WO2015145524A1 - Document analysis system, document analysis method, and document analysis program - Google Patents

Abstract

In order to perform highly accurate analysis of document information, this document analysis system comprises: a category selection unit that selects categories that are indices whereby each document included in a plurality of documents can be classified; and a score calculation unit that calculates, for each category selected by the category selection unit, a score indicating the strength of connection between a document constituting document information and a sorting code, said sorting code indicating the degree of association between the document information and a lawsuit or a fraud investigation.

Description

Document analysis system, document analysis method, and document analysis program

The present invention relates to a document analysis system for analyzing document information recorded in a predetermined computer or server.

Conventionally, when computer crimes and legal disputes such as unauthorized access and leakage of confidential information occur, the equipment, data, and electronic records necessary for investigation and investigation are collected and analyzed, and the legal evidence is revealed. Means and techniques to make it have been proposed.

In particular, eDiscovery (electronic disclosure), etc. is required in US civil lawsuits, and both the plaintiff and the defendant in the lawsuit are responsible for submitting relevant digital information as evidence. Therefore, the digital information recorded on the computer server must be submitted as evidence.

On the other hand, with the rapid development and spread of IT, since most information is created by computers in today's business world, a lot of digital information is flooded even within the same company.

Therefore, in the process of preparing for submission of evidence to the court, it is easy to make mistakes that include confidential digital information not necessarily related to the lawsuit as evidence. Moreover, it has been a problem to submit confidential document information not related to the lawsuit.

Recently, technologies related to document information in a forensic system have been proposed in Patent Documents 1 to 3. However, for example, in a forensic system such as Patent Documents 1 to 3, a large amount of document information of users using a plurality of computer servers is collected.

The task of sorting out whether such a large amount of digitized document information is valid as evidence for a lawsuit requires a user called a reviewer to visually confirm and sort out the document information one by one. There was a problem that it took a lot of labor and cost.

A document separation system for solving the above problem is proposed in Patent Document 4. In Patent Document 4, after collecting digitized document information, a classification code is automatically assigned to the document information, thereby reducing the burden of sorting the document information used in the lawsuit. A document separation system is disclosed.

JP 2011-209930 A (released on October 20, 2011) JP 2011-209931 A (released on October 20, 2011) JP 2012-032859 A (released on February 16, 2012) JP 2013-182338 A (released on September 12, 2013)

However, in the document classification system disclosed in Patent Document 4, when documents belonging to different categories are mixed in documents to be investigated, it is not possible to deal with the difference in description included in each document, and the classification code is There is a possibility that the accuracy of automatically giving a document (scoring the document) may be insufficient.

The present invention has been made in view of the above problems, and an object of the present invention is to provide a document analysis system and the like that can analyze the document information with high accuracy.

In order to solve the above problems, the document analysis system of the present invention acquires information recorded in a predetermined computer or server and analyzes document information composed of a plurality of documents included in the acquired information. A document selection system that selects a category that is an index that can classify each document included in the plurality of documents, and the document that constitutes the document information includes a document or a lawsuit or fraud And a score calculation unit that calculates a score indicating the strength associated with the classification code indicating the degree of association with the survey for each category selected by the category selection unit.

In the document analysis system of the present invention, the category selection unit sequentially selects the category from a plurality of categories, and the score calculation unit calculates the score for each category sequentially selected by the category selection unit. it can.

In the document analysis system of the present invention, the category selection unit classifies according to the type of the lawsuit or fraud investigation, the phase of the predetermined action that causes the lawsuit or fraud investigation, and the document information. At least one of the attributes can be selected as the category.

The document analysis system of the present invention further includes a survey category input receiving unit that receives an input of a category to which the lawsuit or fraud investigation belongs, and the category selection unit selects a category received by the survey category input receiving unit it can.

In addition, the document analysis system of the present invention determines a survey category to be surveyed based on the category received by the survey category input reception unit, and extracts a necessary information type from the survey basic database. An investigation type determination unit can be further provided.

In addition, the document analysis system of the present invention can further include an information extraction unit that extracts keywords and / or sentences included in the document information from the document information as information related to the lawsuit or fraud investigation.

In addition, the document analysis system of the present invention may further include a search unit that searches for keywords and / or sentences included in the document information from the plurality of documents.

In addition, the document analysis system of the present invention can further include a presentation unit that presents the score calculated by the score calculation unit to the user so as to be grasped.

In order to solve the above problems, the document analysis method of the present invention acquires information recorded in a predetermined computer or server, and analyzes document information composed of a plurality of documents included in the acquired information. A document selecting method for selecting a category that is an index capable of classifying each of the documents included in the plurality of documents, and the document that constitutes the document information is a lawsuit or fraud A score calculation step of calculating a score indicating the strength associated with the classification code indicating the degree of association with the survey for each category selected in the category selection step.

In order to solve the above problem, the document analysis program of the present invention acquires information recorded in a predetermined computer or server, and analyzes document information composed of a plurality of documents included in the acquired information. A document analysis program for selecting a category that is an index capable of classifying each document included in the plurality of documents, and a document constituting the document information includes: A score calculation function for calculating the score indicating the strength associated with the classification code indicating the degree of association with the lawsuit or the fraud investigation for each category selected by the category selection function is realized.

The document analysis system, document analysis method, and document analysis program of the present invention can analyze document information with high accuracy by calculating a score according to the category to which the document belongs.

1 is a block diagram showing a main configuration of a document analysis system according to an embodiment of the present invention. Schematic diagram showing how the score calculator calculates the score for a document for each category The chart which shows the flow of a process in the document analysis method concerning embodiment of this invention The chart which shows the flow of a detailed process in the document analysis method concerning embodiment of this invention The chart which shows the flow of the investigation and the classification process according to the investigation type in the document analysis method according to the embodiment of the present invention The chart which shows the flow of predictive coding according to the investigation kind in the document analysis method concerning embodiment of this invention The chart which showed the flow of processing for every step in an embodiment The chart which shows the processing flow of the keyword database in an embodiment The chart which showed the processing flow of the related term database in this embodiment The chart which showed the processing flow of the 1st automatic classification part in this embodiment The chart which showed the processing flow of the 2nd automatic classification part in this embodiment The chart which showed the processing flow of the classification code reception grant part in this embodiment Chart showing the processing flow of the document analysis unit in this embodiment The graph which showed the analysis result in the document analysis part in this embodiment The chart which showed the processing flow of the 3rd automatic classification part in one example of this embodiment The chart which showed the processing flow of the 3rd automatic classification part in other examples of this embodiment The chart which showed the processing flow of the quality inspection part in this embodiment Document display screen in this embodiment

Embodiments of the present invention will be described with reference to FIGS.

