Patent Publication Number: US-8990106-B2

Title: Information categorisation systems, modules, and methods

Description:
TECHNICAL FIELD 
     The present invention relates to systems, components of systems (such as modules) and methods generally associated with the generation of a recipient profile, the categorisation of information using a recipient profile, and the filtration of information using a recipient profile. 
     BACKGROUND 
     Modern electronic communication systems, such as email, have dramatically decreased the costs associated with the distribution of information. As a consequence, the volume of information distributed by modern communication systems has increased significantly. It is now common for a single communication to be transmitted to many thousands (if not millions) of potentially interested recipients. 
     Distribution lists of contact details (such as email addresses) for recipients interested in particular types of information are now a valuable commodity. These lists may be purchased by content generators and used to target specific sub-sets of potential recipients who are likely to be interested in the information being generated. Alternatively, a content generator may submit information to a distributor and the distributor (who holds the distribution list) may then transmit the information to the potentially interested recipients listed in the distribution list on behalf of the content generator. 
     For example, a clothing company which will shortly launch a new line of clothing may generate an article announced in the launch. The company submits this article to a distributor who transmits the article, by email for example, to potentially interested recipients listed in a distribution list of recipients who may be interested in the content of that article (e.g. recipients interested in fashion). 
     This conventional process is, however, flawed. 
     The distribution lists are often rated on the number of potentially interested recipients listed. Thus, compilers of the distribution lists are inclined to pad the lists with recipients who may, in fact, have little interest in the relevant type of information content. 
     The distribution list compilers have little motivation to provide quality control for the distribution lists which they produce—for example, the users of the distribution lists may not be the list compliers. Thus, even if individual users provide feedback in response to received information, that feedback does not typically filter back to the list compilers or they may not choose to act on the feedback which they do receive. 
     Moreover, there is often no mechanism by which the recipients can provide feedback to the distribution list compiler regarding their interests. 
     Thus, turning back to the article generated by the clothing company, the article may be sent to the journalists at a magazine with a fashion column—these journalists were listed in the distribution list because of the magazine&#39;s fashion column. However, a number of those journalists may not be involved in the production of the fashion column of the magazine but may be involved in the production of a food column (for example). Thus, these journalists—who have no interest in the content of the article—will ignore and/or delete the article when it is received. 
     The fashion company may have selected a particular distributor because of a claim by the distributor regarding the size of the distribution list (and hence the number of recipients) to which that distributor has access. If, however, only 25% of the potentially interested recipients listed in the distribution list are actually potentially interested in the article generated by the clothing company, then that clothing company is effectively paying the distributor to distribute their article to a large number of recipients (75% of the allegedly potentially interested recipients) who will not even consider the content of the article and may, instead, simply delete the article. 
     Thus, the content generator may find that a different distributor—with access to a different and potentially shorter distribution list—would have provided a better service because more of the potential recipients listed in that distributor&#39;s distribution list were actually interested in the content of the article. It is, however, currently impossible for the content generator to obtain any reasonable quality measure for a distribution list which includes an accurate estimation of the number of actually interested recipients who can be targeted. 
     The inexpensive nature of this form of information distribution through modern communication systems has led to a practice among the distributors of information which is effectively a “shot-gun approach” to the distribution of information. This approach relies on information being distributed to a very large number of recipients in the hope that at least some of those recipients are potentially interested in the information being distributed. The collateral damage associated with this approach is that recipients receive vast quantities of information which may be of no or very little interest. 
     The large number of unnecessary communications being distributed imposes a significant burden on the infrastructure of the modern communication systems which are being used in this manner. 
     Moreover, a recipient receiving a large quantity of information of little or no interest, is more likely to miss information which is of interest when compared to a recipient who substantially only receives relevant and interesting information. 
     Simply establishing whether or not a particular piece of information is relevant may consume a large amount of time for a recipient. This is magnified when the recipient receives a multitude of communications each of which contains information which may (potentially) be of interest and which must be reviewed. Cumulatively, the time expended by recipients reviewing communications can consume large quantities of an organisation&#39;s resources. 
     In addition, the trading of distribution lists can be prejudicial to privacy as contact details for potential recipients are passed from distributor-to-distributor, from content generator-to-content generator and/or from list compiler-to-list compiler without the approval of the potential recipients. 
     One example of a conventional information filtration system is an unsolicited bulk email filter (a “SPAM filter”). A typical SPAM filter is configured to identify unsolicited bulk email based on information about the transmitter of the email. For example, the internet protocol (IP) address of the sender of an email may be traced by a SPAM filter and the information provided by that trace used by the SPAM filter to determine (within a degree of likelihood) whether or not the email is, in fact, an unsolicited bulk email (i.e. SPAM). 
     SPAM filtration systems are, however, limited because they identify email as either (i) legitimate or (ii) unsolicited—and unwanted—email. The SPAM filter does not allow for the possibility of one recipient protected by the SPAM filter being interested in the email and another recipient protected by the SPAM filter not being interested in the email. Instead, the SPAM filter applies the same criteria to each email irrespective of the particular interests of the recipient. In other words, SPAM filters typically have no regard for the content of the emails which they are filtering (which may actually be of interest to a recipient). 
     In the case of the distribution of legitimate information, an information distributor is a legitimate source of information for recipients and a SPAM filter, unless configured by the user to reject all communications from an information distributor, will allow communications from an information distributor irrespective of the information content. 
     SPAM filters, therefore, provide a coarse email filtration system but are incapable of handling more advanced filtration tasks. 
     The problems with the prior art are especially prevalent in the field of public relations and in the media industry. 
     The present invention, therefore, seeks to ameliorate one or more of the problems associated with the prior art. 
     SUMMARY 
     Accordingly, one aspect of the present invention provides an information categorisation system comprising: a potential recipient output harvesting module configured to harvest information content generated by a potential recipient; a potential recipient profile generation module configured to generate a recipient profile representing one or more likely interests of the potential recipient, the one or more likely interests being determined by the harvested information content; a comparison module adapted to receive an information content profile representative of content of information content to be categorised and, in response to receipt of the information content profile, to compare the information content profile with the recipient profile to determine a match indicating that the potential recipient is likely to be interested in the information content; and an information categorisation module configured to categorise the information content into information content for which the comparison module has determined a match between the information content profile and the recipient profile and information content for which the comparison module has failed to determine a match between the information content profile and the recipient profile. 
     Preferably, the information categorisation module is further configured to transmit the information content for which the comparison module has determined a match between the information content profile and the recipient profile to the potential recipient with an indicator indicating that a match was determined. 
     Alternatively, the information categorisation module is further configured to transmit only the information content for which the comparison module has determined a match between the information content profile and the recipient profile to the potential recipient. 
     Alternatively, the information categorisation module is further configured to present the information content for which the comparison module has determined a match between the information content profile and the recipient profile to the potential recipient with an indicator indicating that a match was determined. 
     Preferably, the information categorisation module is further configured to present only the information content for which the comparison module has determined a match between the information content profile and the recipient profile to the potential recipient. 
     Conveniently, the system further comprises a database containing a plurality of recipient profiles each recipient profile representing one or more likely interests of a respective potential recipient, wherein the comparison module is adapted to compare the information content profile with each of the plurality of recipient profiles and the information categorisation module is configured to categorise the information content independently for the or each potential recipient whose recipient profile matches the information content profile. 
     Advantageously, the recipient profile generation module is adapted to identify one or more terms and/or phrases in the harvested information content and provide a score for at least one of the terms and/or phrases which is dependent on the frequency of occurrence and location of the term and/or phrase in the harvested information content, the or each score being stored in the recipient profile with the associated term and/or phrase. 
     Preferably, the harvested information content comprises a plurality of harvested information content documents and the frequency of occurrence of the term and/or phrase in the harvested information content includes the frequency of occurrence of the term throughout the plurality of harvested information content documents. 
     Conveniently, the comparison module is adapted to receive an information content profile comprising at least one term and/or phrase and an associated score for the or each term and/or phrase, and the comparison module is further adapted to compare the information content profile score and the recipient profile score for a common term and/or phrase with a threshold value. 
     Advantageously, the comparison module is adapted to multiply the information content profile score and the recipient profile score for the common term and/or phrase to provide a receiver release score, and to compare the receiver release score with a threshold value which is a portion of the total of all the scores in the recipient profile. 
     Preferably, the portion of the total of all the scores in the recipient profile which forms the threshold value is set by a recipient adjustable relevance threshold. 
     Conveniently, the system further comprises a relevance threshold module configured to provide a recipient with an input mechanism to adjust a recipient adjustable relevance threshold, the recipient adjustable relevance threshold being used by the comparison module to determine whether or not a match is sufficient to indicate that the recipient is likely to be interested in the information content. 
     Advantageously, the system further comprises an information content profile generation module which is adapted to identify one or more terms and/or phrases in the information content and provide a score for at least one of the terms and/or phrases which is dependent on the frequency of occurrence and location of the term and/or phrase in the information content, the or each score being stored in the information content profile with the associated term and/or phrase. 
     Preferably, further comprises an information content categorisation coverage prediction module which is configured to receive data from the comparison module regarding matches which the comparison module has identified and to output the data in advance of the categorisation module categorising the information content. 
     Preferably, the system further comprises a recipient feedback module configured to receive feedback from a recipient who in response to the information content for which the comparison module has determined a match between the information content profile and the recipient profile, and to update the recipient profile for that recipient based on the feedback. 
     Conveniently, the feedback module is further configured to adjust the content of one or more lists of terms and/or phrases in the recipient profile, the one or more lists being used by the comparison module. 
     Advantageously, the system further comprises an anti-abuse module which is configured to receive information content to be categorised and to analyse the information content and quarantine information in which: the terms and/or phrases used in the information content are repeated more than a predetermined number of times, and/or there are fewer than a predetermined number of unique terms and/or phrases used in the information content, and/or the terms and/or phrases in the information content are listed in a banned term/phrase list. 
     Preferably, the system further comprises a usage monitoring module configured to monitor output information content generated by a recipient in response to information content for which the comparison module has determined a match between the information content profile and the recipient profile to determine whether the recipient uses the information content in the generation of output information content. 
     Conveniently, the system further comprises a comparison adjustment module configured to adjust one or more parameters of the comparison module based on usage information output by the usage monitoring module to refine the comparison process of the comparison module. 
     Advantageously, the system further comprises an information content submission module adapted to rate and output the quality of the information content. 
     Preferably, the system further comprises an information content submission module adapted to rate and output a predicted coverage of the categorised information content. 
     Conveniently, the comparison module is configured to compare an industry category of the information content profile with an industry category of the recipient profile to determine a match indicating that the potential recipient is likely to be interested in the information content. 
     Advantageously, the system further comprises an information content profile generation module which is adapted to identify one or more terms and/or phrases in the information content and provide a score for at least one of the terms and/or phrases which is dependent on the frequency of occurrence and location of the term and/or phrase in the information content, the or each score being stored in the information content profile with the associated term and/or phrase. 
     Preferably, the information content profile generation module is further adapted to receive an industry profile comprising at least one term and/or phrase and an associated score for the or each term and/or phrase, and to compare the industry profile score and the information content profile score for a common term and/or phrase with a threshold value. 
     Advantageously, the information content profile generation module is adapted to multiply the information content profile score and the industry profile score for the common term and/or phrase to provide an industry release score, and to compare the industry release score with a threshold value which is a portion of the total of all the scores in the industry profile. 
     Conveniently, the potential recipient input harvesting module is configured to harvest information content generated by a potential recipient and output in an information feed associated with the potential recipient. 
     Preferably, the information feed is a RSS or Atom feed. 
     Advantageously, the information categorisation module is further configured to send the information content to the potential recipient in the event of the comparison module determining a match between the information content profile and the recipient profile by transferring or copying the information content into a first information storage location. 
     Conveniently, the information categorisation module is further configured to transfer or copy the information content into a second information storage location in the event of the comparison module failing to determine a match between the information content profile and the recipient profile. 
     Preferably, the modules form part of a computer program operating on a computer. 
     Another aspect of the present invention provides a computer implemented method comprising: harvesting information content generated by a potential recipient in a potential recipient output harvesting module; generating, in a potential recipient profile generation module, a recipient profile representing one or more likely interests of the potential recipient, the one or more likely interests being determined by the harvested information content; receiving, in a comparison module, an information content profile representative of content of information content to be categorised and, in response to receipt of the information content profile, comparing the information content profile with the recipient profile to determine a match indicating that the potential recipient is likely to be interested in the information content; and categorising, using an information categorisation module, the information content into information content for which the comparison module has determined a match between the information content profile and the recipient profile and information content for which the comparison module has failed to determine a match between the information content profile and the recipient profile. 
     Another aspect of the present invention further provides a system configured to generate a recipient profile, the system comprising: a potential recipient output harvesting module configured to receive location information from a potential recipient, the location information representing the location of information content generated by the potential recipient and to harvest at least a portion of the information content; and a potential recipient profile generation module configured to generate a recipient profile representing one or more likely interests of the potential recipient, the one or more likely interests being determined by the harvested information content. 
     Preferably, the location information comprises a location of an information feed. 
     Advantageously, the information feed is a RSS or Atom feed. 
     Conveniently, the location information further comprises a location of one or more emails. 
     Preferably, the one or more emails are stored in the system and the location information comprises a location of the one or more emails in the system. 
     Advantageously, the information content is harvested passively. 
     Another aspect of the present invention provides a computer implemented method to generate a recipient profile, the method comprising: receiving location information from a potential recipient, the location information representing the location of information content generated by the potential recipient; harvesting, in a potential recipient output harvesting module, at least a portion of the information content; and generating, in a potential recipient profile generation module, a recipient profile representing one or more likely interests of the potential recipient, the one or more likely interests being determined by the harvested information content. 
     Preferably, receiving location information comprises receiving a location of an information feed. 
     Advantageously, receiving location information comprises receiving a location of a RSS or Atom feed. 
     Conveniently, receiving location information further comprises receiving a location of one or more emails. 
     Preferably, the one or more emails are stored in the system and receiving the location information comprises receiving a location of the one or more emails in the system. 
     Advantageously, harvesting comprises harvesting the information content harvested passively. 
     Another aspect of the present invention provides a computer readable medium having stored thereon a computer program which, when run on a computer, causes the computer to operate in accordance with any of the methods above. 
     Another aspect of the present invention provides a computer readable medium having stored thereon a database including a plurality of recipient profiles, each associated with a potential recipient and each comprising one or more terms and/or phrases used in information content output by the associated potential recipient and a score for at least one of the terms and/or phrases which is dependent on the frequency of occurrence and location of the term and/or phrase in the harvested information content, such that each recipient profile can be used to determine the likely interests of the associated recipient. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the present invention will now be described, by way of example, with reference to the accompanying drawings in which: 
         FIG. 1A  shows aspects of a recipient profile generation process; 
         FIG. 1B  shows further aspects of the recipient profile generation process; 
         FIG. 2  shows aspects of an information content matching process; 
         FIG. 3  shows aspects of an information content matching process; 
         FIG. 4  shows aspects of a recipient profile generation process and a profile database generation process; 
         FIG. 5  shows aspects of a feedback process; 
         FIG. 6  shows aspects of an information content profile generation process; 
         FIG. 7  shows aspects of an information distribution process; 
         FIG. 8  shows aspects of an analysis process; 
         FIG. 9  shows aspects of an extraction process; 
         FIG. 10  shows aspects of an analysis process; 
         FIG. 11  shows aspects of a recipient profile; 
         FIG. 12   a, b , &amp;  c  show aspects of a graphical user interface; 
         FIG. 13  shows aspect of a form; 
         FIG. 14  shows aspect of a form; 
         FIG. 15  shows aspect of a form; 
         FIG. 16  shows aspects of an analysis process; 
         FIG. 17  shows aspects of an analysis process; 
         FIG. 18  shows aspects of a feedback process; 
         FIG. 19  shows aspects of an industry profile generation process; 
         FIG. 20  shows aspects of an analysis process; 
         FIG. 21  shows aspects of an information quality score generation process; 
         FIG. 22  shows aspects of a coverage predication process; 
         FIG. 23  shows aspects of an information usage tracking process; 
         FIG. 24  shows aspects of an automatic input process; 
         FIG. 25  shows aspects of an anti-abuse system and process; and 
         FIG. 26  shows aspects of a computer and network system. 
     
