Retrieval data storage device

Abstract

The object of the present invention is to provide a system that can appropriately suggest candidates for search terms for each page of a document. The solution of the present invention is a search document information storage device, comprising: a term extraction device 3, a keyword storage device 5, a keyword extraction device 7, a subject word storage device 9, a subject word extraction device 11, a search term candidate extraction device 13, a search term candidate display device 17, a search term input device 19 and a document search information storage device 21.

Description

[0001] This application is a divisional application of an application with a filing date of May 7, 2018, application number 201880035902.5, and invention name “Retrieval Data Information Storage Device”. Technical Field

[0002] The present invention relates to a retrieval material information storage device. More specifically, the present invention relates to a retrieval material information storage device that can effectively suggest retrieval terms related to each page in a manner that effectively retrieves each page of a briefing material, and then associates and stores information related to each page with the retrieval terms related to each page. Background Art

[0003] Japanese Patent Publication No. 2019-16355 discloses a search information management device, a search information management method, and a search information management program. It is common to associate search terms for searching in various materials. Users can find suitable materials by using search terms. On the other hand, the search terms attached to each material may not be suitable for the search. Therefore, it is hoped that search terms suitable for the search can be suggested according to the user's intention.

[0004] Prior art literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent Application Publication No. 2019-16355 Summary of the invention

[0007] Problems to be solved by the invention

[0008] The present invention aims to provide a system that can appropriately suggest candidates for search terms for each page of a document. The present invention aims to provide a search document information storage device that can associate and store information related to each page with search terms related to each page in a manner that effectively searches each page of a document.

[0009] Technical solutions to problems

[0010] The present invention is basically based on the knowledge that after extracting terms included in each page of a document as keywords and extracting subject terms related to the keywords, high-scoring subject terms are displayed, thereby suggesting candidates for search terms applicable to each page of the document.

[0011] The present invention relates to a data information storage device for retrieval.

[0012] This device is a processing device based on a computer, and includes: term extraction means 3, keyword storage means 5, keyword extraction means 7, subject word storage means 9, subject word extraction means 11, search term candidate extraction means 13, search term candidate display means 17, search term input means 19, and data search information storage means 21. Each means is based on computer means, and is achieved through the cooperation of hardware and software.

[0013] The term extraction means 3 is a means for extracting terms in a document, and the term in a document is a term included in a certain page of the document.

[0014] The keyword storage means 5 is a means for storing terms that are keywords associated with terms in the material.

[0015] The keyword extracting means 7 is a means for extracting a plurality of keywords associated with the document terms from the keyword storing means 5 using the document terms extracted by the term extracting means 3 .

[0016] The subject word storage means 9 is a means for storing subject words associated with keywords.

[0017] The subject word extracting means 11 is a means for extracting subject words associated with the keywords from the subject word storing means 9 using the plurality of keywords extracted by the keyword extracting means 7 .

[0018] The search term candidate extracting means 13 is a means for extracting candidates of search terms for a certain page of a document from the subject terms extracted by the subject term extracting means 11 and the plurality of keywords extracted by the keyword extracting means 7 .

[0019] The search term candidate display means 17 is a means for displaying the search term candidates extracted by the search term candidate extraction means 13 on the display unit 15 .

[0020] The search term input means 19 is for receiving an input of a search term designated among the search term candidates displayed on the display unit 15 .

[0021] The document search information storage means 21 is a means for associating and storing the search term input by the search term input means 19 with information related to a certain page of the document.

[0022] The search material information storage device may further include classification term storage means 25 and classification term extraction means 27.

[0023] The classification term storage means 25 is a means for storing classification terms associated with subject words.

[0024] The classifying term extracting means 27 is a means for extracting a classifying term associated with the subject word from the classifying term storing means 25 using the subject word extracted by the subject word extracting means 11 .

[0025] Next, the search term candidate display means 17 of the search material information storage device further extracts the classifying term extracted by the classifying term extracting means 27 as one candidate of the search term.

[0026] In the retrieval material information storage device, the keyword storage means 5 can associate and store a plurality of keywords with individual keyword scores.

[0027] The keyword extraction means 7 can extract a plurality of keywords and extract individual keyword scores at the same time.

[0028] In the retrieval data information storage device,

[0029] The subject word storage means 9 can also associate and store subject words with individual subject word scores.

[0030] The subject word extraction means 11 may also select a predetermined number of keywords (one or more than one) with higher scores among the plurality of keywords extracted by the keyword extraction means 7 as strong subject word candidates, and extract subject words associated with the predetermined number (one or more than one) of the strong subject word candidates from the subject word storage means 9.

