TW201606534A - A document analysis system, document analysis method and document analysis program - Google Patents

Abstract

The purpose of the present invention is to analysis document information by means of computer with high precision. The present invention provides a document analysis system, retrieving the information stored in the predetermined computer or server, and analyzing the retrieved information and the document information comprising a plurality of documents. The document analysis system comprising: a category selection section for sorting the retrieved documents to be a category respectively and selecting the corresponding proper category; and a score calculating section for combining the proper category to the document containing litigation and unfair event investigation, forming a distinguishing mark to represent the combining, and calculating a weight of the distinguishing mark to be a score.

Description

File analysis system, file analysis method, and file analysis program

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

In the past, when illegal or access to confidential information, such as computer crimes or legal disputes, the necessary machines or materials, as well as electronic records, were collected and analyzed. Thus, the present invention provides a proposal as a legally evident evidence means or technique.

In particular, in the US civil litigation, eDiscovery (electronic evidence discovery) is required, and both the plaintiff and the defendant of the litigation, on the other hand, have the responsibility to present relevant digital information as evidence. Therefore, the digital information recorded in the computer ‧ server must be presented as evidence.

On the other hand, with the rapid development and popularization of information technology, almost all information in today's business world is made by computer. Even in the same enterprise, there is a lot of digital information.

Therefore, in the process of preparing the evidence for the court, even the confidential digital information that is not necessarily related to the litigation is likely to be mistaken as evidence. Also, raising confidential information about the file that is not relevant to the lawsuit will cause problems.

In recent years, in the court certification system, technologies related to document information have been proposed in Patent Documents 1 to 3. However, for example, in the court certification system of Patent Documents 1 to 3, a plurality of computers and servers are used to collect huge file information of the user.

In this way, the huge amount of document information that is digitized is regarded as evidence of litigation, and whether the proper operation is to be determined is determined by human visual inspection by a user called a corrector. Differentiating one by one will have the problem of labor and expense.

A document distinguishing system for solving the above problem is suggested in Patent Document 4. In Patent Document 4, a file distinguishing system is disclosed: after collecting the digitized file information, the file information is automatically assigned a distinguishing symbol by the computer system, thereby reducing the use in the litigation. The file information is used to differentiate the burden of the job.

(previous technical literature)

(Patent Document 1) JP-A-2011-209930 (published on October 20, 2011)

(Patent Document 2) JP-A-2011-209931 (published on October 20, 2011)

(Patent Document 3) JP-A-2012-032859 (published on February 16, 2012)

(Patent Document 4) JP-A-2013-182338 (published on September 12, 2013)

However, in the file discrimination system disclosed in the above-mentioned Patent Document 4, when the files belonging to the different categories and the documents to be investigated are mixed, the differences recorded in the respective files are included. It will not correspond, and the accuracy of automatically adding the above-mentioned difference symbol to the document (scoring the file) will be insufficient.

The present invention has been made in view of the above problems, and provides a file analysis system capable of analyzing the document information with high precision.

The document analysis system of the present invention is configured to obtain information recorded in a set computer or a server in order to solve the above problem, and to provide information including the information to be acquired and file information composed of a plurality of files. The analysis file analysis system includes: a category selection unit that selects each of the respective files included in the plurality of files as a categorizable index, that is, as a category, and selects; a score calculation unit that displays the score of the strength of the combination after the document constituting the document information is combined with the difference symbol indicating the degree of connection between the document information and the lawsuit or the non-initiation of the investigation. The category selected by the category selection unit calculates the scores of the above categories one by one.

Further, 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 can select the categories to be sequentially selected by the category selection unit. Ground, calculate the above score.

Further, in the document analysis system of the present invention, the category selection unit classifies the litigation or the type that is not to be investigated, and classifies the behavior according to the behavior of the foregoing litigation or the reason for not performing the survey. At least one of the phase, and the attributes of the aforementioned file information, is selected as the aforementioned category.

Further, in the document analysis system of the present invention, there is further provided a survey item type input receiving unit for receiving the aforementioned lawsuit or for inputting a category to which the survey belongs; the category selection unit can be input by the aforementioned survey category The receiving unit selects the category to be received.

Further, the document analysis system of the present invention further includes a survey type determination unit that determines the survey item to be surveyed based on the category received by the survey category input receiving unit, and then proceeds from the survey base database. , to sample the necessary types of information.

Further, in the document analysis system of the present invention, there is further provided an information sampling unit for treating the keyword and/or the article included in the information of the aforementioned document as being related to the foregoing lawsuit or not being related to the investigation. Information, sampled from the information of the document.

Further, in the document analysis system of the present invention, there is further provided a retrieval unit. The keywords and/or articles contained in the information of the aforementioned documents are retrieved from the plurality of files mentioned above.

Further, in the document analysis system of the present invention, there is further provided a presentation unit for presenting the score calculated by the score calculation unit to the user so as to be possible to grasp.

The document analysis method according to the present invention is a file which is composed of a plurality of files included in the information acquired in order to solve the above problem and obtain information recorded in a set computer or a server. The document analysis method for analyzing the information includes the following steps: a category selection step, and selecting each of the respective files included in the plurality of files by using the categorizable index, that is, the category, And a score calculation step, which is a document that constitutes the information of the aforementioned document, and has a difference symbol indicating the degree of correlation between the document information and the lawsuit or is not implemented as a survey, and is displayed by a score in combination with the difference symbol In the above-described category selection step, the selected categories are calculated one by one.

The file analysis program of the present invention is a type of information that is recorded in a set computer or a server in order to solve the above problem, and includes information on a file composed of a plurality of files in the information to be acquired. The analysis file analysis program, in the computer, performs: a category selection function, and treats each of the respective files included in the plurality of files as a categorizable indicator, that is, as a category, Selecting; and a score calculation function, when the document constituting the information of the aforementioned document is combined with the difference symbol indicating the degree of correlation between the document information and the litigation or not being investigated, the intensity of the combination is displayed. The score is calculated by the category selected by the category selection unit, and the scores of the above categories are calculated one by one.

The file analysis system, the file analysis method, and the file analysis program of the present invention correspond to the category to which the file belongs, and can be analyzed with high precision by calculating the score. File information.

1‧‧‧Document Analysis System

11‧‧‧File display screen

20‧‧‧Investigation category input receiving department

22‧‧‧Investigation Type Determination Department

24‧‧‧Information Sampling Department

26‧‧‧Category Selection Department

30‧‧‧Search Department

201‧‧‧1st automatic distinction department

301‧‧‧2nd Automatic Differences Department

401‧‧‧3rd Automatic Distinction Department

501‧‧‧Quality Inspection Department

601‧‧‧Learning Department

701‧‧‧Reporting Department

100‧‧‧Data Storage Department

101‧‧‧Digital Information Storage

103‧‧‧Investigation basic database

104‧‧‧Keyword database

105‧‧‧Connected Language Database

106‧‧‧Scoring calculation database

107‧‧‧Reporting database

109‧‧‧Database Management Department

116‧‧‧Scoring calculation department

118‧‧‧Document Analysis Department

120‧‧  Speech Judgment Department

122‧‧‧Translation Department

124‧‧‧ Trend Information Generation Department

130‧‧‧ Tips Department

131‧‧‧Differential Symbol Receiving Department

133‧‧ ‧ lawyer correction receiving department

Fig. 1 is a diagram showing a functional block diagram of a main structure of a file analysis system according to an embodiment of the present invention

Fig. 2 The score calculation unit calculates the score of a file, and calculates it one by one, showing the pattern of the pattern.

