Patent Publication Number: US-6665667-B2

Title: Method of and an apparatus for retrieving and delivering documents and a recording media on which a program for retrieving and delivering documents are stored

Description:
This is a continuation application of U.S. Ser. No. 09/518,689, filed Mar. 3, 2000 now U.S. Pat. No. 6,549,898. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates to a document retrieving and delivering technique in which an electronic document is retrieved according to a retrieval condition registered by a user in advance and documents satisfying the condition are delivered to the user. 
     Recently, a large amount of electronic documents (to be referred to as texts herebelow) have been delivered at every moment to users through an electronic mail or e-mail, electronic news, and the like. Information sources which transmit information through the World Wide Web (WWW) are rapidly increasing and hence an immense amount of texts have been collected from such information sources using an information collecting robot or the like. There consequently arises a need for a document retrieving and delivering system in which texts containing information requested by a user are retrieved therefrom and are delivered to the user. 
     JP-A-10-27182 (to be referred to as prior art 1) describes such a document or text retrieving and delivering system. In this system, retrieval condition expressions of a plurality of users are combined with each other to process condition expressions of a plurality of users through one text scanning operation. 
     However, in prior art 1, the user is required to generate retrieval condition expressions, which leads to two problems as follows. 
     First, when a rarely used word is specified in a retrieval condition or when generally used words are complicatedly combined with each other in a retrieval condition specified, there appears texts which cannot be retrieved (retrieval leakage). 
     Second, in contrast with the first problem, when a simple retrieval condition expression containing only generally used words is specified, there are possibly retrieved many documents or texts (to be referred to as retrieval noise) not suitable for an object of the retrieval. This leads to a problem that documents desired by the user cannot be easily attained. 
     In short, to obtain retrieval results in which texts not retrieved as above are minimized and in which the noise is reduced, it is difficult for the user to appropriately generate a retrieval condition expression. 
     Japanese Patent Application Serial No. 10-148721 (to be referred to as prior art 2) describes a technique to improve two problems above in a document retrieval system in which documents containing information desired are retrieved from documents (to be referred to as registered documents herebelow) registered to a text database. 
     In this technique, a keyword (called “feature character string” in prior art 2) is extracted from a text (to be referred to as a seed text) exemplified as a retrieval condition to calculate similarity of the seed document with respect to registered documents. 
     In prior art 2, the user needs only to exemplify a seed document containing information desired. Namely, the user is relieved from the troublesome job to select appropriate retrieval terms for a retrieval condition expression. The user then instructs execution of retrieval to view retrieval results sorted according to the similarity. Therefore, even when the retrieval results include some retrieval noise, the user can easily attain necessary information. 
     Next, description will be given of an outline and problems of the prior arts above. 
     Referring to FIG. 2, an outline of prior art 1 will be described. 
     In this example, three users, i.e., users 1 to 3 have registered retrieval condition expressions to a document retrieving and delivering system, i.e., document containing “new” and “car”, document containing USA, and document containing used and car, respectively. Under this condition, a scanning operation is conducted using a text collected “price of this new car is . . . ” to determine whether or not the three conditions are satisfied. 
     The retrieval condition expressions registered by the users are analyzed to extract retrieval terms “new”, “car”, “USA”, and “used”. 
     The number of retrieval terms extracted is stored for each user in a retrieval term count table. For example, from retrieval condition expression of user  1 , i.e., document containing “new” and “car” registered by user  1 , two retrieval terms “new” and “car” are extracted and hence “2” is stored in an associated field of the table. In a similar fashion, “1” and “2” are stored in associated fields of the table for users  2  and  3 , respectively. 
     Next, the system creates a finite automaton to collate all retrieval terms extracted. 
     In the finite automaton in FIG. 2, a circle indicates a state of the automaton and an arrow denotes a state transition. A character next to the arrow represents input characters which cause the transition of the arrow. A numeral in the circle designates a state number of the automaton state. This example does not include an arrow to an initial state to be used when a character not indicated in the automaton is inputted (to be called a failure herebelow). 
     The system then forms a user list including elements each including a user identifier of a user having specified a retrieval term. The list is linked with retrieval term detection states of the automaton respectively associated with. In this example, when “car” is collated, the system refers to an associated user list item according to the last state “3”. This indicates that users  1  and  3  have specified “car”. 
     Description will next be given of the scanning of a text “price of this new car is” in the automaton shown in FIG.  2 . In this example, it is detected that the text includes partial character strings in which “car” or “new” appears. In this automaton, a retrieval term having a small circle at an end thereof means that a partial character string matching the term exists in the text. Since partial character strings matching with “car” or “new” appear in the text in FIG. 2, end states  3  and  6  are assigned with a small circle. 
     In the texts, the number of retrieval terms matching partial character strings in the text are counted for each user and is stored in a retrieval term appearance count table. For example, since the matching state is detected for “new” and “car” or user  1 , “2” is set to the count value. Only car is matching for user  3 , “1” is counted. For user  2 , the matching state does not occur for any partial character strings, and hence the counting is not achieved and “0” is kept unchanged for the count value. 
     The retrieval term count table in which the retrieval term counts extracted from the retrieval condition expressions are stored is compared with the retrieval term appearance count table in which the numbers of retrieval terms appearing in partial character strings in the text are stored. When these tables match each other, it is assumed that the retrieval condition expressions of the user are satisfied and hence the text is delivered to the user. In FIG. 2, the retrieval term count is “2” for user  1  in both tables and hence the text is delivered to user  1 . The retrieval term counts are respectively different from each other for users  2  and  3  and hence the text is not delivered to users  2  and  3 . 
     Prior art 1 has been briefly described. 
     In accordance with prior art 1, it is possible to implement a document retrieving and delivering system in which a text matching retrieval condition expressions given can be delivered to the user through one scanning operation. 
     However, the user must generate retrieval condition expressions in prior art 1. There consequently arises a problem, namely, it is not easy for the user to appropriately generate retrieval condition expressions. 
     Prior art 2 has been proposed to improve the problem above in a document retrieval system. 
     Referring now to FIG. 20, an outline of prior art 2 will be described. 
     Prior art 2 is a technique to extract keywords from a sentence of a language, e.g., Japanese not using a separation code between words. 
     FIG. 20 shows an example to extract keywords (to be described in accordance with a name “tokuchomojiretsu (feature character string)” in prior art 2 herebelow) from a seed document “. . . . Keitaidenwa no shiyohji no mana ga mondai ni naru (manners of use of a cellular phone causes a problem). . . . ”. 
     In step  1910 , a single character type seed character string extraction program is started to subdivide a seed document  1920  at boundaries of character types such as kanji (Chinese characters) and katakana (angular Japanese phonetic letters) to extract character strings (to be called single character type character strings herebelow)  1921  each including characters of one character type. 
     In step  1911 , a check is made to determine a character type for each of the extracted strings. For a character string of Chinese or angular Japanese characters which possibly configure a complex word, a division probability comparison feature character string extraction program is executed to subdivide any complex word to extract feature character strings. For character strings of the other character types having a low probability of configuration of a complex word, the character strings of a single character type are directly extracted as feature character strings (step  1912 ). 
     In step  1913 , the feature character strings thus extracted are stored in a work area. 
     Description has been given of an example to extract feature character strings from a seed document in prior art 2. 
     In prior art 2, according to the number of appearances of feature character strings in a seed document as extracted above and the number of appearances thereof in each document in a text database, the similarity is calculated for each document to display the documents in the descending order of similarity. A method of calculating similarity has been described in prior art 2 and hence description thereof will be here avoided. 
     An outline of prior art 2 has been described. 
     In accordance with prior art 2, feature character strings (keywords) are extracted from a seed document and then similarity of each registered document to the seed document is calculated using the keywords. The user specifies a document containing information desired by the user such that the user then refers to results of retrieval in the descending order of similarity to obtain texts containing necessary information from the text database. 
     Consequently, when prior art 2 is applied to prior art 1, it is possible to assign, to a registered document including at least one of the keywords extracted from the seed document, similarity with respect to the seed document. This improves the first problem of prior art 1. 
     However, for each text delivered at every moment, prior art 1 makes a check to determine the matching of the retrieval condition to instantaneously deliver any text matching the condition to the user. Therefore, although similarity can be calculated for each text with respect to the retrieval condition in prior art 2, the similarity cannot be compared with similarity thereof with respect to another text. 
     Namely, even when prior art 2 is simply applied to a document retrieving and delivering system of prior art 1, the results of retrieval cannot be sorted in the descending order of similarity to be delivered to the user. The second problem of prior art 1 cannot be improved. 
     Additionally, although statistic information in the text database can be used to extract keywords and/or to calculate similarity, texts delivered is not saved in the document retrieving and delivering system of prior art 1. This leads to a problem that the statistic information cannot be easily obtained. 
     SUMMARY OF THE INVENTION 
     It is therefore an object of the present invention to provide a system in which according to retrieval conditions inputted by a plurality of users, a check is made to determine whether or not the conditions are satisfied with respect to a text inputted such that the text inputted is delivered to users corresponding to the retrieval conditions satisfied to thereby deliver texts desired by the users. 
     To improve the problems above in accordance with the document retrieving and delivering method of the present invention, through the steps described below, similarity of each text acquired is calculated for a document (to be referred to as a seed document herebelow) containing information requested by the user to thereby deliver an appropriate text to the user. 
     Namely, the document retrieving and delivering method of the present invention includes a retrieval condition registering steps of registering retrieval conditions inputted from a plurality of users and a retrieval and delivery step for retrieving texts satisfying the retrieval condition from text data of document information and for delivering the texts retrieved to associated users. 