[0031] The search term candidate extraction means 13 can also extract a predetermined number of keywords (one or more than one) with higher scores from the multiple keywords extracted by the keyword extraction means 7 as search term candidates, and at the same time, use the keyword scores and the subject word scores to extract a predetermined number of subject words (one or more than one) from the subject words extracted by the subject word extraction means 11 as search term candidates.

[0032] In the retrieval material information storage device,

[0033] The search term candidate display means 17 may display a predetermined number (one or more) of keywords extracted as search term candidates and a predetermined number (one or more) of subject terms extracted as search term candidates on the display unit 15 as search term candidates, and may display, on the display unit 15, those of the keywords extracted by the keyword extraction means 7 that have not been extracted as search term candidates and those of the subject terms extracted by the subject term extraction means 11 that have not been extracted as search term candidates as reserve candidates for the search term.

[0034] Search term input means 19,

[0035] When receiving an input indicating that a search term is to be used as a search term, the preliminary candidates for the search term are used as the search term.

[0036] The items displayed as candidates for the search terms may be used as search terms, except for those that have received an input indicating that they should not be used as search terms.

[0037] The present invention also provides a search material information storage program for causing a computer to function as the following means, or a computer-readable recording medium storing the program.

[0038] The means include:

[0039] The term extraction method 3 extracts the terms in the data, that is, the terms contained in a certain page of the data;

[0040] Keyword storage means 5, storing terms that become keywords associated with terms in the data;

[0041] The keyword extracting means 7 uses the terms in the document extracted by the term extracting means 3 to extract a plurality of keywords associated with the terms in the document from the keyword storing means 5;

[0042] A subject word storage means 9 for storing subject words associated with keywords;

[0043] The subject word extraction means 11 uses the multiple keywords extracted by the keyword extraction means 7 to extract subject words associated with the keywords from the subject word storage means 9;

[0044] The search term candidate extracting means 13 extracts the search term candidates for a certain page of the document from the subject terms extracted by the subject term extracting means 11 and the plurality of keywords extracted by the keyword extracting means 7;

[0045] The search term candidate display means 17 displays the search term candidates extracted by the search term candidate extraction means 13 on the display unit 15;

[0046] The search term input means 19 receives an input indicating a search term among the search term candidates displayed on the display unit 15; and

[0047] The document search information storage means 21 associates and stores the search term input by the search term input means 19 with information related to a certain page of the document.

[0048] Effects of the Invention

[0049] The present invention can provide a system that can appropriately suggest search term candidates related to each page of a document. The present invention can provide a search document information storage device that associates and stores information related to each page with search terms related to each page in a manner that can effectively search each page of the document. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a block diagram for explaining the retrieval material information storage device of the present invention.

[0051] Figure 2 It is a block diagram showing the basic structure of a computer.

[0052] Figure 3 This is a conceptual diagram showing an example of a system of the present invention.

[0053] Figure 4 This is an example of a page of presentation material.

[0054] Figure 5 This is a conceptual diagram showing a storage example of keyword storage means.

[0055] Figure 6 This is a conceptual diagram showing a storage example of keyword storage means.

[0056] Figure 7 This is a conceptual diagram showing a storage example of a classifier storage means.

[0057] Figure 8 It is a concept map showing the extracted (classification terms), subject terms, keywords, and terms used in the data.

[0058] Fig. 9 This is an example of a display screen.

[0059] Fig.10 This is a flowchart for explaining an example of using the search material information storage device of the present invention.

[0060] Fig.11 This is a conceptual diagram for explaining an example of using the search material information storage device of the present invention. DETAILED DESCRIPTION

[0061] The following describes specific embodiments of the present invention using the drawings. The present invention is not limited to the embodiments described below, and also includes those appropriately modified within the scope of knowledge by those skilled in the art from the following embodiments.

[0062] Figure 1This is a block diagram for explaining the information storage device for searching of the present invention. This device is a processing device based on a computer. The computer can be any one of a mobile terminal device, a desktop personal computer and a server, or a combination of two or more. These are usually connected in a manner that can send and receive information via the Internet (intranet) or the like. It is also possible to make any one computer have a part of the functions, etc., and use multiple computers to share the functions.