Fig. 3 is a diagram showing a process of analyzing a file according to an embodiment of the present invention flow chart

Fig. 4 is a flow chart showing the detailed processing flow in the file analysis method according to the embodiment of the present invention

Fig. 5 is a flowchart showing a flow of investigation and a process of distinguishing processing according to the type of investigation in the file analysis method according to the embodiment of the present invention.

Fig. 6 is a flow chart showing a flow of predictive coding in accordance with the type of investigation of the file analysis method according to the embodiment of the present invention

Figure 7 is a flow chart showing the flow of processing in each stage in the embodiment.

Figure 8 is a flow chart showing the processing flow of the keyword database in the embodiment.

Figure 9 Flowchart of the processing of the related term database in this embodiment

Fig. 10 is a flow chart showing the processing of the first automatic distinguishing unit in the embodiment.

Fig. 11 is a flow chart showing the processing of the second automatic distinguishing unit in the embodiment.

Fig. 12 is a flowchart showing the processing of the difference symbol reception providing unit in the present embodiment.

Fig. 13 is a flowchart showing the processing of the file analysis unit in the embodiment.

Fig. 14 is a diagram showing the analysis result in the file analysis unit in the embodiment.

Fig. 15 is a flowchart showing the processing of the third automatic distinguishing unit in an embodiment of the embodiment

Fig. 16 is a flowchart showing the processing of the third automatic distinguishing unit in another embodiment of the embodiment.

Figure 17 Flowchart of the quality inspection unit in this embodiment

Figure 18 In this embodiment, the file display screen

Embodiments of the present invention will be described with reference to Figs. 1 to 18 .

[Structure of Document Analysis System 1]

Fig. 1 is a functional block diagram showing the configuration of a main part in the document analysis system 1 according to the embodiment of the present invention. The file analysis system 1 is a system that acquires information recorded in a set computer or a server, and analyzes file information composed of a plurality of files included in the information to be acquired.

As shown in FIG. 1, the file analysis system 1 is provided with a data storage unit 100 (digital information storage area 101, survey basic database 103, keyword database 104, related term database 105, and score calculation database 106). The report creation database 107), the database management unit 109, the information sampling unit 24, the search unit 30, the score calculation unit 116, the file analysis unit 118, the investigation type input reception unit 20, the investigation type determination unit 22, and the presentation unit 130. The category selection unit 26, the first automatic distinguishing unit 201, the second automatic distinguishing unit 301, the difference symbol receiving providing unit 131, and the third automatic distinguishing unit 401. Further, the document analysis system 1 may further include a tendency information generating unit 124, a quality checking unit 501, a learning unit 601, a report creating unit 701, a lawyer correction receiving unit 133, a speech determining unit 120, and a translation unit 122.

The survey category input receiving unit 20 is for receiving a category input by the user. When the category is input, the survey category input receiving unit 20 outputs the category to the survey type determining unit 22 and the category selecting unit 26. Therefore, in the above categories, each individual file included in a plurality of files is used as a categorizable indicator.

For example, the above categories , the lawsuit or the type of investigation that is not implemented (the litigation or the nature of the event that does not appear to be related to the investigation, for example, includes: antitrust, patent, overseas bribery prohibition (FCPA), manufacturing liability (PL) , information leaks, fictional requests, etc.). Alternatively, the above categories may also be used in the properties of the document information (displaying the nature of the information contained in the document information, such as information on competitors, prices, valuations, list of amounts, products, etc.).

In other words, the above-mentioned categories may also be classified according to the progress of the set behaviors in response to the lawsuit or the cause of the non-implementation of the investigation. Here, the "phase" is an index for displaying each stage of the behavior of the above-described setting (classifying according to the progress of the behavior set above). For example, a phase such as "Relationship Building" is a stage in which the relationship between the customer and the competition is established at the stage of the phase of the Competition. Also, in the phase of "Preparation", there will be a stage of exchanging competition-related information with other competing companies (or third parties).

Furthermore, the phase of "Competition" refers to the stage in which the customer is prompted for the price, the feedback is received, and the feedback is related to the feedback and the communication cooperation and communication opinions are obtained. In addition, the above-mentioned "set behavior" refers to litigation or non-implementation of investigations (for example, anti-monopoly, patents, overseas bribery prohibitions, manufacturing liability, information disclosure, fictional requests, etc.) Behavior (for example, attending a price adjustment meeting related to competitive cooperation, etc.), or making an act related to the improper act. When the above-mentioned category is the above-described phase, the file analysis system 1 can perform the most suitable analysis on the behavior set as described above.

The survey type determination unit 22 determines the category to be investigated based on the received category based on the received category, and samples the necessary information type from the survey base database 103. For example, in the case of the above-mentioned document information, if it is an e-mail, a briefing material, a table calculation material, a consultation material, a contract book, an organization chart, or a business plan book, the investigation type determining unit 22 will put each of the above respective documents. The type of information necessary is output to the information sampling unit 24. Therefore, the document analysis system 1 can sample the types of information necessary as described above.

The information sampling unit 24 samples a plurality of files from the file information. Specifically, the information sampling unit 24 receives the information (for example, email, briefing materials, table calculation data, negotiation materials, contract documents, organization charts, etc.) from the investigation type determination unit 22. The business plan book, etc.), the keywords and/or the articles contained in the information are treated as litigation or not related to the survey, and the results of the sampling are stored in the survey basic data. Library 103. Therefore, the document analysis system 1 can identify the above-mentioned lawsuit or does not implement the information related to the investigation and store it in the database.

The item selection unit 26 selects the above-described item and outputs the selected item to the score calculation unit 116. When the category is assumed to have a plurality of categories, the category selection unit 26 may sequentially select one category from among the plurality of categories.

Further, when the category is input from the survey item input receiving unit 20, the category selection unit 26 can select the category to be input. Thereby, the document analysis system 1 can surely select those categories input by the user.

The score calculation unit 116 links the file that is sampled from the file information, and displays the difference between the document information and the lawsuit or the degree of connection that is not implemented as the investigation, and then ranks the strength of the display link one by one. Calculated. The score calculation unit 116 can calculate the score in time series. Further, the method of calculating the above score will be described in detail later.

The presentation unit 130 presents the score calculated by the score calculation unit 116 to the user so that the user can grasp it more. The presentation unit 130 displays the score on the set display unit (not shown), for example, whereby the score can be presented to the user. The file analysis system 1 will prompt the user as a suitable file for any type of item, which can be more easily grasped by the user.

The search unit 30 searches for a keyword and/or an article included in the file information from among a plurality of files. Thereby, the document analysis system 1 can sample the keywords and/or the articles included in the above-mentioned file information.