[Configuration of Document Analysis System 1]
FIG. 1 is a block diagram showing a main configuration of a document analysis system 1 according to an embodiment of the present invention. The document analysis system 1 is a system that acquires information recorded in a predetermined computer or server, and analyzes document information including a plurality of documents included in the acquired information.

As shown in FIG. 1, the document analysis system 1 includes a data storage unit 100 (digital information storage area 101, survey basic database 103, keyword database 104, related term database 105, score calculation database 106, report creation database 107), Database management unit 109, information extraction unit 24, search unit 30, score calculation unit 116, document analysis unit 118, survey category input reception unit 20, survey type determination unit 22, presentation unit 130, category selection unit 26, first automatic classification Unit 201, second automatic sorting unit 301, sorting code reception / giving unit 131, and third automatic sorting unit 401. The document analysis system 1 may further include a trend information generation unit 124, a quality inspection unit 501, a learning unit 601, a report creation unit 701, a lawyer review reception unit 133, a language determination unit 120, and a translation unit 122.

The survey category input receiving unit 20 receives a category input by the user. When a category is input, the survey category input reception unit 20 outputs the category to the survey type determination unit 22 and the category selection unit 26. Here, the category is an index that can classify each document included in a plurality of documents.

For example, the above categories represent the type of litigation or fraud investigation (representing the nature of the case relating to the litigation or fraud investigation, such as antitrust, patents, foreign bribery prohibition (FCPA), product liability (PL), Information leakage, fictitious billing, etc.). Alternatively, the category may be an attribute of document information (representing the nature of information included in the document information, such as competing opponent information, price, estimate sheet, price list, product, etc.).

Alternatively, the category may be a phase classified according to the progress of a predetermined action that causes a lawsuit or fraud investigation. Here, the “phase” is an index indicating each stage in which the predetermined action progresses (classified according to the progress of the predetermined action). For example, the phase “Relationship Building” (relationship building) is a premise of the phase of competition (competition), and is a step of building a relationship with a customer / competition. The “Preparation” phase refers to a stage in which information regarding competition is exchanged with competitors (which may be third parties).

Furthermore, the "Competition" (competition) phase refers to the stage of presenting prices to customers, obtaining feedback, and communicating with the competitors regarding the feedback. Note that the above “predetermined acts” are fraudulent acts (eg, price adjustment with competitors) related to lawsuits or fraud investigations (eg, antitrust, patents, overseas bribery prohibition, product liability, information leakage, fictitious claims, etc.) Such as participating in a meeting) or an act related to the illegal act. When the category is the phase, the document analysis system 1 can perform an optimal analysis corresponding to the predetermined action.

The survey type determination unit 22 determines a category to be surveyed based on the category received by the survey category input reception unit 20 and extracts a necessary information type from the survey basic database 103. For example, when the document information is an e-mail, a presentation material, a spreadsheet, a meeting material, a contract, an organization chart, or a business plan, the investigation type determination unit 22 sets each of the types of necessary information as above. The information is output to the information extraction unit 24. Therefore, the document analysis system 1 can extract the necessary information types.

The information extraction unit 24 extracts a plurality of documents from the document information. Specifically, the information extraction unit 24 uses information input from the survey type determination unit 22 (for example, e-mail, presentation materials, spreadsheet materials, meeting materials, contracts, organization charts, business plans, etc.) The keywords and / or sentences included in the information are extracted as information related to lawsuits or fraud investigations, and the extracted results are stored in the investigation basic database 103. Therefore, the document analysis system 1 can specify information related to the lawsuit or fraud investigation and hold it in the database.

The category selection unit 26 selects the category and outputs the selected category to the score calculation unit 116. When a plurality of categories are assumed, the category selection unit 26 can sequentially select one category from the plurality of categories.

In addition, when a category is input from the survey category input reception unit 20, the category selection unit 26 can select the input category. Thereby, the document analysis system 1 can select reliably the category input by the user.

The score calculation unit 116 calculates, for each category, a score indicating the strength with which the document extracted from the document information is associated with the classification code indicating the degree of association between the document information and the lawsuit or fraud investigation. The score calculation unit 116 may calculate the score in time series. The score calculation method will be described in detail later.

The presenting unit 130 presents the score calculated by the score calculating unit 116 to the user so as to be grasped. The presentation unit 130 can present the score to the user, for example, by displaying the score on a predetermined display unit (not shown). Thereby, the document analysis system 1 can make a user grasp | ascertain which category the document made into object fits.

The search unit 30 searches a plurality of documents for keywords and / or sentences included in the document information. Thereby, the document analysis system 1 can extract the keywords and / or sentences included in the document information.

When the keyword stored in the keyword database 104 is searched by the word search unit 114 and a document including the keyword is extracted from the document information by the document extraction unit 112, the first automatic sorting unit 201 adds the extracted document to the extracted document. On the other hand, a specific classification code is automatically given based on the keyword correspondence information.

The second automatic classification unit 301 extracts a document including related terms stored in the related term database from the document information, and based on the evaluation value of the related terms included in the extracted document and the number of the related terms. When a score is calculated, a predetermined classification code is automatically assigned based on the score and related term correspondence information to a document that includes the related term and whose score exceeds a certain value. To do.

The classification code receiving / giving unit 131 accepts a classification code given by the user based on the relevance to the lawsuit for a plurality of documents that are extracted from the document information and to which the classification code is not given, and outputs the classification code. Give.

The document analysis unit 118 analyzes the document given the classification code by the classification code reception / giving unit 131. Further, the document analysis unit 118, based on the relevance to the lawsuit, in addition to the document that has been given and received the classification code from the user, in the first automatic classification unit 201 and the second automatic classification unit 301, keywords, related terms, Based on the score, the document automatically assigned with the classification code is analyzed, and the above-mentioned document automatically received with the classification code is integrated with the above-mentioned document automatically received with the classification code. You may obtain a simple analysis result. In this case, the third automatic classification unit 401 can automatically assign a classification code based on the comprehensive analysis result.