[0063] Figure 2 It is a block diagram showing the basic structure of a computer. As shown in this figure, the computer has an input unit 31, an output unit 33, a control unit 35, a computing unit 37 and a storage unit 39, and each element is connected by a bus 41 and the like and is configured in a manner that can send and receive information. For example, the storage unit can store a control program or various information. When predetermined information is input from the input unit, the control unit reads the control program stored in the storage unit. Then, the control unit appropriately reads the information stored in the storage unit and transmits it to the computing unit. In addition, the control unit transmits appropriate input information to the computing unit. The computing unit uses the various information received to perform computing processing and stores it in the storage unit. The control unit reads the computing results stored in the storage unit and outputs them from the output unit. Various processing is performed in this manner. The various processing units are referred to as various means.

[0064] Figure 3 1 is a conceptual diagram showing an example of a system of the present invention. Figure 3 As shown, the system of the present invention (system including the apparatus of the present invention) may also be a system including a mobile terminal device 45 connected to the Internet or an intranet 43, and a server 47 connected to the Internet or the intranet 43. Of course, a single computer or mobile terminal device may also function as the apparatus of the present invention, and a plurality of servers may also exist.

[0065] The retrieval material information storage device 1 associates and stores the information used to read each page of the briefing material (e.g., the identification number and page number of the briefing material) with one or more search terms related to the page in a manner that makes it easy to retrieve the information required by the user. The retrieval material information storage device 1 may also include a storage unit (storage device) of any terminal device, computer (or server). In addition, the retrieval material information storage device may also include a database and database management software. In the retrieval material information storage device, the briefing material pages may also be graded or scored for each search term. For example, consider the case where multiple pages are associated with and stored with a search term called diabetes. In this case, for the search term called diabetes, information on the highest-ranked page, the second-highest-ranked page, the highest-scoring page, and the second-highest-scoring page may be stored in the storage unit.

[0066] like Figure 1As shown, the retrieval material information storage device 1 includes a term extraction means 3, a keyword storage means 5, a keyword extraction means 7, a subject word storage means 9, a subject word extraction means 11, a search term candidate extraction means 13, a search term candidate display means 17, a search term input means 19, and a material retrieval information storage means 21. Each means is a means relying on a computer, and each process is achieved through the cooperation of hardware and software.

[0067] The term extraction means 3 is a means for extracting terms from data, wherein the terms from the data are terms contained in a certain page of the data. Examples of data include so-called presentation data. The format of the presentation data is not particularly limited. Examples of presentation software include Microsoft Corporation (registered trademark) PowerPoint (registered trademark), Kingsoft Software Co., Ltd. (registered trademark) WPS OFFICE (registered trademark), Apache (registered trademark) OpenOffice Impress (registered trademark), Keynote (registered trademark), Lotus Freelance (registered trademark), Illustrator (registered trademark), PDF (registered trademark) and Prezi (registered trademark). Examples of data include, for example, data produced using any of these presentation software. Presentation software is software that can display the contents of each page in a display unit such as a screen.

[0068] Figure 4 is an example of a page from a presentation. Figure 4 As shown, the presentation material includes multiple texts input by the creator. The user can recognize multiple characters with his eyes. On the other hand, the computer stores the text input by the user, or the input information related to the text (text size, text color, whether there is animation) and other information together with the text. A preferred example of the term extraction means 3 is to assign a score (score) to the text according to the input information related to the text (text size, text color, whether there is animation) when extracting the text. For example, the larger the text, the more likely it is to represent the content of the presentation material, so a high score is assigned. In addition, for example, when the text color is red or the text is animated, it represents more content in the presentation material, so a high score is assigned. The term extraction means 3 pre-stores the score (score) of the effect associated with the text. When extracting the term, the score associated with the text is read. When calculating the score described later, it can be added or multiplied with other scores and scored.

[0069] On the other hand, the term extraction means 3 itself is a known technology. The briefing material has a plurality of text information. Then, the briefing material is stored in a storage unit such as a server (or a computer). The term extraction means 3 reads each page of the stored briefing material and reads the text contained in each page. Then, the term extraction means 3 performs word class analysis on the read text. At this time, for example, there is a word class database in the storage unit, and various terms and their word classes are stored. At this time, the scores of the search terms used as various terms can also be stored in the storage unit according to the purpose. For example, if the data information storage device for retrieval is used for a drug manufacturer, MR (medical information manager), or MS (drug wholesaler), higher scores can be assigned to various disease names compared to general nouns. In addition, although the importance of various drug names or active ingredients is lower than that of disease names, higher scores can also be assigned to general nouns. Then, the term extraction means 3 can extract the terms (especially nouns) contained in the text, and can use their frequency or the term scores stored in the storage unit to extract one or more terms in the data. For example, the term extraction means 3 extracts term A, term B, and term C from a certain page, term C appears twice and terms A and B appear once, and the scores of terms A, B, and C stored in the storage unit are 5, 50, and 40, respectively, the scores of terms A, B, and C can be set to 5, 50, and 80, respectively. For example, when the number of extracted terms in the document is set to 2, the term extraction means 3 can extract terms C and B as terms in the document. Then, the extracted terms in the document (terms C and B) are associated with information related to the number of pages that can read the page, and stored in the storage unit. In this way, terms C and B can be read together with the page. Another example of the term extraction means 3 is to identify the part of a certain page of the presentation that uses the largest font. Then, a predetermined coefficient is assigned to the terms in the document included in the part that uses the largest font. The coefficient (first coefficient: a1) can be stored in the storage unit. The term extraction means 3 stores the first coefficient together with the term in the document included in the portion using the largest font size in the storage unit. Alternatively, the term extraction means 3 may store a coefficient (second coefficient: a2) according to the font size together with the term in the document in the storage unit.