The first automatic distinguishing unit 201 searches the keyword stored in the keyword database 104 by the vocabulary search unit 114, and samples the file information of the file including the keyword by the file sampling unit 112. The sampled file is automatically added with the recognized distinguishing symbol based on the keyword corresponding information.

The second automatic distinguishing unit 301, the related content stored in the related term database is included The document of the term is sampled from the document information, and the score of the relevant term contained in the sampled document, and the score based on the value of the related term, is scored in the document containing the above related term. The file of a certain value is automatically added with the set difference symbol based on the rating and the corresponding information of the related term.

The difference symbol reception providing unit 131 receives a plurality of files from which the file information is sampled without being distinguished, and receives the added difference symbol according to the relationship between the user and the lawsuit. The difference sign should be on.

The file analysis unit 118 analyzes the file to which the distinguishing symbol is added by the difference symbol reception providing unit 131. Further, the file analysis unit 118 adds the difference symbol from the user to the file to be attached based on the relevance to the lawsuit, and the first automatic difference unit 201 and the second automatic difference unit 301 are based on the key. Words, related terms, scores, parsing files that are automatically distinguished by a symbol, and the above-mentioned files that are received by the user and then added with the distinguishing symbols, and the above-mentioned files that automatically add the distinguishing symbols, can be obtained by combining Analytical results of totality. At this time, the third automatic distinguishing unit 401 can automatically add the distinguishing symbol based on the analysis result of the totality.

In addition, the difference and the progress of the investigation work are automatically distinguished by the vocabulary search, the difference between the user and the reception of the survey, the automatic distinction and the survey using the score, the automatic distinction through the learning process, and Surveys, automatic differentiation through quality assurance, surveys, etc., are available in a variety of ways. The above-mentioned various differences and the investigation work are performed in a combination of how and in what order, and the related information is displayed, and a plurality of files to which the distinguishing symbols are added are passed to the file analysis unit 118. The analysis may be performed, and the result of the analysis may be reported in a report creation unit 701 to be described later.

In the third automatic distinguishing unit 401, the difference symbol receiving and providing unit 131 adds a file of the distinguishing symbol, and the file analyzing unit 118 analyzes the plurality of files sampled from the file information based on the analyzed result. Automatically add the difference symbol.

In the trend information generating unit 124, the file analysis unit 118 analyzes the number of occurrences of the word included in each file, and the evaluation value of the word is generated. The tendency information is to show the degree of similarity between the different symbols of each file and the files to which the distinguishing symbols are added.

The quality inspection unit 501 compares the difference symbol received by the difference symbol reception providing unit 131 with the difference symbol added by the file analysis unit 118 with the tendency information, and verifies the difference symbol reception providing unit. The appropriateness of the distinguishing symbols received by 131.

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

The report creation unit 701 outputs the most appropriate investigation report in accordance with the result of the difference processing of the document, in response to the litigation case or the type of investigation in which the investigation is not performed. Also, as mentioned above, in litigation cases, for example, include antitrust, patents, overseas bribery prohibitions (FCPA), manufacturing liability (PL), and the like. Also, in the absence of investigations, for example, including information disclosure, fictitious requests, and so on.

The lawyer corrects the receiving department 133 to improve the quality of the difference investigation and report. In order to make the responsibility for distinguishing the investigation and report more clear, it is necessary to accept the correction of the chief lawyer or the chief patent attorney.

The speech determining unit 120 is configured to determine the type of speech of the sampled file.

The translation unit 122 receives the designation from the user or automatically translates the sampled file. In this case, it is possible to reduce the small paragraph of the speech in the speech determination unit to be smaller than the single text in a compound language in which a single word and a plurality of words are combined. Also, the judgment of speech, Predictive Coding, any encoding of character encoding, or both encodings may be used. Furthermore, HTML (Hyper Text Markup Language) headers and the like can also be removed from the translated object.

The data storage unit 100 stores the digital information acquired from a plurality of computers or servers in the digital information storage area 101 for use in litigation or not for analysis. Further, 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. Further, as shown in FIG. 1, the data storage unit 100 may be a recording medium included in the file analysis system 1, or may be an external recording medium that is communicably connected to the file analysis system 1.

The survey base database 103 is used to store, for example, litigation cases including antitrust, patents, Foreign Corrupt Practices Act (FCPA), Goods Liability (PL), etc., and/or Including the disclosure of information, fictitious requests, etc., is not an investigation, and belongs to any of the above-mentioned event attributes, company name, person in charge, supervisor, and investigation, or the structure of the input screen.

The keyword database 104 is configured to store: a distinguishing symbol included in the acquired digital information, a distinguishing symbol of the file, a keyword closely related to the recognized distinguishing symbol, and a display of the recognized distinguishing symbol and the The keyword correspondence information of the correspondence between the keywords.

In the related term database 105, the related term information is stored. That is, the stored difference symbol is set, and the related words of the high frequency appearing on the file added to the set difference symbol, and Correspondence information corresponding to the correspondence between the set difference symbol and the related term is displayed.

The score calculation database 106 is for storing: in order to calculate a score for displaying the strength of the link of the file and the distinguishing symbol, the weight of the vocabulary included in the file.

The report creation database 107 maintains the form of the prepared report in response to the category, the supervisor, and the content of the assignment.

The database management unit 109 is for managing the survey basic database 103, the keyword database 104, the related term database 105, the score calculation database 106, and the update of the data content of the report creation database 107. The database management unit 109 may also be connected to the information storage device 902 via a dedicated connection line or an internet connection line 901. In this case, the database management unit 109 may also update the survey basic database 103, the keyword database 104, and related terms according to the content of the data stored in the information storage device 902. The database 105, the score calculation database 106, and the data content of the report creation database 107.

As described above, in the conventional file analysis system, if the files belonging to different categories are mixed with the files of the survey object, the differences in the records included in the respective files will not correspond, and the above-mentioned difference symbols will be used. The accuracy of automatically adding to the document (scoring the file) will be insufficient.

On the other hand, in the file analysis system 1, since the score can be calculated in accordance with the category to which the file belongs, the file information can be analyzed with high precision.

Further, the document analysis system 1 includes: a category selection unit that selects each of the respective files included in the plurality of files as a categorizable index, that is, a first category; and a score calculation unit that constructs a file of the file information, and displays a score displayed by the intensity of the combination of the document information and the litigation or the difference symbol of the degree of correlation that is not implemented as the survey, and uses the category selection unit to select the selected The first category is scored and calculated. When the score calculation unit calculates a score for the first category, the category selection unit further selects a second category different from the first category. The score calculation unit may further calculate the score for the selected second category by using the category selection unit.

[Explanation of terms]

"Differential symbol" refers to the identifier used to classify a document, and the identifier for the degree of relevance to the litigation is displayed in order to make the file more easily available in litigation. For example, when using document information as evidence in a lawsuit, the type corresponding to the evidence can be added.

"Document" means information containing more than one word, such as e-mail, briefing materials, table calculation data, consultation materials, contract letters, organization charts, business plans, etc.