     The retrieval and delivery step includes calculating, from the texts, a ratio the text which matches the retrieval conditions (to be called similarity herebelow) and whether or not the retrieval conditions are satisfied is determined according to the similarity to deliver the text to the users corresponding to the retrieval conditions satisfied. 
     By the operation, similarity of the text acquired can be determined with respect to a document including information desired by the user to thereby appropriately deliver the text to the user. 
     The retrieval condition inputted from the user may be provided in the form of a document desired by the user. In such a situation, retrieval conditions are generated according to the document in the present invention. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The objects and features of the present invention will become more apparent from the consideration of the following detailed description taken in conjunction with the accompanying drawings in which: 
     FIG. 1 is a diagram schematically showing a configuration of a first embodiment in accordance with the present invention; 
     FIG. 2 is a diagram to explain an outline of prior art 1; 
     FIG. 3 is a problem analysis diagram (PAD) showing a processing procedure of system control program  110  of the first embodiment; 
     FIG. 4 is a PAD showing a processing procedure of a retrieval condition registration control program  111  of the first embodiment; 
     FIG. 5 is a PAD showing a processing procedure of a text retrieval and delivery control program  112  of the first embodiment; 
     FIG. 6 is a PAD showing a processing procedure of a retrieval automaton generator program  123  of the first embodiment; 
     FIG. 7 is a PAD showing a processing procedure of a text retrieval program  131  of the first embodiment; 
     FIG. 8 is a diagram to explain an outline of retrieval condition registration in the first embodiment; 
     FIG. 9 is a diagram to explain an outline of text retrieval in the first embodiment; 
     FIG. 10 is a flowchart showing a concrete processing flow of retrieval condition registration control program  111  of the first embodiment; 
     FIG. 11 is a flowchart showing a concrete processing flow of text retrieval program  131  of the first embodiment; 
     FIG. 12 is a diagram to explain a method of connecting a finite automaton  114  to a user list  115  in a second embodiment in accordance with the present invention; 
     FIG. 13 is a PAD showing a processing procedure of a text retrieval program  131   a  of the second embodiment; 
     FIG. 14 is a flowchart to explain a concrete processing procedure of text retrieval program  131   a  of the second embodiment; 
     FIG. 15 is a diagram showing a layout of a retrieval condition registration control program  111   a  in a third embodiment; 
     FIG. 16 is a diagram to explain a concrete processing procedure of retrieval condition registration control program  111   a  in the third embodiment; 
     FIG. 17 is a PAD showing a processing procedure of a retrieval automaton generator program  123   a  of the third embodiment; 
     FIG. 18 is a diagram to explain a concrete processing procedure of retrieval condition registration control program  111   a  of the third embodiment; 
     FIG. 19 is a PAD showing a processing procedure of a text retrieval program  151   a  of the third embodiment; 
     FIG. 20 is a flowchart showing a flow of feature character string extraction in prior art 2; 
     FIG. 21 is a diagram showing a configuration of a fourth embodiment in accordance with the present invention; 
     FIG. 22 is a PAD showing a processing flow of a system control program  110   a  in the fourth embodiment; 
     FIG. 23 is a PAD showing a processing flow of a delivery threshold update program  2000  in the fourth embodiment; 
     FIG. 24 is a PAD showing a processing flow of a delivery threshold setting support information program  2001  in the fourth embodiment; 
     FIG. 25 is a flowchart to explain a specific processing flow of program  2001  in the fourth embodiment; 
     FIG. 26 is a graph showing an example of data outputted from program  2001  of the fourth embodiment; 
     FIG. 27 is a graph showing another example of data outputted from program  2001  of the fourth embodiment; 
     FIG. 28 is a PAD showing a processing procedure of a delivery threshold setting trial program  2002  in the fourth embodiment; 
     FIG. 29 is a PAD to explain a concrete processing flow of program  2002  in the fourth embodiment; 
     FIG. 30 is a graph showing an example of data outputted from program  2002  of the fourth embodiment; 
     FIG. 31 is a PAD showing a processing procedure of a text retrieval and delivery control program  112   a  in the fourth embodiment; 
     FIG. 32 is a flowchart to explain a concrete processing flow of program  112   a  in the fourth embodiment; 
     FIG. 33 is diagram showing a configuration of a fifth embodiment in accordance with the present invention; 
     FIG. 34 is a PAD showing a processing flow of a system control program  110   b  of the fifth embodiment; 
     FIG. 35 is a PAD showing a processing flow of an additional delivery text count setting program  2900  of the fifth embodiment; 
     FIG. 36 is a PAD showing a processing flow of a text additional delivery program  2901  of the fifth embodiment; 
     FIG. 37 is a flowchart to explain a concrete processing flow of program  2901  of the fifth embodiment; 
     FIG. 38 is diagram showing a configuration of a sixth embodiment in accordance with the present invention; 
     FIG. 39 is a PAD showing a processing flow of a system control program  110   c  of the fifth embodiment; 
     FIG. 40 is a PAD showing a processing flow of a desired delivery count setting program  3400  of the sixth embodiment; 
     FIG. 41 is a PAD showing a processing flow of a saved text retrieval program  3402  of the sixth embodiment; 
     FIG. 42 is a PAD showing a processing flow of a delivery threshold automatic setting program  3401  of the sixth embodiment; and 
     FIG. 43 is a flowchart showing a specific processing flow of program  3401  of the sixth embodiment. 
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Referring now to the drawings, description will be given of a first embodiment in accordance with the present invention. 
     First, an outline of the first embodiment will be described by referring to FIGS. 8 and 9. 
     Retrieval condition registration will be first briefly described by referring to FIG.  8 . This diagram shows an example in which retrieval conditions of three users are registered, namely, “the information of new car . . . ” of user  1 , “today&#39;s market in USA is . . . ” of user  2 , and “price of used cars are falling . . . ” of user  3 . 
     First, retrieval terms are extracted from the retrieval conditions registered by the users. In this example, three retrieval terms “information”, “new”, and “car” are extracted from retrieval condition user  1  “the information of new car . . . ” registered by user  1 . Similarly, three terms “today”, “market”, and “USA” are extracted from the conditions of user  2  and four terms of “price”, “used”, “car”, and “falling” are extracted from the conditions of user  3 . 
     For all retrieval terms extracted, weights are calculated using a predetermined formula and are stored in a retrieval term weight table. 
     To calculate these weights, there may be used, for example, an inverted document frequency (IDF) formula (1) described in “Information Retrieval” written by William B. Frakes and Ricardo Baeza-Yates in pages 363 to 391 of “Ranking Algorithm” published from Prentice Hall PTR. in 1992 (to be referred to as prior art 3 herebelow).                I                 D                   F        (   i   )         =     1   +       log   2          N     n        (   i   )                     (   1   )                         
     In expression (1), IDF(i) indicates IDF of retrieval term (i), N denotes the total number of documents in the text database, and n(i) designates the number of texts in which retrieval term (i) appears. Assume that the expression is used for a text database in which about 100 thousand texts are stored. When a retrieval term “car” extracted from the retrieval condition of user  1  appears in 2000 texts, the weight of term “car” is calculated as “6.6” to be stored in the retrieval term weight table. 
     The total number of documents registered to the text database and the number of texts in which the retrieval term appears may be calculated using the number of texts delivered from each news delivery source and the number n(i) of texts in which retrieval term (i) appears. Alternatively, these items may be calculated by referring to the text database to which texts delivered or other different texts are registered. By such operations, similarity calculating processing in which statistical information is used can be implemented for a document retrieving and delivering system. 
     Subsequently, the system creates a finite automaton to collate all retrieval terms extracted. In the finite automaton in FIG. 8, a circle indicates a state of the automaton and an arrow denotes a state transition. A character next to the arrow represents an input character which causes the transition of the arrow. A numeral in the circle designates a state number of the automaton state. In this example, there is not shown an arrow to an initial state to be used when a character not indicated in the automaton is inputted (to be called “fail” or “failure” herebelow). Part of finite automatons generated in this example is omitted in FIG.  8 . 
     Identifiers of users who have specified the retrieval conditions from which the respective retrieval terms are extracted are set as user list elements to be respectively linked with retrieval term detection states of the automaton. In FIG. 8, for example, when “new” is collated, a user list element is referred to via the last state “6”. Therefore, it is detected user  1  has specified “new”. 
     Delivery threshold values are then extracted from the retrieval conditions registered by the users. A delivery threshold value of 8.0 is extracted from the retrieval condition of user  1 . Similarly, 3.5 and 7.0 are obtained as delivery threshold values from the retrieval conditions of users  2  and  3 , respectively. 
     These values are stored in a similarity control table in association with the respective user identifiers. 
     An outline of the text retrieval will be described by referring now FIG.  9 . In this diagram, the scanning is conducted by the automaton of FIG. 8 according to “the car maker announced a new model car . . . ” to retrieve a matching retrieval condition. 
     The automaton of FIG. 9 first calculates the number of appearance of each retrieval term in the text. In this automaton, the number of appearances of each term is shown in the vicinity of the last state. Since a partial character string matching retrieval term “car” appears twice, “2” is indicated for the last state “3”. A partial character string matching retrieval term “new” appears once and hence “1” is indicated for the last state “6”. 
     For each retrieval term matching a partial character string of the text, a user list element linked with its last state is referred to and similarity of the text is calculated with respect to the retrieval conditions of the users. To calculate the similarity, it is possible to use a similarity calculation formula (2) described in prior art 3.                S                 i                 m                 i                 l                 a                 r                 i                 t                 y     =       ∑   i   Q          (       (     C   +     I                 D                   F        (   i   )           )     ×     (     K   +       (     1   -   K     )            freq        (   i   )         max        (     freq        (   j   )       )             )       )               (   2   )                         
     In the expression, Q indicates the number of retrieval terms extracted from the retrieval conditions of the users, C and K are constants, IDF(i) denotes IDF of retrieval term (i), freq(i) is the number of appearances of retrieval term (i) in the text, and max(freq(j)) is a maximum value of the number of appearances of retrieval term in the text. 