    
    
     DETAILED DESCRIPTION 
     Processes and Systems Overview 
     Aspects of embodiments seek to use the information which is generated by a potential recipient to provide an insight into the interests and priorities of that potential recipient. This insight may then, in an embodiment, be used to match information content with the interests and priorities of the potential recipient in order to target the delivery of specific information content to that potential recipient. 
     In addition, where—for example—access to a potential recipient&#39;s own information content is limited, or not possible, then the use of proxy data may, in an embodiment, be possible. This proxy data may be derived from another party, group of parties, potential recipient, or group of potential recipients, whose interests and priorities can be assessed (by virtue of information content which they generate—for example) and which are likely to share common interests and priorities with the potential recipient who cannot be directly assessed; for example, they may operate in the same industry as the potential recipient. 
     An embodiment includes the hierarchical ranking of the match between an information content profile for information to be sent and a recipient profile for a potential recipient, based on the likelihood that the potential recipient is interested in the information content. In an embodiment, the recipient is given control over the matching process so that the degree of match between the information content and the recipient&#39;s interests which is required for the information content to be sent to the recipient can be controlled. In other words, the recipient may be given control over the level in the hierarchical ranking which determines what information content is sent to the recipient. 
     With reference to  FIGS. 1 to 7 , aspects of embodiments of the present invention are described in brief to provide an overview of these aspects of the invention. The various components of the invention are then described, by way of example, in more detail with reference to the other figures. 
     With reference to  FIG. 1A , an embodiment comprises a recipient profile generation process  1 . In accordance with process  1 , information content generated by a potential recipient  2 —potential recipient outputs  3 —are harvested in an output harvesting process  4  to produce harvested results. 
     The information content which is generated by the potential recipient  2  and harvested by the output harvesting process  4  may comprise, as is discussed in more detail below, for example RSS feeds or any other form of information content which is generated by the potential recipient  2  or as a result of information content which is generated by the potential recipient  2 . Each item of information content (be it an article, a entry in a RSS feed, a webpage, a status update (such as a Tweet), a social networking entry, a microblog entry, blog entry, an email, or the like) is considered to be document. The use of the term “document” herein is to be interpreted accordingly. 
     The output harvesting process  4  may be a passive process in which the potential recipient  2  is not required to input information content. Instead, the process  4  passively harvests existing information content which is available to it. 
     In an embodiment, the potential recipient  2  provides location information which is to be used by the output harvesting process  4  to determine the location of the information content to be harvested. This may include a directory on a computer storage medium, a URL, or any other suitable location information. 
     The harvested results are then analysed  5  to produce a recipient profile  6 . This recipient profile  6  is, therefore, a profile  6  based on the information content which is produced by the potential recipient  2  and, hence, is usually a good indication of the interests of the potential recipient  2 . 
     For example, a clothing company may produce information content which discusses changes in fashion, new lines of clothing and the like. It is reasonable to assume that the clothing company is, therefore, interested in information content from content generators which relates to fashion. The recipient profile  6  reflects these interests. 
     With reference to  FIG. 2 , an embodiment comprises an information content matching process  7 . 
     Recipient profiles  6 —which may be recipient profiles  6  as generated in accordance with the recipient profile generation process  1 —are collated in a recipient profile database  8 . 
     An information content profile  9  is provided. The information content profile  9  is a profile  9  which is generated based on information content which it is intended to be distributed to one or more potentially interested recipients  2 . 
     The recipient profile  6  of each of the potentially interested recipients  2  (as stored in the recipient profile database  8 ) is compared with the information content profile  9  in a comparison (or “matching”) process  10  to identify recipients who are likely to be potentially interested in the information content. 
     If a match is identified then the information content is sent  11  to the recipient  2 . If a match is not identified then the information content is not sent  12  to the recipient  2 . 
     With reference to  FIG. 3 , another embodiment of an information content matching process  7  is similar to the process  7  depicted in  FIG. 2  and corresponding reference numerals are used for common features. 
     In this embodiment of the information content matching process  7 , the recipient profile database  8  comprises, in addition to the potential recipient profiles  6 , a relevance threshold  13  for each recipient profile  6 . 
     The relevance threshold  13  comprises a threshold which has been set by the potential recipient  2 . The relevance threshold  13  comprises an indication of how close the match between the recipient profile  6  and the information content profile  9  must be for the comparison process  10  to identify a match and send the information content to the potential recipient  2 . 
     Thus, a relevance threshold  13  is associated with each potential recipient  2  and allows a potential recipient  2  to control the information content which is sent to them. 
     The process  7  otherwise operates in substantially the same manner as the process  7  of  FIG. 2 . 
     With reference to  FIG. 4 , a recipient profile database generation process  14  is shown. This process  14  includes a process which is similar to the recipient profile generation process  1  of  FIG. 1A , as such like reference numerals have been used for like features. The recipient profile generation process  1  which forms part of the recipient database generation process  14  generally operates in the same manner as the corresponding process  1  of  FIG. 1A . 
     In addition to the recipient profile generation process  1 , the recipient profile database generation process  14  includes a potential recipient relevance threshold determination process. This process allows a recipient  2  to input a relevance threshold  13  which is then stored, along with a generated recipient profile  6 , in the recipient profile database  8 . The process  14  may be repeated for a plurality of potential recipients  2  to generate a database  8  containing information associated with the plurality of potential recipients  2 . 
     Another aspect of an embodiment is shown in  FIG. 5 , this aspect comprises a feedback process  15  through which a recipient  2  who has received  16  information content as the result of a match being determined during an information matching process  7  such as those of  FIGS. 2 and 3 . 
     In accordance with process  15 , the recipient  2  receives  16  information content and is directed  17  to a feedback system  18 . The feedback system  18  receives input  19  from the recipient  2  and updates  20  the recipient profile  6  associated with that recipient  2 . The updated recipient profile  21  is the output and may be stored in a recipient profile database  8  (replacing the existing recipient profile  6  for that recipient  2 ). 
     An information content profile  9  has been mentioned above and an embodiment comprises an information content profile generation process  22 . This process  22  is described with reference to  FIG. 6 . 
     An information content generator  23  generates information content  24  and this is analysed  25 —in accordance with a process described in more detail below—to produce the information content profile  9 . 
     This information content profile generation process  22  produces an information content profile  9  which is in a form which is suitable for comparison with a potential recipient profile  6 —see  FIGS. 2 and 3 . In an embodiment, the information content profile  9  is in a form which is suitable for comparison with a potential recipient profile  6  with reference to a relevance threshold  13  associated with the potential recipient  2 —see  FIG. 3 . 
     Thus, one or more of the above described processes, as a whole or in part, may be combined to form an information content distribution process  26 . An example, of an information content distribution process  26  is shown in  FIG. 7 . 
     The information content distribution process  26  shown in  FIG. 7  comprises and information content profile generation process  22  (which may be a process  22  such as depicted in  FIG. 6 ) and a recipient profile generation process  1  (which may be a recipient profile generation process  1  as depicted in  FIG. 1A  or a recipient profile generation process including a potential recipient relevance threshold as depicted in  FIG. 4 ). 
     Information content  24  is submitted to the information content profile generation process  22  by, for example, an information content generator  23  and the information content  24  is analysed  25  to determine an information content profile  9 . 
     The information content  24  may be, for example, an article prepared by the information content generator  23 . One example of such an article is a news article regarding the launch of a new line of clothing which is generated by a clothing company. 
     The potential recipient generation process  1  is used to generate recipient profiles  6  for respective potential recipients  2 . If applicable, each potential recipient  2  may also set a relevance threshold  13 . 
     The recipient profiles  6  of potential recipients  2  are stored in a recipient profile database  8  (which may also store the respective relevance thresholds  13 —see  FIG. 4 ). 
     The information content distribution process  26  further comprises an information content matching process  7  (which may be an information content matching process  7  as depicted in  FIG. 2  or  FIG. 3 ). As part of the information content matching process  7 , the information content profile  9  is compared  10  with the recipient profile  6  of each potential recipient  2  for which a profile  6  is stored in the database  8 . If a match is identified, then the information content is sent  11  to the associated recipient  2 . If a match is not identified, then the information content is not sent  12  to the associated recipient  2 . If applicable, the relevance threshold  13  associated with each potential recipient  2  is used to confirm whether or not a match between an information content profile  9  and a recipient profile  6  is sufficient to qualify as an actual match (and hence result in the sending  11  of the information content  24  to the recipient  2 ). 
     The information content distribution process  26  may further comprise a feedback process  15  (which may be a feedback process  15  as depicted in  FIG. 5 ). The feedback process  15  allows a recipient  2 , who has been sent  11  information content as a result of the application of the information content matching process  7 , to input  19  feedback into a feedback system  18  which causes the recipient profile to be updated  20  to provide an updated recipient profile  21  which is then stored in the recipient profile database  8  (replacing the existing recipient profile  6  associated with that potential recipient  2 ). 
     Thus, in the example of a clothing company issuing an article about the launch of a new line of clothing, the information content matching process  7  uses recipient profiles  6  for potential recipients  2  (and the associated relevance threshold  13 —if applicable) to identify potential recipients  2  who are likely to be interested in the content of the article. The article is then sent  11  to those identified potential recipients  2 . 
     The potential recipients  2  who receive  16  the article may then, in an embodiment, be offered the opportunity to provide feedback (or input  18 ) through a feedback process  15 . If a recipient  2  is not, in fact, interested in the article then the feedback is used to update that recipient&#39;s  2  recipient profile  6  to produce an updated recipient profile  21 . For example, the recipient  2  may be interested in fashion and so was originally identified as a match by the information content matching process  7  but that recipient&#39;s  2  interest in fashion may be limited to shoes rather than clothing as such. The updated recipient profile  20  may indicate a greater interest in shoes (in particular) within the field of fashion. 
     On the other hand, if a potential recipient  2  who received the article is particularly interested in the article, then the feedback provided by the recipient  2  may be used to produce an updated recipient profile  21  which reflects a greater interest in, for example, new lines of clothing (within the broader field of fashion). 
     Recipient Profile Generation Process and Recipient Profile Database Generation Process 
     A recipient profile generation process  1  is depicted in  FIG. 1A  and a similar recipient profile generation process  1  including the setting of a potential recipient relevance threshold  13  is depicted in  FIG. 4 .  FIG. 4  also depicts a recipient profile database generation process  14  in which the recipient profile  6  and relevance threshold  13  are stored in a recipient profile database  8 . 
     These processes are described below in more detail by way of example. 
     When a potential recipient  2  is interested in being included in an information distribution system of which the recipient profile generation process  1  is a part, various details regarding the potential recipient  2  are collected. 
     In an embodiment, this information is collected from the potential recipient  2  through a website. The potential recipient  2  is, in an embodiment, required to enter contact details—for example an email address to which information content  24  which is identified as being potentially interesting to the recipient  2  can be sent  11 . In an embodiment, contact details may be automatically obtained for the potential recipient  2 ; for example, an IP address for the potential recipient  2  may be obtained. 
     In addition, the potential recipient  2  may be required to enter one or more of the following: name, telephone number, publication/site/blog name, publisher, email address, alternative email address or addresses, website address (for example a Uniform Resource Locator (URL)), the details of any web based information content submission form which the potential recipient  2  wishes to use to submit information content, an indication of the number of unique users of the potential recipient&#39;s  2  website each month (which may be approximate), another form of indication of the number of users of the potential recipient&#39;s  2  website (for example the number of page views), permission to provide publicly available summary of the coverage achieved in relation to information content  24  submitted by the potential recipient  2 , and the geographical location or base of the potential recipient  2 . 
     It will be understood, that a potential recipient  2  is also likely to want to submit information content  24  for distribution. Therefore, in this embodiment, some aspects of some of the types of information which are requested from the potential recipient  2  is relevant to the submission of information content  24  rather than solely for the purposes of generating a recipient profile  6 . 
     It will be appreciated, that in other embodiments, a potential recipient  2  is not interested in submitting information content  24  for distribution. As such, the information required from the potential recipient  2  is tailored accordingly. 
     In addition to the above information, a potential recipient  2  may also be required to enter the details of an information feed. A suitable information feed may comprise, for example, a RSS (Really Simple Syndication/Rich Site Summary) feed which provides summarised information in a standardised format based on a source of information content which may, for example, be information content generated by a potential recipient  2 . This information content may be information content which is generated by the potential recipient  2  and published on their website. 
     Other forms of information feed are also known and are equally applicable to embodiments of the invention—such as the Atom syndication format. 
     The information submitted by the potential recipient  2  is processed in order to harvest  4  potential recipient outputs  3 . 
     The potential recipient outputs  3  may comprise one or more of the content of information feeds, articles published on the potential recipient&#39;s  2  website, published letters and e-mails generated by the potential recipient  2 , the potential recipient&#39;s blog, the potential recipient&#39;s social networking page, the potential recipient&#39;s Twitter outputs, and any other form of information content which was generated or published by the potential recipient  2  and which can be harvested. 
     One possible method of harvesting  4  potential recipient outputs  3  from a RSS feed associated with the potential recipient  2  is to program a computer to generate an Outline Processor Mark-up Language (OPML) file which contains the contents the information feeds (such as a RSS feed) for the potential recipient  2 . The OPML file can then be automatically read by a computer and the URLs listed in the OPML file (typically one for each entry in the OPML file) can be stored in respective rows of a database (for example using Microsoft (registered trade mark) SQL. The URLs can be read from the database using the XML data type and SQLCLR user defined functions in Microsoft (registered trade mark) SQL for analysis. 
     The information gathered by this process is associated with a specific potential recipient  2  in order to build a prediction of the interests of the potential recipient  2  and to form a recipient profile  6 . 
     The gathered information is, in an embodiment, passively gathered in that the potential recipient  2  need not generate the information for the purposes of the generation of a recipient profile  6 ; instead, the information is existing information which is collected by this process. 
     As will be appreciated, the more information which is collected and known about the potential recipient  2  the better the recipient profile  6  may be at indicating the interests and priorities of that potential recipient  2 . 
     Other sources of information content produced by the potential recipient  2  are, of course, applicable to aspects and embodiments of the present invention. It will be appreciated that aspects and embodiments of the present invention can use a wide variety of different sources of information content for the potential recipient  2 . 
     Preferably, the harvested information is stored in a database for analysis. 
     Once the information content from the potential recipient  2  has been harvested  4  the harvested information is analysed  5 . 
     A variety of different methods of analysis of the harvested results  5  are possible. One such method will be described below—by way of example—with reference to  FIG. 8 . 
     The information content which resulted from the harvesting process  4  comprises harvested results  27  (also known as information content documents  3  or potential recipient outputs  3 ). These results  27  are analysed  5  in order to provide a score for substantially each term and/or phrase used in the information content of the results  27 . 
     This score may be based on the frequency of the term or phrase used in the harvested results  27 . Preferably, the score is not solely based on the frequency of occurrence of the term or phrase but is, instead, also based on the location of the term or phrase within the document or which it is a part and the age of that document. 
     A stop list check  28  may be performed in advance of the more detailed analysis. The stop list check  28  may comprise the removal of terms from the information content which has been harvested (i.e. the harvested results  27 ). These removed terms may comprise, for example, common words which have little relevance to the analysis process described below. Examples of such words include one or more of: a, the, it, is, of, or, all, as, at, any, for, have, has, he, she, their, there, its, isn&#39;t, including, more, form, on, or, once, said, such, they, then, than, us, and, them, me, my, we, what, which, when, but, this, just, such, are, with, into, to, give, and had. 
     The stop list check  28  may be performed and configured using database commands such as those provided by Microsoft (registered trademark) SQL. 
     Phrase extraction  30  and term extraction  32  is performed on the information content—preferably after the stop list check  28 —as part of a score determination process  29 . Phrase  30  and term  31  extraction are shown in more detail in  FIG. 9 . 
     Phrase  30  and term extraction  32  are generally similar processes which use a dictionary and linguistic information to tokenise  38 , tag  39 , stem  40 , and normalise  41  the information content of the harvested results  27 . These processes  38 , 39 , 40 , 41  may be preformed in parallel or in series. 
     As used herein a “term” may comprise, for example, a “word”. 
     Tokenisation  38  comprises one or more of: 
     separating text into terms or phrases by using spaces, line breaks, and other word terminators—for example, punctuation marks such as “?” and “:” are word-breaking characters, 
     preserving terms or phrases that are connected by hyphens or underscores—for example, the terms “copy-protected” and “read-only” each remain as one term when tokenised, 
     keeping acronyms that include periods intact—for example, “A.B.C Company” would be tokenised as “ABC” and “Company”, 
     splitting terms or phrases on special characters—for example, the term “date/time” is tokenised as “date” and “time”, “(bicycle)” is tokenised as “bicycle”, and “C#” is tokenised as “C”—special characters are discarded and cannot be lexicalised in the tokenisation process  38 , 
     recognising when special characters such as an apostrophe should not split terms—for example, the word “bicycle&#39;s” is not split into two words and is tokenised as the single term “bicycle”, and 
     splitting time expressions, monetary expressions, e-mail addresses, and postal addresses—for example, the date “Jan. 31, 2004” is separated into the three tokens “January, 31” and “2004”. 
     It will be appreciated, therefore, that information content which is tokenised is separated into tokens for further processing. It will be understood that other tokenisation processes are possible and are applicable to embodiments. Tokenisation  38  can be applied on a term, word, or phase level. 
     Tagging  39  comprises the allocation of terms into linguistic groups (such as nouns, verbs, etc) and may include the tagging of one or more of: 
     nouns in the singular form—for example, bicycle and potato, 
     nouns in the plural form—for example, bicycles and potatoes (all plural nouns are subject to stemming), 
     proper nouns in the singular form—for example, April and Peter, 
     proper nouns in the plural form—for example Aprils and Peters (for a proper noun to be subject to stemming, it must be a identifiable in the dictionary and linguistic information which is available to the process), 
     adjectives—for example, blue, 
     comparative adjectives that compare two things—for example, higher and taller, and 
     superlative adjectives that identify object that have a quality above or below the level of at least one other object—for example, highest and tallest, 
     numbers—for example, 62 and 2004. 
     It will be appreciated that tagging of phrases is also possible. 
     Stemming  40  is the process of lemmatising the terms and may include one or more of: 
     removing “s” from nouns—for example, “bicycles” becomes bicycle, 
     removing “es” from nouns—for example, “stories” becomes story, and 
     retrieving the singular form for irregular nouns from the dictionary information available 
     to the process—for example, geese becomes goose. 
     Normalisation  41  is the process of normalising the case of terms and may be a case-sensitive normalisation process or a case-insensitive normalisation process include: normalising terms that are capitalised only because of their position in a sentence by converting these terms into their non-capitalised form instead—for example, in the phrases “Dogs chase cats” and “Mountain paths are steep”, “Dogs” and “Mountain” would be normalised to “dogs” and “mountains” (with stemming  40  further reducing these terms to “dog” and “mountain”). 
     If the normalisation  40  is case sensitive then terms like “Method” and “method” are not normalised and remain as two different terms. Capitalised terms which are not the first term in a sentence are not normalised and are tagged as proper nouns (see tagging  39  above). 
     If the normalisation  40  is case insensitive then terms like “Method” and “method” are recognized as variants of a single term. The list of extracted terms might include either “Method” or “method”, depending on which term occurs first in the information content being processed. 
     The information content which has been tokenised  38 , tagged  39 , stemmed  40  and normalised  41  can then be processed to extract, for example, one or more of: the nouns and noun phrases. The nouns are processed by the term extraction process  30  and the noun phrases are processed by the phrase extraction process  32 . In an embodiment, both of these processes  30 ,  32  are combined in a single term and phrase extraction process. 
     In an embodiment, the phrase  30  and term  31  extraction processes are implemented using database commands such as those provided by Microsoft (registered trade mark) SQL. In an embodiment, processes are performed on the information content in accordance with Microsoft (registered trademark) DN ms141809. 
     The outputs from the phrase  30  and term  32  extraction processes are fed into respective phrase  31  and term  33  frequency analysis processes which are also part of the score determination process  29 . 
     The phrase  31  and term  33  frequency analysis processes are shown in more detail in  FIG. 10  and are generally comparable in operation. 
     The phrase  31  and term  33  frequency analysis processes are configured to output a frequency value for each extracted noun and/or noun phrase. Thus, the frequency analysis processes  31 ,  33  have respective sub-processes  42 ,  43 . 
     The frequency value (as determined by the frequency value determination sub-process  42 ) for each extracted noun and/or noun phrase is a value which represents the number of occurrences of that noun or noun phrase in the extracted nouns and noun phrases. A threshold frequency  44  may be provided and the frequency of a noun or noun phrase must exceed the threshold frequency  44  if it is to be output by the phrase  31  and term  33  frequency analysis processes. 
     The phrase frequency analysis process  31  (and, in an embodiment, not the term frequency analysis process  33 ) has a maximum phrase length limit  45 . Extracted phrases which exceed this length are not processed by the phrase frequency analysis process  31 . 
     The threshold frequency  44  and maximum phrase length limit  45  may be adjustable. Preferably, an operator of the process is provided with the option to alter these values. The threshold frequency may be set to one or two or any other desired number. 
     Document age determination  34  may be preformed by checking a date and/or time associated with the document being analysed. This process is part of the score determination process  29 . The date and/or time associated with the document may be text in the document or, for example, data stored in a document header. 
     Total word/term count determination  35  can be used to determine the total number of terms (i.e. “words”) in a document being processed by score determination process  29  and forms a part thereof. This may be the total number of words in the original document or the total number of words after the application of the tops check list  28 . 
     A term rank determination process  37  ranks each of the extracted terms in a document in order of their appearance in the document. Each term is provided with a numerical value indicating position within the document. If a term is repeated in a document, then the second and each subsequent occurrence of the term in the document is ignored—only the first occurrence of a term in a document is ranked. 
     It will be understood that the recipient profile  6  may be generated in a score determination process  29  by: 
     phrase extraction  30  and subsequent phrase frequency analysis  31 , 
     term extraction  32  and subsequent term frequency analysis  33 , 
     document age determination  34 , 
     population average length determination  35 , 
     total word count determination  36 , and 
     term rank determination  37 . The outputs from these processes are then input into equations below to generate a recipient profile  6 . 
     In an embodiment, a chronological term rank (CTR) algorithm is used. This algorithm may be: 
     