"Single word" is the simplification of the least meaningful character string. For example, in the article "The so-called file refers to data containing more than one word." This article contains "documents", "one", "above", "single word", "include", "data", "so-called" Some words.

"Keyword" refers to a cluster of text columns that have a certain meaning in a certain language. . For example, if a keyword is selected from an article that "distinguishes a file", "file" and "distinction" can be used as keywords. In the present embodiment, keywords such as "infringement" or "litigation" or "patent bulletin ○○" can be selected in an important manner. Further, the above "keyword" may include a form factor.

The "keyword correspondence information" refers to information showing the correspondence between the keyword and the recognized difference symbol. For example, in a lawsuit, a distinguishing symbol such as "important" of an important document is displayed. If there is a close relationship with a keyword such as "infringer", the above "keyword correspondence information" can distinguish the symbol "important". And the keyword "infringer" is connected in series as management information.

The "relevant term" is a term that is common to the file to which the set difference symbol is added. The word whose evaluation value is equal to or greater than a certain value among the words with a high frequency of occurrence. Here, the frequency of occurrence may also refer to, for example, the proportion of the related words among the total number of words appearing in a file.

The "evaluation value" is a value in which each word is displayed in a file and the amount of information it plays. The "evaluation value" can also be calculated by using the amount of information to be transmitted as a reference. For example, when the set product name is added as a difference symbol, the above-mentioned "connected language" may also refer to the name of the technical field to which the product belongs, the country of sale of the product, the similar product name of the product, and the like. Wait. Specifically, the product name of the device for symbolizing the image is treated as a "connected language" that is added to the symbol, and "symbolized", "Japan", "encoder", etc. are cited. Wait.

"Related language correspondence information" is information indicating the correspondence between related terms and distinguishing symbols. For example, if the distinguishing symbol of the "product A" of the product name related to the lawsuit is the related term of "image symbolization" of the function of the product A, the "relational term correspondence information" may be the difference symbol "product". A" and the related term "portrait symbolization" are linked and managed.

"Score" refers to the combination of the recognized distinguishing symbols in the document. Intensity is stated by quantitative evaluation values. In each of the embodiments of the present invention, for example, the score is calculated by the following formula (1) by the evaluation value held by the single word and each word appearing in the document.

Scr: the score of the instrument

m _i : the frequency of the ith keyword or related language

: the weight of the i-th keyword or related language

In the second drawing, the score calculation unit 116 calculates the scores of a certain file, and classifies them one by one, and displays a pattern diagram of the pattern. As described above, the score calculation unit 116 calculates the scores one by one. Specifically, as shown in FIG. 2, the weight (coefficient, parameter) of each category is determined in advance (learning), and the score calculation unit 116 corresponds to the category selected by the category selection unit 26. Substituting the above weights in turn, the scores for the files are calculated one by one according to the category.

In other words, the category selection unit is a coefficient for calculating the score by the score calculation unit, and sequentially substitutes the coefficient determined in advance for each category to select the category. The score calculation unit calculates the score of the category one by one using the corresponding coefficient by the category selected by the category selection unit.

Therefore, in the document analysis system 1, the above score can be calculated one by one for all the categories that are desired. For example, although the classifications A, B, and C have low scores, it is possible to calculate a high score for the category D. Thereby, the above-mentioned score calculated one by one for each category is used, and since the case can be analyzed in a multi-dimensional manner, the file analysis system 1 can analyze the file information with high precision.

In the file analysis system 1, the distinguishing symbols added by the user can also sample the words that appear frequently in the common file. Then, for each file, the type of the sampled word, the evaluation value of each word, and the tendency information of the number of occurrences are included, and each file is analyzed one by one. Among the files in which the difference symbol reception providing unit 131 cannot receive the difference symbol, the file having the same tendency as the analyzed tendency information is also A common difference symbol can be added.

Here, "prone information" means that each file has a similar ratio of information to a file with a distinguishing symbol, and the number of occurrences of the word included in each file, the evaluation value of the word, Information showing the degree of correlation with the set difference symbol. For example, if the file, the file with the set difference symbol, and the degree of association with the set difference symbol are similar, the two files can be said to have the same tendency information. Further, even if the types of the included words are different, the words having the same number of occurrences can be regarded as files having the same tendency if the words having the same evaluation value are included.

[Processing performed in the file analysis system 1]

FIG. 3 is a flowchart showing an example of the executed processing (the file analysis method according to the embodiment of the present invention) in the file analysis system 1. In the following description, the "~ step" in the parentheses displays the respective steps included in the above-described file analysis method (the control method of the file analysis system 1).

First, the category selection unit 26 selects each of the respective files included in the plurality of files as a categorizable index, that is, a category (category selection step, step 41, hereinafter "step" is simply referred to as " S"). Next, in the score calculation unit 116, the file constituting the document information is combined with the difference symbol indicating the degree of relevance of the document information and the litigation or the non-implementation of the survey, and the score of the strength of the display is combined, and the category is utilized. The selection unit 26 calculates one by one based on the selected category (score calculation step, S42). The item selection unit 26 determines whether or not all the items are selected, and if there are still unselected items remaining (displayed as NO in S43), the item selected from the previous selection category is selected (S41), and the score calculation unit 116 The above score is further calculated (S42).

Next, the details of the document analysis method of the present invention will be specifically described with reference to the drawings. Note that the following description is merely an example, and the present invention is not limited to this example.

Figure 4 A detailed flowchart of the file analysis method according to the embodiment of the present invention. Further, the flow shown in FIG. 3 can be executed as an independent process from the flow shown in FIG. 4, or can be used as an internal sub-flow anywhere in the flow shown in FIG. Process it to execute it.

In response to the display of the displayed screen in the display section, receiving the arguments specified by the user, for example, a litigation involving an antitrust, patent, FCPA, PL, or a non-implementation of a fictitious request containing an information disclosure, may The category corresponding to these arguments is identified (S11).

In accordance with the identified category, the use database of the survey basic database, the document analysis database, and the like can be identified (S12).

In order to confirm whether the use of the database is up-to-date, the information storage device storing the latest database can be accessed. The information storage device is sometimes disposed inside the organization that implements the difference, and is sometimes placed outside the organization. The information storage device is set outside the organization, for example, sometimes in a cooperative law firm or a patent office.

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

After the authentication is completed, the information storage device is allowed to be accessed, and the usage database such as the basic database, the file analysis database, and the like can be updated, and the database can be updated to an index (S14).

The searched basic database is searched (S15), and the name of the company, the person in charge, and the name of the supervisor can be presented on the screen of the display device (S16).

The name of the person in charge and the supervisory administrator displayed on the screen of the display device, if the name of the actual person in charge and the supervisor is different, the user will correct the person in charge and the supervisor in the screen of the display device. name. The file analysis system can receive the user's correction input to identify the actual person in charge and the supervisor's name (S17).

Next, in order to carry out the file analysis job, the digital file information can be sampled (S18).

As the updated file analysis database, the updated keyword database, the related term database, and the score calculation database are searched (S19), and the sample file information can be distinguished (S20).