     Assume in this example that constants C and K are zero. Similarity of text to the retrieval condition of user  1  is calculated as 9.2 (6.6×2/2+5.1×1/2=9.15). Similarly, the values of similarity for users  2  and  3  are obtained as 0 and 6.6, respectively. 
     When the similarity exceeds a predetermined delivery threshold value, the text is delivered to the user associated with the pertinent retrieval condition. Since the threshold value of user  1  is 8.0 and the text similarity is 9.2, the text is sent to user  1 . However, users  2  and  3  have respectively threshold values 3.5 and 7.0 and the text similarity values thereof are respectively 0.0 and 6.6. The threshold values are not exceeded and hence the text is not delivered to users  2  and  3 . 
     In this embodiment above, the text is scanned by a finite automaton to calculate, for each retrieval term, the number of appearances thereof in the text. Similarity of the text to the retrieval conditions of the users is calculated referring to the user list. The text is delivered to any user who has specified a retrieval condition for which the similarity satisfies a delivery condition predetermined for the user. 
     Resultantly, the similarity of the text with respect to the retrieval conditions of a plurality of users can be calculated through one scanning operation of the text. Since the delivery threshold value is compared with the similarity for each user, even a text having a low similarity value can be delivered to a user who requests a large amount of information. Moreover, a text having a high similarity value can be delivered to a user requesting only important information. 
     Referring now to FIG. 1, description will be given in detail of a first embodiment in accordance with the present invention. 
     The first embodiment of a document retrieving and delivering system in accordance with the present invention includes a display  100 , a keyboard  101 , a central processing unit (CPU)  102 , a main memory  104 , and a bus  103  connecting these constituent units to each other. 
     Bus  103  is linked via a communication line  105  such as a local area network (LAN) with a news delivery source  106  to deliver news and a user  107  who accesses the document retrieving and delivering system. News delivery source  106  delivers electronic texts of new data via e-mail and/or “electronic news” to this system or presents texts via the Internet. User  107  registers retrieval conditions via e-mail to this system. The system delivers texts retrieved according to the retrieval condition to the user. 
     In the description of this embodiment, news source  106  delivers texts via e-mail or the like to the system. However, it is also possible that source  106  presents texts only onto the Internet such that the texts are collected by an information collecting robot. Moreover, user  107  registers texts via e-mail to the system. However, the user may use the Internet for the registration. Additionally, this system delivers the texts retrieved according to the retrieval conditions via an e-mail to the pertinent users. The system may present the texts via the Internet or the like. 
     Memory  104  is loaded with a system control program  110 , a retrieval condition registration control program  111 , a text retrieval and delivery control program  112 , an e-mail program  113 , a finite automaton  114 , a user list  115 , a retrieval term weight table  116 , a similarity control table  117 , a work area  118 , a retrieval condition acquiring program  120 , retrieval term extraction program  121 , a retrieval term weight calculation program  122 , a retrieval automaton creation program  123 , a delivery threshold setting program  124 , a text acquiring program  130 , a text retrieval program  131 , and a text generator program  132 . 
     Retrieval automaton creation program  123  includes a finite automaton creation program  140  and a user list creation program  141 . 
     Although a finite automaton is employed to extract retrieval terms from a text in this embodiment, the term extracting technique is not restricted by this example. Namely, in addition to the finite automaton, there may be used an extended BM method (to be referred to as prior art 3 herebelow) described in pages 175 to 189 of “Nikkei Byte” published in August 1987. When the extended BM method is used, the expression of “retrieval automaton creation program” and “finite automaton creation program” are not appropriate. Using a more general expression, these program will be designated, for example, “retrieval character string collation table creation program” and “multiple character string collation table creation program”. 
     Text retrieval program  131  includes a text scan program  150 , a similarity calculation program  151 , and a text delivery determination program  152 . 
     These programs may be stored on a recording media such as a hard disk (not shown) or a floppy disk (not shown) on which data can be written or from which data can be read by a computer. 
     System control program  110  initiates its operation on receiving an instruction of a manager of the document retrieving and delivering system from a keyboard  101 . 
     The retrieval condition registration control program  111  and text retrieval and delivery program  112  are activated by system control program  110  in response to an indication of registration of a retrieval condition from user  107  or for text delivery from news source  106 . The program  111  controls programs  120  to  123 , and the program  112  controls programs  130  to  132 . 
     An existing mail program generally employed in a workstation is used as E-mail program  113 . Program  113  is initiated by system control program  110  according to a result of processing of text retrieval and delivery control program  112 . 
     Description will next be given of a processing procedure of the embodiment of the document retrieving and delivering system. 
     First, a processing procedure of system control program  110  will be described by referring to a PAD (Problem Analysis Diagram) of FIG.  3 . 
     In step  300 , program  110  repeatedly executes subsequent steps until an end command is inputted from keyboard  101 . 
     In this processing, program  110  checks to determine in step  301  whether or not a retrieval condition has been received via e-mail from user  107 . If the condition has been received, program  110  initiates program  111  in step  303  to generate and to register a retrieval condition. 
     In step  302 , program  110  makes a check to determine whether or not a text has been received via e-mail from news source  106 . If the text has been received, program  110  initiates program  112  in step  304  to retrieve the text. 
     In step  305 , program  110  checks a result of the text retrieval conducted by program  112 . If at least one retrieval condition is satisfied, program initiates e-mail program  113  in step  306  to deliver the text via e-mail to the user having specified the pertinent retrieval condition. 
     The processing procedure of program  110  has been described. 
     Referring next to a PAD of FIG. 4, description will be given of a processing procedure of program  111  activated in step  303  shown in FIG.  3 . 
     In step  400 , program  111  initiates program  120  to acquire a retrieval condition received via e-mail from user  107  to store the condition in work area  118 . 
     In step  401 , program  111  activates program  121  to extract retrieval terms from a seed document in the retrieval condition stored in work area  118  and stores the terms in work area  118 . 
     In step  402 , program  111  initiates program  122  to calculate weights of the retrieval terms stored in work area  113  and stores the weights in table  116 . 
     In step  403 , program  111  activates program  123  to generate finite automaton  114  to collate all retrieval terms contained in the retrieval condition. 
     In step  404 , program  111  initiates program  124  to store in table  117  a delivery threshold value specified in the retrieval condition. 
     The processing procedure of program  111  has been described. 
     Referring now to the PAD of FIG. 6, description will be given of program  123  initiated by program  111  in step  403  of FIG.  4 . 
     In step  600 , program  123  initiates program  140  to create finite automaton  114  to collate all retrieval terms which are extracted and stored in work area  118  by program  121 . 
     In step  601 , program  123  initiates program  141  to gather identification numbers of users  107  having specified the retrieval condition to generate a user list  115 . 
     In step  602 , program  123  links user list  115  via a pointer to an associated output table of finite automaton  114 . 
     The processing procedure of retrieval automaton creation program  123  has been described. 
     Referring now to FIG. 10, description will be given of a processing flow of retrieval condition registration control program  111  shown in FIG.  4 . 
     In step  1000 , program  111  extracts retrieval terms  1011  from retrieval conditions  1010  sent via e-mail from users  107 . In this example, three retrieval terms “information”, “new”, and “car” are extracted from retrieval condition user  1  “the information of new car . . . ” registered by user  1 . In a similar way, three terms “today” and “market” are extracted from the condition of user  2  and four terms “price”, “used”, “car”, and “falling” are extracted from the condition of user  3 . 
     In the retrieval term extracting technique in a language including a space to separate words from each other as above, words other than those frequently used such as “or” and “the” are used as retrieval terms to be extracted. However, there may be used other methods. 
     For example, in a language such as Japanese which does not use a separation code between words, there may be used a method described in JP-A-8-335222 in which words included in a seed document are extracted as retrieval terms by referring to a word dictionary through morphological analysis. However, the words not contained in the word dictionary cannot be extracted. Consequently, it is favorable, as described in prior art 2 to use statistic information in the text database to extract all words written in the document as retrieval terms without using the word dictionary. Although prior art 2 employs probability of appearance of each n-gram in the text database, it may be possible in a document retrieving and delivering system to use probability of appearance of each n-gram in all texts delivered, in a text database to which the texts delivered are registered, or in a text database to which documents other than the texts delivered are registered. 
     In step  1001 , program  111  calculates, according to a predetermined calculation formula, importance for all retrieval terms  1011  extracted from retrieval conditions  1010  and stores the values of importance in weight table  116 . The IDF formula, i.e., expression (1) described above may be used for this purpose. Using expression (1), when retrieval term “car” appears in 2000 texts in a text database including, for example, 100 thousand texts, 6.6 is obtained as importance of “car”. The total number of documents in the database and the number of documents in which the retrieval term appears may respectively be the number of texts delivered from news source  106  and the number of texts in which the term appears. Alternatively, these values may be obtained by referring to a text database to which texts other than the texts delivered are registered. 
     In step  1002 , program  111  creates finite automaton  114  to collate all retrieval terms  1011  extracted from conditions  1010 . The retrieval terms can be registered to the finite automaton in a method of prior art 1. 
     Description has been given of a specific processing procedure of retrieval condition registration control program  111  of FIG.  4 . 
     Referring now to the PAD of FIG. 5, description will be given of text retrieval and delivery control program  112  initiated by system program  110  in step  304  of FIG.  3 . 
     In step  500 , program  112  initiates program  130  to store a text sent via e-mail or the like from news source  106  in work area  118 . 
     In step  501 , program  112  initiates program  131  to retrieve the text stored in work area  118 . 