In addition, the classification and investigation work can be carried out through automatic classification by word search, acceptance of classification and investigation by users, automatic classification and investigation using scores, automatic classification and investigation through the learning process, and automatic classification through quality assurance. There are various ways to proceed, such as surveys. The document analysis unit 118 analyzes a plurality of documents assigned classification codes together with a progress history that indicates in what order and how the various classification and investigation operations have progressed in combination, and will be described later. The report creation unit 701 may report the analysis result.

The third automatic classification unit 401 assigns a classification code to a plurality of documents extracted from the document information based on a result obtained by analyzing the document to which the classification code is given by the classification code receiving / giving unit 131 by the document analysis unit 118. Grant automatically.

The trend information generation unit 124 is similar to a document to which a classification code possessed by each document is assigned based on the type, number of occurrences, and evaluation value of the word included in each document for the document analysis unit 118 to analyze. The trend information indicating the degree of the is generated.

The quality inspection unit 501 compares the classification code received by the classification code reception / giving unit 131 with the classification code given by the trend information by the document analysis unit 118, and the classification code received by the classification code reception / granting unit 131. Verify the validity of.

The learning unit 601 learns the weighting of each keyword or related term based on the result of sorting the document. The learning unit 601 learns the weight of each keyword or related term based on the first to fourth processing results (described later) using Expression (2). The learning unit 601 may reflect the learning result on the keyword database 104, the related term database 105, or the score calculation database 106.

The report creation unit 701 outputs an optimal investigation report according to the type of litigation or the investigation type of the fraud investigation based on the result of separating the documents. As described above, the lawsuit includes, for example, antitrust, patent, foreign bribery prohibition (FCPA), product liability (PL), and the like. In addition, the fraud investigation includes, for example, information leakage and fictitious billing.

The lawyer review reception unit 133 receives reviews of the chief attorney or the lead patent attorney in order to improve the quality of the classification survey and the report and clarify the responsibility of the classification survey and the report.

The language determination unit 120 determines the language type of the extracted document.

The translation unit 122 receives the designation from the user or automatically translates the extracted document. In this case, it is desirable that the language delimiter in the language determination unit be smaller than one sentence so that it can be used for a single-sentence multilingual compound language. In addition, one or both of predictive coding and character coding may be used for language determination. Furthermore, a process of excluding an HTML (Hyper Text Markup Language) header or the like from translation targets may be performed.

The data storage unit 100 stores digital information acquired from a plurality of computers or servers in the digital information storage area 101 for use in analysis of lawsuits or fraud investigations. The data storage unit 100 includes a survey basic database 103, a keyword database 104, a related term database 105, a score calculation database 106, and a report creation database 107. As shown in FIG. 1, the data storage unit 100 may be a recording medium included in the document analysis system 1 or an external recording medium connected to the document analysis system 1 so as to be communicable. It may be.

The basic research database 103 includes, for example, litigation matters including antitrust, patents, foreign bribery prohibition (Foreign Corrupt Practices Act) (FCPA), product liability (Products Liability, PL), and / or information leakage, fictitious claims, etc. It holds the case attribute, company name, person in charge, custodian, and the structure of the investigation or classification input screen indicating which of the fraud investigations includes

The keyword database 104 includes a specific classification code of a document, a keyword having a close relationship with the specific classification code, and a correspondence relationship between the specific classification code and the keyword included in the acquired digital information. Holds keyword correspondence information.

The related term database 105 includes a predetermined classification code, a related term composed of words having a high appearance frequency in a document to which the predetermined classification code is assigned, and a relationship indicating a correspondence relationship between the predetermined classification code and the related term. Holds term correspondence information.

The score calculation database 106 holds weights of words included in the document in order to calculate a score indicating the strength of connection between the document and the classification code.

The report creation database 107 holds a report format determined according to the category, custodian, and contents of the classification work.

The database management unit 109 manages the update of data contents of the survey basic database 103, the keyword database 104, the related term database 105, the score calculation database 106, and the report creation database 107. The database management unit 109 may be connected to the information storage device 902 via a dedicated connection line or the Internet line 901. In this case, the database management unit 109 determines whether the survey basic database 103, the keyword database 104, the related term database 105, the score calculation database 106, and the report creation database 107 are based on the contents of data stored in the information storage device 902. Data content may be updated.

As described above, according to the conventional document analysis system, when documents belonging to different categories are mixed in the documents to be investigated, it is not possible to cope with the difference in description included in each document, and the classification code is assigned to the document. The accuracy of automatically assigning to (scoring the document) may be insufficient.

On the other hand, since the document analysis system 1 can calculate the score according to the category to which the document belongs, it can analyze the document information with high accuracy.

In addition, the document analysis system 1 includes a category selection unit that selects a first category that is an index that can classify each document included in the plurality of documents, and the document that forms the document information includes the document information A score calculation unit that calculates a score indicating a strength associated with a classification code indicating a degree of association with a lawsuit or fraud investigation with respect to the first category selected by the category selection unit, and the category selection unit includes: After the score calculation unit calculates a score for the first category, the score calculation unit further selects a second category different from the first category, and the score calculation unit selects the second category selected by the category selection unit. The score may be further calculated for two categories.

[Explanation of terms]
The “classification code” is an identifier used for classifying documents, and is an identifier indicating the degree of relevance with the lawsuit so that the document can be easily used in the lawsuit. For example, when document information is used as evidence in a lawsuit, it may be given according to the type of evidence.

“Document” is data including one or more words, and may be, for example, e-mail, presentation materials, spreadsheet materials, meeting materials, contracts, organization charts, business plans, and the like.

“Word” is a group of the smallest character strings having meaning. For example, a sentence “document means data including one or more words” includes “document”, “one”, “more”, “word”, “include”, “data”, “ The word "" is included.

“Keyword” is a group of character strings having a certain meaning in a certain language. For example, if a keyword is selected from a sentence “classify a document”, it can be set to “document” or “classify”. In the present embodiment, keywords such as “infringement”, “lawsuit”, or “patent publication XX” are selected with priority. The “keyword” may include a morpheme.

