KR102085214B1 - Method and system for acquiring word set of patent document - Google Patents

Abstract

Disclosed is a method for obtaining a word set of a patent document. The method for obtaining a word set of a patent document comprises the following steps. A server obtains a patent document. The server obtains a plurality of word sets by analyzing a morpheme based on the patent document. The server determines an error word set among the obtained plurality of word sets. The server corrects the error word set. The server obtains at least one complex noun phrase set based on a degree of association between the plurality of word sets.

Description

Method and system for acquiring word set of patent document {METHOD AND SYSTEM FOR ACQUIRING WORD SET OF PATENT DOCUMENT}

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

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

On the other hand, for patent applications, prior art research is performed to determine whether a technology can be patented, and prior art research may be performed by searching various patent documents published in the past.

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

Prior art surveys, such as search-type inputs, are often dependent on the ability of a person to conduct prior art surveys, and thus there are cases where a stable result for prior art surveys is not guaranteed.

Therefore, there is a need for a prior art research method that can guarantee stable results with little time investment.

Patent Application Publication No. 10-0481598, 2005.03.29

The problem to be solved by the present invention is to provide a method and system for acquiring a word set of a patent document.

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

According to an aspect of the present invention, there is provided a method of obtaining a word set of a patent document, the method comprising: obtaining, by a server, a patent document; Obtaining, by the server, a plurality of word sets by analyzing a morpheme based on the patent document; Determining, by the server, an error word set among the obtained plurality of word sets; The server correcting the error word set; And acquiring, by the server, at least one compound noun phrase set based on the degree of association between the plurality of word sets.

In this case, the obtaining of the plurality of word sets may include: obtaining a sentence included in the patent document; Dividing the sentence into syllable units; Matching a part-of-speech tag to the divided syllables; Obtaining a morpheme included in the sentence based on the divided syllables; It may include.

In this case, the determining of the error word set may include: matching semantic information on 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 having an accuracy score equal to or less than a preset value as an error word set.

In this case, the correcting of the error word set may include: obtaining a plurality of semantic information about the error word; Obtaining a plurality of weights for each of the obtained plurality of semantic informations; Obtaining a weight having the highest association with the patent document among the plurality of weights; Matching semantic information corresponding to a weight having the highest association with the patent document to the error word set; It may include.

In this case, the obtaining of the at least one compound noun phrase set may include: obtaining a plurality of word sets for a sentence 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 at which a compound noun phrase set identical to the obtained plurality of compound noun phrase set candidates is included in a patent document; Determining a compound noun phrase set candidate whose frequency is equal to or greater than a preset frequency as a compound noun phrase set; 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 candidate 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 a compound noun phrase set candidate; The preset condition may include a condition in which the plurality of word sets are adjacent to each other, a condition to include a predetermined code between the plurality of adjacent word sets, and a word set positioned at the right side of the plurality of adjacent word sets includes parenthesis. May be a condition in which the set of words and the plurality of words are different languages.

In this case, the obtaining of the word set may include analyzing a sentence included in the patent document; When the sentence has a preset structure, obtaining at least one word set except for a word set corresponding to the preset structure among the word sets included in the sentence; Obtaining semantic information of the at least one word set; Obtaining a word set corresponding to the semantic information; It may include.

In this case, the acquiring of the word set may include: acquiring a plurality of identification item information and a template included in the patent document; Obtaining a plurality of sentences except the obtained template; Weighting each of the plurality of pieces of identification information; Determining a priority of identification items to be morphologically analyzed based on weights of the plurality of identification item information; Acquiring word sets for a plurality of sentences in which the template is excluded, according to the determined priority; It may include.

The acquiring of the word set may include determining whether an identification item adjacent to the image exists when the patent document includes an image; Acquiring text included in the image when the identification item is adjacent to the image; Obtaining a word set based on the obtained text; It 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, a word set for efficiently analyzing a patent document may be obtained.

Effects of the present invention are not limited to the above-mentioned effects, 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 word set obtaining method 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 error 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 obtaining a word set according to an embodiment of the present invention.
9 is a flowchart illustrating a method of obtaining a word set except for a template of a patent document according to an embodiment of the present invention.
10 is a flowchart illustrating a method of obtaining 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 for achieving them will be apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be embodied in various different forms, only the present embodiments are intended to complete the disclosure of the present invention, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully inform the skilled person of the scope of the invention, which is defined only by the scope of the claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and / or “comprising” does not exclude the presence or addition of one or more other components in addition to the mentioned components. Like reference numerals refer to like elements throughout, and "and / or" includes each and all combinations of one or more of the mentioned components. Although "first", "second", etc. are used to describe various components, these components are of course not limited by these terms. These terms are only used to distinguish one component from another. Therefore, of course, the first component mentioned below may be the second component within the technical spirit of the present invention.