Also, the reviewer can receive the distinguishing symbol and add a distinguishing symbol to the sampled document information (S21).

The difference result is used as the guidance material, and the database is searched, and the difference symbol can be added to the sample file information (S22).

It is possible to receive a correction made by a chief lawyer or a patent attorney (S23). In this way, the quality of the survey can be improved.

The category is identified by the user's argument designation (S24), and the report can be identified as a database in accordance with the identified category (S25). The report is created using the identified report, and the form of the report is determined, and the report can be automatically output (S26).

Fig. 5 is a flow chart showing the flow of the investigation and the difference processing in accordance with the type of investigation in the document analysis method according to the embodiment of the present invention.

Initially, the type of survey can be entered (S31). That is, in response to the indication of the display screen, the user can input: for example, a lawsuit containing antitrust, patent, overseas bribery prohibition (FCPA), manufacturing liability (PL), or a request containing information leakage, fictitious request Conduct surveys that are conducted for the investigation and the categories that correspond to the different assignments. The document analysis system can receive the user's category input and identify the category to be investigated.

The type of the investigation and the file analysis processing and the type of the database to be used can be determined in accordance with the identified item (S32).

Corresponding to the identified category, access to the information stored in the usage database of the survey basic database, the file analysis database, etc. may be accessed (S33).

In response to the identified category, the survey base database is accessed, and an input screen corresponding to each keyword of the identified category can be displayed (S34).

The access to the survey basic database is performed corresponding to the identified category, and an input screen corresponding to each article of the identified category can be displayed (S35)

The survey basic database is accessed corresponding to the identified category, and the keyword or the file may be sampled corresponding to the identified category (S36).

By performing the above processing, it is possible to automatically add a distinguishing symbol (predictive editing) The guidance data of the code is added by weighting (S37).

The sample file and the information can be narrowed down by performing keyword search on the document analysis database (S38).

Fig. 6 is a flow chart showing the flow of predictive coding in accordance with the type of survey in the file analysis method according to the embodiment of the present invention.

In the file analysis method according to the embodiment of the present invention, initially, the file analysis system requests the user to input according to the type of the investigation, and can receive the user's input of the event. For example, related to antitrust law, about joint monopoly, related object products, related parties (name and e-mail address), relationship organization (name and department), and period, requiring user input, can receive users The input of the event. In addition, for the related organization, the competitor company and the customer enterprise are required to input the user, and the user can receive the input of the event (S51).

Next, by inputting a keyword, it is possible to add a distinguishing symbol and perform weighting (S52). Then, predictive coding can be performed (S53).

In the embodiment of the present invention, as an example, in the first to fifth stages, the registration processing, the difference processing, and the inspection processing are performed in accordance with the flowchart shown in FIG.

In the first stage, the update registration of the keyword and the related term is performed in advance using the result of the past difference processing (S100). At this time, the keywords and related terms, the distinguishing symbols and the keywords or the corresponding information of the related terms, that is, the keyword corresponding information and the related information of the related terms, will be updated and registered at the same time.

In the second stage, the file containing the keyword for updating the registration in the first stage is sampled from the full file information, and if the file is found, the updated keyword corresponding information recorded in the first stage is referred to. The first difference processing is performed by adding the difference symbol corresponding to the keyword (S200).

In the third stage, the file containing the related term of the update registration in the first stage is sampled from the file information in which the difference symbol is not added in the second stage, and the score of the file including the related term is calculated. . The calculated score is referred to in the first In the stage, the related term information corresponding to the login is updated, and the difference symbol is added to perform the second difference process (S300).

In the fourth stage, for the file information that has not been distinguished by the third stage, the difference symbol added by the user is received, and the difference information received by the user is added to the file information. Next, the file information added by the difference symbol received by the user is parsed, and according to the analysis result, the files without the distinguishing symbols are sampled, and the sampled file is distinguished by the difference symbol, and the third difference is made. deal with. For example, the distinguishing symbols added to the user are sampled in the vocabulary frequently appearing in the common file, and are included in each file, the type of the sampled word, the evaluation value of each word, and the number of occurrences. The tendency information is analyzed one by one for each file, and a common difference symbol is added to the file having the same tendency as the tendency information (S400).

In the fifth stage, for the file in which the user has a distinguishing symbol in the fourth stage, the difference symbol to be added is determined based on the analyzed tendency information, and the difference symbol of the decision is added to the user. The difference symbol is compared with the two to verify the validity of the difference processing (S500). Also, if necessary, the learning process can be performed based on the results of the file analysis processing.

The tendency information used in the processing of the fourth stage and the fifth stage refers to a similar ratio between the files and the files with the distinguishing symbols, which can be said to be based on the words included in each file. Type, number of occurrences, information generated by the evaluation value of a word. For example, when each file is a file with a set difference symbol, and the degree of connection of the set difference symbol is similar, it can be said that the two files have the same tendency information. Further, even if the types of the included words are different, the files having the same number of occurrences for the same word having the same evaluation value can be regarded as files having the same tendency.

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

<Phase 1 (S100)>

The detailed processing flow of the keyword database 104 in the first stage will be described using FIG.

In the past, the keyword database 104 identifies the keywords for each of the different distinguishing symbols one by one based on the result of distinguishing the documents, and identifies the keywords corresponding to the respective distinguishing symbols (S111). ). This identification is an embodiment of the present invention, and the file to which each distinguishing symbol is added is analyzed, and the number of occurrences of each keyword in the file and the evaluation value are used, and the method of transmitting the information amount by the keyword is used. The user can also adopt the method of selecting by hand.

In the embodiment of the present invention, for example, when keywords such as "important" are distinguished, "infringement" and "patent" are recognized, "infringement" and "patent" and distinguishing symbols are displayed. "Important" keyword correspondence information of a keyword having a closely related relationship (S112). The identified keywords are then logged in the keyword database 104. At this time, the recognized keyword and the keyword correspondence information are added to the relationship and registered in the management list of the distinguishing symbol "important" of the keyword database 104 (S113).

Next, the detailed processing flow of the related term database 105 will be described using FIG. In the related litigation database 105, in the past litigation, based on the result of discriminating the documents, a list for management is created for each of the respective distinguishing symbols, and the related terms corresponding to the respective distinguishing symbols are registered (S121). . In the embodiment of the present invention, for example, as a related term of "product A", "decoding" and "product b" are registered as "signification processing" and "product a" and "product B" related terms. .

Each of the related terms after the registration is displayed, and the relevant difference symbol is displayed, and the related term correspondence information is generated (S122), and is recorded in each management list (S123). At this time, in the related information of the related language, the evaluation value of each related term and the limit value of the score necessary for determining the difference symbol are also recorded.

In fact, before the difference operation, the keyword and keyword correspondence information, as well as the related terms and related terms, will be updated and registered with the latest information (S113, S123).

<Second stage (S200)>

The detailed processing flow of the first automatic distinguishing 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 distinguishing unit 201 is used. The difference symbol "important" is added to the file for processing.