     In step  502 , program  112  checks to determine whether or not at least one retrieval condition exceeds a predetermined delivery threshold value. If such a condition is present, program  112  executes step  503 . 
     In step  503 , program  112  initiates program  132  to transform the text in work area  118  into a format which can be delivered by e-mail program  113 . 
     The processing procedure of  112  has been described. 
     Referring now to the PAD of FIG. 7, description will be given of a processing procedure of text retrieval program  131  initiated in step  501  of FIG.  5 . 
     In step  700 , program  131  resets to zero a retrieval term appearance count storage area in an output table of finite automaton  114 . 
     In step  701 , program  131  initiates program  150  to scan by finite automaton  114  the text stored in work area  118  by program  130  to count the number of appearances of a retrieval term in partial character strings of the text. 
     In step  702 , program  131  initiates program  151  to calculate similarity of the text to each retrieval condition registered by users  107  according to a predetermined similarity calculating formula using the number of appearances of the retrieval term in the text obtained by program  150  and a weight of the retrieval term stored in table  116  by program  122 . Program  131  stores the similarity in table  117 . 
     In step  703 , program  131  initiates program  152  to output to program  112  an identifier of each user having specified a retrieval condition for which the similarity of the text exceeds the delivery threshold value stored in table  117 . 
     The processing procedure of text retrieval program  131  has been described. 
     Referring now to FIG. 11, description will be given in detail of a processing flow of program  131  shown in FIG.  7 . 
     In step  1100 , program  131  collates by finite automaton  114  the retrieval terms extracted from the retrieval conditions registered by users  107  with a text  1100  stored in work area  118  by program  130  to count the number of appearance of each retrieval term in the text. 
     In this example, a text “the car maker announced a new model car . . . ” is scanned by finite automaton  114  of FIG. 10 to retrieve a matching retrieval condition. In automaton of FIG. 11, the number of appearance of each retrieval term in the text is shown in the vicinity of the last state. A partial character string matching retrieval term “car” appears twice and hence “2” is indicated for the last state “3”. A partial character string matching retrieval term “new” appears once and therefore “1” is indicated for the last state “6”. 
     In step  1101 , for the retrieval terms matching any partial character strings of the text, program  131  calculates similarity of the text to the retrieval conditions of the users by referring to user list elements respectively connected to the end states. Although this embodiment uses similarity calculating expression (2) to calculate the similarity, there may be employed other methods. According to expression (2), the similarity of text “the car maker announced a new model car . . . ” to the retrieval conditions of the users is attained as follows. 
     User  1 : 9.2 
     User  2 : 0 
     User  3 : 6.6 
     In step  1102 , program  131  determines whether or not the similarity exceeds an associated delivery threshold value in table  117 . When the condition is satisfied, the pertinent user identifier is outputted to program  112 . This embodiment sets the delivery threshold values of users  1  to  3  as 8.0, 3.5, and 7.0, respectively. However, the other values may be set as conditions. 
     The similarity of the text to the retrieval conditions of the users is checked according to the text delivery conditions. Since the similarity of the retrieval condition registered by user  1 , i.e., “the information of new car . . . ” exceeds the delivery threshold value “8.0” of user  1 . Accordingly, user identifier  1111 , i.e., “user  1 ” is outputted to program  112 . 
     In this embodiment as described above, the similarity of text to the retrieval conditions of a plurality of users can be calculated through only one scanning operation of the text. For each user, the delivery threshold value is compared with the similarity. Consequently, even a text having a low similarity value can be delivered to a user who requests a large amount of information. Furthermore, a text having a high similarity value can be delivered to a user requesting only essential information. 
     In the description of the embodiment, the delivery threshold value can be set for each user. However, there may be used common delivery threshold values in the system. This minimizes the storage capacity necessary for similarity control table  117 . 
     To calculate similarity of the users, similarity calculation program  151  of the first embodiment sequentially processes user list  115  connected to the output table of finite automaton  114 . When the number of users increases, this leads to a problem that a period of time to completely calculate similarity for all users becomes quite long. For example, even if processing for one user identifier connected to its user list takes only 0.01 second, 100 seconds are required for 10,000 user identifiers. Namely, a period of one minute 40 seconds lapse from when the similarity calculation is started to when the calculation is completed. 
     To solve the problem, the second embodiment of a document retrieving and delivering system in accordance with the present invention assigns priority of delivery to each user identifier to conduct the similarity calculation beginning at a user having highest priority. The delivery is more quickly achieved for users having higher priority. 
     The second embodiment is almost the same in constitution with the first embodiment of FIG.  1 . These embodiments differ from each other in the processing procedure of text retrieval program  131   a  and connection between finite automaton  114  and user list  115 . As shown in PAD of FIG. 13, step  1300  is added to program  131   a  in the second embodiment. As can be seen from FIG. 12, a user list  115  is connected via a priority identifier  1200  to finite automaton  114 . 
     Referring now to the PAD of FIG. 13, description will be given of text retrieval program  131   a  of the second embodiment. 
     In step  700 , program  131   a  resets a retrieval term appearance count storage area in an output table of finite automaton  114  to zero. 
     In step  701 , program  131   a  initiates program  150  to scan by finite automaton  114  the text stored in work area  118  by program  130  to count the number of appearances of a retrieval term in partial character strings of the text. 
     In step  1300 , program  131   a  repeatedly executes steps  702  and  703  in a descending order of priority indicated by priority identifier  1200  connected to finite automaton  114 . 
     In step  702 , program  131   a  initiates program  151  to calculate similarity of the text to each retrieval condition registered by users  107  according to a predetermined similarity calculating formula using the number of appearances of the retrieval term in the text obtained by program  150  and a weight of each retrieval term stored in table  116  by program  122 . Program  131   a  then stores the similarity in table  117 . 
     In step  703 , program  131   a  initiates program  152  to output to program  112  an identifier of each user having specified a retrieval condition for which the similarity of the text exceeds the delivery threshold value stored in table  117 . 
     The processing procedure of text retrieval program  131   a  has been described. 
     Referring to a specific example shown in FIG. 14, description will now be given of a concrete processing procedure of text retrieval program  131   a.    
     In step  1100 , program  131   a  counts the number of appearances of each retrieval term in text  1110  stored in work area  118  by program  130 . In this example, there is obtained a result  1410  indicating that retrieval terms “car” and “new” respectively appear twice and once in text  1110  “the car maker announced a new model car . . . ” 
     In step  1400 , program  131   a  calculates similarity of text  1110  by referring to a user list connected to “superexpress” priority identifier  1200  shown in FIG.  12 . In the example of FIG. 14, similarity of text  1110  with respect to the retrieval condition of user  1  is obtained as 9.2. 
     In step  1401 , program  131   a  checks to determine whether or not the similarity exceeds the delivery threshold value stored in the similarity control table. If the condition is satisfied, the user identifier is outputted to program  112 . In this example, the delivery threshold value is 8.0 for user  1 . However, any other text delivery condition may be employed. Since similarity “9.2” exceeds threshold value “8.0”, “user  1 ” is outputted as the user identifier. 
     In step  1402 , program  131   a  calculates similarity of text  1110  by referring to a user list connected to “local train” priority identifier  1200  shown in FIG.  12 . In the example of FIG. 14, similarity of text  1110  to the retrieval conditions of users  2  and  3  are attained as 0 and 6.6, respectively. 
     In step  1403 , program  131   a  checks to determine whether or not each similarity exceeds the delivery threshold value stored in the similarity control table. If the condition is satisfied, the user identifier is output to program  112 . As a result, since the similarity values are less than the respective delivery threshold values, the identifiers of these users are not outputted. 
     In this embodiment described above, the text retrieval can be preferentially conducted for users having higher priority. It is therefore possible to provide a document retrieving and delivering system in which even when the number of users becomes greater, texts can be immediately delivered to users having higher priority. 
     In the description of the embodiment, priority identifier  1200  includes “superexpress” and “local train” assigned with respective priority levels set by the user. However, the identifier may include delivery priority according to, for example, posts in a firm such as “division manager” and “section manager” or according to a contract charging rate such as a rate for “user” and a rate for “trial user”. 
     Referring now to FIG. 15, description will be given of a third embodiment in accordance with the present invention. 
     In the first and second embodiments, the similarity is calculated assuming that the retrieval terms extracted from the seed document have the same importance regardless of a type of the seed document. However, this leads to a problem that even if the subject of the seed document changes, the retrieval terms have the same weight, and hence the subject of the seed document cannot be appropriately reflected in the results. 
     Fog example, retrieval term “HiRetrieval” extracted from retrieval condition “bunsho kensaku shisutemu toshitewa HiRetrieval ga yoku shirarete (HiRetrieval is well known as a document retrieval system) . . . ” is an example of a document retrieving system. For retrieval condition “HiRetrieval”, it is possible to conduct logical operations such as AND and OR. For HiRetrieval, it is possible to register structured documents or texts described in the standard generalized markup language (SGML), the extensible markup language (XML), or the like. Furthermore, in HiRetrieval, retrieval term “HiRetrieval” extracted is a word representing the theme of the document and is quite important. 
     In the third embodiment of the document retrieving and delivering system of the present invention, the problem above is removed by adding a retrieval term weight to the user list with respect to each retrieval condition. 
     The third embodiment is almost the same in constitution as the first embodiment of FIG. 1, but includes a different retrieval condition registration control program  111  and an additional program, i.e., retrieval condition weight calculation program  1500  as shown in FIG.  15 . The format of user list  115  created by user list creation program  141   a  and the processing procedure of similarity calculation program  151  are different from those of the first embodiment. 
     Referring now to FIG. 16, description will be given of a processing procedure of retrieval condition registration control program  111   a  which is different from program  111  of the first embodiment. 