       
         
           
             
               
                 
                   
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                       ( 
                       
                         E 
                         · 
                         F 
                         · 
                         
                           
                             log 
                             ⁡ 
                             
                               ( 
                               
                                 
                                   
                                     termr 
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                                   30 
                                 
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                               ) 
                             
                           
                           
                             log 
                             ⁡ 
                             
                               ( 
                               
                                 
                                   docl 
                                   30 
                                 
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                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   ] 
                 
               
             
           
         
       
     
     In this equation: 
     CTR, is the chronological term rank which is the ranked sequence of unique words and phrases as they are encountered in an information content document being analysed as it is read from start to finish—as determined by the above equation, 
     doc1, is the count of all the words in the information content document (i.e. the document length), 
     termr, is the rank of each unique term in a document as they appear from start to finish of the document, 
     E is a constant (e.g. value=1), and 
     F is also a constant (e.g. value=0.8). 
     As will be appreciated, termr can be replaced by phraser (which is the rank of each unique phrase in a document as they appear from start to finish of the document) in equation 1 for the analysis of phrases. 
     The chronological term rank is used in order to calculate a receiver (or recipient) key word score (ReceiverKS) using, for example, the following equation: 
     
       
         
           
             
               
                 
                   ReceiverKS 
                   = 
                   
                     
                       
                         tf 
                         
                           0.5 
                           + 
                           
                             0.1 
                             · 
                             
                               
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                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                   ] 
                 
               
             
           
         
       
     
     In this equation: 
     tf, is the frequency which a term appears in an information content document, and 
     avdl, is the average length of all the information content documents in the population of information content documents (i.e. average document length)—the other variables are as specified above. 
     As will be appreciated, tf can be replaced by pf (which is the frequency which a phrase appears in an information content document) in equation 2 for the analysis of phrases. 
     The receiver key word score (ReceiverKS) can then be used to calculate a receiver key word rank score (rkrs)  80  in accordance with, for example, the following equation: 
     
       
         
           
             
               
                 
                   
                     R 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     K 
                     ⁢ 
                     
                         
                     
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                     R 
                     ⁢ 
                     
                         
                     
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                     S 
                   
                   = 
                   
                     ReceiverKS 
                     · 
                     
                       1 
                       
                         ln 
                         ⁡ 
                         
                           ( 
                           
                             aa 
                             
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                               ⁢ 
                               
                                   
                               
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                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
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                     3 
                   
                   ] 
                 
               
             
           
         
       
     
