performed. The reasons for performing the adjustment are as follows. First, because the clusters (eg 91, 92, 111, 112) are skewed in the XY plane and the XZ plane, it is necessary to adjust the position. As shown in FIG. 18, the same entity (eg wage) is a cluster at different positions. This is because it can exist in duplicate. Hereinafter, it will be described with reference to FIG. 19.

In order to coordinate the cluster (e.g. 91, 92, 111, 112), a representative entity (e.g. wages) within the cluster may be determined. The representative entity may be, for example, an entity (or an entity above the reference layer; see FIG. 7) extracted from a law above the reference layer (eg, the highest layer, the next higher layer law) in the legal system, but the scope of the present disclosure is limited thereto. It does not become.

It is determined whether the representative entity (eg wage) overlaps the cluster of the XY plane and the cluster of the YZ plane, and in response to the determination of the existence of the overlap, the XY coordinates of the representative entity (eg wage) (ie, two-dimensional coordinates; eg 1, 2) , (0)) and XZ coordinates (ie, two-dimensional coordinates; eg 3, (0), 3) XYZ coordinates (eg 2, 1, 1.5) may be calculated. The XYZ coordinates (ie, three-dimensional coordinates) may be determined in various ways, such as an intermediate point of two coordinates, a point at which the distance from a straight line composed of two coordinates to an origin point is minimum.

When the XYZ coordinates (e.g. 2, 1, 1.5) of the representative entity (e.g. wage) are calculated, the clusters (e.g. 91, 92, 111, 112) may be adjusted based on the calculated coordinates. For example, the positions of the first cluster 91 and the second cluster 111 may be adjusted according to the changed coordinates (e.g. 2, 1, 1.5) of the representative entity (e.g. wage). At this time, the first cluster 91 and the second cluster 111 may be simply merged, or may be reconfigured into a new cluster 121, and may be disposed adjacent to the changed coordinates (eg 2, 1, 1.5) without merging. May be.

In some embodiments, after the cluster reposition step is performed, the cluster reposition step may be further performed. For example, the adjusted cluster (e.g. 121) may be included in the specific region 120 of the XYZ space, and the position of the entire cluster (e.g. 121) may be generally readjusted so that the position within the region 120 is not deflected.

Meanwhile, in some embodiments, entities in the cluster (e.g. 121) may be visualized in the form of a word cloud. That is, it may be visualized in a form in which colors, sizes, and/or arrangements are different according to the importance of each entity. In this case, information can be delivered more effectively.

In addition, in some embodiments, the service providing system 1 may automatically generate and provide consulting information for a company based on the analysis information. Hereinafter, the present embodiment will be described in more detail.

For example, the service providing system 1 may be configured between the first correspondence point of the law located on the X axis, the second correspondence point of the standard job related to the target job located on the Y axis, and the third correspondence point of the target job located on the Z axis. Consulting information (or evaluation information) for the system of a target job may be generated based on the positional relationship. For example, if the distances from the origin to the first correspondence point, the second correspondence point, and the third correspondence point are the same or similar (eg, the shape of the figure connecting the first, second and third correspondence points is balanced), the service providing system ( 1) can generate consulting information notifying that the target job system is well established. In the opposite case, the service providing system 1 may generate consulting information notifying that it is necessary to re-establish the system of the target job by referring to the standard job information. The principle that can generate such consulting information is as follows. In general, higher-level jobs (eg higher-ranking management jobs) tend to take on an abstract form of work, so there is a high probability that they are related to higher-level laws (eg Labor Standards Act). Specific detailed tasks) tend to take on a specific form of work, so there is a high probability that it is related to lower-level laws (eg rules, precedents, etc.). In addition, since the standard job is well established systematically, the difficulty of the job and the level of the related law tend to be generally proportional (eg, the standard job of high difficulty is usually related to the law of the upper level, and the corresponding point of the standard job is The corresponding point of the law is located at a similar distance from the origin). Accordingly, by comparing the positions of the third correspondence point with the first correspondence point and the second correspondence point, it can be automatically evaluated whether the target job is well established systematically.