[0070] The keyword storage means 5 is a means for storing terms, and the terms are keywords associated with the terms in the data. The keyword storage means 5 can be realized by a storage unit and a requirement (such as a control program) for reading information from the storage unit. Keywords are not only terms in multiple documents, but also terms that are used to easily search each page by using terms related to these as search terms when searching each page. As a result, the number of search terms associated with each page and stored is reduced, and a method for rapid retrieval is achieved. However, there are also cases where the terms in the data are directly keywords. Keywords can be called first conversion terms related to the terms in the data. Keywords can also be selected from terms in multiple types of data to be suitable for use in retrieval.

[0071] The terms in the data are the terms included in the brief. Therefore, the terms in the data may not necessarily be consistent with the search terms or may not be suitable as search terms. For example, the brief contains terms called ob gene or ob / ob mouse. They are associated with obesity genes (and obesity, obesity experimental animals). Therefore, the keyword storage means 5 associates and stores the obesity gene (and obesity, obesity experimental animals) as keywords with the ob gene or ob / ob mouse as the terms in the data.

[0072] Since the keyword storage means 5 exists, the search terms stored in association with each page become unified terms, so that the number of related pages can be quickly read when searching.

[0073] Figure 5 2 is a conceptual diagram showing a storage example of a keyword storage means. Figure 5 As shown, the keyword storage means associates and stores one or more keywords with each term in a plurality of documents, and associates and stores scores (the score is b1) with each keyword. The scores are preferably pre-entered so that the more suitable the term is for retrieval, the higher the score is.

[0074] The keyword extraction means 7 is a means for using the terms in the document extracted by the term extraction means 3 and extracting a plurality of keywords that are associated with the terms in the document from the keyword storage means 5. The keyword storage means 5 stores terms that are keywords associated with the terms in the document. Therefore, the keyword extraction means 7 can use the terms in the document and read the terms that are keywords associated with the terms in the document from the keyword storage means 5. Usually, a plurality of terms in the document are extracted from a certain page. Therefore, usually, a plurality of terms that are keywords related to this page are extracted. In addition, there are usually a plurality of terms that are keywords associated with the terms in the document (scores can also be assigned to each of them). Therefore, usually, a plurality of terms that are keywords related to this page are extracted. In addition, of course, there are also cases where the terms in the document become keywords. That is, the terms in the document can also be directly extracted as keywords. The keyword extraction means 7 can use the coefficients of the terms in the document stored in the storage unit and the scores of the keywords and evaluate the scores of individual keywords. An example of the score of a keyword is a1×a2×b1. In order to calculate the score, the storage unit stores a control program for performing the above operation. Therefore, the control unit can read the control program and read the coefficients and scores stored in the storage unit at the same time, and perform the operation of a1×a2×b1 in the operation unit, and store the operation result in the storage unit. In addition, the frequency of occurrence of the term in the data (the coefficient is a 21 ) or the addition coefficient when extracting a specific keyword from multiple types of data (the coefficient is a22 ) is stored in advance in the storage unit, and by calculating a1×a2×a 21 ×a 22 ×b1, thereby calculating the score of the keyword and storing it in the storage unit. In addition, a stronger coefficient can be assigned to the emphasized color contained in a certain page. In this case, there can be a means for analyzing the color of the term from the page, and a storage unit for storing the coefficient of each color, and the color of the term that has been analyzed can be used to read the coefficient related to the color from the storage unit. In addition, not only keywords, but also subject words and classification terms basically store coefficients or scores for various requirements in advance, and read them and perform multiplication or addition operations to calculate the scores, store the scores of individual languages ​​and compare them, thereby calculating the candidates for priority.