Unless otherwise defined, all terms used in the present specification (including technical and scientific terms) may be used in a sense that can be commonly understood by those skilled in the art. In addition, terms that are defined in a commonly used dictionary are not ideally or excessively interpreted unless they are specifically defined clearly.

The term "part" or "module" as used herein refers to a hardware component such as software, FPGA or ASIC, and the "part" or "module" plays certain roles. However, "part" or "module" is not meant to be limited to software or hardware. The “unit” or “module” may be configured to be in an addressable storage medium or may be configured to play one or more processors. Thus, as an example, a "part" or "module" may include components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Functions provided within components and "parts" or "modules" may be combined into smaller numbers of components and "parts" or "modules" or into additional components and "parts" or "modules". Can be further separated.

The spatially relative terms " below ", " beneath ", " lower ", " above ", " upper " It can be used to easily describe a component and its correlation with other components. Spatially relative terms are to be understood as including terms in different directions of components in use or operation in addition to the directions shown in the figures. For example, when flipping a component shown in the drawing, a component described as "below" or "beneath" of another component may be placed "above" the other component. Can be. Thus, the exemplary term "below" can encompass both an orientation of above and below. Components may be oriented in other directions as well, so spatially relative terms may be interpreted according to orientation.

In the present specification, a computer refers to any kind of hardware device including at least one processor, and according to an embodiment, it may be understood as a meaning encompassing a software configuration that operates on the hardware device. For example, a computer may be understood as including, but not limited to, a smartphone, a tablet PC, a desktop, a notebook, and a user client and an application running on each device.

Hereinafter, with reference to the accompanying drawings will be described an embodiment of the present invention;

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

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

The system for acquiring 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 a component for obtaining a patent document and obtaining a word set from the obtained patent document.

In detail, the server 10 may receive a patent document from the electronic device 20, obtain a patent document from an external server, and obtain a word set of the obtained patent document.

In the present specification, the patent document may be a document on the technical content submitted by the applicant for patent registration with the respective patent offices. 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 job inventions and papers for patent application.

The electronic device 20 is a component for providing 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 example, and may include a smartphone, a tablet personal computer, a mobile phone, a video phone, E-book readers, desktop PCs, laptop PCs, netbook computers, workstations, servers, personal digital assistants, portable multimedia It may include at least one of a player or a wearable device.

Hereinafter, the present invention will be described in detail with reference to 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 operation S110, the server 10 may acquire a patent document.

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

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

In one embodiment, the server 10 may perform a morphological analysis of the patent document using the Mecab morpheme analyzer. However, the present invention is not limited thereto, and various morpheme analyzers such as Okt, Komoran, Hannanum, and Kkma morpheme analyzers may be used. Furthermore, the server 10 may use various morpheme analyzers according to the language of the patent document to be analyzed.

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

In an embodiment, when the acquired plurality of word sets does not satisfy a preset condition, the server 10 may determine the corresponding word as an error word. Detailed description thereof will be described later.

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

In operation S150, the server 10 may obtain at least one compound noun phrase set 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 operation S210, the server 10 may acquire a sentence included in the patent document. In detail, 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 operation S220, the server 10 divides a sentence into syllable units. In operation S230, the server 10 matches a part-of-speech tag to the divided syllables. In operation S240, the server 10 based on the divided syllables. The morpheme included in the sentence can be obtained.

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

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

In operation S310, the server 10 may match semantic information on each of the acquired 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, the natural language understanding unit may grasp the intent of the word set included in the entity and the sentence based on the sentence analysis result, and furthermore, the natural language understanding unit may Analyze sentences through structure and major component analysis and sentence analysis using statistics / analysis.

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

In operation 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, in step S310, semantic information about the word set acquired through one sentence is obtained, but the semantic information obtained through step S310 may be inaccurate. Accordingly, the server 10 may obtain 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 of the semantic information of the word set obtained through sentence analysis based on the semantic information of the same word set found in the entire patent document.

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

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

That is, when the accuracy score is less than or equal to a preset value, the server 10 may determine that semantic information matched to the corresponding word set is incorrectly matched, and may acquire the word set as an error word set.