     In this equation, 
     ReceiverKS is the receiver key word score as calculated by, for example, (equation 2 above), 
     aa is the age of the information content document in days, and 
     AC is a constant (of value 1.00E-10). 
     An rkrs may be calculated for each term and/phrase in a document. 
     As mentioned above, the term “document” as used in relation to the above equations is for ease of reference only. A “document” is, for example, an item of information content such as a webpage, blog entry, information feed entry, status update, email or the like. 
     If, for example, a term or phrase appears in multiple information content documents within the same potential recipient&#39;s RSS feed or other collection of information content documents  3 , then the scores for that particular term are summed or otherwise combined. 
     The result of this process is a recipient profile  6 . The recipient profile preferably comprises a list of terms and phrases used in information content generated by the potential recipient  2 . The profile  6  also comprises a receiver key word rank score  80  for each of these terms. 
     As will be appreciated, the receiver key word rank score  80  is calculated based on elements of an OkapiBM25 ranking function combined with the chronological term rank algorithm. 
     The constant AC in equation 3 (and, indeed, the constants E and F), may be set on a system-wide level such that each all recipient profiles  6  are generated using the same constant values. In an embodiment, one or more of the values of the variables AC, E, and F are stored in the respective recipient profiles  6  for one or more potential recipients  2 ; one or more of the constant values for a potential recipient  2  may, therefore, be different from the constant values for another potential recipient  2 . 
     In an embodiment, the recipient profile  6  may further comprise an industry category  46  (see  FIG. 11 ). The industry category  46  is initially set of a group of potential recipients  2  manually. This manual setting of an industry category  46  may be performed by an operator of the information content distribution process  26 . In an embodiment, an industry category  46  is part of the information which is provided by the potential recipient  2  as described above. 
     In an embodiment, the recipient profile  6  may further include one or more of a white list  47 , a trust list  48 , an ignore list  49 , and a black list  50  (see  FIG. 11 ). 
     The trust list  48  is a list of terms and/or phrases which are favourably (i.e. positively) weighted in the information content matching process  7 . The terms and/or phrases in the trust list  48  may be positively weighted by 10% in the information content matching process  7  such that they are more likely to result in a positive match (and information content  24  which includes these terms and/or phrases is more likely to be sent  11  to the potential recipient  2 ). 
     The white list  47  is a list of terms and/or phrases that have been automatically identified by the information content distribution process  26  as being positively matched in the information content matching process  7  frequently. The terms and/or phrases in the white list  47  may be positively weighted and this positive weighting may be by 2% in the information content matching process  7  such that they are more likely to result in a positive match (and information content  24  which includes these terms and/or phrases is more likely to be sent  11  to the potential recipient  2 ). Each term and/or phrase can receive multiple 2% positive weightings (in this example) up to a maximum positive weighting (which may be 10%). Thus, frequently matched terms and/or phrases are more likely to cause a match in the future. 
     The ignore list  49  comprises a list of terms and/or phrases which are not weighted (positively or negatively). 
     The black list  50  comprises a list of terms and/or phrases that have been automatically identified by the information content distribution process  26  as not being positively matched in the information content matching process  7  frequently. The terms and/or phrases in the black list may be negatively weighted and this negative weighting may be by 2% in the information content matching process  7  such that they are less likely to result in a positive match (and information content  24  which includes these terms and/or phrases is more likely to be sent  11  to the potential recipient  2 ). Each term and/or phrase can receive multiple 2% negative weightings (in this example) up to a maximum negative weighting (which may be 10%). Thus, infrequently matched terms and/or phrases are less likely to cause a match in the future. 
     The analysis of the harvested results  5  is a computer implemented method and is achieved by a computer under the control of a computer program which is configured to perform the above processes. 
     As shown in, for example,  FIG. 4 , a potential recipient  2  may also indicate a potential recipient relevance threshold  13 . As is explained in more detail below with reference to the information content matching process  7 , the potential recipient relevance threshold  13  is an indication which may be set by a potential recipient  2  as to how closely information content  24  must match that particular recipient profile  6  in order for the potential recipient  2  to be sent the information content  24 . 
     In order to allow potential recipients  2  to modify the potential recipient relevance threshold  13 , a potential recipient  2  may be provided with a graphical user interface (which may be supplied over the internet through a webpage—for example) which allows the potential recipient  2  to modify the level of the relevance threshold  13 . The modified level is then stored in the recipient profile database  8  and replaces the previous relevance threshold  13  associated with that potential recipient  2 . 
     Various different forms of this graphical user interface are envisaged and some of these forms are depicted  FIGS. 12   a ,  12   b  and  12   c . Each graphical user interface  51  comprises a level indicator  52  and an interactive user controllable element  53 . In addition, further graphical elements  54  may be provided. 
     With reference to  FIG. 12   a , a graphical user interface  51  comprises an interactive user controllable element  53  in the form of a bar which extends across a further graphical element  54 . The further graphical element  54  comprises an isosceles or equilateral triangle. In the centre of the bar which forms the interactive user controllable element  53  is a level indicator  52 . A potential recipient  2  can select the interactive user controllable element  53  and move the element  53  with respect to the further graphical elements  54  up or down the equilateral/isosceles triangle which forms a further graphical element  54 . As the interactive user controllable element  53  is moved with respect to the further graphical element  54 , an indication of the relevance threshold level  13  is provided in the level indicator  52  located on the interactive user controllable element  53 . 
     When the interactive user controllable element  53  is towards the top of the triangle which forms one of the further graphical elements  54  the relevance threshold  13  is set such that a high degree of match must exist between a recipient profile  6  and an information content profile  9  in order for the information content  24  associated with the profile  9  to be sent  11  to that potential recipient  2 . This is graphically indicated by the narrow upper end of the triangle of the further graphical element  54 —indicating that there will be less information content which meets this criterion. 
     If the potential recipient  2  wishes to receive more information content  24  which may be less relevant (information content  24  for which the match between the recipient profile  6  and the information content profile  9  is weaker) then the potential recipient  2  can adjust the interactive user controllable element  53  so that the element  53  is closer to the base of the triangle of the further graphical element  54 . Thus, a greater quantity of information  24  will be sent  11  to the recipient  2  but this information may be less relevant to the interests and priorities of the potential recipient  2 . 
     At the base of the triangle forming one of the further graphical elements  54  is an additional further graphical element  54 . This additional further graphical element  54  may, for example, provide an indication of the type of information content  24  which would be sent  11  to the potential recipient  2  if the relevance threshold  13  is at its current level (i.e. when the interactive user controllable element  53  is in its current location with respect to the further graphical elements  54 ). 
       FIGS. 12   b  and  12   c  show respective alternative embodiments of the graphical user interface  51 —like reference numerals have been used for like features. 
     In the embodiments in  FIG. 12   b , the graphical user interface comprises a dial which forms the interactive user controllable element  53 . A number of values are indicated around the dial and these form respective level indicators  52 . In this embodiment, the further graphical elements  54  comprise the additional, optional features of the dial such as the ribbing around the outside of the dial. 
     In order to adjust the relevance threshold  13  using the graphical user interface  51  of the embodiment depicted in  FIG. 12   b , a user may select the interactive user controllable element  51  in the form of the dial and rotate the dial with respect to the plurality of level indicators  52  until the dial is set such that the pointer of the dial points towards the relevant level indicator  52 . 
     In accordance with the embodiment shown in  FIG. 12   c , the graphical user interface  51  comprises a tap. The interactive user controllable element  53  of the tap is the rotatable member. The other features of the tap comprise further graphical elements  54 . A water droplet is graphically represented as indicated in  FIG. 12   c  and provides the level indicator  52 . The potential recipient  2  wishes to adjust the relevance threshold  13 , then the potential recipient  2  can select and rotate the interactive user controllable element  53 . By rotating the interactive user controllable element  53  in a first direction, the water droplet depicted as the level indicator  52  will increase in size and a numerical value indicating the relevance threshold  13  is shown in the centre of the water droplet. When the interactive user controllable element  53  is rotated in the opposing direction, then the water droplet depicted as the level indicator may decrease in size and a corresponding numerical value may be depicted in the centre of the water droplet indicating the relevance threshold  13 . 
     It will be appreciated that there are numerous different embodiments of a graphical user interface  51  which allow a user to select and control the level of the relevance threshold  13 . 
     Not all embodiments of the present invention incorporate a relevance threshold  13 . In such embodiments, it may be that a relevance threshold  13  is, in fact, used but that this relevance threshold  13  is set at a predetermined value for all potential recipients  2 . This relevance threshold  13  may be controllable by the operators of the information content distribution process  26  and may not be adjustable by the potential recipients  2 . 
     The relevance threshold  13  is explained in more detail below in relation to the information content matching process  7 . 
     It will be appreciated that a potential recipient  2  may not have suitable information content outputs  3  to be harvested  4  or the information content outputs  3  may be inadequate to form a reasonable recipient profile  6 . In such instances, the recipient profile generation process  1  may provide an approximate recipient profile  6  based on the recipient profiles  6  of other similar potential recipients  2 . Thus, for example, is a potential recipient  2  is determined as belonging to a particular industry category  26 , the recipient profile generation process  1  may identify other potential recipients  2  whose profiles are stored in the recipient profile database  8  and who have be allocated the same industry category  46 . The process  1  may then copy the one of the recipient profiles  6  of such a matched potential recipient  2  to form the new potential recipient&#39;s  2  recipient profile  6 . In an embodiment, as shown in  FIG. 1B , the process  1  takes information from the recipient profiles  6  of several such matched recipients  2  to derive a new recipient profile  6 ′ for the new potential recipient  2 —this may be an averaging process in which the receiver keyword rank score for a number of terms and/or phrases which are common to all of the recipient profiles  6  are averaged to form the respective receiver keyword rank scores for those terms and/or phrases in the new recipient profile  6  for the new potential recipient  2 . 
     Information Content Profile Generation and Information Content Matching Process 
     An information content generator  23  may generate information content  24  which is then analysed  25  to generate an information content profile  9 —as shown in FIG.  6 —in an information content generation process  22 . 
     The information content generator  23  may submit the information content  24  to the information content distribution process  26  through an electronic form or series of electronic forms—such as the forms  55 , 56 , 57  shown in  FIGS. 13 ,  14  and  15 . 
     A main form  55  shown in  FIG. 13  comprises a title entry field  58  and a release body field  59 . The information entered by the information content generator  23  into the fields  58 , 59  forms part of the information content  24  for analysis  25 . As will be understood, the term “release” as used herein generally refers to information content  24  which is being submitted for distribution. Similarly, as used herein, the term “receiver” is intended to have generally the same meaning as the term “recipient”. 
     A multimedia form  56  shown in  FIG. 14 , in an embodiment, comprises one or more image title entry fields  61  for the entry of respective image titles, a corresponding number of description fields  60  for the entry of respective image descriptions, a corresponding number of image file selection fields  63  for the selection of respective image files, and a corresponding number of radio buttons  62  for selecting an image as a primary image. The form  56  may also comprise a check box  64  to identify whether or not images are to be included in the information content  24 . In a preferred embodiment, there are facilities for including three images in the information content  24 . 
     Similarly, an embodiment of the multimedia form  56  in  FIG. 14  comprises one or more audio title entry fields  66  for the entry of respective audio titles, a corresponding number of description fields  65  for the entry of respective audio descriptions, and a corresponding number of audio file selection fields  67  for the selection of respective audio files. The form  56  may also comprise a check box  64  to identify whether or not audio files are to be included in the information content  24 . 
     Similarly, an embodiment of the multimedia form  56  in  FIG. 14  comprises one or more video title entry fields  69  for the entry of respective video titles, a corresponding number of description fields  68  for the entry of respective video descriptions, and a corresponding number of video file selection fields  70  for the selection of respective video files. The form  56  may also comprise a check box  64  to identify whether or not video files are to be included in the information content  24 . 
     In an embodiment, the summary form  57  comprises one or more summary fields  71 ,  72 ,  73 . These summary fields  71 ,  72 ,  73  may comprise a boilerplate field  71 , a release summary field  72  and a SEO keywords field. 
     The boilerplate field  71  is for entry of a re-usable summary of the information content  24 . The release summary field  72  is for entry of a summary of the information content  24 . The SEO keyword field  73  is for entry of search engine optimisation keywords associated with the information content  24 . 
     In an embodiment, additional supplementary files may be added to the information content  24  using a similar form. 
     In an embodiment, a form (not shown) is also provided for the addition of relevant URLs to the information content  24 . 
     The type/format and size of image files which can be added to the information content  24  may be limited. For example, suitable image file formats may include: GIF, JPG (JPEG), BMP, PNG, EPS, PSD, and TIF (TIFF); and a suitable file size limit may be 5 Mb. 
     The type/format and size of audio files which can be added to the information content  24  may be limited. For example, suitable audio file formats may include: MP3, WMA, WAV, SND, AU, AIF, AIFC, AIFF, and RA; and a suitable file size limit may be 10 Mb. 
     The type/format and size of video files which can be added to the information content  24  may be limited. For example, suitable image file formats may include: AVI, MOV, WMV, MPG (MPEG), M1V, MP″, MPA, MPE, ASF, FLV, SWF, and RM; and a suitable file size limit may be 10 Mb. 
     The type/format and size of image files which can be added to the information content  24  may be limited. For example, suitable image file formats may include: GIF, JPG (JPEG), BMP, PNG, EPS, PSD, and TIF (TIFF); and a suitable file size limit may be 5 Mb. 
     The type/format and size of supplementary files which can be added to the information content  24  may be limited. For example, suitable supplementary file formats may include: TXT, DOC, PDF, XML, XLS, PPT and RTF; and a suitable file size limit may be 1 Mb. 
     There may be a total file size limit (i.e. an accumulated maximum file size) and a suitable limit may be 26 Mb. 
     The title fields  58 , 61 , 66 , 69  may each have a maximum character limit and this may be 300 characters. The release body field  59  may have a maximum character limit and this may be 11000 characters. The boilerplate field  71  may have a maximum character limit and this may be 1500 characters. The release summary field  72  may have a maximum character limit and this may be 600 characters. The SEO keywords field  73  may have a maximum character limit and this may be 250 characters. A field for entering relevant URLs may have a maximum character limit and this may be 250 characters. 
     After an information content generator  23  has generated information content  24  and input that information content into the information content distribution process  26  (as described above), the information content is analysed  25 . 
     The analysis of the information content  24  is substantially similar to the analysis  5  of the harvested results from a potential recipient  2  as described above—see  FIG. 16 . 
     An initial step in the analysis  25  of the information content  24  may comprise the application of a stop list check  28  (as will be apparent, like referenced numerals will be used for like features). As described above, the stop list check  28  filters the information content so as to exclude certain common words from further analysis. 
     The information content (which may have already been passed through the stop list check  28 ) is then passed to a score determination process  29 . As part of the score determination process  29 , phrase  30  and/or term  32  extraction processes are performed on the information content  24  and the results of these extraction processes  30 ,  32  are passed to respective phrase  31  and term  33  frequency analysis processes. 
     The score determination process  29  for the information content  24  may also include a population average length determination process  35 , a total word count termination process  36  and a term rank determination process  37  (all these processes have been described above in relation to the analysis of information content harvested from a potential recipient  2 ). 
     In addition, the score determination process  29  for the information content  24  may also include a document population count determination process  74  and a term and/or document frequency determination process  75 . 
     The document population count determination process  75  comprises of process to determine the total number of documents in the population of documents being analysed. In other words, the information content  24  may comprise a plurality of documents which are each analysed as part of the information content  24  analysis process  25 . 
     The term and/or document frequency determination process  75  is a process to determine the number of documents within the population of documents which contain the term and/or phrase which is being analysed. 
     The outputs from the processes involved in the score determination process  29  for the information content  24  are fed into equations in order to derive a release keyword score (RKS)—an example of a method of calculating the release keyword score is described below. 
     Initially, the relevant outputs may be fed into a chronological term rank (CTR) algorithm such as equation 1 above. The output from this algorithm along with the relevant outputs from the above processes may then be fed into a release key word score algorithm: 
     