As another example, the service providing system 1 may generate consulting information (or evaluation information) for a system of a target job based on a comparison result between target job information and standard job information. Specifically, the service providing system 1 may generate a list of target jobs that do not correspond to a standard job or may generate and provide information on a target job that does not conform to the standard job system.

So far, a method of visualizing legal information related to a target job according to some embodiments of the present disclosure has been described with reference to FIGS. 15 to 19. As described above, since legal information related to a target job can be provided as visualized information along with a related standard job, legal information can be provided more effectively.

In the following, an exemplary computing device 200 capable of implementing the service providing system 1 or components thereof (eg, service platform 10, service server 11, etc.) according to some embodiments of the present disclosure will be described with reference to FIG. 20. Let me explain about it.

20 is an exemplary hardware configuration diagram illustrating the computing device 200.

As shown in FIG. 20, the computing device 200 is a memory for loading a computer program executed by one or more processors 210, bus 230, communication interface 240, and processor 210 ( 220) and a storage 250 for storing the computer program 260. However, only components related to the embodiment of the present disclosure are shown in FIG. 20. Accordingly, those of ordinary skill in the art to which the present disclosure pertains can recognize that other general-purpose components may be further included in addition to the components illustrated in FIG. 20. That is, the computing device 200 may further include components other than the components illustrated in FIG. 20. Hereinafter, each component of the computing device 200 will be described.

The processor 210 controls the overall operation of each component of the computing device 200. The processor 210 includes at least one of a CPU (Central Processing Unit), MPU (Micro Processor Unit), MCU (Micro Controller Unit), GPU (Graphic Processing Unit), or any type of processor well known in the technical field of the present disclosure. It can be configured to include. In addition, the processor 210 may perform an operation on at least one application or program for executing the method/operation according to the embodiments of the present disclosure. The computing device 200 may include one or more processors.

Next, the memory 220 stores various types of data, commands, and/or information. The memory 220 may load one or more programs 260 from the storage 250 in order to execute a method/operation according to embodiments of the present disclosure. For example, when the computer program 260 is loaded in the memory 220, a module as shown in FIG. 2 may be implemented on the memory 220. The memory 220 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

Next, the bus 230 provides a communication function between components of the computing device 200. The bus 230 may be implemented as various types of buses such as an address bus, a data bus, and a control bus.

Next, the communication interface 240 supports wired/wireless Internet communication of the computing device 200. In addition, the communication interface 240 may support various communication methods other than Internet communication. To this end, the communication interface 240 may be configured to include a communication module well known in the technical field of the present disclosure.

Next, the storage 250 may non-temporarily store the at least one program 260. The storage 250 is a nonvolatile memory such as a Read Only Memory (ROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), and a flash memory, a hard disk, a removable disk, or well in the technical field to which the present disclosure pertains. It may be configured to include any known computer-readable recording medium.

Next, the computer program 260 may include one or more instructions that when loaded into the memory 220 cause the processor 210 to perform a method/operation according to some embodiments of the present disclosure. have. That is, the processor 210 may perform a method/operation according to some embodiments of the present disclosure by executing the one or more instructions.

For example, the computer program 260 analyzes legal information using an artificial intelligence model, an operation of interpreting standard job information using an artificial intelligence model, and an analysis result of legal information and standard job information. It may contain instructions that make it possible to perform an action to derive a law related to a specific standard job as a basis. In this case, the analysis platform 20 according to some embodiments of the present disclosure may be implemented through the computing device 200.

As another example, the computer program 260 may perform an operation of acquiring relationship information between a standard job and a law analyzed using an artificial intelligence model, and an operation of providing legal services for a target job based on the acquired relationship information. It may contain instructions to do. In this case, the service platform 20 according to some embodiments of the present disclosure may be implemented through the computing device 200.

As another example, the computer program 260 analyzes legal information and standard job information using an artificial intelligence model to derive a law related to a specific standard job and provides legal services for a target job based on the result. Instructions for performing the provided operation may be included. In this case, the service providing system 1 according to some embodiments of the present disclosure may be implemented through the computing device 200.

