KR20120069206A - Method and system on analysis map information of similar patents - Google Patents

Abstract

PURPOSE: A similar patent analysis map information providing method and system thereof are provided to enable a user to simultaneously grasp similar patent groups by simultaneously displaying the similar patent groups. CONSTITUTION: Information for a central patent is acquired(S21). Similar patent group information for the central patent is acquired(S22). Selection information for the display level of the similar patent group information is acquired(S23). A similar patent uses level configuration elements as an intermediate node. The similar patent corresponding to the level configuration element is arranged by contacting to the intermediate node(S25).

Description

Method and system for providing similar patent analysis map information {Method and System on Analysis Map Information of Similar Patents}

The present invention relates to a system for providing similar patent analysis map information and a method of providing the same. More particularly, the present invention relates to a system for providing similar patent analysis map information and a method for providing the same, which provide a network diagram by classifying similar patent groups of a central patent in multiple fields. will be.

When there is a patent, a patent that is technically similar to this patent plays an important role in searching the technical literature or acquiring related technical knowledge. In order to find such a similar patent, a method of precisely adjusting a search word or reading a searched result and arranging relevant patent documents has been used. However, these methods may actually: 1) be very similar, resulting in missing similar documents due to limitations in entering keyword search terms or problems with the search system, or 2) providing too many search results, There have been many problems, such as the case where it takes too much time to extract high similarity patents. On the other hand, due to the limitation of the keyword search formula, a method of preventing the omission of related patent documents by adding a patent classification to the search formula is widely used.

However, finding similar documents based on these searches is time consuming, and how similar documents are to a central patent is unknown until the document is read, and searching for similar patents took a long time. There is a problem that it is difficult to see only similar patents having specific properties by analyzing patents effectively.

Accordingly, there is an urgent need for the development of a method and system for providing similar patent analysis map information that can effectively extract similar patents similar to the core patents and analyze the extracted similar patents from various viewpoints.

The first problem to be solved by the present invention is to provide an information processing method of a composite viewpoint similar patent analysis map information providing system.

SUMMARY OF THE INVENTION The first problem to be solved by the present invention is to provide a system of providing similar patent analysis map information in a composite view.

In order to achieve the problem to be solved by the present invention, an information processing method of a similar patent analysis map information providing system, the method comprising: (A) receiving information on a central patent; (B) obtaining similar patent group information on the obtained central patent; (C) receiving selection information on a display dimension for the similar patent group information; And (D) arranging similar patents in such a manner as to link similar patents corresponding to the dimensional components to the intermediate nodes, using the dimensional component corresponding to the dimension from which the selection information has been obtained as an intermediate node. And a similar patent in which the central patent and the registration are maintained are provided with an information processing method of the similar patent analysis map information providing system, wherein the patent evaluation information is associated with the patent.

Preferably, the display dimension is any one of a time dimension, a patent classification dimension, a key keyword dimension, and an evaluation score dimension.

The time dimension is either the filing year or the year of registration, the patent classification dimension is a predetermined classification level on the IPC patent classification system, or the evaluation score dimension includes at least two evaluation elements for the similar patent. It is preferable that the evaluation score generated as a result of applying the evaluation element value for the similar patent to the evaluation model to be processed in interval units.

Preferably, the preset classification level is any one of a class, a subclass, a main group, and an n (n is a natural number) dot subgroup.

The evaluation score is preferably a total evaluation score for the similar patent or an evaluation score for each evaluation criteria item.

It is preferable that the said evaluation criteria item is any one or more of rights, technology, and marketability.

Preferably, the dimensional intermediate node is a dimensional component included in the similar patent processing information, which is processing information about the similar patent constituting the similar patent group, with respect to the display dimension obtained with the selection information.

The display dimension may be any one of a time dimension, a patent classification dimension, a key keyword dimension, and an evaluation score dimension, and the dimension intermediate node is configured of the filing years of the similar patent included in the similar patent processing information. Consisting of registered years, consisting of patent classifications of a predetermined classification level included in the similar patent, consisting of key keywords extracted from the similar patent, or at least for the similar patent. It is preferable that the evaluation scores resulting from applying the evaluation element values for the similar patents to an evaluation model including two or more evaluation elements are composed of evaluation score sections processed in sections.

The central patent and similar patents are those represented by one or more of an application number display, a registration number display, and an applicant / right holder display. The application number display, a registration number display, and an applicant / right holder display are the user or the similar patent. It is preferable that the administrator of the analysis map information providing system can set.

It is preferred that the central patent and the similar patent have a similarity score.

In order to achieve the problem to be solved by the present invention, there is provided a similar patent analysis map information providing system for implementing the above method.

Implementing the present invention has the following effects.

First, since the similar patent group is grouped in various display dimensions, the similar patent group can be identified at a glance. In particular, as the display dimension becomes an intermediate node, similar patent groups are visually distinct and intuitively grouped to increase efficiency of searching for similar patent groups because node attributes are different from other nodes constituting the network diagram.

Second, the search time for the similar patent group is significantly shortened, and it is possible to quickly find patents suitable for the user's search purpose.

Third, there is an advantage provided by intuitively visualizing multiple analysis information on the similar patent group.

Fourth, various additional services such as bibliography, full text, and patent evaluation information can be linked to patents in each analysis patent map, so that patent contents or evaluation information can be immediately recognized for highly related patents or major patents. .

Fifth, since all patents include patent automatic evaluation result information, it is possible to reconstruct an analysis patent map by restricting high-evaluation patents, thereby providing intuitive and quick access to high-quality patents.

1 is a diagram illustrating an exemplary configuration of a system for providing a patent analysis map of the present invention.
2 is a diagram illustrating an exemplary method of an information processing method of a system for providing a patent analysis map of the present invention.
3 is a diagram illustrating an exemplary method of an information processing method of a system for providing a patent analysis map of the present invention.
4 is a diagram of an exemplary configuration of the patent automatic evaluation system of the present invention.
5 is a diagram illustrating an exemplary configuration of a specification analysis engine of the present invention.
6 is a diagram of an exemplary configuration of a DB unit of the present invention.
7 illustrates a patent evaluation result screen UI including an example of patent evaluation data.
8 is an embodiment of a similar patent analysis map of the present invention with the application year of the present invention as an intermediate node.
9 is a conceptual diagram illustrating a method of implementing a similar patent analysis map of the present invention with the filing year of the present invention as an intermediate node.
10 is an embodiment of a similar patent analysis map of the present invention with the patent classification of the present invention as an intermediate node.
11 is a conceptual diagram illustrating a method of implementing a similar patent analysis map of the present invention with the patent classification of the present invention as an intermediate node.
12 is a conceptual diagram illustrating a method of implementing a similar patent analysis map of the present invention with the evaluation score of the present invention as an intermediate node.
13 is an embodiment of a similar patent analysis map of the present invention with the keyword of the present invention as an intermediate node.

Hereinafter, it demonstrates further in detail, referring drawings.

The patent analysis map providing system 100 of the present invention provides a patent analysis map service to a user computer 2000 through a wired / wireless network 4000.

The patent analysis map providing system 100 includes a patent DB 110, an analysis map generation module 120, and a patent automatic evaluation system 1000. The patent analysis map providing system 100 includes patent bibliographic data 111, patent evaluation data 112, patent classification data 113, patent text data 114, similar patent data 115, and DBMS 116. May be included. The bibliographic information includes various number information such as application number, publication number, registration number, etc., various number information such as application date, publication date, registration date, etc., various subject related information such as applicant, inventor, agent, etc., name or abstract information of invention, priority Related information, patent classification information, reference information related to citations, prior art research literature information, examination validation literature information, and the like may be included. The patent evaluation data may include various evaluation information about a patent evaluated by the automatic patent evaluation system 1000. As will be described later, the patent evaluation data includes evaluation scores for evaluation items such as total evaluation scores, rights / technologies / marketability, etc., as systemized processing information on patents, and evaluations corresponding to evaluation scores according to evaluation rating grant models. More grades may be included. Patent classification data includes patent classification information such as IPC, and may include a hierarchy of patent classification and title information on the patent classification. Patent text data may include patent full text information (including drawings). Similar patent data includes information on similar patents generated by the similar patent generation module 121 of the analysis map generation module 120 of the present invention. DB construction and information extraction are performed through the DBMS.