       
         
           
             
               
                 
                   
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     In this equation: 
     dp, is the document population which is a total count of all the documents in the population of documents being analysed, and 
     df, is the document term frequency which is the number of documents containing the term and/or phrase being analysed 
     (the other variables are as described above). 
     As will be appreciated, in equation 4, the variable tf can be replaced with the variable pf in the case of the analysis of phrases rather than terms. 
     The list of phrases and/or terms along with the associated release key word score (RKS) is stored as the information content profile  9 . 
     In an embodiment of an information content distribution process  26 , the information content profile  9  is compared with recipient profiles  6  (which may be stored in a recipient profile database  8 ) to determine which of the potential recipients  2  are likely to be interested in the information content  24 . 
     The process of determining which of the potential recipients  2  is likely to be interested in the information content  24  has been briefly described above in relation to  FIGS. 2 and 3 . These figures disclose an information content matching process  7 . In accordance with an embodiment, the information content matching process comprises a matching or comparison process  10  in which the recipient profile  6  for each potential recipient  2  is compared with the information content profile  9 . If a positive match is found, then the information content is sent  11  to the recipient  2  and if no match is found then the information content  24  is not sent  12  to the recipient  2 . 
     An example implementation of the matching or comparison process  10  is described below. 
     All of the terms and/or phrases  79  which are listed in the recipient profile  6  and in the information content profile  9  are compared  76  (see  FIG. 17 ) in order to identify the terms and/or phrases  81  which are common to both the recipient profile  6  and the information content profile  9 . 
     The receiver key word rank scores  80  which are stored in the recipient profile  6  in relation to respective common terms and/or phrases  81  which are also found to be present in the information content profile  9  are extracted  77 . Similarly, the release key word scores  82  which are stored in the information content profile  9  in respect of each term and/or phrase  81  which was identified as being in common with the recipient profile  6  are also extracted  78 . 
     For each term and/or phrase  81  which is found to be common to both the recipient profile  6  and the information content profile  9 , the receiver keyword rank score  80  and the release keyword score  82  are multiplied to provide a receiver release keyword score  83  in accordance with equation 5 below:
 
RRKS=RKRS·RKS  [Equation 5]
 
     In this equation: 
     RRKS, is the receiver release keyword score, 
     RKRS, is the receiver keyword rank score, and 
     RKS, is the release keyword score. 
     A receiver release total keyword score  84  is also calculated. The receiver release total keyword score  84  is determined by the sum of the receiver release key word scores  83 . 
     In an embodiment, the receiver release keyword scores  83  and receiver release total keyword scores  84  are only calculated for a portion of the terms and/or phrases  81  which are identified as being common to both the recipient profile  6  and the information content profile  9 . In an embodiment, the receiver release keyword scores  83  are calculated for every term and/or phrase  81  which is found to be common to both the recipient profile  6  and the information content profile  9  but the receiver release keyword scores which are below a threshold level are discarded and are not used further in the matching process  10  and are not used to calculate the receiver release total keyword score  84 . 
     The value of the receiver release total keyword score  84  is used to determine whether or not a match has occurred (and, therefore, whether or not to send 11 the information content  24  to the recipient  2 ). 
     Thus, in an embodiment, the receiver release total keyword score  84  is compared with a predetermined threshold value in order to determine whether or not a match has occurred. This threshold value may be dependent on the relevance threshold  13 . The relevance threshold  13  may be set by a potential recipient  2  or may be set by operators of the information content matching process  7 . 
     In an embodiment, the top 10% of the receiver keyword rank scores  80  for a particular potential recipient  2  (as stored in the recipient profile  6 ) are summed in order to arrive at a sub-total of the receiver keyword rank scores  80  for that potential recipient  2 . This sub-total is then graduated in order to provide a plurality of possible threshold values for determining whether or not a particular receiver release total keyword score  84  has reached the required threshold in order for a match to be found. 
     The graduation may comprise the determination of ten threshold values. The first threshold value (relevance threshold number one) may be 30% of the sub-total of the receiver keyword rank scores  80 . The last threshold value (relevance threshold number ten) may be 10% of the sub-total of the receiver keyword rank scores  80 . 
     The graduation of the intermediate threshold values (i.e. numbers two to nine) may be in accordance with equation 6 below:
 
Threshold value SL   =Y %−[(SL−1)·( Y %− Z %)/(NSL−1)]  [Equation 6]
 
     In this equation: 
     Y %, is the percentage of the sub-total of the receiver keyword rank scores  80  which forms the first level of the threshold (number one), in the present example this is 30% of the sub-total, 
     Z %, is the percentage of the sub-total of the receiver keyword rank scores  80  which forms the last level of the threshold (number ten), in the present example this is 10% of the sub-total, 
     SL, is a slider level or relevance threshold (and may take a value between 2 and 9), and 
     NSL, is the total number of slider levels or relevance thresholds (which is 10). 
     It will be appreciated that various different values are possible for NSL, Y % and Z % —the above values are given as an example only. 
     When a receiver release total keyword score  84  is calculated and this value is greater than the threshold value as determined by a particular slider level or relevance threshold  13  (which is either set by the potential recipient  2  or an operator of the information content matching process  7 ), then the information content  24  is sent  11  to the recipient  2 . If no match is found (i.e. the value is less than the threshold value) then the information content  24  is not sent  12  to the recipient  2 . 
     It will be understood that a potential recipient may be provided with a graphical user interface  51 —as described above—such that the potential recipient  2  can alter the relevance threshold  13 . 
     As discussed above, a recipient profile  6  may include a white list  47 , a trust list  48 , an ignore list  49  and a black list  50 . The terms and/or phrases which are listed in each of these lists  47 ,  48 ,  49 ,  50  may have an impact on the receiver release total key word score  84 . As mentioned above, terms and/or phrases on the trust list  48  which appear in the list of common terms and/or phrases  81  may have their respective receiver release key word scores increased by a first relative amount—for example 10%. 
     Similarly, terms and/or phrases which are present in the white list  47  and which are found in the list of common terms and/or phrases  81  may have their respective receiver release key word scores  83  increased by a second relative amount (which is generally less than the first relative amount—for example 2%). It may be that multiple 2% increases in the receiver release key word score  83  for a particular term or phrase  81  are implemented (for example up to a maximum of 10%)—as is explained in more detail below. 
     Common terms and/or phrases  81  which are listed in the ignore list  49  have respective receiver release key word scores  83  which remain the same. 
     Common terms and/or phrases  81  which are also listed in the black list  50  have their respective receiver release key word scores  83  reduced by a third relative amount. The third relative amount is preferably the same as the second relative amount (but is a reduction rather than an increase in the score). For example, the third amount may be 2%. Again, a plurality of applications of the reduction in the receiver release key word score  83  may be applied up to a maximum value (which may be 10%). 
     Feedback Process 
     A feedback process  15  has been described above, briefly, in relation to  FIG. 5 . Accordingly, a feedback process  15  generally comprises a recipient  2  being directed  17  to a feedback system  18  in which recipient input  19  is received and the recipient profile  6  updated  20  to provide an updated recipient profile  21 . 
     In some embodiments, a feedback process  15  is provided in which the recipient  2  is not directed  17  to a feedback system  18 . Instead, the feedback process  15  relies on automatically generated feedback information in order to update  20  the recipient profile  6  and output an updated recipient profile  21 . 
     In an example of a negative feedback process (see  FIG. 18  which shows a generic feedback process which may be a positive or a negative feedback process), a recipient  2  is sent  11  information content  24 . The recipient  2  generates feedback  85  which is negative (in this case). 
     The recipient  2  may be directed  86  to a webpage through which the recipient  2  is, preferably, thanked for their feedback  85  and shown a graphical user interface  51  which allows the recipient  2  to adjust the relevance threshold  13  associated with their recipient profile  6 . The recipient  2  may be instructed on the use of the graphical user interface  51  and may be provided with an opportunity to generate advanced feedback  87 . 
     In the case of this negative feedback process it is assumed that the recipient  2  does not take the opportunity to provide advanced feedback  87 . 
     The feedback system  18 , therefore, obtains and stores the terms and/or phrases  79  from the relevant information content profile  9 . These terms and/or phrases  79  are compared with terms and/or phrases which have been stored and associated with the recipient profile  6  for the potential recipient  2  concerned. In cases in which a term and/or phrase  79  is common to both the information content profile  9  for the current information content  24  and the terms and/or phrases  79  which were stored from previous negative feedback results, those terms and/or phrases  79  are added to the black list  50 . 
     As discussed above, terms and/or phrases  79  which appear in the black list  50  are negatively weighted in future matching processes  10 . In an embodiment, a term and/or phrase  79  must appear at least a predetermined number of occasions in the stored terms and/or phrases  79  associated with negative feedback for a particular recipient  2  before the term and/or phrase  79  is added to the black list  50 . In an embodiment, there must be three occurrences of a term and/or phrase  79  before that term and/or phrase  79  is added to the black list  50 . Each additional occurrence of the term and/or phrase  79  in an information content profile  9  associated with information content  24  which has been given negative feedback, will result in an additional negative weighting. There may be a maximum negative weighting which can be applied to any term in the black list  50 . This maximum negative weighting may be 10%. 
     A positive feedback process is described below. However, it would be appreciated that a term and/or phrase  79  which is in the black list  50  associated with a particular potential recipient  2  may receive a positive weighting such that terms and/or phrases in the black list  50  may have their weighting increased or decreased for the purposes of the matching process  10  in accordance with the negative and positive feedback processes. If a term and/or phrase  79  in the black list  50  has a zero or positive weighting, then the term and/or phrase  79  is removed from the black list  50 . 
     If the recipient  2  decides to provide advanced feedback  87  then the recipient  2  may select terms and/or phrases  79  from a list of terms and/or phrases associated with the information content  24  and forming part of the information content profile  9  for that information content  24 . The recipient  2  may select one or more terms and/or phrases  79  in which they have no interest and these terms and/or phrases  79  may be added to the ignore list  49 . 
     The information content distribution process  26  may allow a recipient  2  to adjust the content of the ignore list  49  at any time. 
     A positive feedback process is similar to the negative feedback process discussed above. However, instead of terms and/or phrases  79  being allocated a negative weighting, the terms and/or phrases are allocated a positive weighting—as discussed above—thus, terms and/or phrases  79  may be added to white list  47 . 
     Similarly, an advance positive feedback process may be provided which corresponds with the advance negative feedback process but in which a recipient  2  selects terms and/or phrases  79  which, instead of being placed in the ignore list  49  are placed in the trust list  48 . 
     If a recipient  2  attempts to provide feedback which would result in a term and/or phrase  79  from the trust list  48  being added to the ignore  49  or black  50  list, then a warning may be issued to the recipient  2 . 
     If a recipient  2  attempts to provide feedback which would result in a term and/or phrase  79  from the black list  50  being added to the trust  48  or white  47  list, then a warning may be issued to the recipient  2 . 
     Further Aspects of Embodiments 
     The use of a industry category  46  in a recipient profile  6  is discussed above in relation to the generation of a recipient profile  6  for a potential recipient  2  for whom limited or insufficient information content is available for analysis. 
     In addition, in an embodiment, the industry category  46  may be used to as part of the matching or comparison process  10  of the information content matching process  7 . This can be used, for example, to match an information content profile  9  to a recipient profile  6  even if the matching or comparison process  10  described above does not result in a match. Thus, the industry category  46  can be used in a process which seeks to ensure that potential recipients  2  receive information content  24  which is relevant to their industry category—for example. 
     An embodiment includes an industry profile generation process  88  (see  FIG. 19 ). The industry profile generation process  88  produces a profile for an industry category  46  based on the recipient profiles  6  of the potential recipients  2  who are associated with that industry. 
     Thus, the industry profile generation process  88  may comprise the identification  89  of one or more potential recipients  2  associated with an industry. This identification  89  process may be based on industry categories  46  which were entered by the potential recipient  2  as discussed above. 
     For the identified potential recipients  2 , the respective recipient profiles  6  are acquired  90 . The list of terms and/or phrase  79  which forms part of the recipient profiles  6  along with the respective receiver keyword rank scores  80  are then combined. The receiver keyword rank scores  80  for a term and/or phrase  79  which is common to more than one of the recipient profiles  6  are summed to provide an industry keyword rank score  91  for that term or phrase  79 . 
     The resultant list of terms and/or phrases  79  and their respective industry keyword rank scores  91  form an industry profile  92  for that industry. 
     In an embodiment, an information content profile  9  for information content  24  is passed to a matching or comparison process  93  (see  FIG. 20 ). An industry profile database  94  may be provided which stores a plurality of industry profiles  92  each of which is associated with an industry. The industry profile database  94  may feed industry profiles  92  into the matching or comparison process  93 . The matching or comparison process  93  may operate in a similar manner to matching and comparison process  10 . If a match is found between an information content profile  9  and an industry profile  92 , then the information content  24  associated with the information content profile  9  is identified as being potentially interesting to the industry associated with the industry profile  92 . 
     The matching or comparison process  93  comprises the generation of a release industry score for each term and/or phrase  79  using the equation shown below:
 