“Keyword correspondence information” is information representing the correspondence between a keyword and a specific classification code. For example, when the classification code “important” representing an important document in a lawsuit has a close relationship with the keyword “infringer”, the above “keyword correspondence information” uses the classification code “important” and the keyword “infringer”. It may be information managed in association with each other.

The “related term” is a term having an evaluation value of a certain value or more among words having a high appearance frequency in common with a document to which a predetermined classification code is assigned. Here, the appearance frequency may be, for example, a ratio of related terms appearing in the total number of words appearing in one document.

“Evaluation value” is a value indicating the amount of information that is exhibited in a document with each word. The “evaluation value” may be calculated based on the amount of transmitted information. For example, when a predetermined product name is assigned as a classification code, the “related term” may refer to the name of the technical field to which the product belongs, the country where the product is sold, the name of a similar product of the product, and the like. Specifically, “related terms” in the case of assigning the product name of the apparatus that performs the image encoding process as a classification code includes “encoding process”, “Japan”, “encoder”, and the like.

“Related term correspondence information” refers to information indicating the correspondence between related terms and classification codes. For example, when the classification code “product A”, which is the product name related to the lawsuit, has a related term “image encoding”, which is a function of the product A, the “related term correspondence information” is the classification code “product A”. And the related term “image coding” may be managed in association with each other.

“Score” refers to a value obtained by quantitatively evaluating the strength of association with a specific classification code in a document. In each embodiment of the present invention, for example, a score is calculated from the words appearing in the document and the evaluation value of each word using the following formula (1).

FIG. 2 is a schematic diagram showing how the score calculation unit 116 calculates a score for a certain document for each category. As described above, the score calculation unit 116 calculates the score for each category. Specifically, as shown in FIG. 2, the weights (coefficients, parameters) are determined (learned) in advance for each category, and the score calculation unit 116 responds to the category selected by the category selection unit 26. Then, by sequentially switching the weights, a score for a certain document is calculated for each category.

That is, the category selection unit is a coefficient used by the score calculation unit to calculate the score, and selects the category by sequentially replacing coefficients determined in advance for each category, and calculates the score. The unit calculates the score for each category using the coefficient corresponding to the category selected by the category selection unit.

Therefore, the document analysis system 1 can sequentially calculate the above scores for all assumed categories. For example, a low score may be calculated for categories A, B, and C, but a high score may be calculated for category D. Accordingly, it is possible to analyze the case from various angles using the score calculated for each category, and the document analysis system 1 can analyze the document information with high accuracy.

The document analysis system 1 may extract words that frequently appear in documents having a common classification code assigned by the user. Then, for each document, the extracted word type, the evaluation value of each word, and the trend information of the number of appearances included in each document are analyzed for each document, and the classification code is not accepted by the classification code acceptance and grant unit 131. Among them, a common classification code may be assigned to documents having the same tendency as the analyzed trend information.

Here, the “trend information” is information representing the degree of similarity of each document with a classification code, and is based on the type of word, the number of occurrences, and the word evaluation value included in each document. , Information represented by the degree of association with a predetermined classification code. For example, when each document is similar in degree of relevance between a document given a predetermined classification code and the predetermined classification code, the two documents are said to have the same trend information. In addition, even if the types of words included are different, documents having the same evaluation value and the same number of occurrences may be documents having the same tendency.

[Processes executed in the document analysis system 1]
FIG. 3 is a flowchart showing an example of processing executed in the document analysis system 1 (document analysis method according to the embodiment of the present invention). In the following description, parenthesized “˜steps” represent steps included in the document analysis method (control method of the document analysis system 1).

First, the category selection unit 26 selects a category which is an index that can classify each document included in a plurality of documents (category selection step, step 41, hereinafter “step” is abbreviated as “S”). Next, the score calculation unit 116 selects a score indicating the strength with which the document constituting the document information is associated with the classification code indicating the degree of association between the document information and the lawsuit or the fraud investigation by the category selection unit 26. It calculates for every category (score calculation step, S42). The category selection unit 26 determines whether or not all categories have been selected. If an unselected category remains (NO in S43), the category selection unit 26 selects a category different from the previously selected category (S41), and calculates a score. The unit 116 further calculates the score (S42).

Subsequently, details of the document analysis method of the present invention will be specifically described with reference to the drawings. In addition, the example demonstrated below is an example, Comprising: It is not limited to this example.

FIG. 4 is a detailed flowchart of the document analysis method according to the embodiment of the present invention. Note that the flow shown in FIG. 3 may be executed as a process independent of the flow shown in FIG. 4 or may be executed as a process included in an arbitrary part of the flow shown in FIG. .

Accepts designation of arguments from the user according to the display screen on the display unit, and identifies the corresponding category from litigation cases including antitrust, patents, FCPA, PL, or fraud investigations including information leakage, fictitious claims, etc. (S11).

使用 According to the specified category, the use database such as the survey basic database and the document analysis database can be specified (S12).

In order to check whether the database used is the latest, it is possible to access an information storage device that stores the latest database. The information storage device may be installed inside an organization that performs sorting or may be installed outside the organization. As a case where the information storage device is installed outside the organization, for example, there is a case where the information storage device is installed in an affiliated law firm or patent office.

When accessing the information storage device, authentication by ID and password can be performed to maintain security (S13).

After the authentication is performed, access to the information storage device is permitted, and the usage database such as the survey basic database and the document analysis database can be updated to the guideline database (S14).
The updated survey basic database is searched (S15), and the name of the company, the person in charge, and the custodian can be presented on the screen of the display device (S16).

If the name of the person in charge and the custodian displayed on the screen of the display device is different from the name of the person in charge and the custodian actually, the user corrects the names of the person in charge and the custodian on the screen of the display device. The document analysis system can accept the user's correction input and specify the names of the actual person in charge and the custodian (S17).

Next, digital document information can be extracted in order to perform document analysis work (S18).

As the updated document analysis database, the updated keyword database, related term database, and score calculation database can be searched (S19), and a classification code can be assigned to the extracted document information (S20).

Also, the classification code by the reviewer can be received and the classification code can be given to the extracted document information (S21).