Next, the analysis map generation module 120 will be described. The analysis map generation module 120 may include a similar patent generation module 121, a similar patent acquisition module 122, an analysis map visualization module 123, and an option setting module 124. The similar patent generation module 121 generates information on at least one or more similar patents for the central patent, and the similar patents generated may have a similarity relationship score with the central patents.

The patent included in the group of similar patents has a keyword similarity or higher than a predetermined level between the central patent and the plurality of keywords extracted from the central patent and the plurality of keywords extracted from the similar patent, while the similar patent is the center. It refers to a forward similar patent relationship whose filing date is earlier than the patent, or that the similar patent is a rear similar patent relationship whose filing date is later than the central patent.

The patent automatic evaluation system 1000 includes a specification analysis engine, an evaluation element processor, an evaluation engine, a reporting processor, a DB unit, and the like. The patent automatic evaluation system 1000 will be described in detail later in the present invention.

Next, the information processing method of the said patent analysis map providing system 100 of this invention is demonstrated, referring FIG. As illustrated in FIG. 2, the patent analysis map providing system 100 obtains information on the central patent (S11), obtains similar patent group information on the obtained central patent (S12), and similar patent group. Obtaining selection information about the display dimension for the information (S13), and connecting a similar patent corresponding to the dimension component to the intermediate node using the dimension component corresponding to the dimension from which the selection information is obtained as an intermediate node. Information processing is performed by placing similar patents in a manner (S14). The patent analysis map providing system 100 may provide various options through the option setting module 124. In this case, the patent analysis map providing system 100 obtains information on the central patent (S21), obtains similar patent group information on the obtained central patent (S22), and displays a display dimension of similar patent group information. Obtain the selection information for (S23), obtain the option selection information (S24), and limit only to similar patents satisfying the options, with the dimensional component corresponding to the dimension from which the selection information is obtained as the intermediate node, Information processing may be performed by arranging similar patents corresponding to dimensional components to the intermediate node in a manner of disposing similar patents (S25).

The display dimension may be bibliographic information of the patent. For example, it may be a time dimension such as an application date, publication date, registration date, etc., a patent classification dimension such as IPC, an evaluation score dimension, a key word dimension, and the like. FIG. 8 shows various display dimensions such as the year of similar patent group by year, the similar patent group by IPC, the similar patent group by keyword, the similar patent group by evaluation score, and the similar patent group by family. .

The option may be an option, a detailed evaluation score section or an evaluation score grade such as depth in the patent classification system, an evaluation score section, an evaluation score grade, rights / technologies / marketability in the case of IPC. Of course, the application number, the applicant, etc. may be set as an option in the node display.

Hereinafter, the analysis map generation module 120 of the present invention will be described in more detail.

8 * shows an example of information processing of the patent analysis map providing system 100 for the central patent 1019990000069.

Meanwhile, since the central patent and the similar patents may correspond to bibliographic matters, the central patent and the similar patent may be displayed by any one or more of application number display, registration number display, and applicant / right holder display, the application number display, The registration number display and the applicant / right holder display may be set by the user or the administrator of the similar patent analysis map information providing system. The visualization of the analysis patent map as shown in FIGS. 8 to 15 is performed by the analysis map visualization module 123 of the present invention.

Although the above description has been given of the case where there is one central patent, it will be apparent to those skilled in the art that the present invention can be extended even when the central patent is n (n> 1, n is an integer). The analysis map generation module 120 treats each of the n central patents as Pi, extracts similar patents as shown in Tables 1 to 3, and arranges similar patents with respect to each Pi for each of the n central patents. However, it is possible to generate an analysis patent map by treating similar duplicate patents with one node and connecting the node and each Pi with different lines.

Meanwhile, the central patent and the similar patent may be linked with bibliographic information, full text information, patent evaluation information, or other services of the system or the third system 3000 of the present invention.

Hereinafter, the patent automatic evaluation system 1000 of this invention and the information processing method of the system are demonstrated.

4 is an exemplary configuration of a patent automatic evaluation system 1000 of the present invention.

In the present invention, the patent automatic evaluation system 1000 and the user computer 2000 are connected to the wired / wireless network 4000. The automatic patent evaluation system 1000 may optionally be connected to at least one external data source group 3000. The external data source group 3000 includes a national authority system 3100 such as the Korea Patent Office and the like, an national authority system 3200 such as the EPO, and a public institution system 3300 having publicity such as the Korea Patent Information Service. In addition, there are public institution systems 3300 or private institution systems 3400 that provide market information including corporate information, and these can provide data to the patent automatic evaluation system 1000 through wired and wireless networks 4000. have. The user computer 2000 is a comprehensive computer including a personal computer, a mobile computer, as well as a server or system using the patent automatic evaluation system 1000 as a user.

The patent automatic evaluation system 1000 may include a specification analysis engine 1100 for analyzing and processing a specification, an evaluation element processing unit 1200 for processing an evaluation element, and an evaluation engine 1300 for generating, managing, and evaluating evaluation models. At least one of a reporting processing unit 1400 for providing an evaluation result, a DB unit 1500 for storing various data, a user interface unit 1600, and a system manager function unit 1700 for an administrator for managing a system. It may include. Then, separate them one by one.

The automatic patent evaluation system 1000 includes a specification analysis engine 1100 that analyzes a patent specification, extracts key technology key keywords from the patent specification, and extracts evaluation elements through pattern processing. This will be described in detail. As illustrated in FIG. 5, the specification analysis engine 1100 includes a morpheme analyzer 1111, a terminology recognizer 1112 for recognizing a technical term, a technical term, and a field weighting process (core). Core keyword extractor 1110 including keyword processor 1113, similar patent group generator 1120 including cluster analyzer 1121 to perform cluster analysis, synonym / thesaurus dictionary 1131, stemming dictionary 1132 Dictionary 1130 including terminology dictionaries 1133, user dictionaries 1134, post-processing rules 1135, and the like, and pattern / rule-based evaluation element extractors 1141, claim-related evaluation element extractors 1142. There is an evaluation element extractor 1140 that includes. The key keyword processor 1113 may include a technical term extractor 1113-1 and a field weight application unit 1113-2 for applying different weights for each field in which the key keywords appear. The data processed by the specification analysis engine 1100 is stored in the specification analysis result DB 1510.

The specification analysis engine 1100 obtains a specification, processes the specification data for each field, extracts a preliminary keyword for each field, and the stemmer 1111 natural-processes the patent specification while preliminary keyword for each field on the patent specification. Select and save.

For example, as follows. When the sentence "the present invention relates to a visitor notification service system through the network 4000 system for listing the image information and identity information for the visitors of the apartment house and transmits to the resident terminal." 1111 performs morpheme analysis and compound noun analysis to generate the following preliminary keywords.

"Public housing", "visitor", "public housing visitor", "image", "information", "image information", "identity information", "listing", "resident", "terminal", "resident terminal" , "Transfer", "network (4000)", "system", "network (4000) system", "visitor", "service", "system", "service system"

Subsequently, the core keyword extractor 1110 of the present invention extracts preliminary air pair information. Air pair information refers to information about key keywords that appear together but are spaced apart. In the above example sentence, the following preliminary air pairs can be extracted.

"Community Visitor | Image Information", "Community Visitor | Identity Information", "Identity Information | Image Information", "Listing | Image Information", "Listing | Identity Information", "Resident Terminal | Identity Information", "Resident Terminal | Listing", "Listing | Send", "Resident Terminal | Send", "Resident Terminal | Network (4000) System", "Network (4000) System | Transfer", "Visitor | Send", " Network (4000) System | Visitor "," Network (4000) System | Service System "," Visitor | Service System "

Subsequently, the key keyword extractor 1110 generates a term frequency (TF) for the preliminary keyword and the preliminary air pair. For example, there are one resident, one resident terminal | network 4000 system, and one visitor two times.