RIS=RKS·IKRS  [Equation 7]
 
     In this equation: 
     RIS, is the release industry score, 
     RKS, is the release keyword score, and 
     IKRS, is the industry keyword rank score. 
     A total release industry score is also calculated and is the sum of the release industry scores for the terms and/or phrases  79 . In an embodiment, the total release industry score is the sum of a portion of the release industry scores. In an example embodiment, the total release industry score is the sum of the top 10% of the release industry scores in that industry profile  92 . 
     An industry relevance threshold  95  is used by a matching or comparison process  93  in a similar manner as the relevance threshold  13  is used by the matching and comparison process  10 . 
     The industry relevance threshold  95  may be set by an operator of the matching and comparison process  10 . 
     Thus, in an embodiment, the release industry score is compared with a predetermined threshold value in order to determine whether or not a match has occurred. This threshold value may be dependent on the industry relevance threshold  95 . 
     A portion of the industry keyword rank scores are summed. This portion may be the top 10% of the industry keyword rank scores. 
     The summed total of the portion of the industry keyword rank scores are then graduated in order to provide a plurality of possible threshold values for determining whether or not a particular release industry score has reached the required threshold in order for a match to be found. 
     The graduation may comprise the determination of ten threshold values. The first threshold value (relevance threshold number one) may be 30% of the summed total of the portion of the industry keyword rank scores. The last threshold value (relevance threshold number ten) may be 10% of the summed total of the portion of the industry keyword rank scores. 
     The graduation of the intermediate threshold values (i.e. numbers two to nine) may be generally in accordance with equation 6 above—with the relevant variables for this process (as will be appreciated). 
     When a release industry score is calculated and this value is greater than the threshold value as determined by a particular industry relevance threshold  95 , then the information content  24  is classified as being from the associated industry. 
     As will be appreciated, information content  24  may be associated with a plurality of industries. 
     The industry or industries which are determined by this process may be stored in the information content profile  9  for that information content  24 . 
     The industry or industries which are determined by this process may then be compared to the industry category  46  associated with a potential recipient  2  as part of the matching or comparison process  10 . 
     Once a potential recipient  2  has received a predetermined number of information content  24  documents which have been classified into a particular industry category (in accordance with the information content matching process  7  described herein), that industry category is added to the industry categories  46  which are stored in that recipient&#39;s recipient profile  6 . 
     In an embodiment, the industry category of an information content  24  document is used compared  10  against the industry category or categories  46  which are stored in the recipient profile  6  for each potential recipient  2 . If a match is found, then the information content  24  is sent to the potential recipient  2 . Thus, the comparison of the terms and/or phrases  79  listed in a recipient&#39;s recipient profile  2  with those in the information content profile  9 , to arrive at a receiver release keyword score  83  in accordance with the matching or comparison process  10  discussed above, need not be performed unless the recipient profile  2  for a potential recipient  2  does not include an industry category  46 . This reduces the number of occasions on which the comparison of the terms and/or phrases  79  listed in a recipient&#39;s recipient profile  2  with those in the information content profile  9 , to arrive at a receiver release keyword score  83  in accordance with the matching or comparison process  10  discussed above, need be performed. 
     Aspects of embodiments which relate to the use of an industry category  46  or industry categorisation may collectively be known as a categorisation process and aspects of embodiments which relate to the use of industry categories  46  in a matching or comparison process  10  in order to determine whether 11 or not 12 information content  24  is sent to a potential recipient  2  may be known, collectively, as a release categorisation system. 
     An embodiment comprises an industry specific information feed, such as an RSS feed. In such an embodiment, an information feed contains references to submitted information content which has been classifies (as discussed above) into an industry category. The information feed may be provided over the internet (a wide area network) or a local area network (for example, to the users of a company or corporation&#39;s computer network). The information feed may allow subscription thereto—as is possible in relation to RSS feeds and Atom feeds. The information feed may be provided through a server operated or controlled by the operator of an information content distribution process  26 . A plurality of information feeds may be provided. 
     It will be appreciated that information content  24  may be matched with an industry category through the matching or comparison process which is described above. As such, an industry relevance threshold  95  may be set for the formation of the or each information feed. The relevance threshold for one information feed (for example, relating to one industry category) may differ from that of another information feed. Thus, an industry which has typically has relatively specific interests may have a different industry relevance threshold  95  than an industry which typically has relatively broad interests. 
     In an embodiment (with reference to  FIG. 21 ) information content  24  which is submitted as described above is analysed  96  to generate an information content quality score  97 . The information content quality score  97  provides an indication of the perceived quality of the information content  24  which has been submitted. 
     Various different quality measures are envisaged for use in the analysis  96 . 
     The information content quality score  97  may be a score with a maximum of one hundred points. The allocation of points to the score  97  may include consideration of various factors each of which is associated with a total maximum number of points. The factors may include one or more of: title length, body length, multimedia presence, related links presence, type of release, content details presence, and boilerplate presence. 
     One example distribution of points is: 
     title length: 30 points; 
     body length: 20 points; 
     multimedia presence: 10 points; 
     related links presence: 10 points; 
     type of release: 10 points; 
     content details presence: 10 points; and boilerplate presence: 10 points (total 100 points). 
     The each score (e.g. title length, body length, etc.) may be determined in accordance with a respective score calculation process. 
     The title length score calculation process may comprise awarding the maximum number of points (30 in the above example) if the title is a predetermined number of characters or fewer than this number (e.g. 70 characters). A portion of a point may be subtracted from the total possible score for each character over the predetermined number (up to a point at which the score reaches zero). The portion of a point which is subtracted for each additional character may be the same for each character over the predetermined number or may increase as the number of characters over the predetermined number increases. The total number of characters required in order to receive a score of greater than one may be set (e.g. 200 characters). 
     A similar score calculation process may be applied to the body length score. In this case an example of the predetermined number of characters is 1750 and a maximum number of characters to receive a score of greater then zero may be 10000 characters. 
     For the scores relating to the presence of multimedia and related links may be calculated in accordance with a score calculation process in which a score is added for each multimedia item (e.g. image or video) which is included in the information content  24  until the maximum score for has been reached. Thus, for example, if the presence of each multimedia item causes the addition of five points, with a maximum of ten possible points, the maximum score for the presence of multimedia can be reached by the presence of two or more multimedia items. In the case of related links, the presence of each related link may cause the addition of two and a half points, with a maximum score of ten possible points, four or more related links in the information content  24  will cause the maximum score to be reached (i.e. ten). 
     For other scores, the score calculation process may be binary—the presence of the required information in the information content  24  may result in allocation of the maximum score for that criterion. 
     Different types of information content  24  may receive different scores. For example, a press release may receive an allocation of five points and a social media news release (SMNR—a release specifically designed for distribution over the internet) may receive ten points. 
     The results of the information quality analysis may be output and displayed to the information content generator  23  or other party who submitted the information content  24 . In addition, advice may be provided as to how the information content  24  can be improved (for example, “Adding multimedia to your release will help to increase your information quality score”). The information content generator  23  or other party who submitted the information content  24 . 
     In an embodiment, an information content distribution coverage prediction  98  (see  FIG. 22 ) may be determined. The information content coverage distribution coverage prediction  98  is generated by performing an information content distribution process  7  as discussed above with the exception that the information content  24  is not sent  11  to any potential recipients  2 ; instead, the total number of matched recipient profiles  6  (be that by the use of terms and/or phrases, industry categories, or both) is recorded and output as the information content distribution coverage prediction  98 . This prediction  98  may be displayed to an information content generator  23  or other party submitting information content  24 . 
     Thus, a party (such as an information content generator  23 ) may, in an embodiment, be provided with an indication regarding the number of potential recipients  2  who have been identified as being potentially interested in the information content  24  and to whom the information content  24  will be sent  11  if the party decides to proceed. 
     Other information may also be provided as part of the information content distribution coverage prediction  98  such as the industries which have been identified as being potentially in the information content  24  and/or an indication of the geographical locations of the identified potential recipients  2  (each recipient profile  6  may include a geographical location—such as a continent, country, county/state, town/city, and/or district). Thus, a party submitting information content  24  for distribution may be provided with an indication of the geographical coverage which can be achieved. 
     In accordance with an embodiment, a process  99  (see  FIG. 23 ) is provided which is configured to track a recipient&#39;s  2  use of information content  24  which has been sent to that recipient  2 . This process  99  may be generally known as an information usage tracking process  99 . 
     The information usage tracking process  99  may include a manual input process  100  and an automatic input process  101 . 
     The information usage tracking process  99  is configured to determine different forms of information content  24  uses. These different uses include one or more of: editorial usage  102 , selective usage  103 , non-selective usage  104 , blog usage  105 , and recipient generated usage  106 . 
     Editorial usage  102  is typically usage of information content  24  by a recipient  2  who generates their own content about the information content  24 . 
     Selective usage  103  is typically usage of information content  24  by a recipient  2  who selectively uses information content  24  which they have been sent  11 . 
     Non-selective usage  104  is typically usage of information content  24  by a recipient  2  who uses substantially all information content  24  which they have been sent  11 —for example, the recipient  2  may re-publish the information content  24  on their own website. 
     Blog usage  105  is typically usage of information content  24  by a recipient  2  on the recipient&#39;s  2  blog. 
     Recipient generated usage  106  is typically usage of information content  24  by a recipient  2  in the generation of their own information content and is similar to the editorial usage  102 . 
     As will be appreciated, information content  24  may be used by a recipient  2  in a manner which falls into more than one of the above categories  102 , 103 , 104 , 105 , 106 . 
     The automatic input process  101  may monitor the information content  3  which is output by a recipient  2  for a predetermined period after that recipient  2  has been sent information content  24 . This may be achieved in a similar manner to the recipient profile generation process  1  above. For example, the outputs  3  from a recipient  2  may be harvested  4  and analysed  107 —see  FIG. 24 . The analysis  107  results in the generation of a list of terms and/or phrases  79  along with respective receiver keyword rank scores  80 —as described above. The release keyword scores  82  are already known from the processes which were performed (and which are described above) prior to the sending  11  of information content  24  to one or more recipients  2 . Indeed, the information content profile  9  for the distributed information content  24  is already known. 
     The information content profile  9  is matched or compared  108  in a manner which is comparable to the matching or comparison process  10  discussed above. A relevance threshold  109  may be set (as discussed above in relation to relevance threshold  13 ). If there is a match between an information content profile  24  and harvested  4  and analysed  107  results (i.e. the degree of match exceeds the threshold—see above for more details), then the information content document  3  is marked  110  as a potential match. 
     A record  111  of these potential matches may be kept. 
     The automatic input process  101  may classify the information content document  3  which has been marked  110  as a potential match into the above described categories  102 , 103 , 104 , 105 , 106  based on the type of information content document  3  (e.g. blog, website news article etc) and the level of the match. If a very high match has been found, then this may be an indication that the information content  24  has been re-published by the recipient  2  as the information content document  3  without editing. A lesser degree of match may indicate that the information content document  3  is based on (but not a direct copy of) the information content  24 . 
     The record  111  may form an input into the manual input process  100 . An operator may then review the information content documents  3  which were identified by the automatic input process  101  to determine if there is, indeed, a match and/or to classify the information content document  3  as discussed above. 
     The record  111  may store an identifier for the information content  24  (such as an identification code), an identifier for the recipient  2  (such as an identification code), an alternative identifier for the recipient  2  (such as a name), a title of the information content document  3 , and a URL for the information content document  3 . 
     Information content usage results  112  may be generated by the information usage tracking process  99 . These results  112  may comprise one or more of: the number of uses found by the automatic input process  101  and/or the manual input process  100 , the categories  102 , 103 , 104 , 105 , 106  of the recorded usage of the information content  24 , dates of the recorded usage, and the like. 
     The information content usage results  112  may be presented to the party who submitted the information content  24  for distribution. 
     In an embodiment, the outputs  3  of a potential recipient  2  are monitored for a predetermined period (e.g. two days) before information content  24  is sent to the recipient  2  such that more accurate results regarding the impact of the information content  24  can be gathered. In an embodiment, the outputs  3  of a potential recipient  2  are monitored for a predetermined period (e.g. seven days) after they have been sent the information content  24  to provide the usage results. 
     The information content document  3  may be the content of an information feed (such as a RSS feed) output by the recipient  2 . 
     The information usage tracking process  99  can be used to validate the information content distribution coverage prediction  98 . Thus, a party who as submitted information content  24  for distribution may be given feedback of the actual use of that information content  24  and this can be compared with the coverage prediction to determine a quality measure for the distribution. In an embodiment, the distribution of information content  24  is automatically given a score which represents a comparison of the predicted coverage  98  with the actual usage of the information content  24 —the results may be graded. This information can then be used to provide predictions for actual usage based on future information content distribution coverage predictions  98 . 
     In an embodiment, a process is provided which monitors information content distribution coverage predictions  98  with actual usage results. The process is configured to use historical matches in order to provide a predicted actual usage along with a predicted coverage. In an embodiment, this process is also configured to adjust the matching process  10 , and/or the processes used to generate the profiles (such as the recipient and information content profiles) to provide better actual usage results in the future. This process is preferably an on going and iterative learning process which may be implemented using neural networking techniques or regression analysis. The adjustments may, for example, involve adjustments to the constants in the above equations or alterations to equations themselves. 
     Information content  24  which is submitted may be allocated a substantially unique identifier. Each potential recipient  2  may be allocated a substantially unique identifier. Each party who submits information content  24  may be allocated a substantially unique identifier (which may be the same identifier as the potential recipient identifier for that party). Information content documents  3  output by a potential recipient  2  may be allocated a substantially unique identifier. Records may be maintained of the information content  24  which is generated, who submitted that information content, who received the information content  24 , who used that information content, and how that information content was used. These records may be used to provide statistical analysis of the operation of a distribution process  26  operating generally as described above. 
     In an embodiment, an anti-abuse process  113  is provided (see  FIG. 25 ). The anti-abuse process  113  is configured to check  114  all information content  24  which is submitted for distribution against a banned term/phrase list  115 . Any information content  24  which is found to include one or more of the terms and/or phrases in the banned term/phrase list  115  will be quarantined  116 . 
     Quarantined information content  24  may be reviewed manually by an operator of the system  26 . The operator may contact the party who submitted the information content  24  to inform them that the information content  24  cannot be distributed. In an embodiment, a party who submitted the information content  24  is automatically informed that the information content  24  has been quarantined  116 . 
     Similarly, information content  24  may be quarantined  116  if the information content  24  uses the same term and/or phrase too many times. For example, if the same term and/or phrase constitutes more than 40% of the total number of terms and/or phrases in the information content  24  then this information content  24  may be quarantined  116 . 
     Similarly, information content  24  may be quarantined  116  if the information content  24  does not include a sufficient number of unique terms and/or phrases. For example, if less than 40% of the terms and/or phrases in the information content  24  are unique then this information content  24  may be quarantined  116 . 
     Applications of Aspects of Embodiments 
     It will be appreciated that embodiments of the present invention may be used to distribute information content  24  in the form of, for example, press releases or other articles to potentially interested recipients  2  in a targeted manner. 
     It will be appreciated that embodiments of the present invention may be used to target other information to potentially interest recipients. For example, emails to a company are often sent to a general email address, these incoming emails may be processed and forwarded to potentially interested recipients  2  within the company (whose email addresses may not be publicly available). 
     It will be appreciated that embodiments may be used to sort e-mails on a local or remote computer into those which are likely to be of interest and those which are unlikely to be of interest. 
     It will be appreciated that embodiments may be used to analyse information content based on the content of that document. 
     It will be appreciated that embodiments may be used to target information content at potentially interested recipients  2 . 
     It will be appreciated that embodiments may be used to provide feedback of potential distribution coverage for information content before that information content is submitted for distribution. 
     It will be appreciated that embodiments of the present invention may be used to allow a party wishing to submit information content  24  to tailor that information content  24  to achieve the most appropriate level of coverage. 
     It will be appreciated that embodiments of the present invention provide a filtration system which reduces the quantity of information which is transmitted through a network. Thus, for example, communications may be more targeted and the strain on hardware resources greatly reduced. 
     It will be appreciated that embodiments of the present invention provide a filtration system which is configured to sort information content into a first or a second storage location based on the match (or lack of a match) between the information content and a likely interests of a recipient  2 . 
     It will be appreciated that embodiments of the present invention may be used to generate collections of information content  24  which is of potential interest to a particular industry. This collection of information content  24  may be output as an information feed—such as a RSS or Atom feed. 
     Embodiments of the present invention seek to provide technical solutions to technical problems, such as excessive use of hardware resources in information transmission, the need to maintain privacy and security, by allowing the targeting of information content to potentially interested recipients wherein, in embodiments, the contact details of the potential recipients need not be provided to the party wishing to distribute information content. 
     Although the transmission and sending of information content  24  has been discussed herein, it will be understood that embodiments of the present invention may not transmit or send information content  24 , as such, but may re-order, categorise, filter, display, organise, or present the information content  24  in some other manner. For example, in a list of available information content  24 , each item in the list may be accompanied by an indication of the likelihood of that information content  24  being of interest to a particular potential recipient  2  or to potential recipients  2  in, for example, a particular industry. This indication may be a binary indication (i.e. of interest/not of interest) or may be a score representing the likelihood of the information content  24  being of interest or could be any other form of indication (e.g. a traffic light-type indication). As will be appreciated information content  24  may be transmitted to a potential recipient  2  and then presented to the recipient  2  or, for example, filtered or organised or re-ordered and then transmitted and/or presented to the recipient. 
     Yet Further Aspects of Embodiments 
     According to an aspect of an embodiment, equation 8 below is used in a process as described above instead of equation 2. 
     