The technical idea of the present disclosure described with reference to FIGS. 1 to 20 so far may be implemented as a computer-readable code on a computer-readable medium. The computer-readable recording medium is, for example, a removable recording medium (CD, DVD, Blu-ray disk, USB storage device, removable hard disk) or a fixed recording medium (ROM, RAM, computer-equipped hard disk). I can. The computer program recorded on the computer-readable recording medium may be transmitted to another computing device through a network such as the Internet and installed in the other computing device, thereby being used in the other computing device.

In the above, even if all the components constituting the embodiments of the present disclosure are described as being combined into one or operating in combination, the technical idea of the present disclosure is not necessarily limited to these embodiments. That is, as long as it is within the scope of the object of the present disclosure, one or more of the components may be selectively combined and operated.

Although the operations are illustrated in a specific order in the drawings, it should not be understood that the operations must be executed in the specific order shown or in a sequential order, or all illustrated operations must be executed to obtain a desired result. In certain situations, multitasking and parallel processing may be advantageous. Moreover, the separation of the various components in the above-described embodiments should not be understood as necessitating such separation, and the program components and systems described are generally integrated together into a single software product or may be packaged into multiple software products. It should be understood that there is.

Although the embodiments of the present disclosure have been described with reference to the accompanying drawings above, those of ordinary skill in the art to which the present disclosure pertains may implement the present disclosure in other specific forms without changing the technical idea or essential features. I can understand that there is. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not limiting. The protection scope of the present disclosure should be interpreted by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the technical ideas defined by the present disclosure.

Claims (16)