[0075] The keyword storage means 9 is a means for storing keyword-related keywords. The keyword storage means 9 can be realized by a storage unit and a component (eg, a control program) for reading information from the storage unit.

[0076] For example, in the subject word storage means, a subject word named obesity can be associated and stored with keywords named obesity gene, obesity, obesity experimental animals, etc. The subject word can be a term or a superordinate conceptual term that further unifies multiple keywords. By using the subject word, retrieval can be performed more quickly. Examples of subject words include disease names, drug names, active ingredient names, and pharmaceutical company names. That is, the subject word can be called a second conversion word related to the term in the data. The subject word can also be a term suitable for retrieval selected from multiple types of keywords. In addition, the subject word can also be information related.

[0077] The subject word extracting means 11 is a means for using the plurality of keywords extracted by the keyword extracting means 7 to extract subject words associated with the keywords from the subject word storing means 9 .

[0078] The subject word storage means 9 stores subject words associated with keywords. Therefore, the subject word extraction means 11 uses the plurality of keywords extracted by the keyword extraction means 7 and extracts subject words associated with the keywords from the subject word storage means 9.

[0079] Figure 6 : is a conceptual diagram showing a storage example of a keyword storage means. Figure 6 As shown, the subject word storage means associates and stores one or more subject words for each of the plurality of keywords, and associates and stores scores for the individual subject words. The scores are preferably pre-entered so that the more suitable the term is for search, the higher the score is.

[0080] The search term candidate extracting means 13 is a means for extracting candidates of search terms for a certain page of a document from the subject terms extracted by the subject term extracting means 11 and the plurality of keywords extracted by the keyword extracting means 7 .

[0081] For example, a subject word associated with a certain page is stored in one or more storage units. In addition, a plurality of keywords associated with a certain page are stored.

[0082] For example, when the control program controls the subject words as candidates for all search terms and the keywords as candidates for several search terms (for example, considering that the size displayed on the display unit is four), the search term candidate extraction means 13 uses the read subject words as candidates for all search terms and uses four keywords among the keywords as candidates for search terms.

[0083] In addition, the keyword storage means 5 may be a device that associates and stores multiple keywords with scores of individual keywords, and the keyword extraction means 7 may be a device that extracts scores of individual keywords while extracting multiple keywords. In this case, for example, keywords with higher scores are extracted as candidates for search terms.

[0084] The subject word storage means 9 can also associate and store subject words with the scores of individual subject words, and the subject word extraction means 11 can also take a predetermined number of keywords (one or more than two) with higher scores among the multiple keywords extracted by the keyword extraction means 7 as strong subject word candidates, and extract subject words associated with the predetermined number of strong subject word candidates from the subject word storage means 9.

[0085] The above-mentioned search material information storage device may further include classification term storage means 25 and classification term extraction means 27.

[0086] The classification term storage means 25 is a means for storing classification terms associated with subject words.

[0087] The classification term extraction means 27 is a means for using the subject terms extracted by the subject term extraction means 11 and extracting classification terms associated with the subject terms from the classification term storage means 25. The classification term can be called a third conversion term related to the term in the data. The classification term can also be a term suitable for classification search selected from multiple types of subject terms. Examples of classification terms can also be objects that show interest in the data. For example, if a certain page of the data is a certain diabetes drug for MR (this can be, for example, associated with the subject term and stored), then examples of classification terms can also be "MR", "diabetes", "medicine". In addition, if a certain page of the data is accounting information for bank clerks, then examples of classification terms can be bank clerks, etc., or "bank clerks" and "accounting", etc. In addition, the classification term can be information related to the product. Then, the search term candidate display means 17 of the search data information storage device further extracts the classification term extracted by the classification term extraction means 27 as one of the candidates for the search term. Figure 7 This is a conceptual diagram showing a storage example of a classifier storage means. The classifier storage means associates and stores one or more classifiers with each of a plurality of subject words, and simultaneously associates and stores scores with each of the classifiers. The scores are preferably input in advance so that the more suitable the term is for use in the search, the higher the score is.

[0088] Figure 8 It is a concept map showing the extracted (classification terms), subject terms, keywords, and terms used in the data.