     In step  400 , program  111   a  initiates program  120 , which acquires retrieval conditions sent via e-mail from users  107  and which stores the conditions in work area  118 . 
     In step  401 , program  111   a  initiates program  121  to extract the retrieval terms from a seed document in the retrieval conditions in work area  118  and to store the terms in work area  118 . 
     In step  402 , program  111   a  initiates program  122 , which calculates weights of retrieval terms in work area  118  and which stores the weights in retrieval term weight table  116 . 
     In step  1600 , program  111   a  initiates program  1500 , which calculates weights of the retrieval terms in work area  118  for each retrieval condition and which stores the weights in work area  118 . 
     In step  1601 , program  111   a  initiates a retrieval automaton creation program  123   a  to create finite automaton  114  to collate all retrieval terms in the retrieval conditions. 
     In step  404 , program  111   a  initiates program  124  to store in table  117  the delivery threshold values specified in the retrieval conditions. 
     The processing procedure of retrieval condition registration control program  111   a  has been described. 
     Referring next to the PAD of FIG. 17, description will be given of a processing procedure of program  123   a  initiated in step  1601  by program  111   a.    
     In step  600 , program  123   a  initiates program  140  to generate finite automaton  114  to collate all retrieval terms which are extracted and stored in work area  118  by program  121 . 
     In step  1700 , program  123   a  initiates program  141   a  which couples an identifier number of user  107  having specified the retrieval condition with a weight of the retrieval term for the retrieval condition, the weight being stored in work area  118  by program  1500 . Program  141   a  resultantly creates a user list  115   a.    
     In step  1701 , program  123   a  connects user list  115   a  via a pointer to an output table of finite automaton  114 . 
     The processing procedure of retrieval automaton creation program  123   a  has been described. 
     Referring now to FIG. 18, description will be given of a processing flow of a retrieval condition registration control program in the third embodiment shown in FIG.  15 . 
     In step  1000 , program  111   a  extracts retrieval terms  1011  from retrieval conditions  1010  sent via e-mail from users  107 . In this example, three retrieval terms “information”, “new”, and “car” are extracted from retrieval condition user  1  “the information of new car . . . ” registered by user  1 . In a similar fashion, three terms “today”, “market”, and “USA” are extracted from the condition of user  2  and four terms “price”, “used”, “car”, and “falling” are extracted from the condition of user  3 . 
     In the technique to extract retrieval terms in a language including a space to separate words from each other as above, words other than whose frequently used such as “or” and “the” are used as retrieval terms to be extracted. However, there may be used other methods. 
     For example, in a language such as Japanese which does not use a separation code between words, there may be used a method described in JP-A-8-335222 in which words contained in the seed document are extracted as retrieval terms by referring to a word dictionary through morphological analysis. However, the words not contained in the word dictionary cannot be extracted. Consequently, it is favorable, as described in prior art 2 to use statistic information in the text database to extract all words written in the document as retrieval terms without using the word dictionary. Although prior art 2 employs probability of appearance of each n-gram (character strings each having n continual characters) in the text database, it may be possible in a document retrieving and delivering system to utilize probability of appearance of each n-gram in all texts delivered, in a text database to which the texts delivered are registered, or in a text database to which documents other than the texts delivered are registered. 
     In step  1001 , program  111   a  calculates, according to a predetermined calculation formula, importance for all retrieval terms  1011  extracted from retrieval conditions  1010  and stores the values of importance in weight table  116 . IDF formula (1) described above may be used to calculate the weight for each retrieval term. Using expression (1), when retrieval term “car” appears in 2000 texts in a text database including, for example, 100 thousand texts, 6.6 is obtained as importance of “car”. The total number of documents in the database and the number of documents in which the retrieval term appears may respectively be the number of texts delivered from news source  106  and the number of texts in which the term appears. Alternatively, these values may be obtained by referring to a text database to which texts other than the texts delivered are registered. 
     In step  1800 , program  111   a  calculates, according to a predetermined calculation formula, retrieval condition importance of each retrieval term  1011  extracted from retrieval conditions  1010  with respective to each retrieval condition and then stores the importance in work area  118 . The importance may be the number of appearances of the term in the retrieval condition. 
     In step  1002 , program  111   a  creates finite automaton  114  to collate all retrieval terms  1011  extracted from conditions  1010 . The retrieval terms can be registered to the automaton in a method of prior art 1. 
     Description has been given of a specific processing procedure of retrieval condition registration control program  111   a  of FIG.  15 . 
     Referring now to the PAD of FIG. 19, description will be given of a processing procedure of a similarity calculation program  151   a  of third embodiment which is different from those of the first and second embodiments above in accordance with the present invention. 
     In step  1900 , program  151   a  repeatedly executes steps  1901  to  1905  for all retrieval terms collated by program  150 . 
     In step  1901 , program  151   a  obtains by program  150  the number of appearances of the retrieval term. In step  1902 , program  151   a  acquires a weight of the retrieval from weight table  116 . 
     Program  151   a  then repeatedly executes steps  1904  and  1905  for the user identifiers of user list  115   a . In step  1904 , program  151   a  acquires a user identifier and a retrieval condition weight of the pertinent retrieval condition. In step  1905 , program  151   a  calculates similarity for each retrieval condition according to a predetermined calculation formula. 
     The processing procedure of similarity calculation program  151   a  has been described. 
     In accordance with the third embodiment described above, a high weight can be added to retrieval terms representing subjects of the retrieval conditions registered by the respective users. Consequently, it is possible to provide a document retrieving and delivering system having high precision. 
     Description will now be given of a fourth embodiment in accordance with the present invention. 
     In the first to third embodiments above, a text having similarity equal to or more than a predetermined value (to be referred to as a delivery threshold value herebelow) is delivered in step  1102  of FIG.  11 . However, this is attended with a problem that a delivery threshold value cannot be appropriately assigned for the retrieval conditions set by the users as follows. 
     For example, when a too great value is set as the threshold value, desired texts cannot be delivered to some users. Conversely, when a too small value is specified, some users receive a large amount of texts not requested. It is therefore necessary to modify the delivery threshold value initialized. This leads to a problem, i.e., how to modify the threshold value for the user to acquire all desired texts without noise. 
     To solve the problem, in the fourth embodiment of a document retrieving and delivering system of the present invention, information (to be referred to as delivery threshold setting information) useful for the user to set an appropriate delivery threshold value is presented to the user. Moreover, the system displays texts in the past of which similarity calculated exceeds the delivery threshold value set by the user (to be referred to “trial of delivery threshold value setting” herebelow). Using these information items, the user can appropriately set a suitable delivery threshold value. 
     FIG. 21 shows a system configuration of a fourth embodiment in accordance with the present invention. 
     The fourth embodiment is almost the same in constitution as the first embodiment shown in FIG.  1 . As can be seen from FIG. 21, the configuration of the fourth embodiment additionally includes a delivery threshold update program  2000 , a delivery threshold setting information program  2001 , and a delivery threshold setting trial program  2002 . The system further includes a personal similarity determination information control area  2003  and a text save area  2004 . 
     In area  2003 , there are stored similarity calculated in the past for texts with respect to retrieval conditions of users and flags indicating whether or not texts are delivered to users. Stored in area  2004  are contents and reception time of texts received in the past. 
     In the fourth embodiment, according to information stored in areas  2003  and  2004 , program  2001  presents delivery threshold setting information to users. Using information in areas  2003  and  2004 , program  2002  similarly presents a function to set a delivery threshold value to users. The user can therefore determine an appropriate delivery threshold to register a determined threshold value to the system by program  2000 . The value registered is used by text retrieval and delivery control program  112   a  to determine whether or not a text is delivered to each user. 
     In the description of the fourth embodiment, a user request for presentation of delivery threshold setting information, a user request for delivery threshold setting operation, and a user request for delivery threshold setting trial are transmitted in the form of e-mail. However, these requests may be sent to the system via other network applications such as Web browser. Moreover, the system sends delivery threshold setting information and results of delivery threshold setting trial via e-mail to the pertinent user. However, other network applications such as Web browser may be used for this purpose. 
     Description will now be given of a processing procedure of each program in the fourth embodiment. 
     Referring now to the PAD of FIG. 22, description will be given of system control program  110   a  in the fourth embodiment. 
     The procedure of program  110   a  of this embodiment is implemented by adding steps  2100  to  2105  to that of system control program  110  of the first embodiment. 
     In iterative processing step  300 , program  110   a  checks after processing of steps  301  and  302  whether or not a delivery threshold value has been sent from a user. If such a value has been received, program  110   a  initiates program  2000  in step  2103  to set a delivery threshold value of the user. 
     In step  2101 , program  110   a  checks to determine whether or not a request for presentation of delivery threshold value setting information has been sent from user  107 . If such a request has been received, program  110   a  initiates program  2001  to send presentation of delivery threshold value setting information to the user. 
     In step  2102 , program  110   a  checks to determine whether or not a request for delivery threshold setting trial has been sent from user  107 . If such a request has been received, program  111   a  initiates program  2002  to try setting a delivery threshold. 
     The processing procedure of system control program  110   a  has been described. 
     Referring now to the PAD of FIG. 23, description will be given of delivery threshold update program  2000  initiated by system program  110   a  in step  2103  of FIG.  22 . 
     In step  2200 , program  2000  acquires a delivery threshold value sent from user  107  via e-mail. 
     In step  2201 , program  2000  updates user list  115   b  to replace the old delivery threshold value of the user with the value received. In list  115   b , each delivery threshold value may be initialized to a value determined by a manager or may be set to a value inputted by user  107  when user  107  registers a retrieval condition. 
     The processing procedure of delivery threshold update program  2000  has been described. 
     Referring next to a PAD shown in FIG. 24, description will be given of delivery threshold setting support information program  2001  initiated by the system program in step  2104  of FIG.  22 . 