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

In operation S410, the server 10 may obtain a plurality of semantic information about an error word. In detail, the server 10 may obtain a plurality of semantic information of an error word from a plurality of patent documents.

In operation S420, the server 10 may obtain a plurality of weights for each of the obtained plurality of semantic information.

In one embodiment, the server 10 may obtain a weight based on the patent document including the error word. In detail, the server 10 may acquire at least one sentence including an error word in a patent document, and obtain semantic information on the same word as an error word included in the obtained at least one sentence. The server 10 may obtain weights for a plurality of semantic information based on semantic information about at least one word that is the same as an error word.

In operation S430, the server 10 may obtain a weight having the highest correlation with the patent document among the plurality of weights.

In operation S440, the server 10 may match semantic information corresponding to the weight having the highest association with the patent document to the error word set. That is, the server 10 may determine the semantic information having the largest weight among the plurality of semantic information of the error word as semantic information of the error word, and correct the error word.

Meanwhile, according to various embodiments of the present disclosure, the error word set may be not only a case in which wrong semantic information is matched with the word set, but also a case in which the word set is incorrectly parsed due to an error in the morphological analysis process. In one embodiment, when the sentence for acquiring the word set is "natural language processing using machine learning", the server 10 may acquire the machine, the learning, the use, the natural language, and the processing as the word set.

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

In detail, after acquiring semantic information about each of the acquired word sets, the server 10 may determine whether the word is an error word based on the degree of association between the semantic information and the patent document. According to an embodiment of the present disclosure, when the acquired word set is found in a patent document including a word set having a predetermined frequency or more, the server 10 may determine that the word set is an error-free word set. In another embodiment, when the word set is found more than a predetermined frequency in the entire patent document, the server 10 may determine that the word set is not an error word set by determining that the word set is generally used. In another embodiment, when the semantic information about the word set is not found, the server 10 may determine the word set as an error word set. In another embodiment, if the semantic information matched with the word set is heterogeneous with the patent document including the word set, the server 10 may determine the corresponding word set as an error word set.

In detail, the server 10 may obtain a word set included in the entire patent document, and match and store semantic information on each of the acquired word sets. The server 10 may cluster the matched word set and the semantic information to obtain a cluster of a plurality of word sets having relevant semantic information. The server 10 may acquire a word set cluster of a plurality of word set clusters, in which a word set included in the cluster is equal to or less than a preset number. In this case, the preset number may not mean the number of the plurality of identical word sets used in the patent document but may mean the number of the plurality of word sets having different forms. For example, even when the word "use" is found a plurality of times in a patent document, since "use" is the same word set, 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 error word set.

However, the present invention is not limited thereto, and the server 10 may determine, as an error word set, a word set included in a cluster of a plurality of word set clusters having a distance from another cluster greater than or equal to a preset distance. In detail, the server 10 may acquire center points of the plurality of clusters and determine the distance between 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 may include 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. When each of the distances is obtained and all three acquired distances are greater than or equal to a predetermined distance, the first word set cluster may be determined as an error 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. Thus, there is a need for the server 10 to obtain a complex noun phrase set from a single word set. Accordingly, the server 10 may obtain a compound noun phrase set based on the plurality of word sets.

In operation 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 "to perform natural language processing using machine learning", the server 10 may obtain a word set of a machine, a learning, a use, a natural language, and a processing.

In operation 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 a compound noun phrase set candidate such as machine learning, learning use, user language, natural language processing, etc. from the obtained combination of the word sets.

In operation S530, the server 10 may acquire a frequency at which the compound noun phrase set identical to the obtained plurality of compound noun phrase set candidates is included in the patent document.

In operation S540, the server 10 may determine a compound noun phrase set candidate having an acquired frequency greater than or equal to a preset frequency as a compound noun phrase set.

For example, the server 10 obtains information about machine learning total 301 times, learning usage once, user language total 0 times, and natural language processing 58 times, and the frequency of occurrence is set in advance. A compound noun phrase candidate set of more than a frequency can 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 one embodiment, a compound noun phrase set is determined by a combination of two or more adjacent word sets, but in the present invention, a combination of word sets in which words are spaced may also be obtained as a compound noun phrase set. For example, when the server 10 obtains a compound noun phrase set from a sentence including "a first device of a plurality of devices, a second device of a plurality of devices, a third device of a plurality of devices", The first device of the device, the second device of the plurality of devices, and the third device of the plurality of devices can be obtained as a set of independent compound noun phrases, but a " device of a plurality of devices (spaced word 1) " Of course it can also be obtained. In this case, the complex noun phrase set may include spaced information. In the above-described embodiment, the separation information may be information indicating that one word is included between the word “in” and the word “device”. According to various embodiments, a word and a plurality of words may be included between the 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, if there are n words included in the sentence for acquiring the compound noun phrase set, and the order between the word sets is not changed, the number of the compound noun phrase sets obtained is