[0089] The search term candidate extraction means 13 may also extract a predetermined number of keywords (one or more than one) with higher scores from the multiple keywords extracted by the keyword extraction means 7 as search term candidates. In addition, the search term candidate extraction means 13 may also use the scores of keywords and the scores of subject terms, and extract a predetermined number (one or more than one) of search term candidates from the subject terms extracted by the subject term extraction means 11. For example, the subject term storage means 9 associates and stores the subject terms with the scores of individual subject terms. In addition, the keyword storage means 5 associates and stores multiple keywords with the scores of individual keywords. A subject term has a keyword from which it is derived. That is, a subject term is read using a keyword. A subject term is often associated with one or more keywords. At this time, the search term candidate extraction means 13 reads the scores of each keyword from the extracted subject term from the keyword storage means 5 while reading the score related to a certain subject term from the subject term storage means 9. Next, for example, when there are multiple keywords for a certain subject word, the search term candidate extraction means 13 causes the calculation unit to sum up the scores of the keywords and simultaneously multiply the score of the subject word by the score of the keyword (or the total score of the keywords). By performing the above-described operation, the total score for the subject word is obtained and stored in the storage unit. The search term candidate extraction means 13 reads the total scores for multiple subject words and causes the calculation unit to compare and extract a predetermined number (one or more than two) of subject words. By performing the above-described operation, the search term candidate extraction means 13 can extract a predetermined number of subject words even if the number of subject words to be extracted has been determined.

[0090] The search term candidate display means 17 is a means for displaying the search term candidates extracted by the search term candidate extraction means 13 on the display unit 15 .

[0091] The search term candidate display means 17 is:

[0092] While a predetermined number (one or more than one) of keywords extracted as candidates for the search term and a predetermined number (one or more than one) of subject words extracted as candidates for the search term are displayed on the display unit 15 as candidates for the search term,

[0093] The keywords extracted by the keyword extraction means 7 that have not been extracted as search term candidates and the subject words extracted by the subject word extraction means 11 that have not been extracted as search term candidates are displayed on the display unit 15 as preliminary candidates for search terms.

[0094] Search term input means 19

[0095] When receiving an input indicating that the search term is to be used as a search term, the preliminary candidates for the search term may be used as the search term.

[0096] The items displayed as search term candidates can be used as search terms, except for those receiving an input indicating that they should not be used as search terms.

[0097] The document search information storage means 21 is a means for associating and storing the search term input by the search term input means 19 with the information of a certain page of the document.

[0098] The device of the present invention may further display candidates for content types according to the type of presentation material, and associate and store the type of content with each page of the presentation (or the presentation itself). At this time, the device of the present invention reads the format of the presentation stored in the storage unit (PowerPoint (registered trademark), PDF (registered trademark), Word (registered trademark), etc.). The device of the present invention reads the text contained in the format that has been read. The device of the present invention includes a content analysis term database that has stored terms for content analysis. Next, the device of the present invention uses the terms stored in the content analysis term database to analyze the type of content. For example, when the material is PDF (registered trademark) and a text named "Attached File" exists at the beginning, "Attached File" is extracted as a candidate for the content type of the material. Next, "Attached File" is displayed as a content type on the display unit, and when the user enters confirmation, it is associated with the material, and the "Attached File" related to the content type is stored.

[0099] Fig. 9 is an example of a display screen. In this example, a certain page of the presentation material is displayed in the upper half of the display screen. Next, icons (check boxes) indicating whether to use or not use the search term are displayed, and the search term candidates are displayed at the same time. Fig. 9 In the example, the data are arranged in the order of classification terms, subject terms, and keywords from the left. The terms used in the data can also be displayed in the display section. Fig. 9 In the example, the term extracted as the search term by the search term candidate extraction means 13 is checked in the adoption check box. A confirmation button is provided at the bottom of the display screen. When the user uses the confirmation button and inputs a confirmation instruction in the computer (terminal device), the candidate of the search term is confirmed. The device 1 that receives the input from the computer associates a certain page of the briefing with the confirmed search term (and the score of the individual search term) and stores it in the storage unit.

[0100] The search term input means 19 is a means for receiving an input indicating a search term among the search term candidates displayed on the display unit 15 . Fig. 9In the example, the function is performed as a search term input means 19 by inputting a check box. When the user inputs a candidate of the search term in the adopted state as not adopted, for example, a check is input in the not adopted check box. The device 1 that receives the not adopted input from the check box will make the indicated candidate of the search term into a not adopted state. Then, if the user uses the confirmation button and inputs a confirmation instruction in the computer (terminal device), the candidate of the search term is not adopted. In addition, after the device 1 reduces the score of the candidate of the search term that is not adopted (for example, the score is halved), it is stored as a search term associated with the page. The search term candidate extraction means 13 checks the not adopted check box (or does not check any check box) for the term that is not extracted as a search term. When the user inputs a candidate of the search term that is not adopted as adopted, for example, a check is input in the adopted check box. The device 1 receiving the adoption input from the check box will make the indicated candidate of the search term into the adopted state. Then, if the user uses the confirmation button to input the confirmation instruction on the computer (terminal device), the candidate of the search term will be adopted. That is, the search term will be associated with this page as the search term of a certain page and stored. At this time, the search term selected by the user can also be stored in a state where the score is added or multiplied.