The following table shows an example of information on the frequency and weight of the preliminary keyword for each patent specification field of each preliminary keyword for the patent application No. 1019900000891.

Application number keyword F1 F2 F3 F4 weight 1019990000069 Data signal 2 4 17 54 506 1019990000069 Driver One 4 0 38 555 1019990000069 Line unit 0 One 2 8 388 1019990000069 shift 6 7 14 85 552 1019990000069 Shift clock signal 5 3 10 59 1000 1019990000069 Signal Wiring 3 0 7 13 452 1019990000069 LCD display 2 2 15 25 491 1019990000069 Clock signal 6 3 13 85 576 1019990000069 timing 2 7 3 29 391 1019990000069 Display device 2 3 13 25 415 1019990000069 Dual One 2 2 5 200 1019990000069 Clock One One One One 50 1019990000069 ... ... ... ... ... ...

F1: Title F2: Claim F3: Prior art F4: Rest of patent specification, TF: Frequency of term

In Table 1, the ... indicates that there are more keywords in the application number 1019990000069.

The preliminary keyword included in the preliminary keyword set may be further weighted, and the preliminary keyword and the weight information are stored in the specification analysis result DB 1510. The key keyword extractor 1110 included in the specification analysis engine 1100 applies the key keyword extraction algorithm to the preliminary keyword and / or the preliminary air pairs, and the patent among the extracted preliminary keywords and / or the preliminary air pairs. Create and store key keywords and / or key air pairs that can represent the technical content of the specification. The key keyword extraction algorithm may use various methods proposed in the field of natural language processing technology. In the present invention, Equation 1 below uses one such algorithm. In Equation 1 below, a field weight Fwi specialized in the patent specification is applied.

tf is the term frequency where the key keywords (indexes) appear in the current document

N : the total number of documents

df : the number of documents in which a key keyword appears in document frequency

slope : slope (arbitrary value, adjustable)

ut : unique terms for the entire document set

pivot : average length of literature (ut / N)

uf : ut of the document

Fwi: weight by field

The key keyword extraction algorithm illustrated above is extracted in consideration of the frequency of a term extracted from one patent specification and the frequency of a document including the term. In the patent specification, since the name of the invention, the abstract is particularly high in weight, and the claims can be given more weight than the detailed description of the invention, the key keywords extracted from these high-weight fields may have a special weight.

At this time, when 10 keywords are extracted from the 1019990000069, the extraction result will be as shown in Table 2 below.

Application number keyword F1 F2 F3 F4 weight 1019990000069 Data signal 2 4 17 54 506 1019990000069 Driver One 4 0 38 555 1019990000069 Line unit 0 One 2 8 388 1019990000069 shift 6 7 14 85 552 1019990000069 Shift clock signal 5 3 10 59 1000 1019990000069 Signal Wiring 3 0 7 13 452 1019990000069 LCD display 2 2 15 25 491 1019990000069 Clock signal 6 3 13 85 576 1019990000069 timing 2 7 3 29 391 1019990000069 Display device 2 3 13 25 415

At this time, it will be apparent that the core air pair may be generated by applying the above algorithm to the preliminary air pair. Hereinafter, since the core air pairs can also be treated as the same as the core keywords, the core keywords below include the core air pairs in addition to the core keywords of the consultation.

Meanwhile, the evaluation element extractor of the specification analysis engine 1100 may generate the evaluation base DB 1520 by pattern matching or rule-based information processing. For example, if a pattern corresponding to the prior patent document number is found in the description of the prior art information in the background art on the patent specification, it can be obtained, and the independent claim / dependent claim of each claim also in the claims. , The depth of the dependent term (the dependent term directly quoting the independent term is depth 2, the depth of the dependent term quoting the dependent term depth n is n + 1), the length of the claim (the claim term, in particular the number of words constituting the independent term, etc.) ), Information about the length of the specification, etc. may be generated to generate specification analysis result data. When the evaluation element value is generated by using the data included in the evaluation basic DB 1520 including the specification analysis result data, the generated evaluation element value is stored in the evaluation element value DB 1530 of the present invention.

The evaluation element processor 1200 of the present invention includes an evaluation element value generator 1210. The evaluation element value generation unit 1210 refers to a generation rule corresponding to each evaluation element, and refers to data for each data source included in the evaluation basic DB 1520 as a result of language processing of the specification analysis engine 1100. To generate an evaluation element value corresponding to each evaluation element. For example, the bibliographic data includes information about the number of claims, the number of applicants, the inventor's nationality, and the like for each patent, and the information is processed to evaluate evaluation factors (for example, the number of claims, the number of co-applicants, the number of inventors, etc.). ), An evaluation element value (for example, five claims, two co-applicants, three inventors' nationalities, etc.) is generated. There may be many different types of evaluation elements. In this invention, it demonstrates centering around the evaluation element peculiar to this invention.

When there is a patent to be evaluated, the patent is subjected to natural language processing (primarily using clustering based on key keywords) to provide a similar patent group composed of patents that are technically similar to the patent to be evaluated. And, providing various analysis information for the similar patent group also exists in the US OceanTomo system or Japan IPB system.

The present invention proposes a technique for processing the similar patent group information to derive an evaluation element based on the similar patent group information, and use the derived evaluation element in the establishment of an evaluation model and automatic patent evaluation. Generating an evaluation element value based on the similar patent group is processed by the evaluation element value generator 1210 of the present invention.

Similar patents constituting the similar patent group may have various meta information as in general patents. The meta information of the similar patent may include bibliographic information, historical information, family information, citation information, citation information, key keyword information extracted from the similar patent, and market information related to the applicant of the similar patent. Can be. The meta information may be included in the patent specification, but may be obtained and processed from an external data source (particularly, market information). External data sources can include DocDB. The market information may be, for example, the applicant type information of the applicant, applicant nationality information, applicant size information, accounting information including financial information, stock price information, handling item information, and affiliation industry information.

Next, a method of deriving similar patent group-based evaluation elements will be described in detail. Derivation of the similar patent group-based evaluation element is performed in the following steps.

First, the automatic patent evaluation system 1000 extracts at least two preliminary keywords included in the patent specification by processing the patent contents included in the patent specification with respect to a plurality of patent specifications. Extraction of the preliminary keyword has been described in detail at the place where the preliminary keyword and the preliminary air pair are extracted as example sentences in the description of the specification analysis engine.

The following is a step of selecting a core keyword by applying a predetermined key keyword selection algorithm to the extracted preliminary keyword. In the present invention, the key keyword selection algorithm of Equation 1 is used as an example.

Next, the selected key keyword is stored in correspondence with the patent specification.

Table 3 shows an exemplary data in which key keywords are stored for each application number.

Application number Subject Application number Subject Application number Subject 1019950044294 Gate line 1019960023819 gate 1019960029124 horizontal 1019950044294 Data line 1019960023819 Gate line 1019960029124 Gate line 1019950044294 repair 1019960023819 Gate insulation layer 1019960029124 Common electrode 1019950044294 Thin Film Transistor Board 1019960023819 Common electrode 1019960029124 Data line 1019950044294 LCD display 1019960023819 Common voltage 1019960029124 LCD display 1019950044294 LCD Display 1019960023819 Data line 1019960029124 LCD Display 1019950044294 Color filter 1019960023819 LCD display 1019960029124 Electrode wire 1019950044294 Color filter substrate 1019960023819 Insulation layer 1019960029124 Insulation Board 1019950044294 Transistor board 1019960023819 Display 1019960029124 Display 1019950044294 Display 1019960023819 Pixel electrode 1019960029124 Pixel electrode 1019960017410 Gate line 1019960027547 Gate line 1019960030262 edge 1019960017410 boundary 1019960027547 Gate voltage 1019960030262 Gate line 1019960017410 Data line 1019960027547 Drive 1019960030262 Data line 1019960017410 Thin film transistor 1019960027547 Data line 1019960030262 Thin film transistor 1019960017410 LCD display 1019960027547 Data voltage 1019960030262 Source electrode 1019960017410 LCD Display 1019960027547 LCD display 1019960030262 LCD display 1019960017410 Accumulation capacity line 1019960027547 LCD Display 1019960030262 LCD Display 1019960017410 transistor 1019960027547 Display 1019960030262 Equipment 1019960017410 Display 1019960027547 Precharge 1019960030262 Display ... ... ... ... ... ...