     Program  2001  provides, according to history of delivery determination for users in the past, information for users to appropriately set a threshold value. 
     In step  2300 , according to a user identifier of user  107  having requested threshold setting support information, program  2001  accesses personal similarity determination information control area  2003  to read therefrom personal similarity determination information of the user with respect to texts received from news source  106  in a predetermined period of time in the past. The similarity determination information includes data items such as similarity of each user for all texts received from news source  106  and a flag of delivery or non-delivery of each text. A specific example thereof will be described later. 
     In step  2301 , program  2001  obtains from text save area  2004  the contents of texts received from news source  106  within a predetermined period of time in the past. 
     In step  2302 , program  2001  extracts from the information acquired in step  2301  information items concerning the texts delivered to the user and produces a list in work area  118 . 
     In step  2303 , program  2001  draws a graph (to be referred to as similarity distribution information herebelow) in work area  118  in which an abscissa represents the number of texts for each similarity calculated for the retrieval condition of each user and the similarity and an ordinate represents time of text reception. The abscissa and the ordinate may represent other information items obtained in steps  2301  and  2302 . 
     In step  2304 , program  2001  transforms the information in work area  118  into a format which can be delivered by e-mail program  113 . 
     Information thus stored in work area  118  is delivered by e-mail program  113 . 
     The processing procedure of delivery threshold setting support information program  2001  has been described. 
     Referring now to FIG. 25, description will be given in detail of a processing flow of program  2001  shown in FIG.  24 . 
     In step  2300 , program  2001  obtains from area  2003  personal similarity determination information  2400  within a predetermined period of time in the past (e.g., in the last 24 hours in this case) corresponding to the user identifier of user  107  having requested the support information. In this example, program  2001  obtains text identifiers respectively of texts  1  to  3  of user  1 , similarity values calculated for the texts, flags of delivery or non-delivery thereof, and delivery threshold values of user  107  at delivery determination. 
     In step  2301 , program  2001  attains from area  2004  the contents of texts received from new sources  106  within the last 24 hours. In this example, program  2001  obtains the contents of texts  1  to  3 . 
     Steps  2300  and  2301  of this embodiment process the texts received from new sources  106  within the last 24 hours. However, the period to receive texts may be changed, the entire period may be specified to process all texts stored, or the period may be specified by user  107 . 
     In step  2302 , program  2001  collates the information acquired in steps  2300  and  2301  according to the text identifier to extract therefrom information concerning texts delivered to the pertinent user and outputs the information in work area  118 . In this example, program  2001  outputs in work area  118  a list  2401  including text identifiers, similarity values, delivery threshold values, and delivery time for texts  1  and  3  delivered to user  1 . The information items to be output may include any combination of information obtained in steps  2300  and  2301 . The items are outputted in a similarity order in this embodiment. However, the items may be outputted in a text delivery time sequence, or the user may select the similarity order or the delivery time sequence. Alternatively, in place of text identifiers, a first sentence may be extracted from the contents of text to be outputted to work area  118 . Moreover, if there is an attribute item such as “title”, the item may be outputted to area  118 . 
     In step  2303 , program  2001  collates the information attained in steps  2300  and  2301  according to the text identifier to generate similarity distribution information of the texts and further outputs the information to work area  118 . In the example of FIG. 25, program  2001  produces the number of texts for each similarity within the past 24 hours and generates a graph  2403  of text distribution in which the ordinate represents time and the abscissa represents similarity. 
     By referring to the graph, user  107  can visually and easily know the amount of texts to be delivered and the period of time in which the amount of texts are delivered for each value set to the threshold value. User  107  can also recognize change of the text delivery state with respect to time. For example, when the graph of FIG. 26 is produced, user  107  can understand that the amount of desired texts (with high similarity) from the news delivery source becomes gradually decreased. In this situation, user  107  may lower the delivery threshold value. 
     User  107  cal also recognize a time zone in which texts desired are frequently delivered. For example, according to the graph of FIG. 27, it is known that many texts desired are delivered in a time zone from 18:00 to 21:00. User  107  can therefore avoid an unfavorable event, for example, when a delivery threshold value is set to a time zone in which few texts desired are delivered, there is conducted local optimization, and hence the delivery threshold value is set to a value lower than an appropriate value. 
     Although step  2303  of the embodiment processes all texts delivered from news source  106  in the last 24 hours, it is also possible to process only texts not delivered to pertinent user  107 . The graph may include information to indicate whether or not texts are delivered to pertinent user  107 . Change with respect to time of the delivery threshold value set by pertinent user  107  may be presented at the same time. Although similarity is stored in personal control area  2003  in this embodiment, the texts in text save area  2004  may be again scanned and similarity thereof is again calculated with respect to the retrieval condition of the user at the pertinent point of time to use a result of the calculation as similarity. 
     In step  2304 , program  2001  transforms information in work area  118  into a format suitable for e-mail program  113 . 
     The specific processing flow of program  2001  has been described. Although delivery threshold setting support information is presented in response to a request from the user, the information may be presented to all users  107  at a predetermined point of time. 
     Referring next to the PAD of FIG. 28, description will be given of a processing procedure of delivery threshold setting trial program  2002  initiated by the system control program in step  2105  of FIG.  22 . 
     Program  2002  presents, according to the similarity calculated for texts of each user received in the past, texts in the past of which similarity values exceed a delivery threshold value specified by the user. 
     In step  2500 , program  2002  acquires a delivery threshold value sent from user  107  via e-mail. 
     In step  2501 , program  2002  accesses area  2003  according to a user identifier of the user and reads, from personal similarity determination information of the user, information of texts of which similarity exceeds the threshold value obtained in step  2500 . 
     In step  2502 , program  2002  reads from area  2004  the contents and reception time of a text corresponding to the text identifier of similarity determination information attained in step  2501  and outputs the contents, the reception time, and the information to work area  118 . 
     In step  2503 , program  2002  transforms the information in work area  118  into a format of e-mail program  113 . 
     Program  113  then delivers the information from work area  118  to the user. 
     The processing procedure of delivery threshold setting trial program  2002  has been described. 
     Referring now to FIG. 29, description will be given in detail of a processing flow of a delivery threshold setting trial program  2002  of FIG.  28 . 
     In step  2500 , program  2002  obtains delivery threshold value  2600  sent from user  107 . 
     In step  2501 , program  2002  acquires information  2601  of a text of which similarity is greater than threshold value  2500  obtained in step  2500  from area  2003 . In the example of FIG. 29, the user of user identifier “user  1 ” has specified 9.0 for the delivery threshold value and hence the contents and reception time of text  1  of which similarity is 10.0 (more than 9.0) are acquired from area  2003 . In this connection, “User of user identifier ‘user  1 ’” indicates a user having a user identifier of “user  1 ”. Although similarity stored in area  2003  is used in this embodiment, it is also possible that the texts in text save area  2004  is again scanned and similarity thereof is again calculated with respect to the retrieval condition of the user at the pertinent point of time to use a result of the calculation as similarity. 
     In step  2502 , program  2002  accesses text information stored in the text save area to obtain text information  2602  corresponding to the text identifier obtained in step  2501  and then outputs text information  2602  and similarity determination information  2601  also corresponding to the text identifier obtained in step  2501  to work area  118 . In this example, the similarity, the delivery time, and the contents of text of text  1  attained in step  2501  are outputted to work area  118 . It is also possible to output, in place of the contents of text, a first sentence of the contents of text to work area  118 . Alternatively, an attribute such as a title is present, such a title may be outputted to work area  118 . 
     In step  2503 , program  2002  transforms the information stored in work area  118  into a format which can be delivered by e-mail program  113 . 
     Assume that area  2003  contains information of texts delivered from news source  106  within the last 24 hours, a text title is outputted to area  118  in step  2502 , and user  107  desires reception of two texts within the last 24 hours. If user  107  specifies 10.0 as delivery threshold value  2600  for the trial, only one title (text  1 ) is outputted to area  118 . If user specifies 6.0 as value  2600 , two text titles “text  1 ” and “text  2 ” are outputted to area  118 . As a result, if user  107  changes the delivery threshold value to 6.0 by delivery threshold update program  2000 , it can be expected that two texts are delivered within the subsequent 24 hours. 
     Assume the first sentence of the contents of text is outputted together with a title of text in area  118  In step  2502 . If threshold value  2600  is fully lowered, titles of texts and the first sentences of texts which have not been delivered because similarity thereof is less than the delivery threshold value are presented. User  107  checks the texts presented, and when user  107  detects a desired text, user  107  lowers the delivery threshold value below the similarity of the text by program  2000 . Resultantly, the delivery threshold value can be set such that all of the texts desired are delivered. 
     Using information outputted to area  118  in step  2502 , the system generates a graph of text similarity distribution in which the similarity and the text reception time are indicated respectively along the ordinate and the abscissa as described in conjunction with program  2001 . For example, as can be seen from FIG. 30, of the texts delivered from news source  106  in the past, those having similarity exceeding trial delivery threshold value  2600  are presented in another color or with another symbol in the distribution graph. 
     In this example, it is known that if the delivery threshold value is set to the trial value, five texts are delivered within the last 24 hours. Therefore, it can be recognized that if the threshold value is set to the trial value, a similar amount of texts will be delivered within the subsequent 24 hours. As above, user  107  can visually and easily predict results of delivery in response to modification of trial delivery threshold value  2600 . It is therefore possible for user  107  to set an appropriate delivery threshold value. 
     The specific processing flow of delivery threshold setting trial program  2002  has been described. 
     Referring now to the PAD of FIG. 31, description will be given of a processing procedure of text retrieval and delivery control program  112   a  initiated by the system control program in step  304  of FIG.  22 . 