The database can be searched using the classification result as teacher data, and a classification code can be assigned to the extracted document information (S22).

[Reviews by the chief attorney or patent attorney can be accepted (S23). This can improve the quality of the survey.

The category is specified by the user's argument designation (S24), and the report creation database can be specified according to the specified category (S25). The format of the report can be determined by the identified report creation database, and the report can be automatically output (S26).

FIG. 5 is a chart showing the flow of investigation and classification processing according to the investigation type in the document analysis method according to the embodiment of the present invention.

First, the survey type can be input (S31). In other words, depending on the display screen, the user will try to carry out from a fraud investigation including antitrust, patents, litigation cases including overseas bribery prohibition (FCPA), product liability (PL) or information leakage, fictitious claims, etc. Enter the category corresponding to the survey and sorting work. The document analysis system can accept a user category input and specify a category to be investigated.

Depending on the specified category, the type of survey and document analysis processing and the type of database to be used can be determined (S32).

Depending on the specified category, information stock stored in a usage database such as a survey basic database or a document analysis database may be accessed (S33).

調査 The survey basic database is accessed according to the specified category, and each keyword input screen corresponding to the specified category can be displayed (S34).

∙ Access the survey basic database according to the specified category, and display each text entry screen according to the specified category (S35)

調査 The survey basic database is accessed according to the specified category, and keywords or documents can be extracted according to the specified category (S36).

By executing the above-described processing, weighting can be added to the teacher data for automatic classification code assignment (predictive coding) (S37).

The extracted documents and information can be narrowed down by performing a keyword search in the document analysis database (S38).

FIG. 6 is a chart showing the flow of predictive coding according to the investigation type in the document analysis method according to the embodiment of the present invention.

In the document analysis method according to the embodiment of the present invention, first, the document analysis system can ask the user for input according to the type of survey, and can accept the user's input for that. For example, regarding cartels in relation to the antitrust law, user input is requested for target products, parties (name and email address), related organizations (name and department), and time, and user input is accepted. it can. In addition, regarding related organizations, it is possible to request user input regarding competitor companies and customer companies, and accept user input in response to the input (S51).

Next, it is possible to weight the classification code with the input keyword (S52). Then, predictive coding can be performed (S53).

In the embodiment of the present invention, as an example, the registration process, the classification process, and the inspection process are performed in the first to fifth stages according to the flowchart shown in FIG.

In the first stage, the keyword and related terms are updated and registered in advance using the result of the past classification process (S100). At this time, the keyword and the related term are updated and registered together with the keyword correspondence information and the related term correspondence information which are correspondence information between the classification code and the keyword or the related term.

In the second stage, a document including the keyword updated and registered in the first stage is extracted from all document information. When the document is found, the updated keyword correspondence information recorded in the first stage is referred to, and the classification corresponding to the keyword is performed. A first separation process for assigning a code is performed (S200).

In the third stage, the document including the related term updated and registered in the first stage is extracted from the document information that has not been given the classification code in the second stage, and the score of the document including the related term is calculated. With reference to the calculated score and the related term correspondence information updated and registered in the first stage, a second classification process is performed in which a classification code is assigned (S300).

In the fourth stage, the classification code given by the user is accepted for the document information that has not been given the classification code by the third stage, and the classification code accepted from the user is given to the document information. Next, the document information provided with the classification code received from the user is analyzed, the document without the classification code is extracted based on the analysis result, and the third classification for adding the classification code to the extracted document Process. For example, words that frequently appear in documents with a common classification code assigned by the user are extracted, and the types of extracted words, evaluation values possessed by each word, and trend information on the number of appearances are included for each document. The common classification code is assigned to the document having the same tendency as the trend information (S400).

In the fifth stage, the classification code to be given is determined based on the analyzed trend information for the document to which the user has given the classification code in the fourth stage, and the determined classification code and the classification code given by the user are determined. The validity of the classification process is verified by comparison (S500). Moreover, you may perform a learning process based on the result of a document analysis process as needed.

The trend information used in the fourth and fifth stage processing refers to the degree of similarity between each document and the document to which the classification code is assigned. The type of word included in each document, the number of occurrences, This is based on the evaluation value of a word. For example, when each document is similar in degree of relevance between a document assigned a predetermined classification code and the predetermined classification code, the two documents have the same tendency information. In addition, even if the types of words included are different, documents having the same evaluation value and the same number of occurrences may be documents having the same tendency.

The detailed processing flow in each stage from the first stage to the fifth stage will be described below.

<First stage (S100)>
A detailed processing flow of the keyword database 104 in the first stage will be described with reference to FIG.

The keyword database 104 creates a management table for each classification code based on the result of classifying documents in past lawsuits, and specifies keywords corresponding to each classification code (S111). In the embodiment of the present invention, in the embodiment of the present invention, the document to which each classification code is assigned is analyzed, and the number of occurrences of each keyword in the document and the evaluation value are used. A method, a method of manual selection by the user, or the like may be used.

In the embodiment of the present invention, for example, when keywords “infringement” and “patent attorney” are specified as keywords of the classification code “important”, “infringement” and “patent attorney” are closely related to the classification code “important”. The keyword correspondence information indicating that the keyword has a special relationship is created (S112). Then, the identified keyword is registered in the keyword database 104. At this time, the identified keyword is associated with the keyword correspondence information and recorded in the management table of the classification code “important” in the keyword database 104 (S113).

Next, a detailed processing flow of the related term database 105 will be described with reference to FIG. The related term database 105 creates a management table for each classification code based on the result of classifying documents in past lawsuits, and registers the related terms corresponding to each classification code (S121). In the embodiment of the present invention, for example, “encoding process” and “product a” are registered as related terms of “product A”, and “decoding” and “product b” are registered as related terms of “product B”.

The related term correspondence information indicating which classification code each registered related term corresponds to is created (S122) and recorded in each management table (S123). At this time, the related term correspondence information also records a threshold value serving as a score necessary for determining an evaluation value and a classification code of each related term.

Before actually performing the sorting operation, the keyword and the keyword correspondence information, and the related term and the related term correspondence information are updated and registered (S113, S123).

<Second stage (S200)>
A detailed processing flow of the first automatic sorting unit 201 in the second stage will be described with reference to FIG. In the embodiment of the present invention, in the second stage, the first automatic classification unit 201 performs a process of assigning the classification code “important” to the document.