[0101] Fig.10 This is a flowchart for explaining an example of using the search material information storage device of the present invention. That is, this figure is a figure for explaining a search material information storage method using the search material information storage device. In the figure, S represents a process (step).

[0102] The user creates a presentation material (S101). In this way, the user's terminal device or computer stores the presentation material in a storage unit (or a storage unit of a server).

[0103] The device 1 extracts the terms contained in each page of the briefing material, that is, the terms in the material (S102). At this time, the device 1 can also assign scores to the terms in the material. For example, when the frequency of occurrence of the terms in the material is high, or when accompanied by bold characters, colored text, animations, etc., points can be added in advance and logged in, and the logged-in point information can be used to assign scores to the terms in the material. In addition, the device 1 can have a dictionary of terms in the material, and in the dictionary, for various terms in the material, the terms in the material can be associated with scores and stored, and the device 1 can also read the scores of the terms in the material. Afterwards, the scores of the terms in the material existing in the dictionary and the scores related to the points (such as addition or multiplication) can also be used to calculate the scores of the terms in the material. At this time, when the number of terms in the material is pre-set, the one with the higher score can be used as the term in the material.

[0104] The device 1 uses the terms extracted from one or more documents and extracts multiple keywords that are associated with the terms in the documents from the storage unit (S103). The storage unit records the terms that are keywords associated with the terms in the documents. Therefore, the device 1 can use the terms in the documents to extract keywords related to them from the storage unit. At this time, a score as a search term can be assigned to each keyword. In addition, when the same keyword is selected from terms in different documents, the possibility that the keyword will become a search term is higher, so it can also be used as a bonus object. In this case, the bonus for keywords with higher frequency can be pre-registered, and the bonus based on the number of times the keyword is repeated can be read, and the scores can be added or multiplied. In this way, the scores of multiple keywords (and individual keywords) can be calculated.

[0105] If the device 1 uses a plurality of keywords and extracts subject words associated with the keywords from the storage unit ( S104 ), this process is the same as the keyword extraction step.

[0106] The device 1 can use the extracted subject words and extract classification terms associated with the subject words from the storage unit (S105). This process is an arbitrary process.

[0107] The device 1 extracts candidates for search terms for a certain page of the document from the subject words and multiple keywords (and classification terms) (S106). The device 1 may pre-store control instructions for extracting candidates for search terms, and may extract candidates for search terms for a certain page of the document from the subject words and multiple keywords (and classification terms) according to the control instructions. An example of a control instruction is to extract four keywords with higher scores among multiple keywords and two keywords with higher scores among the subject words (and all classification terms) as candidates for search terms. In this way, candidates for search terms for a certain page of the briefing document are automatically extracted. In addition, the storage unit may also store the extracted candidates for search terms as search terms for a certain page.

[0108] Next, when the user confirms or decides the search term, the device 1 may display the extracted search term candidates on the display unit (S107). At this time, the target page (reduced version) of the presentation, the subject words that are not candidates for the search term, and a plurality of keywords (and classification terms) may also be displayed on the display unit. In this case, the user can select the search term.

[0109] When the user directly confirms, the terminal device receives the confirmation input and directly stores the search term candidates extracted by the apparatus 1 as search terms associated with a certain page of the presentation material in the storage unit ( S111 ).

[0110] On the other hand, when the terminal device receives input of a candidate that does not use the search term, or when the terminal device receives input of a candidate term that is not a search term, the candidate search terms reflecting these corrections are used as search terms associated with a page in the storage unit (S121).

[0111] After the correction is made, if the user has confirmed it, the terminal device receives an input regarding the confirmation and stores the corrected search term candidate in the storage unit as a search term associated with a certain page of the presentation material (S122).

[0112] The present invention also provides a search material information storage program for causing a computer to function as the following means, or a computer-readable recording medium storing the program in a readable manner.