       
         
           
             
               
                 
                   ReceiverKS 
                   = 
                   
                     
                       
                         tf 
                         
                           0.5 
                           + 
                           
                             1.5 
                             · 
                             
                               
                                 ⅆ 
                                 l 
                               
                               
                                 av 
                                 ⁢ 
                                 
                                   ⅆ 
                                   l 
                                 
                               
                             
                           
                           + 
                           tf 
                         
                       
                       · 
                       C 
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     T 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     R 
                   
                 
               
               
                 
                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     8 
                   
                   ] 
                 
               
             
           
         
       
     
     According to aspects of embodiments, equation 9a or 9b below is used in a process as described above instead of equation 4. 
     
       
         
           
             
               
                 
                   
                     R 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     K 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     S 
                   
                   = 
                   
                     ln 
                     ⁢ 
                     
                       
                         
                           
                             ⅆ 
                             p 
                           
                           - 
                           
                             ⅆ 
                             f 
                           
                           + 
                           0.5 
                         
                         
                           
                             ⅆ 
                             f 
                           
                           + 
                           0.5 
                         
                       
                       · 
                       
                         ( 
                         
                           
                             tf 
                             
                               0.5 
                               + 
                               
                                 1.5 
                                 · 
                                 
                                   
                                     ⅆ 
                                     l 
                                   
                                   
                                     av 
                                     ⁢ 
                                     
                                       ⅆ 
                                       l 
                                     
                                   
                                 
                               
                               + 
                               tf 
                             
                           
                           + 
                           
                             C 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             T 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             R 
                           
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     9 
                     ⁢ 
                     a 
                   
                   ] 
                 
               
             
             
               
                 
                   
                     R 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     K 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     S 
                   
                   = 
                   
                     ln 
                     ⁢ 
                     
                       
                         
                           
                             ⅆ 
                             p 
                           
                           - 
                           
                             ⅆ 
                             f 
                           
                           + 
                           0.5 
                         
                         
                           
                             ⅆ 
                             f 
                           
                           + 
                           0.5 
                         
                       
                       · 
                       
                         ( 
                         
                           
                             
                               tf 
                               
                                 0.5 
                                 + 
                                 
                                   1.5 
                                   · 
                                   
                                     
                                       ⅆ 
                                       l 
                                     
                                     
                                       av 
                                       ⁢ 
                                       
                                         ⅆ 
                                         l 
                                       
                                     
                                   
                                 
                                 + 
                                 tf 
                               
                             
                             · 
                             C 
                           
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           T 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           R 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     9 
                     ⁢ 
                     b 
                   
                   ] 
                 
               
             
           
         
       
     
     An embodiment further comprises a context filter process. This process may occur after the receiver release keyword scores  83  have been calculated. This process may occur after the matching process. 
     According to the context filter process, a predetermined number of words/phrases may be selected—the selected words/phrases having the highest receiver release keyword scores  83 . According to an embodiment, all of the words/phrases are selected. 
     For example the top five receiver release keyword  83  scored words/phrases may be selected: 
     
       
         
           
               
               
               
               
               
               
             
               
                   
                   
               
               
                   
                   
                   
                   
                   
                 Receiver 
               
               
                   
                 Receiver 
                   
                 Release 
                 Release 
                 Release 
               
               
                   
                 Word 
                   
                 Word 
                 Keyword 
                 Keyword 
               
               
                   
                 Match 
                 RKRS 
                 Match 
                 Score 
                 Scores 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Some 
                 16.5517 
                 some 
                 1.057055 
                 17.49605 
               
               
                   
                 Partner 
                 6.594141 
                 partner 
                 2.606629 
                 17.18848 
               
               
                   
                 Make 
                 21.81457 
                 make 
                 0.738626 
                 16.11281 
               
               
                   
                 Search 
                 9.167579 
                 search 
                 1.676226 
                 15.36693 
               
               
                   
                 Top 
                 21.81708 
                 top 
                 0.704117 
                 15.36178 
               
               
                   
                   
               
            
           
         
       
     
     According to an embodiment, the receiver release keyword score  83  does not occur prior to the content filter process. 
     The selected words/phrases are then analysed to determine the number of times that the word/phrase appears in information content on their own or in conjunction with each other. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                   
                   
               
               
                   
                 some 
                 partner 
                 make 
                 search 
                 top 
                   
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Some 
                 6 
                 3 
                 2 
                 1 
                 2 
                 14 
               
               
                 some + partner 
                   
                   
                 1 
                   
                 1 
                 2 
               
               
                 some + partner + make 
                   
                   
                   
                 2 
                 1 
                 3 
               
               
                 some + partner + make + 
                   
                   
                   
                   
                 1 
                 1 
               
               
                 search 
               
               
                 Partner 
                   
                 9 
                 3 
                 2 
                 4 
                 18 
               
               
                 partner + make 
                   
                   
                   
                 2 
                 1 
                 3 
               
               
                 partner + make + search 
                   
                   
                   
                   
                 1 
                 1 
               
               
                 Make 
                   
                   
                 10 
                 2 
                 3 
                 15 
               
               
                 make + search 
                 1 
                   
                   
                   
                 1 
                 2 
               
               
                 make + search + top 
                 1 
                   
                   
                   
                   
                 1 
               
               
                 Search 
                   
                   
                   
                 4 
                 2 
                 6 
               
               
                 search + top 
                 2 
                 2 
                   
                   
                   
                 4 
               
               
                 Top 
                   
                   
                   
                   
                 12 
                 12 
               
               
                   
                   
                   
                   
                   
                   
                 82 
               
               
                   
               
            
           
         
       
     
     The totals from the preceding step are then summed to give a combination count—for example: 
     
       
         
           
               
             
               
                   
               
               
                 Duplicate combination 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 1 word/phrases 
                 41 
               
               
                   
                 2 words/phrases 
                 24 
               
               
                   
                 3 words/phrases 
                 11 
               
               
                   
                 4 words/phrases 
                 5 
               
               
                   
                 5 words/phrases 
                 1 
               
               
                   
                   
                 82 
               
               
                   
                   
               
            
           
         
       
     
     A context multiplier is calculated. In an embodiment, the context multiplier for a word/phrase on its own is 1 and for combinations of two or more words/phrases is determined by equation 10 below:
 
Context Multiplier=2^ P^X   [Equation 10]
 
where P is a constant which may be 1 and X is the total number of words minus 1.
 
     A context value is then calculated. In an embodiment, the context value is determined by equation 11:
 
Context Value=Combination Count(for a particular number of words/phrases).
 
Context Multiplier(for that number of words/phrases).  [Equation 11]
 
     For example: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                 Duplicate 
                 Context 
                   
                 Context 
               
               
                 combination 
                 Multiplier 
                 Power 
                 value 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 1 word/phrases 
                 41 
                 1.0000 
                 N/A 
                 41.0000 
               
               
                   
                 2 words/phrases 
                 24 
                 2.0000 
                 1 
                 48.0000 
               
               
                   
                 3 words/phrases 
                 11 
                 4.0000 
                 2 
                 44.0000 
               
               
                   
                 4 words/phrases 
                 5 
                 8.0000 
                 3 
                 40.0000 
               
               
                   
                 5 words/phrases 
                 1 
                 16.0000 
                 4 
                 16.0000 
               
               
                   
                   
                 82 
                   
                   
                 189.0000 
               
               
                   
                 Power factor = 
               
               
                   
                 P 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     In an embodiment, a receiver release context value (RRCV) is then determined using equation 12:
 
RRCV=Sum of the context value for each word/phrase combination  [Equation 12].
 
     In an embodiment, a final receiver release relevance score (RRRS) is calculated using equation [13]:
 
RRRS=ΣRRCV·RRKS for all matched words/phrases  [Equation 13]
 
     The matching/comparison process can then be implemented as above using a threshold value. It will be appreciated that the threshold value levels will need to be adjusted accordingly and that this may be achieved by selecting appropriate values for Y and Z in Equation 6. 
     It will be appreciated that the above described context filter could be implemented as part of a matching/comparing process without other aspects of the processes described above. 
     According to an embodiment, a phrase factor is used during the matching process to increase the perceived relevance of any matched phrases proportional to the length of that matched phrase. 
     The RRKS is multiplied by the phrase factor to produce a Phrase Matched Score. This is performed for any matched phrases of 2 or more words—in an embodiment. 
     The phrase factor may be calculated according to equation 14:
 
PF=PC (Number of words in phrase−1)   [Equation 14]
 
where PF is the phrase factor and PC is a phrase constant which may be 2.
 