The first automatic sorting unit 201 extracts documents including the keywords “infringement” and “patent attorney” registered in the keyword database 104 in the first step (S100) from the document information (S211). A management table in which the keyword is recorded is referred to from the keyword correspondence information to the extracted document (S212), and a classification code of “important” is given (S213).

<Third stage (S300)>
A detailed processing flow of the second automatic sorting unit 301 in the third stage will be described with reference to FIG.

In the embodiment of the present invention, the second automatic classification unit 301 assigns the classification codes “product A” and “product B” to the document information that has not been assigned the classification code in the second stage (S200). Process.

From the document information, the second automatic classification unit 301 records a document including related terms “encoding process”, “product a”, “decoding”, and “product b” recorded in the related term database 105 in the first stage. Extract (S311). For the extracted document, a score is calculated by the score calculation unit 116 using Expression (1) based on the appearance frequency and evaluation value of the four related terms recorded (S312). The score represents the degree of association between each document and the classification codes “product A” and “product B”.

When the score exceeds the threshold, the related term correspondence information is referred to (S313), and an appropriate classification code is assigned (S314).

For example, in a document, the appearance frequency of the related terms “encoding process” and “product a” and the evaluation value of the related term “encoding process” are high, and the score indicating the degree of association with the classification code “product A” is a threshold value. Is exceeded, the document is given a classification code “Product A”.

At this time, when the appearance frequency of the related term “product b” is high in the document and the score indicating the degree of association with the classification code “product B” exceeds the threshold, the document is also combined with the classification code “product A”. "Product B" is also given. On the other hand, when the appearance frequency of the related term “product b” is low in the document and the score indicating the degree of association with the classification code “product B” does not exceed the threshold, the classification code “product A” is included in the document. "Is granted.

The second automatic sorting unit 301 recalculates the evaluation value of the related term using the score calculated in S432 in the fourth stage according to the following equation (2), and weights the evaluation value (S315). ).

For example, if there are more than a certain number of documents where the appearance frequency of “decryption” is very high but the score is lower than a certain value, the evaluation value of the related term “decoding” is lowered and the related term correspondence information is again displayed. Record.

<Fourth stage (S400)>
In the fourth stage, as shown in FIG. 12, in the process up to the third stage, the classification code from the reviewer is given to a certain percentage of the document information extracted from the document information to which the classification code is not given. Acceptance and the accepted classification code are assigned to the document information. Next, as shown in FIG. 13, the document information given the classification code received from the reviewer is analyzed, and based on the analysis result, the classification code is given to the document information to which the classification code is not given. In the embodiment of the present invention, in the fourth stage, for example, a process of assigning classification codes of “important”, “product A”, and “product B” is performed on the document information. The fourth stage is further described below.

The detailed processing flow of the classification code reception assigning unit 131 in the fourth stage will be described with reference to FIG. From the document information to be processed in the fourth stage, the information extraction unit 24 first samples a document at random and displays it on the document display unit 130. In the embodiment of the present invention, 20% of the document information to be processed is extracted at random and set as a classification target by the reviewer. Sampling may be an extraction method in which documents are arranged in order of document creation date and time or in order of name, and 30% of documents are selected from the top.

The user views the document display screen 11 shown in FIG. 18 displayed on the document display unit 130, and selects a classification code to be assigned to each document. The classification code reception / giving unit 131 receives the classification code selected by the user (S411) and classifies the classification code based on the given classification code (S412).

Next, a detailed processing flow of the document analysis unit 118 will be described with reference to FIG. The document analysis unit 118 extracts words that frequently appear in the documents classified by classification code by the classification code reception and grant unit 131 (S421). The evaluation value of the extracted common word is analyzed by equation (2) (S422), and the appearance frequency of the common word in the document is analyzed (S423).

Further, based on the results analyzed in S422 and S423, the trend information of the document assigned the classification code “important” is analyzed (S424).

FIG. 14 is a graph showing a result of analyzing words frequently appearing in the document to which the classification code “important” is assigned in S424.

In FIG. 14, the vertical axis R_hot includes a word selected as a word associated with the classification code “important” among all documents to which the classification code “important” is assigned by the user, and the classification code “important” is assigned. Shows the percentage of documents that were used. The horizontal axis indicates the ratio of documents including the word extracted in S421 by the classification code receiving and assigning unit 131 among all documents subjected to the classification process by the user.

In the embodiment of the present invention, the classification code receiving / giving unit 131 extracts words that are plotted above the straight line R_hot = R_all as common words in the classification code “important”.

The processing from S421 to S424 is also executed for the documents to which the classification codes “product A” and “product B” are assigned, and the trend information of the documents is analyzed.

Next, a detailed processing flow of the third automatic sorting unit 401 will be described with reference to FIG. The third automatic classification unit 401 performs processing on the document that has not been given the classification code by the classification code reception / giving unit 131 in step S411 out of the document information to be processed in the fourth stage. In the third automatic classification unit 401, a document having the same trend information as the trend information of the document assigned with the classification codes “important”, “product A”, and “product B” analyzed in S 424 from such a document. Are extracted (S431), and a score is calculated for the extracted document using equation (1) based on the trend method (S432). Further, an appropriate classification code is assigned to the document extracted in S431 based on the trend information (S433).

The third automatic sorting unit 401 further reflects the sorting result in each database using the score calculated in S432 (S434). Specifically, a process of lowering the evaluation values of keywords and related terms included in a document having a low score and increasing the evaluation values of keywords and related terms included in a document having a high score may be performed.

Furthermore, an example of a detailed processing flow of the third automatic sorting unit 401 will be described with reference to FIG. The third automatic classification unit 401 may perform a classification process on the document information that has not been accepted by the classification code reception / giving unit 131 in step S411 out of the document information to be processed in the fourth stage. . In the case where no argument is given in the third automatic classification unit 401 (S441: None), the same trend information as the trend information of the document to which the classification code “important” is assigned is analyzed from the document in S424. Is extracted (S442), and the score of the extracted document is calculated using equation (1) based on the trend information (S443). Further, an appropriate classification code is assigned to the document extracted in S442 based on the trend information (S444).