, And the number of compound noun phrase sets obtained when the order between word sets changes

Dog. Therefore, the longer the sentence is, the longer the server 10 has to inject excessive resources to search for a 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 in the same technical field as the patent document for performing the word set acquisition, and calculates the maximum number of word sets included in the compound noun phrase set included in the acquired patent document. The maximum number of word sets constituting the set may be determined.

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 positions of the plurality of word sets.

In general, when trying to obtain a compound noun phrase in a sentence, it is unnecessary to change the order of the word set included in the sentence. For example, in the sentence of "~" using machine learning, the server 10 needs to obtain a complex noun phrase set "machine learning", but a compound noun phrase set of "treadmill" that changes the order of the word set. It will not need to be acquired. Rather, when a compound noun phrase set of a "treading machine" is obtained, there is a possibility that a compound noun phrase set having a completely different meaning is obtained, thereby degrading the performance to be achieved in the present invention.

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

For example, when the sentence to be analyzed includes "Machine Learning", the server 10 may obtain a machine, a running, a machine, and a learning as a word set. Furthermore, the server 10 may obtain "machine learning" as a compound noun phrase set. Furthermore, the server 10 may determine that a parenthesis code, which is a preset condition, is included, and acquire “Machine Learning Machine Learning” as a compound noun phrase set. That is, the server 10 is expressed in a different language when a compound noun phrase (or word) called "machine learning" and a compound noun phrase of another language for the compound noun phrase (or word) are present together. Two compound noun phrases (or words) having a can be obtained as a compound noun phrase, and a compound noun phrase can be obtained by changing the order of the two compound noun phrases. 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 may be represented as "Machine Learning", but a patent document written in English may be represented as "Machine Learning", so that the server 10 may be complex in both cases. Acquire a noun phrase set to increase the efficiency of the search.

Meanwhile, in the above-described embodiment, the condition in which the preset code includes the parentheses is described, but the present invention is not limited thereto. According to various embodiments of the present disclosure, the predetermined sign may be a sign such as a hyphen (-), a slash (/), a tilde (~), a quote ('or "), or the like.

In operation S620, the server 10 may determine the changed plurality of word sets as a compound noun phrase set candidate.

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

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

In operation S720, when the analyzed sentence has a predetermined structure, the server 10 may obtain at least one word set except for a word set corresponding to the predetermined structure among the word sets included in the sentence.

According to an embodiment, the preset structure may be a structured structure such as "to be defined as". Specifically, in a patent document, "~ is defined as", "hereinafter" is called. " "Can be understood as." A sentence of structure such as "describes" may be used to define terms that are not commonly used in the art. Accordingly, the server 10 may determine a sentence defining a term not generally used in the technical field, and obtain a word set defined in the determined sentence.

For example, if the sentence to be analyzed is a sentence "Machine learning is described (or defined) as OOO", the server 10 determines that the sentence is a predetermined structure, and the machine, running, OOO The word set can be obtained.

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

In detail, 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 with OOO. However, when the term OOO is newly defined by the user and the semantic information is not clear, the server 10 may match the semantic information corresponding to the machine learning with the semantic information of the OOO.

In operation S740, the server 10 may acquire a word set corresponding to 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 association between the machine learning and the OOO is greater than or equal to a preset degree of association.

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

In detail, the patent document is a formal document, which is not significantly related to the essential contents of the present invention, but there may exist a paragraph describing a necessary essential component in a formal method. Therefore, when the formal paragraph is removed and a clue set is obtained, there is an effect of reducing the amount of computation of the server 10.

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

In one embodiment, the template may be set to a paragraph existing at a specific location. For example, the template may include the first word in FIG. 1 from the sentence included in the line following the [detail for carrying out the invention] identification item included in the patent document. The text up to the sentence to be searched may be.

In another embodiment, the template may mean a part in common with other patent documents prepared by the creator 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 the obtained plurality of patent documents, and determines that the paragraph is similar or more similar to the preset ratio through the comparison result. Alternatively, the identification item may be obtained as a template. Since the patent document created by the same author is likely to have a similar sentence structure, the criteria of the server 10 similar judgment may be set to a paragraph or an identification item instead of a sentence unit.

