KR102297962B1 - Method and system for acquiring word set meaning information of patent document - Google Patents

Abstract

A method for obtaining a word set of a patent document is disclosed. The method for obtaining a word set of the patent document includes, by a server, obtaining a patent document; obtaining, by the server, a plurality of word sets by analyzing morphemes based on the patent document; determining, by the server, an erroneous word set among the obtained plurality of word sets; correcting, by the server, the erroneous word set; and obtaining, by the server, at least one complex noun phrase set based on the degree of association between the plurality of word sets.

Description

Method and system for obtaining information on the meaning of word sets in patent documents

The present invention relates to a method and system for obtaining a word set of a patent document.

With the development of the 4th industry, the value of intellectual property rights is increasing. Accordingly, many people are trying to protect their technology and acquire the right to the technology, and interest in patent application for technology is increasing.

Meanwhile, in order to apply for a patent, prior art research is performed to determine whether one's own technology can be patented, and prior art research can be performed by searching various patent documents published in the past.

However, it is virtually impossible to analyze all patent documents published in the past due to the large amount of patent documents and time constraints. It is a reality that prior art research is being conducted.

Prior art research through search-type input, etc., is often dependent on the ability of human resources to conduct prior art research, so there are cases in which stable results for prior art research cannot be guaranteed.

Therefore, the need for a prior art research method that can guarantee stable results with a small investment of time is emerging.

Registered Patent Publication No. 10-0481598, 2005.03.29

An object of the present invention is to provide a method and system for obtaining a word set of a patent document.

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

A method for obtaining a word set of a patent document according to an aspect of the present invention for solving the above-described problem includes, by a server, obtaining a patent document; obtaining, by the server, a plurality of word sets by analyzing morphemes based on the patent document; determining, by the server, an erroneous word set among the obtained plurality of word sets; correcting, by the server, the erroneous word set; and obtaining, by the server, at least one complex noun phrase set based on the degree of association between the plurality of word sets.

In this case, the acquiring of the plurality of word sets may include: acquiring a sentence included in the patent document; dividing the sentence into syllable units; matching the part-of-speech tag to the divided syllables; and obtaining morphemes included in the sentence based on the divided syllables. may include.

In this case, the determining of the erroneous word set may include: matching semantic information for each of the obtained plurality of word sets; obtaining an accuracy score for each of the plurality of word sets based on the matched semantic information and the patent document; and determining a word set whose accuracy score is less than or equal to a preset value as an erroneous word set.

In this case, the step of correcting the erroneous word set includes: obtaining a plurality of semantic information for the erroneous word; obtaining a plurality of weights for each of the obtained pieces of semantic information; obtaining a weight having the highest degree of relevance to the patent document from among the plurality of weights; and matching semantic information corresponding to a weight having the highest degree of relevance with the patent document to the erroneous word set. may include.

In this case, the acquiring of the at least one compound noun phrase set includes: acquiring a plurality of word sets for the sentences included in the patent document; obtaining a plurality of compound noun phrase set candidates obtained by combining the plurality of word sets; obtaining a frequency in which a compound noun phrase set identical to the obtained plurality of compound noun phrase set candidates is included in a patent document; and determining a compound noun phrase set candidate whose frequency is equal to or greater than a preset frequency as a compound noun phrase set. and, the compound noun phrase set candidate may include order information and spacing information of word sets included in the compound noun phrase set candidate.

In this case, the obtaining of the compound noun phrase set candidates may include: changing positions of the plurality of word sets when the plurality of word sets satisfy a preset condition; determining the changed plurality of word sets as compound noun phrase set candidates; The preset condition includes: a condition in which the plurality of word sets are adjacent; a condition in which a preset sign is included between the plurality of adjacent word sets; and a condition in which the plurality of word sets are in different languages.

In this case, the acquiring of the word set may include: analyzing a sentence included in the patent document; when the sentence has a preset structure, acquiring at least one word set excluding a word set corresponding to the preset structure from among the word sets included in the sentence; obtaining semantic information of the at least one word set; and obtaining a word set corresponding to the semantic information. may include.