Next, a step of generating a similar patent group consisting of at least one similar patent for the evaluation target patent by clustering the extracted key keywords. When there are N keywords from n sources, their clustering algorithms are publicly available, and the K-means algorithm is representative. Cluster analysis is apparent to those skilled in the art and detailed description is omitted. Similar patent group data were generated by selecting 150 patents similar to the keywords extracted from 1019990000069 based on the similarity ranking. Application numbers of the generated similar patent group were as follows, and these data may be stored in relation to 1019990000069. However, when the similar patent group data is generated in real time, the similar patent group data may be generated and used in real time.

1019950044294, 1019960017410, 1019960023819, 1019960027547, 1019960029124, 1019960030262, 1019960031403, 1019960036724, 1019960051361, 1019960053734, 1019960078427, 1019960078429, 1019960079342, 1019960079357, 1019960079362, 1019970001015, 1019970002770, 1019970008154, 1019970027109, 1019970027110, 1019970033050, 1019970034424, 1019970038661, 1019970040662, 1019970049956, 1019970053512, 1019970055513, 1019970059144, 1019970059145, 1019970061456, 1019970066154, 1019970080196, 1019970080214, 1019980008213, 1019980008236, 1019980008244, 1019980008468, 1019980008470, 1019980012770, 1019980014682, 1019980014683, 1019980017518, 1019980020791, 1019980020792, 1019980023852, 1019980028107, 1019980028547, 1019980029215, 1019980029867, 1019980030588, 1019980036227, 1019980036229, 1019980038209, 1019980039649, 1019980041100, 1019980042708, 1019980042709, 1019980048364, 1019980053033, 1019980053659, 1019980054811, 1019980055271, 1019990000069, 1019990001009, 1019990001014, 1019990001015,1999 789, 1019990005827, 1019990005934, 1019990006084, 1019990010036, 1019990012287, 1019990015012, 1019990020521, 1019990026024, 1019990028821, 1019990047564, 1019990047566, 1019990050046, 1019990054161, 1019990067761, 1020000018919,02783200000200000 1020000064396, 1020000071330, 1020000072377, 1020010002726, 1020010002918, 1020010008339, 1020010009672, 1020010012592, 1020010018149, 1020010027258, 1020010029742, 1020010031642, 1020010032974, 1020010036541, 1020010040127, 1020010047487, 1020010049966, 1020010050420, 1020010052492, 1020010054965, 1020010057472, 1020010061903, 1020010062619, 1020010069379, 1020010072591, 1020010074896, 1020010077839, 1020010084419, 1020020000023, 1020020001630, 1020020002229, 1020020005606, 1020020011886, 1020020012121, 1020020015364, 1020020017007, 1020020018504, 1020020029290, 1020020042657, 1020020046814, 1020020050134,200 20053803, 1020020054923, 1020020055349, 1020020058993, 1020020064184, 1020020066135, 1020020071920, 1020020072443, 1020030034679, 1020040040291, 1020040086805, 1020040096211, 1020050013585, 1020060054382, 1020070041270, 1020080007610, 1020080033766

Of course, bibliographic matters can be applied to all these patents.

The method may further include generating evaluation element values for at least one or more similar patent group based evaluation elements by processing at least one or more meta information of the similar patent included in the similar patent group.

Hereinafter, an evaluation element based on a similar patent group will be described.

An evaluation factor based on a similar patent group is the average sales volume of related market participants. The average sales volume of the relevant market participants is calculated by an exemplary formula as follows for the right holder (mainly a company) having financial information in a similar patent group. This evaluation factor has a positive effect on patent evaluation (particularly, marketability evaluation of patents) as the number of firms in a similar patent group and the firm's sales scale are large. In general, in terms of marketability of patent technology, patents of corporations (large and foreign companies) tend to be more marketable than patents of universities, research institutes, and individuals, and this trend should be reflected as an evaluation factor.

1) Average sales volume of relevant market participants = sum (Revenue of company i belonging to similar patent group / Number of registered patents of company i * Number of patents in similar patent group of company i)

The above formula is illustrative and the average sales volume of the relevant market participants may also be calculated as follows.

2) Average sales volume of relevant market participants = sum (Revenue of company i belonging to similar patent group * Number of patents in similar patent group of company i) / Number of similar patent group

On the other hand, the average sales increase rate, average profit rate, etc. of the right holder (company) constituting the similar patent group may also be an evaluation factor using the similar patent group information of the present invention.

On the other hand, when the proportion of companies among the rights holders belonging to the similar patent group is higher than that of universities, research, or individuals, or when the proportion of large companies is higher than that of SMEs, the marketability of the technology in the technical field that characterizes the similar patent group is greater. Can be good. Therefore, the ratio of the owner attribute constituting the similar patent group may also be an important evaluation factor. For example, the following evaluation factors could be introduced.

3) Ratio of Large Company Patents Constituting Similar Patent Groups = Number of Large Company Patents in Similar Patent Groups / Total Number of Corporate Patents in Similar Patent Groups * 100

4) Ratio of large company patents composed of similar patent group = Number of large company patents belonging to similar patent group / total number of similar patent group

5) Proportion of company patents in similar patent group = Number of large company patents in similar patent group / total number of similar patent group

On the other hand, when a large number of foreign companies are included in the similar patent group, if the average technology gap with foreign companies or other market leadership can be expected, the ratio of foreign companies in the similar patent group is also a good evaluation factor. Can be The following is an example.

6) Percentage of foreign companies in the similar patent group = Number of foreign company patents in the similar patent group / total number of company patents in the similar patent group * 100

7) Percentage of Foreign Companies in Similar Patent Group = Number of Foreign Enterprise Patents in Similar Patent Group / Total Number of Patents in Similar Patent Group * 100

8) Percentage of foreign companies in the similar patent group = Number of foreign company patents in the similar patent group / Number of company patents in a specific country (for example, Korea) in the similar patent group * 100

In this way, various evaluation factors are utilized by utilizing various meta-information (right holder nationality, property of right holder (company, university, individual, research institute, etc.), sales size, profit rate, sales growth rate, etc.) included in similar patents that make up similar patent group. Can be envisioned. The above is merely an example. As illustrated for each evaluation element, a formula defining an evaluation element may correspond, and when processing information on each component constituting the formula (mainly, the basic data for processing such information in the evaluation basic DB 1520). The evaluation element values for each evaluation element can be generated. On the other hand, it is a matter of course that the above-described evaluation element may be configured in a complex. For example, the following equation can be considered.

9) Proportion of high commercial right holders in similar patent groups = (Number of large company patents in patent group / Number of total company patents in similar patent group * 100) * (Number of foreign company patents in similar patent group / All company patents in similar patent group Number * 100)

On the other hand, the various ratios exemplified above may be an evaluation factor of similar patent group utilization that is also important to increase or decrease the ratio. For example, if the proportion of large companies with similar patent groups is increasing over time, the activity of patents (applications or registrations) of large companies in this technology field is active, which usually has a positive impact on the marketability of this technology field. Gives. For example, the rate of increase or decrease of the rate may be obtained by calculating a rate in units of years and calculating the degree of increase or decrease of the rate for each year.