In another embodiment, the server 10 may obtain a text corresponding to a predetermined identification item as a template. For example, the server 10 may obtain the text included in the identification item such as [a brief description of the drawings], [description of the reference] as a template.

In the above-described embodiment, a method for obtaining a template for a general identification item of a patent document for a domestic application has been described, but is not limited thereto. That is, the server 10 may identify various identification items, such as identification items of the national document for PCT applications, identification items of English forms for PCT applications, and furthermore, identification items of forms used in US, Japan, China, and European applications. Of course, the template can be obtained.

In operation S820, the server 10 may obtain a plurality of sentences except for the obtained template.

In operation 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 form and identification as the present specification, the server 10 is the name of the invention, the technical field, the background of the invention, the problem to be solved, the means for solving the problem, the invention The weights of the effects, the brief description of the drawings, the detailed contents for carrying out the invention, the description of the signs, the claims, the summary, the representations, and the drawing identification items can be given.

In this case, the weight may be given by various methods. In one embodiment, the server 10 is a technical field, the background technology of the invention, claims, the effects of the invention, the specific contents for carrying out the invention, the description of the symbols, a brief description of the drawings, a summary, the problem to be solved The weights can be given in the order of the solution, the solution to the problem, and the representation.

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

Meanwhile, the importance score for each identification item may mean a value obtained by summing the importance scores of a word set (or a compound noun phrase set) included in the identification item and dividing the number of word sets (or a compound noun phrase set). In this case, the word set (or compound noun phrase set) used to obtain the importance score for each identification item may be all word sets (or compound noun phrase set) included in the identification item, but is not limited thereto. For example, a word set (or compound noun phrase set) used to obtain an importance score for each identifier is a word set (or compound nominal set) of all the word sets (or compound noun phrase sets) included in the identifier. It may be a word set (or a compound noun phrase set) except a compound noun phrase set).

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

In operation S850, the server 10 may obtain a word set for a plurality of sentences in which the template is excluded according to the determined priority.

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

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

In operation S910, when the image is included in the patent document, the server 10 may determine whether an identification item adjacent to the image exists.

In one embodiment, if there is an identification item of [mathematical formula] 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 likely to be the essential content of the patent document, the server 10 may obtain the equation text included in the image.

In 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 operation 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, image analysis may be performed by optical character recognition (OCR), but is not limited thereto. For example, when the image is a chemical formula image, the server 10 may analyze the chemical formula structure to obtain a chemical formula corresponding to the image.

In operation S930, the server 10 may acquire a word set based on the obtained text.

Meanwhile, the server 10 may acquire the importance score of each word set through a method described below. In one embodiment, the server 10 may obtain a word set that is the object of calculating the importance score from the patent document. The server 10 may determine the first detail importance of the word set in the entire patent document and the second detail of the word set in the patent classification information corresponding to the technical field information of the patent document including the word set to determine the importance score. One or more detailed importance of the third detailed importance of the searched patent document including the importance and the word set among the entire patent documents may be calculated.

In detail, the server 10 may calculate the first detailed importance 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 to obtain the importance score in the entire patent document compared to the total number of word sets of the entire patent document, the second appearance ratio is a word set of the sentences of the entire patent document The number of appearance sentences that appear may be counted, and may mean the total number of sentences of the entire patent document compared to the number of appearance 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 detail importance, wpw is the number of occurrences of the word set in the entire patent document, WPW is the total number of words set in the entire patent document, wps is the appearance of the word set appeared in the sentences of 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 adjustment constant of the second appearance rate.

In another embodiment, the server 10 may calculate the first detail importance based on the first appearance rate and the second appearance rate. In this case, the third appearance ratio refers to the number of appearances of the word set to obtain the importance score in the patent classification information to the total number of word sets of the patent classification information, and the fourth appearance ratio is a word set from the sentences of the entire patent document. It may mean the total number of sentences of the entire patent document compared to the number of appearance sentences appeared.

Here, the patent classification information is a code that can classify patents according to the technical field, and may be any one of International Patent Classfication (IPC), Cooperative Patent Classification (CPC), and F-Term.

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

Where W ₂ is the second level of importance, ipcw is the number of occurrences of the word set in the patent classification information, IPCW is the total number of words in the patent classification information, and ipcs is the word set in the sentences of the entire patent document. Is the number of appearance sentences, IPCS is the total number of sentences in all patent documents, and a2 is the adjustment constant of the fourth appearance ratio.