     Program  112   a  determines for each user similarity of each text from news source  106  with respect to a retrieval condition of each user, determines delivery or non-delivery of the text for each user, and saves the contents of text and history of delivery determination of each user. 
     In step  2700 , program  112   a  initiates text acquiring program  13   a  to store a text from news source  106  in work area  118 . Program  112   a  further stores the contents of text and text reception time in text save area  2004 . 
     In step  2701 , program  112   a  initiates text retrieval program  131  to retrieve a text stored in work area  118  to calculate similarity thereof with respect to a retrieval condition set by each user. Program  131  determines delivery or non-delivery of the text for each user and stores results of determination in area  2003 . 
     In step  2702 , program  112   a  checks to determine whether or not at least one retrieval condition satisfying a predetermined condition is present. If such a retrieval condition is present, processing goes to step  2703 . 
     In step  2703 , program  112   a  initiates text generator program  132  to transform the text in area  118  into a format for e-mail program  113 . 
     The processing procedure of program  112   a  has been described. 
     Referring now to FIG. 32, description will be given in detail of a processing procedure of program  112   a  in the fourth embodiment of the present invention. 
     In step  2700 , program  112   a  initiates text acquiring program  130   a  to store a text  2810  via e-mail or the like from news source  106  in work area  118 . Program  130   a  then assigns a text identifier to the text and stores the contents and reception time of text in text save area  2004 . 
     In step  2800 , the system executes steps  700  to  702  in a procedure described in conjunction with FIG. 7 of the first embodiment to store similarity in similarity control table  117 . 
     In step  2801 , identifiers of users of which similarity in table  117  exceeds delivery threshold values in user list  115   b  are passed to program  112   a . Text identifiers, similarity calculated, delivery or non-delivery of text, current delivery threshold values are respectively stored in areas  2003  of the respective users. In this example, similarity is obtained as 10.0 for user  1 . Since this does not exceed delivery threshold value “12.0” of user  1  in user list  115   b , the text is not delivered to user  1 . Similarity of “6.6” is calculated for user  3 . Since this value exceeds delivery threshold value “5.0” of user  2  in list  115   b , the text is delivered to user  3 . Furthermore, information items such as text identifier “text  1 ”, Delivery or non-delivery “NO”, and current threshold value “12.0” are stored in a field of user  1  in area  2003 . Processing is similarly conducted also for users  2  and  3  as shown in FIG.  32 . These similarity determination information items are used in programs  2001  and  2002  as already described above. 
     In step  2703 , program  112   a  transforms the information in work area  118  into a format for e-mail program  113 . 
     The processing procedure of program  112   a  has been described. 
     Description has been given of the respective programs of the fourth embodiment. 
     As above, the texts received from the news delivery source and history of similarity calculation for each user are saved in the embodiment above. When the user sets a delivery threshold value, these information items are presented to the user. Therefore, the user can set an appropriate delivery threshold value by referring to the information. When it is necessary to modify a delivery threshold value initialized, it is possible for the user to set an appropriate delivery threshold value to receive all necessary texts without noise. This resultantly solves the problem of the prior art concerning the retrieval leakage and retrieval noise. 
     The delivery text selection described in this embodiment is not limited to the retrieval method of the finite automaton or the extended BM method. Namely, the selection method is similarly applicable to a system using other retrieval methods. 
     The delivery text selection described in this embodiment is not limited to the similarity calculation method for the text with respect to retrieval conditions, but may be similarly used for the similarity calculation method of the first to third embodiments as well as other similarity calculation methods. 
     Next, description will be given of a fifth embodiment of the present invention. 
     In the configuration of the fourth embodiment, the program refers to the history of similarity calculation in the past to set an appropriate delivery threshold value. However, this cannot completely cope with the text delivery state which continuously changes with respect to time. For example, even if the delivery threshold value is increased because a large amount of texts are delivered during a period of time, there may occur thereafter a period of time in which the number of texts of which similarity exceeds the delivery threshold value and texts are not delivered to the user as a result. This leads to a problem that the user cannot understand whether or not texts desired are present or whether the delivery threshold value set is too great. 
     To solve this problem in accordance with the fifth embodiment of a document retrieving and delivering system of the present invention, in addition to texts of which similarity exceeds the delivery threshold value set by the user, the number of texts specified by the user is delivered to the user (to be referred to as additional delivery). 
     FIG. 33 shows a system configuration of the fifth embodiment of the present invention. 
     This embodiment is almost the same in constitution with the fourth embodiment shown in FIG.  21 . The fifth embodiment additionally includes an additional delivery text count setting program  2900  and a text additional delivery program  2901 . 
     In the fifth embodiment, program  2901  additionally delivers texts to users by referring to information in areas  2003  and  2004 . The number of texts additionally delivered is set by program  2900 . 
     In the description below, it is assumed that the user sends a request to set the number of additional texts via e-mail. However, other network applications such as the Web browser may be used to send the request to this system. Moreover, it is assumed that the additional texts are delivered from the system via e-mail to the user. However, other network applications such as the Web browser may be used for this purpose. 
     Description will now be given of processing procedures of respective programs of the fifth embodiment. 
     Referring to the PAD of FIG. 34, description will be given of system control program  110   b  of the fifth embodiment. 
     The processing procedure of program  110   b  of this embodiment is implemented by adding steps  3000  to  3003  to that of system control program  110   a  of the fourth embodiment. 
     In step  3000 , program  110   b  determines whether or not a request to set the number of additional delivery texts has been received from user  107 . If such a request is present, program  110   b  initiates program  2900  in step  3002  to set the number of additional delivery texts for the user. 
     In step  3001 , program  110   b  determines whether or not the current or present time satisfies a predetermined condition. If the time satisfies the condition, program  10   b  initiates program  2901  in step  3003 . It is possible to initiate program  2901  by setting, for example, a condition “initiate program  2901  at 0:00 every day”. 
     The processing procedure of program  110   b  has been described. 
     Referring next to the PAD of FIG. 35, description will be given of a processing procedure of program  2900  initiated by program  110   b  in step  3002  of FIG.  34 . 
     In step  3100 , program  2900  acquires the number of additional delivery texts received via e-mail from user  107 . 
     In step  3101 , program  2900  updates the number of additional delivery texts of user list  115   c  for the user. In user list  115   c , the initial value of the number of additional delivery texts may be beforehand determined by the manager or may be inputted when user  107  registers a retrieval condition. 
     The processing procedure of program  2900  has been described. 
     Referring next to the PAD of FIG. 36, description will be given of a processing procedure of program  2901  initiated by program  110   b  in step  3003  of FIG.  34 . 
     Program  2901  additionally delivers texts of which similarity does not exceed the delivery threshold value so that the user receives a desired number of texts. 
     In step  3200 , program  2901  repeatedly executes step  3201  to  3204  for all users in user list  115   c.    
     In step  3201 , program  2901  reads from list  115   c  additional delivery texts desired by the user. 
     In step  3202 , program  2901  accesses area  2003  and obtains, in a similarity descending sequence, similarity determination information from the texts not delivered to the user within a predetermined period of time in the past, the number of texts being equal to that of texts read in step  3201 . 
     In step  3203 , program  2901  reads from area  2004  the contents of texts corresponding to text identifiers attained in step  3202  and then outputs the contents of texts and the similarity determination information obtained in step  3202  to work area  118 . 
     In step  3204 , program  2901  transforms the contents of texts in work area into a format for e-mail program  113 . 
     E-mail program  113  then delivers the information stored in area  118  to the user. 
     The processing procedure of program  2901  has been described. 
     Referring now to FIG. 37, description will be given in detail of a processing flow of program  2901  shown in FIG.  36 . 
     In this example, program  110   b  initiates program  2901  at an interval of 24 hours. However, program  2901  may be initiated at another interval of time or at a predetermined point of time. Moreover, the program initiating time can be set for each user. 
     In the processing of program  2901 , steps  3201  to  3204  are repeatedly executed for all users as follows. 
     In step  3201 , program  2901  acquires the number of additional delivery texts for each predetermined period of time from user list  115   c . In this example, user  107  desires that two texts are additionally delivered at an interval of 24 hours. 
     In step  3202 , program  2901  obtains in the similarity descending order from area  2003  the desired number of text identifiers of texts not delivered to the user. In this example, from texts  2 ,  4 , and  5  not delivered to user  107  within the past 24 hours, program  2901  selects texts having two larger similarity values, i.e., texts  2  and  5  to read therefrom text identifiers and similarity of texts  2  and  5 . In this regard, it is also possible to add a delivery threshold modification presentation step after step  3202 . In the presentation step, program  2901  counts the number of texts delivered to the user. If the count value is less than a predetermined value, program  2901  outputs a predetermined warning message to lower the delivery threshold value to be sent to the user. If the count value is more than a predetermined value, program  2901  outputs a predetermined warning message to increase the delivery threshold value to work area  118  to send the message to the user. 
     In step  3203 , program  2901  reads from area  2004  text information corresponding to the text identifiers obtained in step  3202 . Program  2901  outputs the text information and similarity determination information obtained in step  3202  to work area  118 . In this example, program  2901  outputs the reception time, the similarity, and the contents respectively of texts  2  and  5  to area  118 . 
     In step  3204 , program  2901  transforms the information in area  118  into a format which can be delivered by e-mail program  113 . 
     The specific processing flow of program  2901  has been described. 
     Description has been given of the processing flows of respective programs of the fifth embodiment. 
     In accordance with the configuration of the fifth embodiment of the present invention, all texts received from the news delivery source and the history of similarity calculation of each user are saved such that texts of which similarity is equal to or less than the delivery threshold value are additionally delivered in the similarity descending order. Resultantly, even when the number of texts of which similarity exceeds the delivery threshold value set by the user is less than that of texts desired by the user, a predetermined number of texts can be additionally delivered to the user. Therefore, when no text is delivered to the user, the user can understand whether or not desired texts are absent or whether or not the delivery threshold value is too great. 