On the other hand, companies generally tend to apply for a large number of research results with commercialization in mind, and universities tend to apply for leading and pioneering research results independently of commercial performance. If the proportion of companies is very low and the proportion of universities is quite high, the technology belonging to the similar patent group is likely to be the technology of the introduction stage. Therefore, it is also possible to estimate whether the evaluation target patent technology is likely to belong to one of the technology change cycles such as the introduction phase, the growth phase, the mature phase, and the decline phase, based on the property of the right holder included in the similar patent group of the evaluation target patent.

The advantage of introducing an evaluation element based on such a similar patent group is to look at the collective properties of similar patent group constituent patents that are technically similar to the patent to be evaluated, thereby estimating properties that are difficult to estimate in the evaluation target patent itself. You can do it.

Next, a description will be given of a method for generating an evaluation element that does not use a similar patent group.

An exemplary information processing method for an evaluation factor of the degree of right of published technology is described. If the extracted key keywords are evenly distributed in the detailed description of the invention and the registered claims, it is likely that various technical elements posted in the detailed description of the invention have been well reflected in the contents of the patent. On the contrary, if the key keywords derived are intensively distributed only in the detailed description of the invention and there is little distribution in the registered claims, it is likely that the disclosed technical elements are not well reflected in the claims. Although the claims tend to be claimed in a higher concept, this is equivalent to the independent claims, and claims having only the higher concept have a high possibility of invalidity. In addition, it is highly likely that a good patent right is included in the subordinate terms such as subordinate concepts. Therefore, the key keywords derived from the patent specification are incorporated into the evaluation factor as to how many times the technology disclosed in the detailed description of the invention is reflected in the claims in the ratio of the number of times derived from the detailed description of the invention and the number of claims derived from the claims. Could be.

In order to generate an evaluation element value of an evaluation element, which is the degree of right of the publication technology, the automatic patent evaluation system 1000 processes the patent contents included in the patent specification for a plurality of patent specifications and includes them in the patent specification. Extract the at least two preliminary keywords for each field included in the patent specification, select the core keywords by applying a predetermined keyword selection algorithm, and store the core keywords for each field in correspondence with the patent specification; For the key keywords extracted from the patent to be evaluated, the number of key keywords included in the claims and the number of key keywords included in the detailed description of the invention are calculated, and the keyword and the detailed description of the invention are included in the claims. Create an evaluation factor value as a percentage of the number of included keywords .

Next, an evaluation element information processing method using the degree of keyword matching with the core keyword set extracted from the set reference evaluation patent set is presented. A baseline evaluation set is a set of high-evaluation patents, for example, a set of patents with a large number of patent citations, a set of patents recognized as technology leaders, a set of high-evaluation patents, and a set of patents applied or registered in various countries overseas. This is an example. The reason for introducing such evaluation element is that the higher the consistency of the key keywords extracted from the patents to be evaluated with the key keywords included in the patent set, the more likely it is to be a better patent than otherwise.

An information processing method for generating an evaluation element measurement value of the present evaluation element for evaluating the degree of correspondence with the leading keyword is described. The information processing method may include extracting at least two preliminary keywords included in the patent specification by processing the patent contents included in the patent specification with respect to the entire patent specification; Selecting a key keyword by applying a predetermined keyword selection algorithm to the extracted preliminary keyword; Storing the key keyword in correspondence with the patent specification; Calculating a ratio or number of keywords included in a keyword set extracted from a predetermined reference evaluation patent set among core keywords extracted from the evaluation target patent; And generating an evaluation element value by the ratio or number. The preset reference evaluation patent set becomes the high evaluation patent set described above.

Next, an evaluation model generation method for patent evaluation of the present invention will be briefly described. In order to generate an evaluation model, for patent sample selection and various other purposes, 1) an evaluation basic DB 1520 is generated, 2) a sample patent is selected as an evaluation population to conduct expert investigation, and 3) an expert is provided for the sample patent. Evaluate and generate evaluation criteria information with the scores evaluated by experts. 4) Create evaluation models using the evaluation factor measurement values of the sample patents with expert evaluation results as true values, 5) Verify the generated evaluation models, 6) confirm the evaluation model. When the evaluation model is confirmed, the importance or weight information of each evaluation element is determined for each evaluation model. An evaluation factor with zero importance can be excluded from the evaluation factor.

Next, the evaluation model is briefly described. Evaluation model generation is one of the most important elements of the present invention. The generation of an evaluation model may also perform the simplest regression analysis (polynomial regression analysis with the true value (the expert evaluation score for the sample patent) set to Y value and the value of each evaluation factor as an independent variable), but in this case However, there is a problem such as multilinearity between the evaluation elements, and there is a disadvantage in that the problem when each evaluation element has a nonlinear characteristic cannot be solved. Thus, the present invention does not exclude polynomial regression analysis, but applies advanced statistical analysis techniques.

First, the first optimal evaluation model tree (classifier) f ₁ (X) is generated by using the evaluation element measurement value of the evaluation element X and the evaluation criteria score Y evaluated by the expert. F ₁ (X) may be in the following form.

I is an indicator function that has a value of 0 or 1 depending on the condition.

mji refers to the average of the evaluation scores of the patents when the i-th indicator function is applied with respect to the evaluation factor j. The indicator function may vary depending on each evaluation factor, but may have the same value as 2 or 3. i corresponds to the number of branches in the tree.

The above equation will be described with a simple example. For example, if the number of evaluation elements is one in the number of claims, j = 1, and if there are two branches in the tree, the maximum value of i is 2. For example, if X ₁ is the number of claims and the number of claims is optimally diverged at 10, the above equation may be expressed as follows.

I ₁ (claim number = <10) is the case where the number of claims is 10 or less, m ₁₁ is the average of the evaluation criteria scores of patents having the number of claims 10 or less, and m ₁₂ is the average of the evaluation criteria scores of patents having more than the number of claims 10 Becomes

Finding the number of claims being a branch = 10 may branch by several branch candidates and repeatedly calculate the minimum value of the error to find the optimal branch by evaluation factor. Since this will be easy for those skilled in the art to find the optimal value, detailed descriptions are omitted.

Next, the difference value (residual) between the first evaluation model tree value generated from the true value Y and the predicted value f ₁ is obtained. A second evaluation model tree f ₂ is generated from the difference value Yf ₁ (X), and a difference value (residual) Yf ₁ (X) -f ₂ (X) is obtained. The third evaluation model tree f ₃ (X) is generated using the difference value Yf ₁ (X) -f ₂ (X), and the difference value (residual) Yf ₁ (X) -f ₂ (X) -f ₃ (X ) In this way, f _i (X) is generated, and then the Mth evaluation model tree f _M (X) is generated using the values Yf ₁ (X) -f ₂ (X)-.. f _{M - 1} (X). Perform iteratively. A weighted average of f ₁ (X) to f _M (X) completes the evaluation model. On the other hand, when generating a _{f M (X), f M} -1 (X) takes a value obtained by multiplying the _{shrinkage parameter s Y-sf 1 (} X) -sf 2 (X) to the - .. to sf _M (X) It can also be processed. In this case, the weights may be the same, or may be differently given in consideration of the magnitude of the error. The following final evaluation model is an example in which the weights become equal.

The evaluation model generation of the present invention is equally applied not only to the evaluation model for the entire patent, but also to the evaluation model for each evaluation item (rights, technicality, marketability, etc.) and the detailed evaluation items constituting subordinate items of the evaluation item. That is, the Y value may be an expert's evaluation score for the entire patent, but may be an expert's evaluation score for each evaluation item or an expert's evaluation score for each detailed evaluation item. According to the property of the Y value, the entire evaluation model, the evaluation model for each evaluation item, and the evaluation model for the detailed evaluation item are generated.

On the other hand, the present invention introduces an evaluation model for each technical field, owner attribute, and time. In order to generate an evaluation model for each technical field, sampling is performed to limit the sample itself to a specific technical field, and after the sampling process, the evaluation model for the specific technical field is completed. On the other hand, sampling for each property of the rights holder, and if the process after sampling, the evaluation model for each property of the rights owner is generated. The same applies to the time-based evaluation model. Generating several evaluation models is in charge of the evaluation model generator 1310 of the present invention. The generated evaluation model is managed by the evaluation model manager 1320.