In this case, the acquiring of the word set includes: acquiring information about a plurality of identification items and templates included in the patent document; obtaining a plurality of sentences excluding the obtained template; assigning a weight to each of the plurality of pieces of identification item information; determining a priority of an identification item to be morphologically analyzed based on a weight of each of the plurality of identification item information; and obtaining, according to the determined priority, a word set for a plurality of sentences from which the template is excluded. may include.

In this case, the acquiring of the word set may include, when an image is included in the patent document, determining whether an identification item adjacent to the image exists; obtaining text included in the image when the identification item is adjacent to the image; and obtaining a word set based on the obtained text. may include.

Other specific details of the invention are included in the detailed description and drawings.

According to various embodiments of the present invention described above, it is possible to obtain a word set for efficiently analyzing a patent document.

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

1 is a system diagram for implementing a method for obtaining a word set of a patent document according to an embodiment of the present invention.
2 is a flowchart illustrating a method of obtaining a word set of a patent document according to an embodiment of the present invention.
3 is a flowchart illustrating a method of obtaining a plurality of word sets from a patent document according to an embodiment of the present invention.
4 is a flowchart illustrating a method of determining an erroneous word set according to an embodiment of the present invention.
5 is a flowchart illustrating a method of correcting an error word set according to an embodiment of the present invention.
6 is a flowchart illustrating a method of obtaining a compound noun phrase set according to an embodiment of the present invention.
7 is a flowchart illustrating a method of obtaining a compound noun phrase set according to another embodiment of the present invention.
8 is a flowchart illustrating a method of acquiring a word set according to an embodiment of the present invention.
9 is a flowchart illustrating a method of acquiring a word set excluding a template of a patent document according to an embodiment of the present invention.
10 is a flowchart illustrating a method of acquiring a word set when an image exists in a patent document according to an embodiment of the present invention.
11 is a block diagram of an apparatus according to an embodiment of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. As used herein, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium or to reproduce one or more processors. Thus, by way of example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or as additional components and “parts” or “modules”. can be further separated.

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

In this specification, a computer means all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

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

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

1 is a system diagram for implementing a method for obtaining a word set of a patent document according to an embodiment of the present invention.

A system for obtaining a word set of a patent document according to the present invention includes a server 10 and an electronic device 20 .

The server 10 is configured to acquire a patent document and to acquire a word set from the acquired patent document.

Specifically, the server 10 may receive a patent document from the electronic device 20 , or may acquire a patent document from an external server, and acquire a word set of the acquired patent document.

In the present specification, a patent document may be a document on technical content submitted by an applicant to obtain a patent registration at each country's patent office. However, the present invention is not limited thereto, and the patent document may be understood as a concept including various documents including technical contents, such as a job invention for a patent application, and a thesis.

The electronic device 20 is configured to provide a patent document to the server 10 . The electronic device 200 according to the present invention may be implemented as a smart phone, but this is only an embodiment, and a smart phone, a tablet personal computer (PC), a mobile phone, a video phone, e-book reader, desktop PC, laptop PC, netbook computer, workstation, server, PDA (personal digital assistant), PMP (portable multimedia) player) or a wearable device.

Hereinafter, the present invention will be described in detail through various embodiments of FIGS. 2 to 10 .

2 is a flowchart illustrating a method of obtaining a word set of a patent document according to an embodiment of the present invention.

In step S110, the server 10 may obtain a patent document.

In an embodiment, the server 10 may obtain a job invention as a patent document from the electronic device 20 . In another embodiment, the server 10 may receive a patent publication or registration publication from an external server and obtain it as a patent document.

In step S120, the server 10 may obtain a word set by analyzing morphemes based on the patent document.

In an embodiment, the server 10 may perform morpheme analysis of a patent document using a Mecab morpheme analyzer. However, the present invention is not limited thereto, and in some cases, various morpheme analyzers such as Okt, Komoran, Hannanum, and Kkma morpheme analyzers may be used. Furthermore, it goes without saying that the server 10 may use various morpheme analyzers according to the language of the patent document to be analyzed.

In step S130 , the server 10 may determine an erroneous word set among the plurality of obtained word sets.

In an embodiment, when the plurality of acquired word sets do not satisfy a preset condition, the server 10 may determine the corresponding word as an erroneous word. A detailed description thereof will be given later.

In step S140 , the server 10 may correct the erroneous word set.

In operation S150 , the server 10 may acquire at least one set of compound noun phrases based on the degree of association between the plurality of word sets.