[0113] The means include:

[0114] The term extraction method 3 extracts the terms in the data, that is, the terms contained in a certain page of the data;

[0115] Keyword storage means 5, storing terms that become keywords associated with terms in the data;

[0116] The keyword extracting means 7 uses the terms in the document extracted by the term extracting means 3 and extracts a plurality of keywords associated with the terms in the document from the keyword storing means 5;

[0117] A subject word storage means 9 for storing subject words associated with keywords;

[0118] The subject word extraction means 11 uses the multiple keywords extracted by the keyword extraction means 7 and extracts the subject words associated with the keywords from the subject word storage means 9;

[0119] The search term candidate extracting means 13 extracts the search term candidates for a certain page of the document from the subject terms extracted by the subject term extracting means 11 and the plurality of keywords extracted by the keyword extracting means 7;

[0120] The search term candidate display means 17 displays the search term candidates extracted by the search term candidate extraction means 13 on the display unit 15;

[0121] a search term input means 19 for receiving an input indicating a search term among the search term candidates displayed on the display unit 15; and

[0122] The document search information storage means 21 associates and stores the search term input by the search term input means 19 with information related to a certain page of the document.

[0123] Fig.11 This is a conceptual diagram (block diagram) for illustrating an example of using the retrieval data information storage device of the present invention. In this example, the basic database (DB) includes a content DB, a customer DB, a record DB, and a DB that stores other information. Next, these databases are connected to an engine called the Interactive-PRO framework through an interface. This engine can send and receive information with various terminal devices (such as tablets, mobile terminal devices, and mobile phones) through an application programming interface (API). In addition, this engine can send and receive information with user-side control programs or applications, HTML materials, animation materials, PowerPoint materials, PDF materials, file materials, and database management software. In addition, the engine can synchronize with a server (cloud) and send and receive information. On the other hand, Fig.11 In the example of , this server can send and receive information with various databases and software including customer BI (business intelligence), CRM (customer relationship management), and DWH (data warehouse).

[0124] Industrial Applicability

[0125] The present invention can be utilized in the information providing industry.

[0126] Description of Reference Numerals

[0127] 1. Data storage device for retrieval

[0128] 3. Terminology extraction methods

[0129] 5 Keyword storage method

[0130] 7 Keyword extraction methods

[0131] 9. Keyword storage method

[0132] 11. Keyword extraction methods

[0133] 13. Method for extracting candidate search terms

[0134] 15 Display

[0135] 17 Search term candidate display method

[0136] 19 Search term input method

[0137] 21 Data retrieval information storage means

[0138] 23. Storage means for information retrieval

[0139] 25 Classification language storage method

[0140] 27 Classification term extraction method

Claims

1. A method for extracting search term candidates using a computer, characterized in that: Include: A keyword extraction step of extracting a plurality of keywords associated with the terms in the document having relatively high scores from a keyword storage means for storing keywords associated with the terms in the document and scores of the individual keywords in association with each other, wherein the terms in the document are terms included in a certain page of the document; A subject word extraction step of extracting a subject word associated with the plurality of keywords and having a higher subject word score from a subject word storage means that associates and stores subject words associated with the keywords and scores of the individual subject words, using the plurality of keywords extracted in the keyword extraction step; and A search term candidate extraction step extracts candidates for search terms for a certain page of the document from the subject terms extracted in the subject term extraction step and the multiple keywords extracted in the keyword extraction step, including: comparing the total subject term scores obtained by multiplying the subject term scores with the scores of the multiple keywords, and extracting a predetermined number of subject terms as search term candidates.

2. A data retrieval information storage method using a computer, characterized in that: Include: The term extraction process is to extract the terms in the data, that is, the terms contained in a certain page of the data; A keyword extraction step of extracting a plurality of keywords associated with the terms in the document extracted in the term extraction step and having relatively high scores from a keyword storage means (5) for storing keywords associated with the terms in the document and scores of individual keywords in association with each other; A subject word extraction step, using the plurality of keywords extracted in the keyword extraction step, extracting a subject word associated with the plurality of keywords having a higher subject word score from a subject word storage means (9) which associates and stores subject words associated with the keywords and scores of the individual subject words; A search term candidate extraction step, extracting search term candidates for a certain page of the document from the subject terms extracted in the subject term extraction step and the plurality of keywords extracted in the keyword extraction step, comprising: comparing the scores of the subject terms obtained by multiplying the scores of the plurality of keywords by the scores of the subject terms, and extracting a predetermined number of subject terms as search term candidates; a search term candidate display step of displaying the search term candidates extracted in the search term candidate extraction step on a display unit (15); a search term input step of receiving an input indicating a search term among the search term candidates displayed on the display unit (15); and The document search information storage step associates and stores the search term input in the search term input step with information related to a certain page of the document.

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