When the evaluation model is established, the evaluation factor is reflected in the evaluation model, and when the evaluation element measurement value for each evaluation element is input, the evaluation element measurement value for each evaluation element can be processed to generate evaluation information. The generation of the evaluation information includes: 1) an evaluation model which receives the selection information about the evaluation target patent and the evaluation model to be used from the user computer 2000, obtains evaluation element value data for the evaluation target patent, and obtains the selection information. The evaluation element value data is applied to the evaluation engine 1300 to generate evaluation information including the evaluation result value, and to report the evaluation information including the evaluation result value to the user computer 2000. In this case, the default evaluation model provided by the patent automatic evaluation system 1000 may be selected as the selection of the evaluation model. Below, it is divided into necessary steps.

The evaluation information may be of various types according to an evaluation model such as an overall evaluation score, an evaluation score for each evaluation item, and an evaluation score for each detailed evaluation item. Since the patent evaluation information may vary depending on the evaluation model, the selection of the evaluation model may precede the generation of the patent evaluation information. The determination or selection of an evaluation model (a general evaluation model, an evaluation model for each technical field, an evaluation model for each attribute of the right holder, and a time-based evaluation model can be selected or selected) for evaluating the patent to be evaluated may be determined by the automatic patent evaluation system ( The user of 1000 may do so, but the patent automatic evaluation system 1000 may do so. In the latter example, the patent automatic evaluation system 1000 may automatically designate which technical field-specific evaluation model is applied with reference to the IPC information of the patent to be evaluated (it may be appropriate to use the main IPC). In addition, it is possible to designate an appropriate evaluation model for each property holder by identifying the property of the right holder with information on the right holder of the patent to be evaluated, and a reasonable timely evaluation model may be specified by referring to the filing date or registration date of the patent to be evaluated. The evaluation model manager 1320 of the present invention may provide a user with a complete set of evaluation models managed by the user and induce selection.

Meanwhile, for special evaluation experts, the evaluation model manager 1320 may provide an interface for manually adjusting the weights of the evaluation elements constituting the evaluation model, and when the weights are input from the experts, An expert-specific evaluation model may be established by reflecting the weight, and the patent evaluation may proceed with the established evaluation model.

When the evaluation target patent and the evaluation model are determined, evaluation element measurement value information may be obtained for each evaluation element of the evaluation target patent. If the evaluation element measurement value information is input to the evaluation model for each evaluation element, the evaluation model may be obtained. Generates preset evaluation information. Generation of the evaluation information is in charge of the evaluation information generation unit 1330 of the present invention. The evaluation information may include an evaluation score and an evaluation grade. When the evaluation score is determined by the evaluation model, the evaluation grade is assigned according to the evaluation grade granting model corresponding to the evaluation score. The evaluation grade may be any grade, but it would be reasonable to be at least 5 and less than 15 grades. There are two methods for assigning an evaluation grade: 1) a score section corresponding rating section method, and 2) a ranking section corresponding rating section section method. 1) is a method of assigning a grade to a score from a few points to a few points, and 2) is an evaluation score for all patents belonging to the population based on the population to which the evaluation target patent belongs. The sequence of is determined, and then the evaluation grade corresponding to the sequence percentile interval is determined.

Next, a point provision evaluation element is demonstrated. Progress information, such as invalid judgments and informational provisions, is a scarce evaluation factor, and because of frequency problems, there is room for statistical models to be missing from the evaluation model. In this case, after generating an evaluation score by applying an evaluation model, if it is confirmed that a special event such as a special event or a license setting according to the progress information such as the invalid judgment is generated in the evaluation target patent, the special event is generated. Additional points or deductions may be given for each type / count and / or for each kind of result of the patent event (for example, determination of a partial invalidation, etc.). In the present invention, the special event processing unit 1331 is in charge of adding or subtracting the special event. The above points or points may be granted to the entire patent evaluation score, but may also be given for each evaluation item, such as rights, technology, and marketability. Accordingly, when a special event occurs, an evaluation grade may be calculated as an evaluation score after performing a point or deduction process on the evaluation score of the evaluation model. Of course, conceptually, since the evaluation model may be a reflection of a point or a penalty according to the patent event, in this case, the evaluation grade may be calculated as the evaluation score of the evaluation model.

The generated evaluation score and evaluation rating may be transmitted to the user computer 2000. The evaluation information may be delivered in a web page or a report (eg, a report in pdf format).

On the other hand, the automatic patent evaluation system 1000 of the present invention can provide similar patent group information for the evaluation target patent. When providing the similar patent group information, the similarity scores / information of the evaluation target patent and the similar patent included in the similar patent group may be further provided. The similarity score / information is information on how similarity or relevance of the patent to be evaluated with the similar patent in terms of key keywords is generated during cluster analysis, which is common in natural language processing, and thus detailed description is omitted. do.

Data processed in the patent automatic evaluation system 1000 is stored in the DB unit 1500 of the present invention. As can be seen in FIG. 6, the DB unit 1500 includes bibliographic data 1511, historical information data 1512, market information data 1513, DocDB data 1514, and a patent specification or reference number of registered patents. An evaluation base DB 1510 including the hit data 1515 and the like is included. Meanwhile, the DB unit 1500 includes a specification analysis result DB 1520 related to specification analysis including a specification analysis result, which includes index terms 1521 for each field, frequency data 1522 for each index term, and key keyword accumulation information for each period. 1523, technical terminology information 1524, associated air pair information 1525, evaluation element information 1526, similar patent group data 1527, and extracted key keyword data 1528 may be included. . The DB unit 1500 may further include an evaluation element value DB 1530 including evaluation element value data 1531 which is an evaluation element value for each evaluation element of each patent. Meanwhile, the DB unit 1500 includes various evaluation models of the present invention (by technical field, by owner attribute, etc.), which are stored in the evaluation model DB 1540. Meanwhile, the information generated by the user of the automatic patent evaluation system 1000 is included in the user DB 1550, which includes user information data 1551, which is information about a user as a user, and data managed by the user. There may be user management data 1552.

The automatic patent evaluation system 1000 may further include a user interface 1600 that receives patent identification information about an evaluation target patent from a user and provides evaluation information. The user interface 1600 may include an input UI for receiving the patent identification information and an output UI for providing evaluation information.

7 shows a patent evaluation result screen UI including an example of patent evaluation data. Patent evaluation data may be generated and provided along with evaluation scores for each item of evaluation criteria as well as evaluation scores for each item of evaluation criteria such as rights, technology, and marketability.

In this manner, an evaluation score may be generated for every patent, and a similar patent group may correspond to every patent. For example, the data of Table No. 1019990000069 may be generated as shown in Table 4 below.

Application number all
score all
Rating Rights
score Rights
Rating Technical
score Technical
Rating marketability
score marketability
Rating 1019990000069 88.253 AA 24.71 BBB 33.543 BBB 30 AAA

Data as shown in Table 5 below may also be generated for patents (150) belonging to the similar patent group of 1019990000069.