In the first automatic segmentation unit 201, in the first stage (S100), the file including the keyword "infringement" and "patent" registered in the keyword database 104 is sampled from the file information (S211). . For the sampled file, the management information list (S212) in which the keyword is recorded is referred to from the keyword correspondence information, and the difference symbol "important" is added (S213).

<Phase 3 (S300)>

The detailed processing flow of the second automatic distinguishing unit 301 in the third stage will be described with reference to FIG.

In the second automatic segmentation unit 301, in the second stage (S200), "product A" and "product B" are added to the file information in which the distinguishing symbols are not added. The difference symbol is to be processed.

In the second automatic distinguishing unit 301, the related information "symbolization processing", "product a", "decoding", and "product b" are included in the related term database 105 from the file information in the first stage. The file is sampled (S311). For the sampled file, the evaluation value is calculated based on the frequency of occurrence of the four related terms recorded, and the score is calculated by the score calculation unit 116 using the equation (1) (S312). This rating shows the degree of connection between each document and the distinguishing symbols "Product A" and "Product B".

When the score exceeds the threshold value, the related term information (S313) is referred to, and an appropriate distinguishing symbol is added (S314).

For example, in a certain file, the frequency of occurrence of the related terms "symbolization processing" and "product a" is higher than the evaluation value of the related term "symbolization processing", and the degree of correlation with the distinguishing symbol "product A" is displayed. When the score exceeds the limit value, the difference symbol "Product A" is added to the file.

At this time, in the document, if the frequency of occurrence of the related term "product b" is high, and the degree of correlation between the display and the distinguishing symbol "product B" exceeds the threshold value, the document is also distinguished by the symbol "product A". Add "Product B". On the other hand, in this document, if the frequency of occurrence of the related term "product b" is low, the display and the distinguishing symbol "system If the score of the degree of correlation of the product B" does not exceed the threshold value, the difference symbol "product A" may be added to the file.

In the second automatic distinguishing unit 301, in the fourth stage S432, the calculated score is used, and the evaluation value of the related term is recalculated by the following formula (2), and the evaluation value is weighted (S315). ).

Wgt _{i, 0} : weight of the i-th selected keyword before learning (initial value)

Wgt _i,L : the weight of the i th selected keyword after the Lth learning

γ _L : learning parameters in the L-th learning

: the threshold value of the learning effect

For example, although the frequency of occurrence of "decoding" is very high, the score is lower than a certain set value. If a file like this is set more than a certain number of times, the evaluation value of the related word "decoding" will be lowered, and then Record again in the related information of the related language.

<Phase 4 (S400)>

In the fourth stage, as shown in Fig. 12, before the third stage, the file information of a certain proportion sampled from the information of the file without the distinguishing symbol is received, and the difference symbol added by the corrector is received, The file information plus the difference symbol it receives. Next, as shown in Fig. 13, the file information of the distinguishing symbols received by the corrector is analyzed, and based on the analysis results, the file information without the distinguishing symbol is added, and the distinguishing symbol is added. Further, in the embodiment of the present invention, in the fourth stage, the processing of adding the distinguishing symbols of "important", "product A" and "product B" in the fourth stage is performed. For the fourth stage, it is further described as follows.

The detailed processing flow of the difference symbol reception providing unit 131 in the fourth stage will be described with reference to FIG. In the file information to be processed in the fourth stage, first, the information sampling unit 24 randomly samples the file and displays it on the file display unit 130. In the embodiment of the present invention, about 20% of the file information to be processed is randomly sampled, and the corrector determines the difference object. Sampling method, You can also sort the files according to the time of the file, sort the names, list the files, and select 30% of the files from the top to sample.

The user views the file display screen 11 shown in FIG. 18 displayed on the file display unit 130, and selects the difference symbol to be added to each file. The difference symbol reception providing unit 131 receives the difference symbol selected by the user (S411), and distinguishes it based on the added difference symbol (S412).

Next, the detailed processing flow of the file analysis unit 118 will be described using FIG. The file analysis unit 118, in the difference symbol reception providing unit 131, performs sampling on the files that are distinguished by the difference symbols one by one, and the frequently appearing words are frequently used. (S421). The evaluation value of the sampled common word is analyzed by the equation (2) (S422), and the appearance frequency in the file of the common word is analyzed (S423).

Further, in S422 and S423, based on the result of the analysis, the file having the "significant" difference symbol is analyzed, and the tendency information is analyzed (S424).

In Fig. 14, a graph of the results of parsing the common and frequently occurring single words in the file of the distinguishing symbol "important" is added to S424.

In Fig. 14, the vertical axis R_hot is a single word that is added to the difference symbol "important" by the user, and is included as a single word with the distinguishing symbol "important", including the selected word, and displayed. The proportion of documents that are distinguished by the symbol "important". On the horizontal axis, the difference symbol reception providing unit 131 is used in the entire file in which the user performs the discrimination processing, and the ratio of the file including the sampled single word is displayed in S421.

In the embodiment of the present invention, the difference symbol reception providing unit 131 performs sampling by using the line in which the straight line R_hot=R_all is drawn on the upper part as a common word in the distinction symbol "important".

The processing of S421 to S424 is performed on a file to which a distinguishing symbol such as "product A" and "product B" is added, and the tendency information of the file is analyzed.

Next, the detailed processing flow of the third automatic distinguishing unit 401 will be described using FIG. In the third automatic distinguishing unit 401, among the file information of the processing target of the fourth stage, the distinguishing symbol receiving providing unit 131 is used in S411, and those that do not receive the distinguishing character are used. The number of files is processed. The third automatic distinguishing unit 401 analyzes such a file in S424, and adds the difference information to the "important", "product A" and "product B" files, and the same tendency information. The file is sampled (S431), and for the sampled document, the score is calculated by the formula (1) according to the tendency rule (S432). Further, for the file sampled at S431, an appropriate distinguishing symbol is added based on the tendency information (S433).

Further, the third automatic distinguishing unit 401 further reflects the difference result on each of the databases using the score calculated in S432 (S434). Specifically, the keyword included in the file with a low score and the evaluation value of the related term may be lowered, and the keyword included in the file with a high score and the evaluation value of the related term may be raised.

An example of the detailed processing flow of the third automatic distinguishing unit 401 will be further described using FIG. In the file information of the processing target of the fourth stage, the third automatic distinguishing unit 401 may perform the distinguishing process by using the difference symbol receiving and providing unit 131 in S411, and the file in which the distinguishing symbol is not received. In the third automatic distinguishing unit 401, if the argument is not available (S441: Nil), the file is analyzed by S424, and then the tendency information of the "important" file is added to the distinguishing symbol, and The file of the same tendency information is sampled (S442), and the score is calculated using the formula (1) based on the tendency information for the sampled file (S443). Further, for the file sampled in S442, an appropriate distinguishing symbol is added based on the tendency information (S444).

The third automatic distinguishing unit 401 further uses the score calculated in S443 to reflect the difference result on each of the databases (S445). Specifically, the keyword and the evaluation value of the related term included in the file with a low score are lowered. On the other hand, the keyword included in the file with a high score and the evaluation value of the related term are raised.