3 is a flowchart illustrating a method of obtaining a plurality of word sets from a patent document according to an embodiment of the present invention.

In step S210, the server 10 may obtain a sentence included in the patent document. Specifically, the server 10 may classify the text included in the patent document into sentence units and perform morphological analysis on each of the classified sentences.

In step S220, the server 10 divides the sentence into syllable units, in step S230, the server 10 matches the part-of-speech tag to the divided syllable, and in step S240, the server 10 divides the divided syllable based on the to obtain the morphemes included in the sentence.

In an embodiment, the server 10 may perform syllable segmentation and part-of-speech tag matching based on LSTM-CRFs and word2vec algorithms, but is not limited thereto.

4 is a flowchart illustrating a method of determining an erroneous word set according to an embodiment of the present invention.

In step S310 , the server 10 may match semantic information for each of the obtained plurality of word sets.

In this case, the semantic information may mean an intent for a word set. The server 10 may perform natural language processing using an artificial intelligence model to obtain semantic information about a word set.

Specifically, the artificial intelligence model includes a natural language understanding unit, and the natural language understanding unit can grasp an entity and an intent of a word set included in a sentence based on the sentence analysis result. It is possible to interpret a sentence through structure and major component analysis, and to perform sentence analysis using statistics/analysis, etc.

In an embodiment, the server 10 may obtain semantic information about the apple by analyzing a sentence including 'apple'. For example, it may be assumed that the word set obtained through sentence passage is “apple.” In this case, the semantic information about the apple may be semantic information indicating one kind of fruit as a noun, but other people as a verb. The server 10 may match semantic information determined to be suitable for a sentence among a plurality of semantic information on 'apple' with 'apple'.

In step S320 , the server 10 may obtain an accuracy score for each of the plurality of word sets based on the matched semantic information and the patent document.

In this case, although semantic information about the word set acquired through one sentence is acquired in step S310, the semantic information acquired in step S310 may be inaccurate. Accordingly, the server 10 may acquire the accuracy score of the semantic information matched to the word set based on the entire patent document including the sentence.

Accordingly, the server 10 may obtain an accuracy score for the semantic information of the word set obtained through sentence analysis based on the semantic information about the same word set found in the entire patent document.

For example, semantic information about 'apple' obtained by analyzing a sentence containing 'apple' is information about an apple representing a fruit, but the semantic information of 'apple' searched in the entire patent document including the sentence is different. In the case of information about telling a person wrong, the server 10 may set the pre-matched semantic information low.

In step S330 , the server 10 may determine a word set having an accuracy score equal to or less than a preset value as an erroneous word set.

That is, when the accuracy score is less than or equal to a preset value, the server 10 may determine that the semantic information matched with the corresponding word set is erroneously matched, and obtain the word set as the erroneous word set.

5 is a flowchart illustrating a method of correcting an error word set according to an embodiment of the present invention.

In step S410, the server 10 may obtain a plurality of semantic information for the erroneous word. Specifically, the server 10 may acquire a plurality of semantic information of the erroneous word from a plurality of patent documents.

In step S420 , the server 10 may acquire a plurality of weights for each of the acquired semantic information.

In an embodiment, the server 10 may obtain a weight based on a patent document including an error word. Specifically, the server 10 may acquire at least one sentence including an erroneous word from the patent document, and acquire semantic information on the same word as the erroneous word included in the at least one acquired sentence. The server 10 may acquire weights for a plurality of semantic information based on semantic information on at least one word identical to the erroneous word.

In step S430 , the server 10 may obtain a weight having the highest degree of relevance to a patent document among a plurality of weights.

In step S440 , the server 10 may match semantic information corresponding to a weight having the highest degree of relevance with the patent document to the erroneous word set. That is, the server 10 may determine semantic information having the greatest weight among a plurality of semantic information on the erroneous word as semantic information on the erroneous word and correct the erroneous word.

Meanwhile, according to various embodiments of the present disclosure, it goes without saying that the erroneous word set may be a case in which incorrect semantic information is matched to the word set, as well as a word set that is erroneously parsed due to an error in the morpheme analysis process. In an embodiment, when the sentence for obtaining the word set is "Natural language processing using machine learning is performed", the server 10 may obtain machine, learning, use, natural language, and processing as a word set.