Application number all
score all
Rating Rights
score Rights
Rating Technical
score Technical
Rating marketability
score marketability
Rating IPC 1019950044294 82.821 A 21.278 BB 31.543 BB 30 AAA 1995 G02F 1/1343 1019960017410 80.791 A 19.248 BB 31.543 BB 30 AAA 1996 G02F 1/136 1019960023819 84.01 A 19.902 BB 34.108 BBB 30 AAA 1996 G02F 1/1343 1019960027547 81.664 A 18.121 B 33.543 BBB 30 AAA 1996 G02F 1/133 1019960029124 80.795 A 16.735 B 34.06 BBB 30 AAA 1996 G02F 1/136 1019960030262 82.418 A 21.358 BB 31.06 BB 30 AAA 1996 G02F 1/136 1019960031403 87.268 AA 21.208 BB 36.06 A 30 AAA 1996 G02F 1/136 1019960036724 85.896 AA 21.836 BB 34.06 BBB 30 AAA 1996 G02F 1/136 1019960051361 86.098 AA 22.038 BB 34.06 BBB 30 AAA 1996 G02F 1/136 1019960053734 89.962 AAA 23.902 BBB 36.06 A 30 AAA 1996 G02F 1/136 1019960078427 87.006 AA 22.946 BBB 34.06 BBB 30 AAA 1996 G02F 1/136 1019960078429 85.422 AA 19.362 BB 36.06 A 30 AAA 1996 G02F 1/136 1019960079342 89.903 AAA 23.843 BBB 36.06 A 30 AAA 1996 G02F 1/136 1019960079357 83.271 A 20.293 BB 34.06 BBB 28.918 A 1996 G02F 1/1343 1019960079362 82.155 A 18.873 BB 33.282 BBB 30 AAA 1996 G09G 3/36 ... ... ... ... ... ... ... ... ... 1020080033766 85.903 AA 22.91 BBB 37.769 AA 25.224 BBB 2008 G02F 1/1343

It will be apparent to those skilled in the art that data such as Table 5 may be generated for all registered patents.

Hereinafter, when there are similar patent group data, evaluation data, and keyword data for every patent as described above, the patent analysis map providing system 100 may generate the following information.

When the patent analysis map providing system 100 obtains 1019990000069 as the central patent, the analysis map generation module 120 obtains similar patent group information of 1019990000069. In this case, when the year of the patent analysis map providing system 100 is selected as the display dimension, the analysis map generation module 120 extracts the filing year from the patents of the similar patent group and treats the filing years as intermediate nodes. do. Group similar patents corresponding to the intermediate nodes. Subsequently, the analysis map visualization module 123 uses the dimensional component corresponding to the dimension for which the selection information has been obtained as an intermediate node, and connects the similar patent to the intermediate node by linking similar patents corresponding to the dimensional component to the intermediate node. Complete the patent analysis map by placing it. This example is well illustrated in FIG. 8. When the intermediate node is introduced in this way, similar patents for each year can be viewed in more detail. That is, the analysis map visualization module connects the central patent with the individual patent through an intermediate node, unlike a conventional network visualization method for linking the central patent and the individual patent.

The patent analysis map generation module 120 may generate the following data for connection through the intermediate node as described above. Let the central patent be Pi and the similar patents P1, P2, P3, P4 ..., Pn. Assume that P1, P2, and P3 are filed in year Y1, P4 in year Y4, and Pn in year Yn. In this case, the data (Pi, Y1), (Pi, Y4), ... (Pi, Yn), and (Y1, P1), (Y1, P2), (Y1, P3), (Y4, P4) You can create data (Yn, Pn). That is, the patent analysis map generation module 120 may generate data for pairing the central patent Pi and the extracted years, and may generate data for pairing each year with an application number corresponding to each year. have. This allows each pair to be drawn on a network diagram. At this time, Pi is positioned at the center, and Y1, Y4, ... Yn connected to Pi form an intermediate node, and similar patents are connected to P at each Y. On the other hand, the patent analysis map generation module 120, if there are similar patents Psij (Yj) (Psi is similar patents Pj of Pi, Yj is the filing year of Pj) of Pi, every similar patent Psij (Yj) , (Pi, Yj) and (Yj, Psij (Yj)). In this case, since there is the most Pi in all the pairs generated, Pi is centered, and because there are more Yj than similar patent Psij (Yj), Yj forms an intermediate node, because (Pi, Yj), Pi and Yj are connected, and because of (Yj, Psij (Yj)), Yj and Psij (Yj) are connected. At this time, more identical pairs form a stronger connection relationship. This strong connection can correspond to the thickness or color of the line or to match the number on the line. That is, in the line connecting Pi and Yj, the number (quantity) of Psij (Yj) corresponding to Yj can be estimated by the thickness of the line, the color of the line, and the numerical value displayed on the line. The number of (Pi, Yj) corresponding to the intermediate node will be proportional to the number of similar patents Psij (Yj) whose application year is Yj. This example is shown in FIG. 9, and an embodiment of the patent analysis map providing system 100 is shown in FIG. 8.

On the other hand, the degree of similarity between Pi and Psij (Yj) is indicated on every Psij (Yj) or a line connecting Psij (Yj) and Yj, or the patent evaluation grade or evaluation score is indicated by display means such as color or symbol. If so, the patent search can be performed by paying attention to the displayed patents. To this end, a link should be placed on the displayed patents, and the link may be a bibliography, a patent text, evaluation information, and the like.

When the IPC is selected as the display dimension, the patent analysis map providing system 100 selects a classification depth (section, class, subclass, main group, subgroup, etc.) in the IPC, or has a classification depth selected as default. In this case, the IPC corresponding to the classification depth is extracted, and the IPCs are treated as intermediate nodes. Group similar patents corresponding to the intermediate nodes. Subsequently, the analysis map visualization module 123 uses the dimensional component corresponding to the dimension for which the selection information has been obtained as an intermediate node, and connects the similar patent to the intermediate node by linking similar patents corresponding to the dimensional component to the intermediate node. Complete the patent analysis map by placing it.

The patent analysis map generation module 120 may generate the following data for connection through the intermediate node as described above. Let the central patent be Pi and the similar patents P1, P2, P3, P4 ..., Pn. Assume that P1, P2, and P3 are assigned IPC1, P4 is assigned to IPC4, and Pn is assigned to IPCn. In this case, the data (Pi, IPC1), (Pi, IPC4), ... (Pi, IPCn) and (IPC1, P1), (IPC1, P2), (IPC1, P3), (IPC4, P4) We can create data called (IPCn, Pn). That is, the patent analysis map generation module 120 generates data pairing between the central patent Pi and the extracted patent classifications, and generates data pairing data between each patent classification and an application number corresponding to each patent classification. Can be generated. This allows each pair to be drawn on a network diagram. At this time, Pi is positioned in the center, and IPC1, IPC4, ... IPCn connected to Pi form an intermediate node, and similar patents are connected to P to each IPC. On the other hand, the patent analysis map generation module 120, if there is a similar patent Psij (IPCj) (Psi is a similar patent Pj of Pi, IPCj is a patent classification of Pj) of Pi, every similar patent Psij (IPCj) , (Pi, IPCj) and (IPCj, Psij (IPCj)). In this case, Pi is the largest in all the pairs generated, so that Pi is centered and that there are more IPCj than similar patent Psij (IPCj), so that IPCj forms intermediate nodes, because (Pi, IPCj), Pi and IPCj are connected, and because of (IPCj, Psij (IPCj)), IPCj and Psij (IPCj) are connected. At this time, more identical pairs form a stronger connection relationship. This strong connection can correspond to the thickness or color of the line or to match the number on the line. That is, in the line connecting Pi and IPCj, the number (quantity) of Psij (IPCj) corresponding to IPCj can be estimated by the thickness of the line, the color of the line, and the numerical value displayed on the line. The number of (Pi, IPCj) corresponding to the intermediate node will be proportional to the number of similar patents Psij (IPCj) whose patent classification is IPCj. This conceptual example is shown in FIG. 11, and an embodiment of the patent analysis map providing system 100 is shown in FIG. 10. Although it can be seen that the processing shown in FIG. 10 is performed in units of IPC class, it will be understood that the scope of the present invention can be extended to a lower patent classification such as an IPC subclass unit.

The patent analysis map providing system 100 may select a patent evaluation score or a patent evaluation grade as the display dimension. The patent evaluation score is section 1 (90 points or more), section 2 (80-90), ..., section n, or the patent grade is S, AAA, AA, A, BBB, BB, B ... In this case, information processing can be performed using the interval of the evaluation score or the patent grade as an intermediate node. Of course, the evaluation score will be able to handle the intermediate node not only with the overall evaluation score, but also with evaluation scores such as rights, technology, and marketability, and so will the evaluation grade.