The third automatic sorting unit 401 further reflects the sorting result in each database using the score calculated in S443 (S445). Specifically, the evaluation value of the keyword and the related term included in the document with a low score is lowered, while the evaluation value of the keyword and the related term included in the document with a high score is increased.

As described above, when the score calculation is performed in both the second automatic classification unit 301 and the third automatic classification unit 401 and the number of score calculations increases, the data for score calculation is collectively stored in the score calculation database 106. May be stored.

<Fifth stage (S500)>
A detailed processing flow of the quality inspection unit 501 in the fifth stage will be described with reference to FIG. In the quality inspection unit 501, the classification code reception / giving unit 131 determines the classification code to be given based on the trend information analyzed by the document analysis unit 118 in S424 for the document received in S411 (S511). .

The classification code received by the classification code reception / giving unit 131 is compared with the classification code determined in S511 (S512), and the validity of the classification code received in S411 is verified (S513).

The document analysis system 1 according to the embodiment of the present invention may include a learning unit 601. The learning unit 601 learns the weighting of each keyword or related term based on the first to fourth processing results using Expression (2). The learning result may be reflected in the keyword database 104, the related term database 105, or the score calculation database 106.

The document analysis system 1 according to the embodiment of the present invention is based on the result of the document analysis processing, and a lawsuit case (for example, a cartel / patent / FCPA / PL if a lawsuit) or a fraud investigation (for example, information leakage, It is possible to provide a report creation unit 701 for outputting an optimum survey report according to the survey type (eg, fictitious billing).

The contents of the survey vary depending on the survey type.
For example,
1. When and how did the competing personnel communicate with the cartel (price adjustment)?
2. Who is the organization involved?
Is the point.

In case of patent infringement,
1. Is the content the same as the technology being infringed?
2. Who, when, what intention (without) infringing or not infringing?
That is the point.

A document survey report system, a document survey report method, and a document survey report program according to another example of the embodiment of the present invention will be described below.

In the document investigation report system according to another example of the embodiment of the present invention, a document that has already been given a classification code is analyzed in correspondence with similar search information, and a range in which the classification code is assigned based on the analysis result is determined. adjust. Then, based on the range to which the adjusted classification code is assigned, the classification work and the survey work are performed, and a report is created based on the results of the classification work and the survey work.

As a method of adjusting the range to which the classification code is assigned corresponding to similar search information, the method of adjusting the range to which the classification code is assigned by clustering similar search information corresponding to the similar search information, and the classification result There is a method to perform prediction classification by learning. In order to adjust the range of clustering similar search information corresponding to similar search information and assigning a classification code, for example, focusing on the commonality of metadata, the original document, the reply document of the original document, A common classification code may be given to the reply document of the reply document of the original document. In the method of learning classification results and performing predictive classification, the same or similar classification codes are given to similar search information by learning to integrate similar search information for the classification results.

In another example of the embodiment of the present invention, the reliability of the analysis result varies depending on the number of documents to be analyzed. A statistical method may be added to the total number of documents to be classified to determine at what time point the percentage of all documents to be adjusted for the range to which the classification code is assigned based on the analysis results. .

In another example of the embodiment of the present invention, as a method of adjusting the range to which the classification code is assigned corresponding to the similar search information, the classification is performed by clustering the search information corresponding to the similar search information. The range of the document to which the classification code is assigned may be adjusted by executing both the method of adjusting the range to be performed and the method of performing the prediction classification by learning the classification result.

In the document survey report system, the document survey report method, and the document survey report program according to another example of the embodiment of the present invention, a report is created based on the results of these sorting operations and surveys.

Thereby, in the document investigation report system, the document investigation report method, and the document investigation report program according to another example of the embodiment of the present invention, it is possible to quickly create an accurate investigation report, The burden associated with report creation can be reduced.

In another example of the embodiment of the present invention, a display screen control unit that controls a display screen that presents the type of information extracted by the survey type determination unit to the user may be provided.

In another example of the embodiment of the present invention, an input receiving unit that receives a keyword and / or sentence input by a user corresponding to the type of information presented on the display screen control unit may be provided.

The embodiment of the present invention automatically updates the database according to a category by accepting a user input for a category of litigation case or fraud investigation case. As a result, the burden of office work for inputting the names of persons in charge, custodians, etc. is reduced. Further, the search word is adjusted by the database automatically updated according to the category, and a classification code is automatically assigned to the document information using the adjusted search word. This reduces the burden of sorting the document information used for litigation or fraud investigation cases. That is, according to the present invention, it becomes easy to analyze document information used in a lawsuit.

[Example of software implementation]
The control block of the document analysis system 1 may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be realized by software using a CPU (Central Processing Unit). . In the latter case, the document analysis system 1 includes a CPU that executes instructions of a program (control program) that is software that realizes each function, and a ROM (in which the program and various data are recorded so as to be readable by the computer (or CPU)). Read only memory) or a storage device (these are referred to as “recording media”), a RAM (Random Access Memory) for expanding the program, and the like. And the objective of this invention is achieved when a computer (or CPU) reads the said program from the said recording medium and runs it. As the recording medium, a “non-temporary tangible medium” such as a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used. The program may be supplied to the computer via an arbitrary transmission medium (such as a communication network or a broadcast wave) that can transmit the program. The present invention can also be realized in the form of a data signal embedded in a carrier wave in which the program is embodied by electronic transmission.

Specifically, the document analysis program according to the present invention obtains information recorded in a predetermined computer or server, and analyzes a document information composed of a plurality of documents included in the obtained information. A computer program, a category selection function for selecting a category that is an index capable of classifying each document included in the plurality of documents, and a document constituting the document information, the document information and the lawsuit or A score calculation function for calculating the score indicating the strength associated with the classification code indicating the degree of association with the fraud investigation for each category selected by the category selection function is realized.

The category selection function can be realized by the category selection unit 26. The score calculation function can be realized by the score calculation unit 116. In either case, the details are as described above.

[Additional Notes]
The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims, and the embodiments can be obtained by appropriately combining technical means disclosed in different embodiments. The form is also included in the technical scope of the present invention. Furthermore, a new technical feature can be formed by combining the technical means disclosed in each embodiment.