In another embodiment, the server 10 retrieves a search patent document including a word set that is a target of calculating importance scores among all patent documents, and based on reference information of each search patent document, The influence value may be calculated, and the third detailed importance may be obtained based on the calculated influence value.

In detail, the server 10 may calculate the influence value based on the number of the same items as the other patent documents, the number of citations and the number of citations of one or more applicants, inventors, and rights holders, which are reference information of the searched patent document. Can be.

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 search patent document is cited from several patent documents, the server 10 may determine that the search patent document has an influence on the patent document and calculate the search patent document as an influence value of the search patent document.

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

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

The processor 102 may include a connection passage (eg, a bus, etc.) that transmits and receives signals with one or more cores (not shown) and graphics processing unit (not shown) and / or other components. .

The processor 102 according to one embodiment executes one or more instructions stored in the memory 104 to perform the method described in connection with FIGS. 2-10.

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

Meanwhile, the processor 102 may include random access memory (RAM) and read-only memory (ROM) for temporarily and / or permanently storing a signal (or data) processed in 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 processor, 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 connection with an embodiment of the present invention may be implemented directly in hardware, in a software module executed by hardware, or by a combination thereof. The software module may be a 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 form of computer readable recording medium well known in the art.

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

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

10: server
20: electronic device

Claims (10)

In the word set acquisition method of the patent document,
Obtaining, by the server, a patent document;
Obtaining, by the server, a plurality of word sets by analyzing a morpheme based on the patent document;
Determining, by the server, an error word set among the obtained plurality of word sets;
The server correcting the error word set; And
Acquiring, by the server, at least one compound noun phrase set based on the degree of association between the plurality of word sets;
Determining the set of error words,
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
And determining a word set having the accuracy score equal to or less than a preset value as an error word set. The method of claim 1,
Acquiring the plurality of word sets,
Obtaining a sentence included in the patent document;
Dividing the sentence into syllable units;
Matching a part-of-speech tag to the divided syllables; And
Obtaining a morpheme included in the sentence based on the divided syllables; Word set acquisition method comprising a. delete Claim 4 was abandoned upon payment of a set-up fee. The method of claim 1,
Modifying the set of error words,
Obtaining a plurality of semantic information on the error word;
Obtaining a plurality of weights for each of the obtained plurality of semantic informations;
Obtaining a weight having the highest association with the patent document among the plurality of weights; And
Matching semantic information corresponding to a weight having the highest association with the patent document to the error word set; Word set acquisition method comprising a. The method of claim 1,
Obtaining the at least one compound noun phrase set,
Obtaining a plurality of word sets for 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 at 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 having the frequency equal to or greater than a preset frequency as a compound noun phrase set; Including,
And the compound noun phrase set candidate comprises order information and spacing information of word sets included in the compound noun phrase set candidate. Claim 6 has been abandoned upon payment of a set-up fee. The method of claim 5,
Acquiring the compound noun phrase set candidate,
Changing the 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 a compound noun phrase set candidate; Including,
The preset condition may include a condition in which the plurality of word sets are adjacent to each other, a condition in which a predetermined code is included between the plurality of adjacent word sets, a condition in which a word set positioned at the right of the plurality of adjacent word sets includes parentheses, and the A method of obtaining a word set, characterized in that the plurality of word sets are in different languages. Claim 7 was abandoned upon payment of a set-up fee. The method of claim 1,
Acquiring the word set,
Analyzing a sentence included in the patent document;
When the sentence has a preset structure, obtaining at least one word set except for a word set corresponding to the preset structure 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; Word set acquisition method comprising a. Claim 8 has been abandoned upon payment of a setup registration fee. The method of claim 1,
Acquiring the word set,
Obtaining a plurality of identification item information and a template included in the patent document;
Obtaining a plurality of sentences except the obtained template;
Weighting each of the plurality of pieces of identification information;
Determining a priority of identification items to be morphologically analyzed based on weights of the plurality of identification item information; And
Acquiring a set of words for a plurality of sentences from which the template is excluded according to the determined priority; Word set acquisition method comprising a. Claim 9 was abandoned upon payment of a set-up fee. The method of claim 1,
Acquiring the word set,
Determining whether an identification item adjacent to the image exists when an image is included in the patent document;
Acquiring text included in the image when the identification item is adjacent to the image; And
Obtaining a word set based on the obtained text; Word set acquisition method comprising a. Memory for storing one or more instructions; And
A processor for executing the one or more instructions stored in the memory;
The processor executes the one or more instructions,
An apparatus for carrying out the method of claim 1.

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