An analysis platform that derives laws related to a specific standard job by analyzing legal information and standard job information using an artificial intelligence model; And
Including a service platform that provides legal services for a target job based on the information derived by the analysis platform,
The analysis platform,
A legal interpreter for interpreting the legal information;
A job interpreter for interpreting the standard job information; And
A relationship analyzer for deriving a law related to the specific standard job by analyzing the relationship between the standard job and the law based on the analysis result of the legal interpreter and the analysis result of the job interpreter,
The service platform provides visualized information on the target job and related laws based on the information derived by the analysis platform, and the target job on a space formed by a first axis, a second axis, and a third axis, The visualized information is generated by arranging a plurality of entities for the relevant law and the relevant standard job of the target job,
The first axis, the second axis, and the third axis each have a corresponding relationship with target job information, legal information, and standard job information,
Legal service delivery system. The method of claim 1,
The standard job information is information collected from the national job competency standard site,
Legal service delivery system. The method of claim 1,
The legal interpreter interprets the legal information through a Named Entity Recognition (NER) model,
The NER model is implemented based on a BERT (Bidirectional Encoder Representations from Transformers) model and a Bi-LSTM-CRF (Bidirectional Long Short-Term Memory with a Conditional Random Field Layer) model,
Legal service delivery system. The method of claim 1,
The legal interpreter extracts a legal entity set from the legal information through a Named Entity Recognition (NER) model,
The job interpreter extracts a standard job entity set from the standard job information through the NER model,
The relationship analyzer,
Deriving a law related to the specific standard job based on the similarity between the extracted legal entity set and the extracted standard job entity set,
Legal service delivery system. The method of claim 4,
Further comprising a dictionary interpreter for configuring a legal dictionary using the extracted legal entity set, and configuring a standard job dictionary using the extracted standard job entity set,
The relationship analyzer,
Deriving a law related to the specific standard job based on the degree of similarity between the configured legal dictionary and the configured standard job dictionary,
Legal service delivery system. The method of claim 5,
The dictionary interpreter,
Construct the legal dictionary based on the explanatory information on the extracted legal entity set,
Correcting the configured legal dictionary using a Neural Tensor Network (NTN) model, but correcting the legal dictionary based on a degree of relation between the explanatory information predicted through the NTN model and the extracted legal entity set,
Legal service delivery system. The method of claim 4,
The legal interpreter classifies the extracted legal entity set into a legal actor entity set and a legal act entity set using the classification result of the NER model,
The job interpreter also classifies the extracted standard job entity set into a job actor entity set and a job act entity set using the classification result of the NER model,
The relationship analyzer,
Deriving a law related to the specific standard job based on a first similarity between the legal actor entity set and the job actor entity set and a second similarity between the legal act entity set and the job act entity set,
Legal service delivery system. The method of claim 7,
The relationship analyzer,
The first degree of similarity is calculated based on whether the legal actor entity and the job actor entity match and the similarity between the vector for the legal actor entity and the vector for the job actor entity,
Law related to the specific standard job based on a first relationship between the legal actor entity and the job act entity predicted through a Neural Tensor Network (NTN) model and a second relationship between the job actor entity and the legal act entity To derive,
Legal service delivery system. The method of claim 1,
The relationship analyzer,
In analyzing the relationship between the specific standard job and the specific law, determining the relationship between the specific standard job and the specific law further based on the relationship between the specific law and the upper job or lower job of the specific standard job,
Legal service delivery system. delete The method of claim 1,
The correspondence relationship between the first axis and the target job information is determined based on the difficulty level of the target job,
The correspondence relationship between the second axis and the legal information is determined based on a hierarchical system of laws,
The corresponding relationship between the third axis and the standard job information is determined based on the difficulty of the standard job,
Legal service delivery system. The method of claim 11,
The service platform,
A user of the legal service based on the positional relationship between the corresponding point of the target job located on the first axis, the corresponding point of the related law located on the second axis, and the corresponding point of the related standard job located on the third axis To generate consulting information for,
Legal service delivery system. The method of claim 1,
The service platform,
Clustering and arranging a first plurality of entities for the target job and the related law on a plane formed by the first axis and the second axis,
Clustering and arranging a second plurality of entities for the related law and the related standard job on a plane formed by the second axis and the third axis,
It is determined whether a first representative entity among the first plurality of entities is the same as a second representative entity among the second plurality of entities,
In response to determining that they are the same, 3D coordinates are calculated based on the 2D coordinates of the first representative entity and the 2D coordinates of the second representative entity,
Adjusting the positions of the first cluster to which the first representative entity belongs and the second cluster to which the second representative entity belongs based on the calculated three-dimensional coordinates,
Legal service delivery system. The method of claim 1,
The service platform,
Determine the standard job corresponding to the target job above,
Providing legal information related to the determined standard job as legal information related to the target job,
Legal service delivery system. In the legal service providing method performed by at least one computing device,
Deriving a law related to a specific standard job by analyzing legal information and standard job information using an artificial intelligence model; And
Including the step of providing a legal service for a target job based on the derived result,
The step of deriving a law related to the specific standard job,
Interpreting the legal information using the artificial intelligence model;
Analyzing the standard job information using the artificial intelligence model; And
Deriving a law related to the specific standard job based on the analysis result of the legal information and the analysis result of the standard job information,
Providing legal services for the target job,
Provides visualized information on the target job and related laws based on the information derived by the step of deriving the law related to the specific standard job, but on the space formed by the first axis, the second axis, and the third axis. The visualized information is generated by arranging a plurality of entities for the target job, the related law, and the related standard job of the target job, and the first axis, the second axis, and the third axis are target job information, respectively, Having a corresponding relationship with legal information and standard job information,
How to provide legal services. Combined with a computing device,
Deriving a law related to a specific standard job by analyzing legal information and standard job information using an artificial intelligence model; And
Based on the result of the derivation, the step of providing legal services for the target job is executed,
The step of deriving a law related to the specific standard job,
Interpreting the legal information using the artificial intelligence model;
Analyzing the standard job information using the artificial intelligence model; And
Deriving a law related to the specific standard job based on the analysis result of the legal information and the analysis result of the standard job information,
Providing legal services for the target job,
Provides visualized information on the target job and related laws based on the information derived by the step of deriving the law related to the specific standard job, but on the space formed by the first axis, the second axis, and the third axis. The visualized information is generated by arranging a plurality of entities for the target job, the related law, and the related standard job of the target job, and the first axis, the second axis, and the third axis are target job information, respectively, Stored in a computer-readable recording medium, having a corresponding relationship with legal information and standard job information,
Computer program.

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