In this case, due to an error in morpheme analysis, a case of acquiring a word set such as machine, learning, using eul, natural language, and processing may occur. In this case, the server 10 may determine 'use E' as an error word set and correct it.

Specifically, after acquiring semantic information for each of the acquired word sets, the server 10 may determine whether the word is an erroneous word based on the degree of association between the semantic information and the patent document. According to an exemplary embodiment, when the acquired word set is found in a patent document including a word set having a frequency greater than or equal to a preset frequency, the server 10 may determine that the corresponding word set is an error-free word set. In another embodiment, when the word set is found in the entire patent document at a frequency greater than or equal to a preset frequency, the server 10 may determine that the word set is not an erroneous word set by determining that the word set is a commonly used word set. In another embodiment, when the server 10 does not find semantic information about the word set, the server 10 may determine the corresponding word set as an erroneous word set. In another embodiment, when semantic information matched to a word set is different from a patent document including a word set, the server 10 may determine the corresponding word set as an erroneous word set.

Specifically, the server 10 may obtain a set of words included in the entire patent document, match and store semantic information for each of the obtained word sets. The server 10 may obtain a cluster of a plurality of word sets having relevant semantic information by clustering the matched word set and semantic information. The server 10 may acquire a word set cluster in which the number of word sets included in the cluster is less than or equal to a preset number from among the plurality of word set clusters. In this case, the preset number does not mean the number of a plurality of identical word sets used in the patent document, but may mean the number of a plurality of word sets having different shapes. For example, even when the word "using E" is searched for multiple times in the patent document, since "using E" is the same set of words, it can be determined as one word set. The server 10 may determine the word set included in the cluster obtained in the above step as the erroneous word set.

However, the present invention is not limited thereto, and it goes without saying that the server 10 may determine, among the plurality of word set clusters, a word set included in a cluster having a distance from another cluster equal to or greater than a preset distance as the erroneous word set. Specifically, the server 10 may obtain the center points of a plurality of clusters, and determine the distance between the clusters based on the obtained center points. For example, the obtained word set cluster may be a first word set cluster to a fourth word set cluster. In this case, the server 10 determines the distance between the first word set cluster and the second word set cluster, the distance between the first word set cluster and the third word set cluster, the first word set cluster and the fourth word set cluster, and If distances of are obtained, and all three obtained distances are equal to or greater than a preset distance, the first word set cluster may be determined as an erroneous word set.

6 is a flowchart illustrating a method of obtaining a compound noun phrase set according to an embodiment of the present invention.

Specifically, the word set obtained through steps S110 to S150 relates to a single word, but when a compound noun phrase is formed by combining single words, the meaning may be different. Accordingly, there is a need for the server 10 to obtain a set of compound noun phrases from a single set of words. Accordingly, the server 10 may obtain a complex noun phrase set based on a plurality of word sets.

In step S510, the server 10 may obtain a plurality of word sets for sentences included in the patent document.

For example, as a result of analyzing the sentence "Natural language processing using machine learning is performed", the server 10 may obtain a word set of machine, learning, use, natural language, and processing.

In step S520, the server 10 may obtain a plurality of compound noun phrase set candidates obtained by combining a plurality of word sets.

For example, the server 10 may obtain complex noun phrase set candidates such as machine learning, learning use, user language, natural language processing, etc. from a combination of the obtained word sets.

In step S530, the server 10 may acquire the frequency at which the same compound noun phrase set as the obtained plurality of compound noun phrase set candidates is included in the patent document.

In step S540 , the server 10 may determine a compound noun phrase set candidate whose obtained frequency is equal to or greater than a preset frequency as the compound noun phrase set.

For example, the server 10 acquires information about machine learning being found a total of 301 times, learning use a total of 1 time, user language a total of 0 times, and natural language processing a total of 58 times, and the frequency of appearance is preset. A compound noun phrase candidate set having a frequency greater than or equal to a frequency may be obtained as a compound noun phrase set.

In this case, the compound noun phrase set candidate may include order information and spacing information of word sets included in the compound noun phrase set candidate.