As described above, the score is calculated by both the second automatic distinguishing unit 301 and the third automatic distinguishing unit 401. When the number of rounds calculated by the score is increased, the data used for the score calculation is stored together in the score calculation database. 106 is no problem.

<Phase 5 (S500)>

The 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 difference symbol reception providing unit 131 is for receiving at S411. The file determines the difference symbol to be added based on the tendency information analyzed by the file analysis unit 118 in S424 (S511).

The difference symbol received by the difference symbol reception providing unit 131 is compared with the determined difference symbol in S511 (S512), and the validity of the difference symbol received in S411 is verified (S513).

The file analysis system 1 according to the embodiment of the present invention may include a learning unit 601. The learning unit 601 learns the weight of each keyword or related term based on the processing results from the first to the fourth, using equation (2). The learning result may also 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 may include a report creation unit 701 that outputs a compliance suit according to the result of the file analysis processing (for example, a joint monopoly ‧ patent ‧ FCPA ‧ PL, etc. common in litigation) Or the most appropriate investigation report for the type of investigation that is not investigated (eg, information disclosure, fictitious requests, etc.).

The content of the survey will vary depending on the type of survey.

For example, if it is a joint monopoly case,

1. How did the person in charge of the competitor’s tacit understanding of the joint monopoly (price adjustment) be obtained?

2. Who is the person in the organization?

These will become the main points of the inspection.

Also, if there is patent infringement,

1. Is the technology and content of the infringing object the same?

2. who is it? when? What is the intention (or not)? Infringement? Or is it not infringing?

These matters are the main points of inspection.

A document investigation report system, a document investigation report method, and a document investigation report program according to another embodiment of the embodiment of the present invention are described below.

In the embodiment of the present invention, the file investigation report system related to other embodiments analyzes the files to which the distinguishing symbols have been added corresponding to the similar search information, and adjusts the range of the distinguishing symbols according to the analysis result. Then, based on the range of the adjusted difference symbols, the difference operation and the investigation work are performed, and the report is made based on the difference operation and the result of the investigation operation.

Corresponding to the similar search information, there is a method for adjusting the range of the differential sign, and there are similar search information, clustering similar search information, adjusting the method of distinguishing the range of the symbol, and learning the difference result to make prediction. The difference between the methods. Corresponding to similar search information, similar search information is clustered, and the method of adjusting the range of distinguishing symbols is, for example, sometimes focusing on the commonality of the original materials, the original documents, the return files of the original documents, and the original documents. In the reply file of the reply letter, the common difference symbol is added. As for the method of learning the difference, the method for predicting the difference means that similar search information is integrated and learned for the difference result, and the same or similar difference symbol is added to the similar search information.

In another embodiment of the embodiment of the present invention, the reliability of the analysis result may vary depending on the number of files to be analyzed. It is also possible to add statistical methods to the total number of files that are the objects of the difference, and at some point, for a certain proportion of the entire file, the difference is added according to the analysis result, and the range is adjusted.

In other embodiments of the embodiments of the present invention, some methods are: adjusting the range of the added differential symbols corresponding to similar retrieval information; and some methods are: after the search information is clustered corresponding to similar retrieval information, Adjust the range of the difference sign; you can also perform the method of predicting the difference after learning the difference result, and execute the two methods to adjust the range of the file to which the difference symbol is added.

The document investigation report system and the document investigation report method according to the other embodiments of the embodiment of the present invention, together with the document investigation report program, create a report based on the results of these different operations and investigations.

Therefore, the document investigation report system and the document investigation report method according to other embodiments of the embodiment of the present invention can be quickly confirmed together with the document investigation report program. In the field, a survey report is prepared, and at the same time, the burden associated with the difference work and the report creation work is alleviated.

In another embodiment of the embodiment of the present invention, the user may be provided with a screen control unit for controlling the display screens for indicating the types of information sampled by the survey type determining unit.

In another embodiment of the embodiment of the present invention, an input receiving unit may be provided to receive a keyword and/or an article input by the user in accordance with the type of information presented to the display screen control unit.

The embodiment of the present invention automatically updates the database by a program in response to a case or a category that is not implemented as a survey case by receiving input from the user. In this way, the burden of the business of inputting the name of the person in charge, the name of the supervisor, etc., is greatly reduced. Further, the search vocabulary is adjusted by the database automatically updated in response to the category, and the adjusted search vocabulary is used to automatically assign a distinguishing symbol to the document information. In this way, the burden of litigation or the non-implementation of the difference in the information used to investigate the documents used in the investigation will greatly reduce the burden. That is, with the present invention, analysis of document information utilized in litigation is made easier.

[Example of execution using software]

The control block of the file analysis system 1 can be executed by a logic circuit (hardware) formed by an integrated circuit (IC chip) or the like, or can be executed by a CPU using a CPU (Central Processing Unit). In the latter case, the file analysis system 1 has a CPU that executes a command of each function software, that is, a program (control program), the above program, and can store various data in a computer (or CPU) to be readable. The ROM (Read Only Memory) is either a memory device (these are referred to as "recording media"), a RAM (Random Access Memory) that expands the above program, and the like. Then, the computer (or the CPU) reads the above program from the recording medium, and executes the program therein to achieve the object of the present invention. The recording medium may be a "non-transitory tangible medium" such as a cassette, a disc, a magnetic card, a semiconductor memory, a programmable logic circuit, or the like. Also, the above program can also use the program to transmit any transmission medium that can be transmitted. (communication network or playing wave, etc.), transferred to the above computer. The above-described program in the present invention can be specifically embodied by electronic transmission, or can be executed by using a form of a data signal embedded in a transmission wave.

Specifically, the file analysis program related to the present invention acquires information recorded in a set computer or a server, and extracts file information composed of a plurality of files included in the acquired information. Analysis of the file analysis program. In the computer, it can be executed: a category selection function, which will include each of the plurality of files in the foregoing plurality of files, and select a categorizable index, that is, a category; and a score calculation function, which constitutes the information of the aforementioned file. In the document, the difference between the document information and the litigation or the degree of correlation that is not implemented as the survey is displayed, and the score of the strength of the link is calculated, and the scores of each of the selected categories are calculated one by one by the above-mentioned category selection function.

The above-described category selection function can be executed by the above-described category selection unit 26. Further, the above-described score calculation function can be executed by the above-described score calculation unit 116. In either case, the detailed practices are as described above.

[payment matters]

The present invention is not limited by the respective embodiments described above, and the scope shown in the scope of the patent application is In the different embodiments, different technical means can be used to obtain different embodiments through appropriate combinations. Even in this case, it is included in the technical scope of the present invention. Further, new technical features can be formed by using a combination of the technical means disclosed in the respective embodiments.

The present invention is a file analysis system, which is a digital information recorded in a plurality of computers or servers, and analyzes the file information composed of a plurality of files included in the acquired digital information. A document analysis system that can make litigation or is not easy to use for investigation. It has: a survey base repository for storing information related to the aforementioned litigation or not for investigation; a survey category input and reception The department is configured to receive the above-mentioned lawsuit or input of a category that is not to be investigated; and a survey type determination unit; and based on the category received by the survey category input receiving unit, determine the survey item to be investigated, and then Investigate the basic database and put the necessary information Species are sampled.