The patent analysis map providing system 100 extracts score sections or evaluation grades corresponding to evaluation score sections, and treats the score sections or evaluation grades as intermediate nodes. Group similar patents corresponding to the intermediate nodes. Subsequently, the analysis map visualization module 123 uses the dimensional component corresponding to the dimension for which the selection information has been obtained as an intermediate node, and connects the similar patent to the intermediate node by linking similar patents corresponding to the dimensional component to the intermediate node. Complete the patent analysis map by placing it.

The patent analysis map generation module 120 may generate the following data for connection through the intermediate node as described above. Let the central patent be Pi and the similar patents P1, P2, P3, P4 ..., Pn. Assume that P1, P2, and P3 correspond to the G1 evaluation grade / rating score interval, P4 to the G4 evaluation grade / rating score interval, and Pn corresponds to the Gn evaluation grade / rating score interval. In this case, the data (Pi, G1), (Pi, G4), ... (Pi, Gn) and (G1, P1), (G1, P2), (G1, P3), (G4, P4) We can create data called (Gn, Pn). That is, the patent analysis map generation module 120 generates data that forms a pair between the central patent Pi and the extracted evaluation grade / evaluation score intervals, and each evaluation grade / evaluation score intervals and each evaluation grade / evaluation score interval Data pairing between the application number corresponding to these can be generated. This allows each pair to be drawn on a network diagram. At this time, Pi is positioned in the center, and G1, G4, ... Gn connected to Pi form an intermediate node, and similar patents are connected to P with each G. Meanwhile, the patent analysis map generation module 120 includes all similar patents Psij when Pi and similar patents Psij (Gj) (Psi are similar patents Pj of Pi and Gj is an evaluation grade / evaluation score section of Pj). For each (Gj), (Pi, Gj) and (Gj, Psij (Gj)) are generated. In this case, Pi is the largest in all the pairs generated, so that Pi is centered, and there are more Gj than similar patent Psij (Gj), so that Gj forms intermediate nodes, and because of (Pi, Gj), Pi and Gj are connected, and because of (Gj, Psij (Gj)), Gj and Psij (Gj) are connected. At this time, more identical pairs form a stronger connection relationship. This strong connection can correspond to the thickness or color of the line or to match the number on the line. That is, in the line connecting Pi and Gj, the number (quantity) of Psij (Gj) corresponding to Gj can be estimated by the thickness of the line, the color of the line, and the numerical value displayed on the line. The number of (Pi, Gj) corresponding to the intermediate node will be proportional to the number of similar patent Psij (Gj) whose application evaluation grade / evaluation score interval is Gj. This conceptual example is shown in FIG. 12.

The patent analysis map providing system 100 may process information in two ways when a keyword is selected in the display dimension. First, the keyword of the central patent is obtained, and the keyword of the obtained central patent is treated as an intermediate node. Information may be processed in such a manner that a similar patent having the keyword of the intermediate node as its keyword is connected to the intermediate node. 13 illustrates one such example.

The second can generate (Keyi, Keyj) for all keywords of the central patent, generate (Keyi, Keyj) for all keywords per similar patent, and then draw a network diagram for the keyword pair. In this case, since all of the similar patents have keyword similarities with the central patent, the keywords based on Pi can be linked to the central patent Pi, and there can be a linking relationship between the keywords linked to the Pi and the paired keywords resulting from the similar patent. . Furthermore, these keywords become intermediate nodes, and similar patents including keywords which become intermediate nodes as their keywords can be linked to the keywords.

The patent analysis map generation module 120 may generate the following data for connection through the intermediate node as described above. Let the central patent be Pi and the similar patents P1, P2, P3, P4 ..., Pn. Pi includes Keyi1, Keyi2, ... Keyin. In this case, a keyword set consisting of (Keyii, Keyij), which is a pair of keywords corresponding to Pi, is generated, and the keyword set can be drawn in a network diagram. On the other hand, when the similar patent Pj contains Keyin, the intermediate node Keyin can be connected to the similar patent Pj in a line. 13 is an embodiment of a similar patent analysis map of the present invention with the keyword of the present invention as an intermediate node.

In the present invention, an intermediate node is introduced to generate a patent analysis map. In a typical network diagram, all nodes participating in the diagram have equivalent attributes. For example, in a diagram representing various networks such as a patent citation map, a co-applicant network, a co-inventor network, a keyword network, and the like, each node has the same property, such as a patent, an applicant, an inventor, and a keyword. However, in the present invention, since the intermediate nodes such as the time display intermediate node such as the filing year, evaluation score, IPC, etc. are introduced, and all other nodes except the intermediate node are patent documents, the intermediate node and other nodes have different attributes. Accordingly, the information processing method for generating data for drawing a network diagram is different.

In this manner, the patent analysis map providing system 100 may generate a similar patent analysis map.

The present invention can be utilized in the patent industry, evaluation industry, and intellectual property consulting industry.

100: Patent analysis map providing system
110: Patent DB
111: Patent bibliography data
112: Patent evaluation data
113: Patent classification data
114: Original patent data
115: Similar patent data
116: DBMS
120: analysis map generation module
121: Similar patent generation module
122: similar patent acquisition module
123: Analysis Map Visualization Module
124: Option setting module
1000: patent automatic evaluation system
131: Specification Analysis Engine
132: evaluation element processing unit
133: rating engine
134: reporting processing unit
135: DB unit
2000: your computer
3000: third system
4000: wired and wireless network

Claims (11)

In the information processing method of the similar patent analysis map information providing system, the similar patent analysis map information providing system is
(A) receiving information on the central patent;
(B) obtaining similar patent group information on the obtained central patent;
(C) receiving selection information on a display dimension for the similar patent group information; And
(D) arranging similar patents in such a manner as to link similar patents corresponding to the dimensional components to the intermediate nodes, using the dimensional component corresponding to the dimension from which the selection information has been obtained as an intermediate node; And the similar patent in which the central patent and the registration are maintained are associated with patent evaluation information. The method of claim 1,
And the display dimension is any one of a time dimension, a patent classification dimension, a key keyword dimension, and an evaluation score dimension. The method of claim 2,
The time dimension is either the filing year or the registration year
The patent classification dimension is a predetermined classification level on the IPC patent classification system,
The evaluation score dimension is a similar patent, characterized in that the evaluation score generated as a result of applying the evaluation element value for the similar patent to the evaluation model including at least two evaluation elements for the similar patent in units of intervals Analysis Map Information processing system. The method of claim 3,
And the predetermined classification level is any one of a class, a subclass, a main group, and n (n is a natural number) dot subgroup. The method of claim 3,
The evaluation score is the total evaluation score for the similar patent, or
An information processing method of a similar patent analysis map information providing system, characterized in that it is an evaluation score for each evaluation criteria item. The method of claim 5,
The evaluation criteria item is one or more of rights, technical, and marketability The method of claim 1,
Similar dimension analysis map information, wherein the dimension intermediate node is a dimension component included in the similar patent processing information which is the processing information about the similar patent constituting the similar patent group with respect to the display dimension that has received the selection information. How the information processing system provides. The method of claim 7, wherein
The display dimension is any one of a time dimension, a patent classification dimension, a key keyword dimension, and an evaluation score dimension.
The dimensional intermediate node may be composed of the filing years of the similar patent included in the similar patent processing information, the registration years, or the patent classifications of a predetermined classification level included in the similar patent. An evaluation generated by applying an evaluation element value for the similar patent to an evaluation model including one or more key elements extracted from the similar patent or consisting of key keywords extracted from the similar patent Information processing method of a similar patent analysis map information providing system, characterized in that consisting of the evaluation score intervals processing the score in interval units. The method of claim 1,
The central patent and the similar patent is to be represented by any one or more of the application number display, registration number display, applicant / rights holder display,
The application number display, registration number display, applicant / right holder display can be set by the user or the administrator of the similar patent analysis map information providing system, the information processing method of the similar patent analysis map information providing system. The method of claim 1,
And said central patent and said similar patent have similarity scores. A similar patent analysis map information providing system for implementing the method of any one of claims 1 to 10.

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