     For example: 
     
       
         
           
               
               
               
               
               
               
             
               
                   
               
               
                 Number of 
                   
                   
                   
                   
                   
               
               
                 words in 
                   
                   
                 Standard match 
                   
                 Phrase matched 
               
               
                 phrases 
                 RKS 
                 RKRS 
                 score 
                 Power 
                 score 
               
               
                   
               
             
            
               
                 1 
                 4 
                 3 
                 12 
                 N/A 
                 12 
               
               
                 2 
                 4 
                 3 
                 12 
                 1 
                 24 
               
               
                 3 
                 4 
                 3 
                 12 
                 2 
                 48 
               
               
                 4 
                 4 
                 3 
                 12 
                 3 
                 96 
               
               
                 PC = 
                 2 
               
               
                   
               
            
           
         
       
     
     The Phrase Matched Score is, in an embodiment, summed with the RRKS for single words to produce a revised receiver release total keyword score  84  and this is then used for the matching process. 
     In accordance with embodiments, the processes, systems and modules described herein include the processes, systems and modules—or parts thereof—as described in  FIGS. 27 ,  28 , and  29 . 
     In accordance with embodiments, the processes, systems and modules described herein include the processes, systems and modules—or parts thereof—as described in  FIGS. 30 and 31 . 
     Still Further Aspects of Embodiments 
     A further aspect of embodiments provides a search query interface which allows a user to search information content  24  to identify information content which may be of particular interest to that user (as a potentially interested recipient  2 ). 
     Information content  24  is stored on one or more storage media—which may be local or remote storage media and/or may be distributed storage media. 
     The search query interface is presented to the user through a portal provided which may be provided over a network—such as the internet. 
     The search query interface is configured to receive a text string from the user (which may be input through an input device such as a keyboard or through a voice recognition system for example). 
     The text string is, in an embodiment, transmitted to a server which is remote from the user. 
     In an embodiment, the text string undergoes analysis which includes some or all of the same analysis steps generally as described above in relation to the analysis  5  of harvested results to produce a recipient profile  6  and/or the process  25  which generates the information content profile  9 . The analysis preferable includes tokenisation  38  of the text string and extraction of one or more words or phrases and may include one or more of stemming  40 , tagging  39 , and normalisation  41 . 
     The extracted words or phrases are converted from text to numeric identifiers by checking whether each word or phrase has previously been identified in either the harvested content  4  from user or the information content  24  to be displayed/distributed by looking up each word or phrase in a terms table (a table that stores every term (i.e. word or phrase) that has been extracted from either of these two data sets). 
     Words or phrases that do not appear in this terms table terms are ignored for the purpose of calculating a Search Relevance Score—as no match will be found in relation to these words or phrases in the information content  24 . 
     The Search Relevance Score is calculated by assigning each identified word or phrase with a value of, for example, one. 
     The information content  24  is searched to identify information content  24  that includes at least one of the words or phrases identified within the text string. The information content  24  identified becomes the search corpus. 
     For each information content item  24  within the search corpus the Search Relevance Score (SRS) is calculated as described below:
 
Search Relevance Score=
 
For all matched terms
 
[[ΣRKSs for SWMT+(ΣRKSs for MWMT×MTC (A−1) )]×SRC B ×[(RTS+1) RTP ]]  [Equation 15]
 
wherein:
 
     Single word matching terms=SWMT; 
     Multiple word matching terms=MWMT; 
     Number of words in MWMT=A; 
     Matching Terms Constant=MTC (e.g. 4); 
     Search Relevance Constant=SRC (e.g. 2); 
     Number of search query terms matched=B; 
     Number of Receivers to whom the Release has been matched and has passed their Relevance Threshold=RTS; and 
     Relevance Threshold Power=RTP (e.g. 0.5). 
     The search corpus is sent to the user, preferably through a web service interface, with the relevant SRS for each information content  24  item (i.e. Release). The information content  24  is presented to the user in reverse SRS score order—preferably with a summary or abstract representing each information content  24  item in the presented results (the user being able to “click-through” the summary or abstract to access the complete information content  24 ). 
     An example is shown below: 
                                                Search query   apple iphone               app                        
Release where they all Match as Single Words, Two Word Terms and Three Words Term
 
                                                     Release   Number of   `               Keyword   words in       Term   Type   Score   MWMT (A)   Score                                                    Apple   SWMT   2.00   N/A   2.00       Iphone   SWMT   1.50   N/A   1.50       App   SWMT   1.20   N/A   1.20       apple iphone   MWMT   2.00   2.00   8.00       iphone app   MWMT   2.00   2.00   8.00       apple iphone app   MWMT   1.70   3.00   27.20                       47.90       MTC   4.00       SRC   2.00       B   6.00       RTS   43.00       RTP   0.50               Search Relevance Score 20,334.89            
Release where they all Match as Single Words and as Two Word Terms, but not as a 3 Word Term
 
                                                                 Release   Number of                       Keyword   words in           Term   Type   Score   MWMT (A)   Score                                                                Apple   SWMT   2.00   N/A   2.00           Iphone   SWMT   1.50   N/A   1.50           App   SWMT   1.20   N/A   1.20           apple iphone   MWMT   2.00   2.00   8.00           iphone app   MWMT   2.00   2.00   8.00                           20.70           MTC   4.00           SRC   2.00           B   5.00           RTS   43.00           RTP   0.50                       Search Relevance Score 4,393.86            
Release where they all Match as Single Words but not as 2 or 3 Word Phrases
 
     
       
         
           
               
               
               
               
               
               
             
               
                   
                   
               
               
                   
                   
                   
                 Release 
                 Number of 
                   
               
               
                   
                   
                   
                 Keyword 
                 words in 
               
               
                   
                 Term 
                 Type 
                 Score 
                 MWMT (A) 
                 Score 
               
               
                   
                   
               
             
            
               
                   
                 Apple 
                 SWMT 
                 2.00 
                 N/A 
                 2.00 
               
               
                   
                 Iphone 
                 SWMT 
                 1.50 
                 N/A 
                 1.50 
               
               
                   
                 App 
                 SWMT 
                 1.20 
                 N/A 
                 1.20 
               
               
                   
                   
                   
                   
                   
                 4.70 
               
               
                   
                 MTC 
                 4.00 
               
               
                   
                 SRC 
                 2.00 
               
               
                   
                 B 
                 3.00 
               
               
                   
                 RTS 
                 43.00 
               
               
                   
                 RTP 
                 0.50 
               
               
                   
                   
               
               
                   
                 Search Relevance Score 249.41 
               
            
           
         
       
     
     Embodiments may operate an exact query match process. For example, the user may submit the text string “apple iphone app”. 
     This text string is processed substantially as discussed above; however, the text string is checked to see if all phrases within the text string (of 2 or more words) exist in the terms table (“apple iphone”, “iphone app”, “apple iphone app”). 
     If any phrases do not appear in the terms table then a null result is generated and sent to the user. 
     If all phrases appear in terms table then the process proceeds substantially as discussed above; however, the only information content  24  items meeting the following criteria are sent to the user: 
     a) items which include all phrases 
     b) items in which the phrases are adjacent to each other. For example, if there are three terms to match and the first term matched was ranked tenth, and the last term matched ranked twelfth, then the terms are together. If there are three terms to match and the first term matched was ranked tenth, and the last term matched ranked forty sixth, then the terms are not together. 
     The SRSs are calculated and the information content  24  items ordered substantially as discussed above. 
     Additional Applications and Functionality 
     The information content  24  used in accordance with embodiments of the present invention may be submitted by an information content provider directly to a module of an embodiment of the invention for the generation of an information content profile  9 . 
     In embodiments, the information content  24  is passively harvested from a collection of available information content  24 . For example, this collection may comprise a storage device or distributed storage device storing a plurality of information content  24  items. The storage device or distributed storage device may comprise one or more computers or data storage media attached to a computer. A distributed storage device may comprise a plurality of storage devices linked by a network such as the internet or a local area network. The storage device or distributed storage device may comprise an internet server or an email server or a part thereof. 
     In an embodiment, a location at which the information content  24  is located may provided and the information content  24  may be passively harvested from that location—the location may be an IP address, the location of a blog, the location of a social networking page in a social networking website, or the like, for example. 
     In an embodiment, the presence of new information content  24  at a location is periodically or continuously checked and new information content  24  is then processed to determine an information content profile  9  and to undergo other processes described herein. For example, the email account of a potential recipient may be monitored and new email messages treated as information content  24  in accordance with embodiments described herein. 
     The information content  24  may comprise part of a repository of information content—such as an electronic library. The electronic library may include information content  24  which has been published by one or more authors. The electronic library may comprise information content  24  which has been extracted from non-digital publications (such as books, posters, and the like) and/or digital publications (such as websites, e-books, and the like). The information content  24  may comprise extracts from one or more larger publications. 
     The repository of information may comprise at least part of a social networking website and information stored in relation thereto. The information content  24  may including postings or updates to a social networking page or pages available through a social networking website. 
     The repository of information may comprise a repository of information accessible through the internet. 
     Alternative Feedback Process 
     A feedback process  15  has been described above. An alternative embodiment of the feedback process  15  is implemented in accordance with embodiments of the invention. This alternative feedback process  15  may be implements by a receiver feedback system (RFS)  18 . 
     This receiver feedback system  18  is capable of handling, in an embodiment, positive and negative feedback and may include a receiver input advanced feedback system and an automatic system for when only limited feedback is given by the recipient  2 . 
     In accordance with an embodiment of the system  18 , all keywords (i.e. words or phrases) for each recipient  2  can have three statuses: 
     (i) Whitelisted—which means that the particular word/phrase will always be included in the receiver release total keyword score  84  where a match exists with a factor equal to, for example, 1.0 applied no matter what the Relevance and Irrelevance Counts relating to this word/phrase (see below). 
     (ii) Blacklisted—which this means that the particular word/phrase will be excluded from the matching process completely no matter what the Relevance and Irrelevance Counts relating to this word/phrase (see below) 
     (iii) Default status—which means that the particular word/phrase is included in the matching process and the scoring process but the resulting product of the Release Keyword Score×Receiver Keyword Rank Score for these words is multiplied by a Feedback Likelihood Factor (FLF) that is calculated on the basis discussed below. 
     By default a predetermined number of the top most highly ranked receiver keywords (i.e. words/phrases with the highest receiver keyword rank scores) are given the whitelisted status (Default Whitelist Words) unless the status is overridden either by applying the blacklisted status or by the recipient  2  changing the status to the Default status. The predetermined number may, for example, be one hundred. 
     In an embodiment, all words by default start with a Relevance Count of 1 and an Irrelevance Count of 1. The feedback likelihood factor is calculated using the equation shown below:
 
Feedback Likelihood Factor=Relevance Count/(Relevance Count+Irrelevance Count)  [Equation 16]
 
     In the event that negative feedback is received from a recipient  2  in relation to particular information content  24  sent to that recipient  2  in accordance with embodiments of the invention, the recipient  2  may be directed to an interface which may be a webpage provided over a network (such as the internet). 
     The interface may present a slider or other user actuatable control or graphical user interface. Instructions on the use of the interface may be provided to the recipient  2 . In an embodiment, the recipient is provided with the releases&#39; matched keywords (i.e. words/phrases) ranked in order based on the result of the values of the Release Keyword Score×Receiver Keyword Rank Score match for the information content  24  concerned. 
     The recipient  2  can choose to flag any of these words/phrases as whitelist or blacklist and/or can change the whitelist status of any words in the list that are currently whitelisted. The status associated with the word/phrase may be altered accordingly. 
     If a word/phrase which has a Default whitelist status has its status changed by the recipient  2  to the Default status then this word/phrase is, in an embodiment, not automatically whitelisted again by the default process. 
     All matched words/phrases that relate to the information content  24  concerned have their “Irrelevance Count” increased by 1 even if they are on the Whitelist or Blacklist in case of future need. 
     The recipient  2  can also review the current Whitelist, Blacklist and Default status lists in full and edit these if desired. A user interface may be provided for this purpose which may be provided as a webpage over a network such as the internet. In an embodiment, the words/phrases are ordered in this interface in descending order of Receiver Keyword Rank Score. 
     In the event that positive feedback is received from a recipient  2  in relation to particular information content  24  sent to that recipient  2  in accordance with embodiments of the invention, the recipient  2  may be directed to an interface which may be a webpage provided over a network (such as the internet). 
     The interface may present a slider or other user actuatable control or graphical user interface. Instructions on the use of the interface may be provided to the recipient  2 . In an embodiment, the recipient is provided with the releases&#39; matched keywords (i.e. words/phrases) ranked in order based on the result of the values of the Release Keyword Score×Receiver Keyword Rank Score match for the information content  24  concerned. 
     The recipient  2  can choose to flag any of these words/phrases as whitelist or blacklist and/or can change the whitelist status of any words in the list that currently have the whitelisted status. The status associated with the word/phrase may be altered accordingly. 
     If a word/phrase which has the Default whitelist status has its status changed by the recipient  2  to the Default status then this word/phrase is, in an embodiment, not automatically whitelisted again by the default process. 
     All matched words/phrases that relate to the information content  24  concerned have their “Relevance Count” increased by 1 even if they are on the Whitelist or Blacklist in case of future need. 
     The recipient  2  can also review the current Whitelist, Blacklist and Default status lists in full and edit these if desired. A user interface may be provided for this purpose which may be provided as a webpage over a network such as the internet. In an embodiment, the words/phrases are ordered in this interface in descending order of Receiver Keyword Rank Score. 
     In accordance with embodiments, the recipient  2  can mark a word/phrase as “Absolute” such that any release (i.e. information content  24 ) that includes a word/phrase will be received no matter what receiver release total keyword score  84  the information content  24  is given. In an embodiment, the receiver release total keyword score  84  will still be calculated however. 
     In accordance with embodiments, the recipient  2  can mark a word/phrase as “Very interested” such that the word/phrase will be given a receiver keyword rank score equal to the highest receiver keyword rank score for that recipient  2  and will also be given the Whitelisted status if this is not the current status for that word/phrase. The receiver release keyword score  83  will still, in an embodiment, be calculated though for the word/phrase so that it can revert to this if the status is changed. 
     Systems and System Components (Such as Modules) 
     The present invention includes a computer configured to run the above processes (with aspects of manual operation as described where relevant). The computer  117  (see  FIG. 26 ) is, in an embodiment, a dedicated computer  117 . The computer  117  may be a computer server. The computer may be connect to a local area network  118  and/or to a wide area network  119 . 
     The computer  117  is preferably configured to run database software such as Microsoft (registered trademark) SQL. 
     In an embodiment, dedicated computer hardware is configured to perform the above processes. 
     In an embodiment, a computer readable medium is provided which stores a computer program which, when run on a computer, causes the computer to run one or more of the above described processes. 
     In an embodiment, a plurality of computers  117  are provided and these are connected to each other through a network which may comprise a local area network  118 , a wide area network  119 , or a combination thereof. The plurality of computers  117  may carry duplicates of database information held thereon and/or may provide distributed processing power for performing one or more of the above operations. 
     In an embodiment, the computer  117  is a local personal computer. 
     Thus, it will be understood, that the above described processes may be run on one or more systems and may form modules or parts of modules. The modules may be synonymous with the processes. A system may be operable to perform any combination of the above processes and may, therefore, include any combination of modules configured to perform the above processes. A system may be a computer  117  or group of computers  117 . 
     It will be appreciated that embodiments of the present invention include various combinations of the above processes. Any combination of processes is possible as an embodiment of the invention and, accordingly, a system may be configured to perform any such combination of processes (or individual process). 
     When used in this specification and claims, the terms “comprises” and “comprising” and variations thereof mean that the specified features, steps or integers are included. The terms are not to be interpreted to exclude the presence of other features, steps or components. 
     The features disclosed in the foregoing description, or the following claims, or the accompanying drawings, expressed in their specific forms or in terms of a means for performing the disclosed function, or a method or process for attaining the disclosed result, as appropriate, may, separately, or in any combination of such features, be utilised for realising the invention in diverse forms thereof.