In an embodiment, the compound noun phrase set is determined as a combination of two or more adjacent word sets, but in the present invention, a combination of a word set in which words are spaced apart may also be obtained as a compound noun phrase set. For example, when the server 10 obtains a set of compound noun phrases from a sentence including "a first device among a plurality of devices, a second device among a plurality of devices, and a third device among a plurality of devices", a plurality of A first device of devices, a second device of a plurality of devices, and a third device of a plurality of devices may be obtained as independent compound noun phrase sets, but "(spaced word 1) device of multiple devices" can be obtained as a single compound noun phrase set. Of course, it can also be obtained with In this case, the compound noun phrase set may include spacing information. In the above-described embodiment, the separation information may be information that one word is included between the word “middle” and the word “device”. Of course, according to various embodiments, a plurality of words may be included between words.

개이고, 단어 세트간의 순서가 변경되는 경우 획득되는 복합 명사구 세트의 수는

개이다. 따라서, 문장이 길어지면 길어질수록, 서버(10)는 복합 명사구 세트 검색을 위해 과도한 리소스를 투입하여야 한다. 따라서, 서버(10)는 복합 명사구 세트를 구성하는 단어 세트의 최대 개수를 결정하고, 결정된 개수 내의 복합 명사구 세트를 획득할 수 있다. 이때, 서버(10)는, 단어 세트 획득을 수행하는 특허문서와 동일한 기술 분야인 복수개의 특허문서를 획득하고, 획득된 특허문서에 포함된 복합 명사구 세트에 포함된 단어 세트의 최대 개수를 복합 명사구 세트를 구성하는 단어 세트의 최대 개수로 결정할 수 있다. In another embodiment, the server 10 may determine the maximum number of word sets constituting the compound noun phrase set, and obtain the compound noun phrase set within the determined number. Specifically, when the number of words included in the sentence for obtaining the compound noun phrase set is n and the order between the word sets is not changed, the number of obtained compound noun phrase sets is

, and the number of compound noun phrase sets obtained when the order between word sets is changed is

it's a dog Therefore, as the sentence becomes longer, the server 10 must invest excessive resources for searching the compound noun phrase set. Accordingly, the server 10 may determine the maximum number of word sets constituting the compound noun phrase set, and obtain the compound noun phrase set within the determined number. At this time, the server 10 acquires a plurality of patent documents that are the same technical field as the patent document for obtaining the word set, and sets the maximum number of word sets included in the compound noun phrase set included in the obtained patent document to the compound noun phrase. It can be determined as the maximum number of word sets constituting the set.

7 is a flowchart illustrating a method of obtaining a compound noun phrase set according to another embodiment of the present invention.

In operation S610, when the plurality of word sets satisfy a preset condition, the server 10 may change the positions of the plurality of word sets.

In general, when obtaining a compound noun phrase from a sentence, it is unnecessary to change the order of the word sets included in the sentence. For example, in the sentence of "using machine learning ~~", the server 10 needs to obtain a complex noun phrase set called "machine learning", but a complex noun phrase set called "learning machine" in which the order of the word sets is changed. You won't need to get it. Rather, when acquiring a complex noun phrase set of "treading machine", a complex noun phrase set with a completely different meaning is obtained, and there is a possibility of reducing the performance to be achieved in the present invention.

However, as described in step S610, if a preset condition is satisfied, the server 10 may obtain a compound noun phrase set candidate by changing the order of the word sets included in the sentence. In this case, the preset conditions include a condition in which a plurality of word sets are adjacent, a condition in which a preset sign is included between a plurality of adjacent word sets, a condition in which a word set located to the right of the plurality of adjacent word sets includes parentheses, and a plurality of words It can be a condition that the sets are in different languages.

For example, when the sentence to be analyzed includes "Machine Learning", the server 10 may acquire Machine, Learning, Machine, and Learning as a word set. Further, the server 10 may acquire "machine learning machine learning" as a set of compound noun phrases. Furthermore, the server 10 may determine that a set condition of parentheses is included, and obtain "Machine Learning Machine Learning" as a set of complex noun phrases. That is, the server 10 is expressed in a different language when the compound noun phrase (or word) of "machine learning" and the compound noun phrase (Machine Learning) of another language for the compound noun phrase (or word) exist together, but the same meaning It is possible to obtain two compound noun phrases (or words) having This has the effect of increasing the efficiency of comparison when comparing patent documents written in different languages. That is, a patent document written in Korean is expressed as “Machine Learning”, but a patent document written in English can be expressed as “Machine Learning”, so the server 10 is a composite in both cases. You can increase the efficiency of your search by acquiring it as a set of noun phrases.