Further, the file analysis system further includes a display screen control unit for controlling a display screen for prompting the type of information sampled by the investigation type determination unit.

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

The above-described document analysis system is characterized in that there is further an information sampling unit that samples a keyword and/or an article from the type of information sampled by the investigation type determination unit from the investigation basic database.

The foregoing file analysis system is characterized in that there is further a search unit for retrieving the aforementioned keywords and/or articles from the aforementioned files.

The above-described file analysis system is characterized in that there is an automatic distinguishing symbol giving unit that automatically adds a distinguishing symbol to the aforementioned file. The aforementioned keyword and/or the article can be used to add the aforementioned distinguishing symbol.

The present invention further provides a file analysis method, which is a method for obtaining information recorded in a plurality of computers or servers, and including information on files formed from a plurality of files in the information to be obtained. To make a lawsuit or a method of document analysis that would be easier to use for investigation purposes. The document analysis method includes the following steps: a survey category receiving step, receiving the aforementioned litigation or input of a category not being investigated; and The investigation type determination step is based on the input category receiving and receiving step, and the investigation item to be investigated is determined based on the received category, and is stored in a survey basic database that stores the aforementioned lawsuit or does not implement the information related to the investigation. Extract the type of information necessary.

The present invention further provides a file analysis program, which is a method for obtaining information recorded in a plurality of computers or servers, and including information on files formed from a plurality of files in the information to be obtained. To make a lawsuit or a file analysis program that is easier to use for investigation purposes. The program is tied to a computer and executed: The inspected class receiver can receive the above-mentioned litigation or the input of the category not inspected for investigation; and the function of determining the type of the survey, and inputting the receiver according to the surveyed category, and classifying the surveyed object according to the received category. It is determined that the types of necessary information are extracted from the basic database of investigations that store the aforementioned litigation or that do not implement the information related to the survey.

The present invention further provides a file analysis program, which is a method for obtaining information recorded in a set computer or a server, and analyzing the file information composed of a plurality of files in the information to be obtained. The document analysis system has a generation process model that generates a basic database of investigations, and sets the behavior of the litigation or the behavior that is not caused by the investigation. Because the progress of the set behavior should be classified and stored one by one, and at the same time, the aforementioned litigation is not implemented as the relevant information of the investigation, and the litigation is not carried out as the category to which the investigation belongs and the aforementioned generation process. The mode is further stored one by one, and then the series information of the time series of the aforementioned phase is displayed, and the foregoing litigation or the relationship of the plurality of characters not related to the investigation is further stored; and a recognition unit will The foregoing lawsuit does not implement information related to the investigation, the aforementioned generation process mode, the aforementioned series of information, and the above-mentioned document information are analyzed according to the relationship of the plurality of characters, and the current phase is identified.

1‧‧‧Document Analysis System

11‧‧‧File display screen

20‧‧‧Investigation category input receiving department

22‧‧‧Investigation Type Determination Department

24‧‧‧Information Sampling Department

26‧‧‧Category Selection Department

30‧‧‧Search Department

201‧‧‧1st automatic distinction department

301‧‧‧2nd Automatic Differences Department

401‧‧‧3rd Automatic Distinction Department

501‧‧‧Quality Inspection Department

601‧‧‧Learning Department

701‧‧‧Reporting Department

100‧‧‧Data Storage Department

101‧‧‧Digital Information Storage

103‧‧‧Investigation basic database

104‧‧‧Keyword database

105‧‧‧Connected Language Database

106‧‧‧Scoring calculation database

107‧‧‧Reporting database

109‧‧‧Database Management Department

116‧‧‧Scoring calculation department

118‧‧‧Document Analysis Department

120‧‧  Speech Judgment Department

122‧‧‧Translation Department

124‧‧‧ Trend Information Generation Department

130‧‧‧ Tips Department

131‧‧‧Differential Symbol Receiving Department

133‧‧ ‧ lawyer correction receiving department

Claims (10)

A file analysis system is a file analysis system for analyzing information recorded in a set computer or a server, and analyzing the file information including the information to be obtained and the plurality of files. : Having a category selection unit that treats each of the respective files included in the plurality of files as a categorizable index, that is, as a category, and a selection calculation unit; The document constituting the information of the aforementioned document is combined with the difference symbol indicating the degree of connection between the document information and the lawsuit or the non-implementation of the investigation, and the score indicating the strength of the combination is selected by the category selection unit. The category of the above categories is calculated one by one. The document analysis system according to the first aspect of the invention, wherein the category selection unit sequentially selects the category from a plurality of categories; and the score calculation unit is sequentially selected by the category selection unit. The selected category, one by one, calculates the aforementioned score. For example, in the document analysis system described in claim 1 or 2, wherein the category selection unit refers to the type of the aforementioned lawsuit or is not implemented as a survey, and the reason for causing the foregoing lawsuit or not for the purpose of the investigation is At least one of the progress of the set behavior, the phase to be classified, and the attribute of the aforementioned document information is selected as the aforementioned category. The document analysis system of claim 1, wherein the document type input receiving unit is further configured to receive the foregoing lawsuit or not to input the category to which the survey belongs; the category selection unit is The aforementioned survey category input In the department, to select the category to be received. The document analysis system according to the first aspect of the invention, further comprising: a survey type determination unit that determines the survey item to be investigated based on the category received by the survey category input receiving unit, and Investigate the basic database and sample the necessary types of information. For example, the document analysis system described in claim 1 has an information sampling department that treats keywords and/or articles contained in the aforementioned document information as being in conflict with the foregoing lawsuit or not Investigate information about the company and sample it from the information in the document. The document analysis system according to claim 1, wherein the file analysis system further includes a search unit for retrieving keywords and/or articles included in the file information from the plurality of files. . The document analysis system according to the first aspect of the invention, further comprising a presentation unit that causes the user to present the score calculated by the score calculation unit to a degree that is likely to be grasped by the user. A file analysis method for obtaining a document recorded in a set computer or a server, and analyzing a file information composed of a plurality of files included in the information acquired by the database , characterized in that it comprises the following steps: a category selection step of selecting each of the respective files included in the plurality of files by a categorizable index, that is, a category; and The step of calculating the score is a file that constitutes the information of the aforementioned document, and the document is displayed. When the information and the lawsuit are not the difference symbols of the degree of correlation between the investigations, the intensity of the combination with the difference symbol is displayed by a score, and in the above-described category selection step, the selected category is calculated one by one. A file analysis program for obtaining information recorded in a set computer or a server, and a file analysis program for analyzing file information composed of a plurality of files in the information to be obtained, The method is characterized in that: in a computer, performing: a category selection function, and selecting each of the respective files included in the plurality of files as a categorizable index, that is, as a category, and selecting; a score calculation function, when the document constituting the information of the aforementioned document is combined with the difference symbol indicating the degree of connection between the document information and the lawsuit or the non-implementation of the investigation, the score of the combined strength is displayed by The category selected by the category selection unit calculates the scores of the above categories one by one.