     In the fifth embodiment, the number of texts set by the user are additionally delivered in addition to the texts of which similarity exceeds the delivery threshold value set by the user. However, there may be used a method in which the additional text delivery is conducted such that the total of the number of texts of which similarity exceeds the delivery threshold value set by the user and that of texts to be additionally exceeded satisfies a condition of a number set by the user. 
     Description will now be given of the sixth embodiment of the present invention. 
     In the fourth embodiment, although the delivery threshold value can be changed to a suitable value, it is difficult to appropriately initialize the delivery threshold value. For example, when a retrieval condition is set to a new value, similarity of a text desired by the user with respect to the retrieval condition is unknown to the user. 
     In the fourth and fifth embodiments, the delivery threshold value is set to an appropriate value for the text delivery state changing at every moment. This leads to a problem that the user must quite frequently modify the delivery threshold value. 
     To solve the problem above in accordance with the sixth embodiment of a document retrieving and delivering system of the present invention, the user sets a desired number of delivery texts and the system appropriately modifies the delivery threshold value according to the number of delivery texts set by the user. 
     FIG. 38 shows a system configuration of the sixth embodiment in accordance with the present invention. 
     This embodiment is substantially equal in constitution to the first embodiment of FIG.  1 . As can be seen from FIG. 38, the sixth embodiment additionally includes a desired delivery count setting program  3400 , a delivery threshold automatic setting program  3401 , and a saved text retrieval program  3402 . 
     Moreover, the embodiment includes areas  2003  and  2004  employed in the fourth embodiment. 
     Text delivery determination program  152   a  under text retrieval program  131  is that used in the fourth embodiment. 
     In the sixth embodiment, program  3401  sets an appropriate delivery threshold value for each user according to information in areas  2003  and  2004  and the desired delivery count set by program  3400  in response to a request from the user. Program  112   a  refers to the delivery threshold value to determine whether or not a text received is to be sent to the pertinent user. 
     For the user of which personal similarity determination information has not been saved, for example, because a new retrieval condition is registered, program  3402  calculates similarity of a text saved in area  2004  and stores the similarity in area  2003 . 
     Description will be given of processing procedures of respective programs of the sixth embodiment. 
     Referring to the PAD of FIG. 39, description will be given of a processing procedure of program  110   c  in the sixth embodiment. 
     The processing procedure of program  110   c  of the sixth embodiment is almost the same as that of system control program  110  of the first embodiment. However, the sixth embodiment includes additional steps  3500  to  3504 . 
     After step  303 , program  110   c  initiates desired delivery count setting program  3400  in step  3502 . 
     In step  3503 , program  110   c  initiates saved text retrieval program  3402 . 
     In step  3503 , program  110   c  initiates saved text retrieval program  3402 . 
     In step  3504 , program  110   c  initiates delivery threshold automatic setting program  3401 . 
     During the iterative processing in step  300 , after steps  301  and  302 , program  110   c  checks in step  3500  to determine whether or not a setting request for count of delivery texts within a predetermined period of time has been received from user  107 . If such a request has been received, program  110   c  initiates program  3400  in step  3505 . 
     In step  3501 , program  110   c  determines whether or not the current time is a point of time satisfying a predetermined condition. If the current time satisfies the condition, program  110   c  initiates program  3401  in step  3506 . For example, “initiate program  3401  at 0:00 every day” may be set to initiate program  3401 . 
     The processing procedure of program  110   c  has been described. 
     Referring now to the PAD of FIG. 40, description will be given of program  3400  initiated by program  110   c  in step  3502  or  3505  of FIG.  39 . 
     In step  3600 , program  3400  acquires from user  107  a specified period of time and a desired number of texts to be delivered for each specified period of time. 
     In step  3601 , program  3400  updates, according to a user identifier of the user, the specified period of time and the desired number of delivery texts in user list  115  according to the values obtained in step  3600 . The specified period of time and the desired number of delivery texts in user list  115  may be specified by the manager or may be set when user  107  registers a retrieval condition. 
     The processing procedure of program  3400  has been described. 
     Referring now to the PAD of FIG. 41, description will be given of program  3402  initiated by program  110   c  in step  3503  of FIG.  39 . 
     Program  3402  calculates similarity of a text saved when the history of similarity calculation is absent, for example, immediately after a new retrieval condition is registered. 
     In step  3700 , program  3402  repeatedly executes steps  3701  and  3702  for all texts saved in area  2004 . 
     In step  3701 , program  3402  initiates programs  150  and  151  to calculate similarity of a text for a retrieval condition registered by program  111 . 
     In step  3702 , program  3402  stores the similarity calculated in step  3701  in area  2003 . 
     The processing procedure of program  3402  has been described. 
     Referring now to the PAD of FIG. 42, description will be given of program  3401  initiated by program  110   c  in step  3504  or  3506  of FIG.  39 . 
     Program  3401  sets an appropriate delivery threshold value for each user according to a distribution of similarity calculated for texts received in the past. 
     In step  3800 , program  3401  repeatedly executes steps  3801  and  3804  for all users in user list  151   d.    
     In step  3801 , program  3401  acquires for a user a specified period of time and a number of delivery texts per specified period of time associated with the user from user list  115   d.    
     In step  3802 , program  3401  accesses area  2003  to read therefrom, according to a user identifier of the user, personal similarity determination information of the user for a text received from news source  106  within a predetermined period of time in the past. 
     In step  3803 , program  3401  calculates a new delivery threshold value according to a predetermined calculation formula using the information obtained in step  3802 . 
     In step  3804 , program  3401  sets the threshold value calculated in step  3803  to a delivery value field of the user in user list  115   d.    
     The processing procedure of program  3401  has been described. 
     Referring to FIG. 43, description will be given in detail of a processing flow of program  3401  shown in FIG.  42 . 
     In this example, program  3401  is initiated at an interval of 24 hours by program  110   c . However, the interval of time may be changed or the program  3401  may be initiated at a predetermined point of time. Moreover, the initiating time may be set for each user. 
     In processing of program  3401 , steps  3801  to  3804  are repeatedly executed for all users. 
     In step  3801 , program  3401  acquires for a user a specified period of time and a desired number of delivery texts from user list  115   d . In this example, user  1  requests that four texts are delivered per 48 hours, and hence program  3401  acquires information of “48 hours” as the specified period of time and “four” as the desired number of delivery texts. 
     In step  3802 , program  3401  accesses area  2003  to read similarity for user  107  from personal similarity determination information within a specified period of time in the past. In this example, program  3401  obtains similarity values “10.0”, “5.0”, and “7.0” respectively for texts  1  to  3  delivered within 24 hours in the past. 
     In step  3803 , program  3401  calculates a new delivery threshold value according to a predetermined calculation method using the similarity obtained in step  3802 . In this example, the condition of “four texts per 48 hours” is transformed into a condition of “two texts per 24 hours”. According to the similarity values obtained in step  3802 , an average, i.e., “6.0” of two high-order similarity “7.0” and the subsequent similarity “5.0” is calculated as the new delivery threshold value. Other calculation methods may be used to attain the delivery threshold value in step  3803 . 
     In step  3804 , program  3401  stores the value attained in step  3803  as a delivery threshold value of the user in list  115   d.    
     The specific processing flow of program  3401  has been described. 
     Description has been given of processing procedures of respective program in the sixth embodiment. 
     In accordance with the sixth embodiment above, the history of similarity calculation conducted for user in the past is saved such that the system automatically correct delivery threshold values using the historical information saved. The delivery threshold value can be set to a suitable value for each user, which consequently relieves the users from the troublesome operation to frequently modify the delivery threshold value. This solves the problem that the user frequently modifies the delivery threshold value to set an appropriate delivery threshold value due to change in the text delivery state. 
     Also when a new retrieval condition is set, the texts in the past are scanned to calculate an appropriate similarity value for the retrieval condition. Therefore, an appropriate delivery threshold value can be calculated and is set in the system. This accordingly removes the problem in which the user cannot predict similarity of a particular text with respect to the new retrieval condition. 
     It is also possible to install programs  2000  to  2002  of the fourth and fifth embodiments and programs  3400  to  3402  of the sixth embodiment in one system. In such a configuration, by additionally installing a delivery condition setting mode selection program in which a user or a system manager selects and registers either one of the systems associated with the embodiments above for subsequent operation, the system user can appropriately select the setting of the delivery threshold value or the setting of the number of delivery texts. 
     The selection of delivery texts described in the embodiments is not limited to the text retrieval method using the finite automaton or the extended BM method, but is also applicable to any system using other retrieval methods. 
     The selection of delivery texts in the embodiments above is not limited to the text similarity calculation for a retrieval condition, but can be also used in the similarity calculation described in conjunction with the first to third embodiments and in other similarity calculation. 
     In the first to sixth embodiments, the document retrieving and delivering system including display  100 , keyboard  101 , CPU  102 , memory  104 , and bus  103  connecting these constituent components to each other may be arranged at any position on the network, namely, at a position between news source  106  and communication line  105 , communication line  105  and user  107 , or the like in FIGS. 1,  21 ,  33 , and  38 . 
     In accordance with the present invention, similarity of a text is calculated for retrieval conditions of a plurality of users and is compared with a delivery threshold value for each user, and hence a text having high similarity can be delivered to a user requesting more important information. 
     While the present invention has been described with reference to the particular illustrative embodiments, it is not to be restricted by those embodiments but only by the appended claims. It is to be appreciated that those skilled in the art can change or modify the embodiments without departing from the scope and spirit of the present invention.