Meanwhile, in the above-described embodiment, a condition in which a preset sign includes a condition in which a parenthesis sign is included has been described, but the present invention is not limited thereto. According to various embodiments, the preset sign may be a sign such as a hyphen (-), a slash (/), a tilde (~), or a quotation mark (' or ").

In step S620 , the server 10 may determine the changed plurality of word sets as compound noun phrase set candidates.

8 is a flowchart illustrating a method of acquiring a word set according to an embodiment of the present invention.

In step S710, the server 10 may analyze the sentence included in the patent document.

In operation S720 , when the analyzed sentence has a preset structure, the server 10 may acquire at least one word set excluding a word set corresponding to the preset structure among the word sets included in the sentence.

According to an exemplary embodiment, the preset structure may be a standardized structure such as "~ is defined as ~". Specifically, in the patent document, "~ is defined as ~", "Hereinafter, ~ is referred to as ~." "~ can be understood as ~." A sentence of a structure such as “states ~” may be used to define terms that are not generally used in the technical field. Accordingly, the server 10 may determine a sentence defining a term not generally used in the technical field, and obtain a set of words defined in the determined sentence.

For example, if the sentence to be analyzed is a sentence "machine learning describes (or defines) OOO", the server 10 determines that the sentence is a preset structure, and You can get word sets.

In operation S730, the server 10 may acquire semantic information of at least one word set.

Specifically, when the acquired word set of OOO has semantic information commonly used in the technical field, the server 10 may match semantic information corresponding to OOO to OOO. However, when the semantic information is unclear because the OOO term is a term newly defined by the user, the server 10 may match semantic information corresponding to machine learning with the semantic information of OOO.

In step S740, the server 10 may obtain a word set corresponding to the semantic information.

However, the present invention is not limited thereto, and the server 10 may replace the word set of OOO with the word set of machine learning when the degree of relevance between machine learning and OOO is equal to or greater than a preset degree of relevance.

9 is a flowchart illustrating a method of acquiring a word set excluding a template of a patent document according to an embodiment of the present invention.

Specifically, a patent document is a standardized document and has nothing to do with the core content of the invention, but there may be paragraphs describing necessary essential elements in a standardized way. Therefore, when the standardized paragraph is removed and the clue set is obtained, there is an effect of reducing the amount of calculation of the server 10 .

In step S810, the server 10 may obtain a plurality of identification item information and templates included in the patent document.

As an embodiment, the template may be set as a paragraph existing at a specific location. For example, the template includes the first word of 'Fig. 1' in the [Specific Contents for Implementing the Invention] identification item from the sentence included in the line following the [Specific Contents for Implementing the Invention] identification item included in the patent document. It may be the text before the sentence to be searched.

In another embodiment, the template may mean a part common to other patent documents prepared by the author of the patent document to be analyzed. That is, the server 10 determines the author of the patent document, obtains another patent document of the determined author, compares a plurality of obtained patent documents, and paragraphs determined to be similar by more than a preset ratio through the comparison result Alternatively, the identification item may be acquired as a template. Since there is a high possibility that a patent document written by the same author has a similar sentence structure, the server 10 may set the criterion for judging similarity in a paragraph or an identification item, not in a sentence unit.

As another embodiment, the server 10 may obtain a text corresponding to a preset identification item as a template. For example, the server 10 may obtain the text included in the identification items such as [simple description of drawings] and [description of symbols] as a template.

In the above-described embodiment, a method of obtaining a template for a general identification item of a patent document for a domestic application has been described, but the present invention is not limited thereto. That is, the server 10 relates to various identification items such as identification items of Korean documents for PCT applications, identification items of English forms for PCT applications, and identification items of forms used in US, Japan, China, and European applications. Of course, you can obtain a template.

In step S820, the server 10 may obtain a plurality of sentences excluding the obtained template.

In step S830, the server 10 may assign a weight to each of the plurality of identification items.