A document that acquires digital information recorded on a plurality of computers or servers, analyzes document information comprised of a plurality of documents included in the acquired digital information, and facilitates use in lawsuits or fraud investigations A survey basic database for storing information related to the lawsuit or fraud investigation, a survey category input accepting unit for accepting an input of the category of the lawsuit or fraud investigation, and the survey category input accepting unit A document analysis system comprising: a survey category determination unit that determines a survey category to be surveyed based on a category and extracts a necessary type of information from the survey basic database.

The document analysis system further includes a display screen control unit that controls a display screen for presenting a type of information extracted by the survey type determination unit to the user.

The document analysis system further includes an input reception unit that receives an input of a keyword and / or a sentence by a user corresponding to the type of information presented on the display screen control unit.

The document analysis system further includes an information extraction unit that extracts keywords and / or sentences corresponding to the type of information extracted by the survey type determination unit from the survey basic database. .

The document analysis system further includes a search unit that searches the document for the keyword and / or the sentence.

The document analysis system further includes an automatic classification code assigning unit that automatically assigns a classification code to the document, and the keyword and / or the sentence are used for assigning the classification code. Document analysis system.

A document that acquires digital information recorded on a plurality of computers or servers, analyzes document information comprised of a plurality of documents included in the acquired digital information, and facilitates use in lawsuits or fraud investigations An analysis method comprising: a survey category input receiving step for receiving an input of a category of the lawsuit or fraud investigation; and a survey category to be investigated based on the category received by the survey category input receiving step; A document analysis method comprising: a survey type determination step for extracting a type of necessary information from a survey basic database that stores information related to litigation or fraud investigation.

A document that acquires digital information recorded on a plurality of computers or servers, analyzes document information comprised of a plurality of documents included in the acquired digital information, and facilitates use in lawsuits or fraud investigations An analysis program for determining a survey category to be surveyed based on a survey category input receiving function that accepts an input of a lawsuit or fraud investigation category in a computer and a category received by the survey category input receiving function And a document analysis program for realizing a survey type determination function that extracts a type of necessary information from a survey basic database that stores information related to the lawsuit or the fraud investigation.

A document analysis system that acquires information recorded on a predetermined computer or server and analyzes document information composed of a plurality of documents included in the acquired information, and causes a lawsuit or fraud investigation A generation process model in which a predetermined action occurs is stored for each phase classified according to the progress of the predetermined action, and information related to the lawsuit or fraud investigation includes the category to which the lawsuit or fraud investigation belongs and the generation Further, it stores for each process model, a time series information indicating a temporal order of the phases, and a research basic database for further storing relationships among a plurality of persons related to the lawsuit or fraud investigation, and the lawsuit or fraud investigation. Based on information related to the generation process model, the time series information, and the relationship between the plurality of persons. Document analysis system is characterized in that a specific section analyzes the document information to identify the current phase.

DESCRIPTION OF SYMBOLS 1 Document analysis system 11 Document display screen 20 Investigation category input reception part 22 Investigation type determination part 24 Information extraction part 26 Category selection part 30 Search part 201 1st automatic classification part 301 2nd automatic classification part 401 3rd automatic classification part 501 Quality Inspection unit 601 Learning unit 701 Report creation unit 100 Data storage unit 101 Digital information storage area 103 Survey basic database 104 Keyword database 105 Related term database 106 Score calculation database 107 Report creation database 109 Database management unit 116 Score calculation unit 118 Document analysis unit 120 Language determination unit 122 Translation unit 124 Trend information generation unit 130 Presentation unit 131 Classification code reception grant unit 133 Lawyer review reception unit

Claims (10)

1. A document analysis system for acquiring information recorded in a predetermined computer or server and analyzing document information comprised of a plurality of documents included in the acquired information,
   A category selection unit that selects a category that is an index capable of classifying each document included in the plurality of documents;
   A score calculation unit that calculates, for each category selected by the category selection unit, a score indicating the strength with which the document constituting the document information is associated with a classification code indicating the degree of association between the document information and a lawsuit or fraud investigation A document analysis system characterized by comprising:
2. The category selection unit sequentially selects the category from a plurality of categories,
   The document analysis system according to claim 1, wherein the score calculation unit calculates the score for each category sequentially selected by the category selection unit.
3. The category selection unit includes at least one of a type of the lawsuit or fraud investigation, a phase classified according to progress of a predetermined action causing the lawsuit or fraud investigation, and an attribute of the document information. The document analysis system according to claim 1, wherein the document analysis system is selected as the category.
4. A survey category input receiving unit that receives input of a category to which the lawsuit or fraud investigation belongs;
   The document analysis system according to claim 1, wherein the category selection unit selects a category received by the survey category input reception unit.
5. A survey type determining unit for determining a survey category to be surveyed based on a category received by the survey category input receiving unit and extracting a type of necessary information from the survey basic database; The document analysis system according to any one of claims 1 to 4, wherein the document analysis system is characterized in that:
6. 6. The information extracting unit according to claim 1, further comprising: an information extracting unit that extracts keywords and / or sentences included in the document information from the document information as information related to the lawsuit or fraud investigation. Document analysis system described in the section.
7. The document analysis system according to any one of claims 1 to 6, further comprising a search unit that searches the plurality of documents for keywords and / or sentences included in the document information.
8. The document analysis system according to any one of claims 1 to 7, further comprising a presentation unit that presents the score calculated by the score calculation unit to a user so as to be grasped.
9. A document analysis method for acquiring information recorded in a predetermined computer or server and analyzing document information comprised of a plurality of documents included in the acquired information,
   A category selection step of selecting a category that is an index capable of classifying each document included in the plurality of documents;
   A score calculating step for calculating a score indicating the strength with which the document constituting the document information is linked to a classification code indicating the degree of association between the document information and the lawsuit or fraud investigation for each category selected in the category selecting step; A document analysis method comprising:
10. A document analysis program for acquiring information recorded in a predetermined computer or server and analyzing document information comprised of a plurality of documents included in the acquired information.
    A category selection function for selecting a category that is an index capable of classifying each document included in the plurality of documents;
    A score calculation function for calculating a score indicating the strength with which a document constituting the document information is associated with a classification code indicating the degree of association between the document information and a lawsuit or fraud investigation for each category selected by the category selection function A document analysis program characterized by realizing the above.

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