In one embodiment, when the patent document has the same format and identification items as in the present specification, the server 10 provides the name of the invention, the technical field, the technology that is the background of the invention, the problem to be solved, the means for solving the problem, and the invention. effect, a brief description of the drawings, specific content for carrying out the invention, description of symbols, claims, summary, representation, and weights for each of the drawing identification items can be given.

In this case, the weight may be assigned by various methods. In one embodiment, the server 10 is a technical field, the background of the invention, the claims, the effect of the invention, the specific content for carrying out the invention, the description of the symbols, the brief description of the drawings, the summary, the problem to be solved Higher weights can be given in the order of , problem solving means, and representativeness.

In another embodiment, the weight is obtained by obtaining the importance score of each word set (or compound noun phrase set) included in the patent document, and after obtaining the importance score for each identification item based on this, the weight is high in the identification item having a high importance score weights can be assigned. The importance score is an index indicating the importance of the corresponding word set (or compound noun phrase set) in the patent document. The higher the importance score, the higher the word set (or compound noun phrase set) may be a keyword of the corresponding patent document.

Meanwhile, the importance score for each identification item may mean a value obtained by dividing the number of word sets (or compound noun phrase sets) after summing up the importance scores of the word sets (or compound noun phrase sets) included in the identification items. In this case, the word set (or compound noun phrase set) used to obtain the importance score for each identification item may be any word set (or compound noun phrase set) included in the identification item, but is not limited thereto. For example, the word set (or compound noun phrase set) used to obtain the importance score for each identification item is the word set (or compound noun phrase set) of the part determined as a template among all the word sets (or compound noun phrase sets) included in the identification item. Of course, it may be a set of words (or a set of compound noun phrases) other than a set of compound noun phrases.

In step S840 , the server 10 may determine the priority of the identification item to be morphologically analyzed based on the weight of each of the plurality of identification item information.

In operation S850, the server 10 may acquire a word set for a plurality of sentences from which templates are excluded according to the determined priority.

That is, the server 10 may reduce the amount of calculation by first analyzing an identification item with a high probability of having a keyword, and analyzing an identification item having a relatively low importance based on the analyzed content. Furthermore, when different semantic information for the same word set is matched, the server 10 may determine the semantic information obtained from the high-priority identification item as the correct semantic information.

10 is a flowchart illustrating a method of acquiring a word set when an image exists in a patent document according to an embodiment of the present invention.

In step S910 , when the patent document includes an image, the server 10 may determine whether an identification item adjacent to the image exists.

As an embodiment, when an identification item of [Equation] exists in the patent document and an image exists at the bottom of the identification item, the server 10 may determine the image as an image for the equation. In the case of the equation, since it is highly likely to be the core content of the patent document, the server 10 may obtain the equation text included in the image.

As another embodiment, when an identification item of [Formula] exists in the patent document and an image exists at the bottom of the identification item, the server 10 may determine the image as an image for the chemical formula.

In step S920 , when the identification item is adjacent to the image, the server 10 may analyze the image to obtain text included in the image. In this case, the image analysis may be performed by optical character recognition (OCR), but is not limited thereto. For example, if the image is a chemical formula image, of course, the server 10 may obtain a chemical formula corresponding to the image by analyzing the chemical formula structure.

In step S930, the server 10 may acquire a word set based on the acquired text.

Meanwhile, the server 10 may acquire the importance score of each word set through a method described later. In an embodiment, the server 10 may obtain a word set that is a target for calculating the importance score from the patent document. The server 10 is configured to provide a first detailed importance of a word set in the entire patent document and a second detail of a word set in the patent classification information corresponding to the technical field information of the patent document including the word set for which the importance score is to be determined. At least one detailed importance among the importance and the third detailed importance of the search patent document including the word set among the entire patent document may be calculated.

Specifically, the server 10 may calculate the first detailed importance level based on the first appearance rate and the second appearance rate. In this case, the first appearance ratio means the number of appearances of the word set for which the importance score in the entire patent document is to be obtained compared to the total number of word sets in the entire patent document, and the second appearance ratio is the number of appearances of the word set among the sentences of the entire patent document. The number of appearing sentences may be counted, and it may mean the total number of sentences in the entire patent document compared to the number of appearing sentences in which the word set appears among the sentences of the entire patent document.

Specifically, the server 10 may calculate the first detailed importance level using Equation 1 below.

Here, W1 is the first detailed importance, wpw is the number of occurrences of the word set in the entire patent document, WPW is the total number of word sets in the entire patent document, and wps is the occurrence of the word set in the entire patent document. is the number of sentences, WPS is the total number of sentences in the entire patent document, and a1 is the control constant of the second appearance ratio.

In another embodiment, the server 10 may calculate the first detailed importance level based on the first appearance rate and the second appearance rate. In this case, the third appearance ratio means the number of appearances of the word set for obtaining the importance score in the patent classification information compared to the total number of word sets in the patent classification information, and the fourth appearance ratio is the number of occurrences of the word set among the sentences of the entire patent document. It may mean the total number of sentences in the entire patent document compared to the number of appeared sentences.

Here, the patent classification information is a code capable of classifying patents according to technical fields, and may be any one of International Patent Classification (IPC), Cooperative Patent Classification (CPC), and F-Term.

Specifically, the server 10 may calculate the second detailed importance level by using Equation 2 below.

Here, W ₂ is the second detailed importance, ipcw is the number of occurrences of the word set in the patent classification information, IPCW is the total number of word sets in the patent classification information, and ipcs is the number of occurrences of the word set in the entire patent document sentence. The number of occurrences is the number of sentences, IPCS is the total number of sentences in the entire patent document, and a2 is the control constant of the fourth occurrence ratio.

In another embodiment, the server 10 searches for a search patent document including a word set to be calculated of an importance score from among all patent documents, and searches for each of the searched patent documents based on the reference information of each of the searched patent documents. An influence value may be calculated, and a third detailed importance level may be obtained based on the calculated influence value.

Specifically, the server 10 calculates an influence value based on the number of items that are the same as at least one of the applicant, inventor, and right holder of the reference information of the search patent document, and the number of citations and citations that are reference information. can

That is, the server 10 may calculate the degree to which the search patent document is related to another patent document as an influence value. For example, when the searched patent document is cited from several patent documents, the server 10 may determine that the searched patent document has an influence on the corresponding patent document, and may calculate it as an influence value of the searched patent document.

Furthermore, the server 10 may calculate the average of the calculated influence values for each of the search patent documents, and calculate the calculated average as the third detailed importance level.

11 is a block diagram of an apparatus according to an embodiment of the present invention.

The processor 102 may include one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus, etc.) for transmitting and receiving signals with and/or other components. .

The processor 102 according to an embodiment performs the method described with reference to FIGS. 2 to 10 by executing one or more instructions stored in the memory 104 .

For example, the processor 102 obtains new training data by executing one or more instructions stored in the memory, performs a test on the acquired new training data by using the learned model, and performs the test result, labeling Extracting the first learning data in which the obtained information is obtained with an accuracy greater than or equal to a predetermined first reference value, deleting the extracted first learning data from the new learning data, and removing the new learning data from which the extracted learning data is deleted It is possible to retrain the learned model using

On the other hand, the processor 102 is a RAM (Random Access Memory, not shown) and ROM (Read-Only Memory: ROM) for temporarily and / or permanently storing a signal (or data) processed inside the processor 102. , not shown) may be further included. In addition, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

The memory 104 may store programs (one or more instructions) for processing and controlling the processor 102 . Programs stored in the memory 104 may be divided into a plurality of modules according to functions.

The steps of a method or algorithm described in relation to an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. The components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , Java, assembler, etc. may be implemented in a programming or scripting language. Functional aspects may be implemented in an algorithm running on one or more processors.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

10 : Server
20: electronic device

Claims (1)

A method for obtaining a word set of a patent document, the method comprising:
server, obtaining a patent document;
obtaining, by the server, a plurality of word sets by analyzing morphemes based on the patent document;
determining, by the server, an erroneous word set among the obtained plurality of word sets;
correcting, by the server, the erroneous word set; and
acquiring, by the server, at least one set of compound noun phrases based on the degree of association between the plurality of word sets;
Obtaining the word set comprises:
analyzing a sentence included in the patent document;
when the sentence has a preset structure, acquiring at least one word set excluding a word set corresponding to the preset structure from among the word sets included in the sentence;
obtaining semantic information of the at least one word set; and
obtaining a word set corresponding to the semantic information; A method of obtaining a word set comprising

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