Patent Abstract:
Question Answering systems can be costly and time-consuming to configure for a particular knowledge base. The proposed Question Answering receives an initial knowledge base, receives user queries without any further configuration, and self-configures based on user feedback. In one embodiment, a method of customizing a question answering system can include searching a plurality of articles based on a user query from a user received via a computer network. The method can further include delivering a set of articles, based on the searching, to the user via the computer network. The method can additionally include receiving user feedback on the set of articles from the user via the computer network. The method can also include associating a given article from among the set of articles with the user query based on the user feedback. In this manner, the proposed question answering system saves the customer resources otherwise spent on configuration.

Full Description:
RELATED APPLICATION 
       [0001]    This application claims the benefit of U.S. Provisional Application No. 61/599,367, filed on Feb. 15, 2012, the entire teachings of which are incorporated herein by reference. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    Question answering systems provide companies (e.g., customers of the question answering service) an ability to have a computer operated system provide automated support to an end-user (e.g., a customer of the company). This allows for the company to provide service to the end-user 24 hours a day, seven days a week. Question answering systems also reduce labor costs by reserving employees for more difficult questions from the end-user. 
       SUMMARY OF THE INVENTION 
       [0003]    In one embodiment, a method of customizing a question answering system can include searching a plurality of articles, based on a user query from a user, received via a computer network. The method can further include delivering a set of articles, based on the searching, to the user via the computer network. The method can additionally include receiving user feedback on the set of articles from the user via the computer network. The method can also include associating a given article from among the set of articles with the user query based on the user feedback. 
         [0004]    In another embodiment, the user feedback can be based on at least one of the following activities performed by the user: (a) selecting an article of the plurality of articles, (b) spending time viewing the article, (c) scrolling through the article, (d) leaving the article within a given time period after viewing the article, (e) selecting a second article, (f) making a manual user entry, or (g) returning to a previously selected article. 
         [0005]    In another embodiment, the method can further include applying a corresponding score to each article within the set of articles based on the user feedback. 
         [0006]    In yet another embodiment, the method can additionally include clustering user queries based on a linguistic distance being below a particular threshold. The method can further include associating a first cluster and second cluster based on a common association with the same article. The method can additionally include presenting a representation of the clusters of user queries to a user and enabling the user to modify the cluster. Modifying the cluster can include performing at least one of the following operations: (a) mapping the cluster to a particular article, (b) creating a new article associated with the cluster, (c) deleting an article associated with the cluster, (d) connecting the cluster to a secondary user query, the secondary user query clarifying the user queries of the cluster, (e) merging a first article and a second article associated with the cluster to be one search result, (f) splitting an article associated with the cluster into a first article and a second article associated with the cluster, or (g) rewriting an article associated with the cluster. The method can also include authorizing a user to modify the cluster. 
         [0007]    In another embodiment, the method can include presenting a dialog mode to a user. The method can also include receiving a natural language query from a user and presenting a natural language answer to the user. The natural language answer can be an article associated with a cluster. The method can further include, if more information is needed, presenting a second natural language query to the user. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    The foregoing will be apparent from the following more particular description of example embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments of the present invention. 
           [0009]      FIG. 1A  is a block diagram illustrating an example embodiment of an Intelligent Virtual Agent (IVA). 
           [0010]      FIG. 1B  is a block diagram of the IVA operatively coupled to a user device over the network. 
           [0011]      FIG. 1C  is a block diagram illustrating an example embodiment of the IVA employed in a dialog mode. 
           [0012]      FIG. 2  is a flow diagram illustrating an example embodiment of training the IVA. 
           [0013]      FIG. 3A  is a block diagram illustrating an example embodiment of the IVA in an encapsulated search mode. 
           [0014]      FIG. 3B  is a block diagram illustrating an example embodiment of the IVA employed in dialog mode. 
           [0015]      FIG. 4A  is a flow diagram illustrating an example embodiment of the IVA employed in encapsulated search mode. 
           [0016]      FIG. 4B  is a flow diagram illustrating an example embodiment of dialog mode employed by the IVA. 
           [0017]      FIG. 5  is a block diagram illustrating an example embodiment of clusters mapping to a knowledge base. 
           [0018]      FIG. 6  illustrates a computer network or similar digital processing environment in which the present invention may be implemented. 
           [0019]      FIG. 7  is a diagram of the internal structure of a computer (e.g., client processor/device or server computers) in the computer system of  FIG. 6 . 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0020]    A description of example embodiments of the invention follows. 
         [0021]    The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety. 
         [0022]      FIG. 1A  is a block diagram  100  illustrating an example embodiment of an Intelligent Virtual Agent (IVA)  102 . An IVA is an automated question answering system deployed by a customer (e.g., a company) that receives queries from an end-user (e.g., a client of the customer). IVAs can require a large amount of work from a vendor (e.g., a company implementing the IVA) and the customer (e.g., a company purchasing the IVA service to solve a problem, such as support, sales or marketing). In the foregoing description, customer refers to the party running the IVA, vendor refers to the party building the IVA, and end-user refers to the client of the customer that is using the IVA for support. 
         [0023]    Quality of an IVA is linked to the quality and amount of configuration of the IVA. An IVA should issue quality answers with a precise scope with a clean content architecture. Configuring the IVA to meet these requirements bridges the gap between intent of end-user queries issued to the IVA and the content of the customer. The proposed method and corresponding system configures an IVA with a customer&#39;s knowledge base without a huge investment of time before deploying the IVA. 
         [0024]    An administrative user  108  employs a setup device  106  to configure the IVA  102 . The setup device  106  is operatively coupled to the IVA  100  over a network  104 . The administrative user  108  employs the setup device  106  to send initialization information  102  to the IVA  102 . The initialization information  110  includes a name of the agent  112 , an avatar  114 , and a knowledge base  116 . 
         [0025]    The name of the agent  112  is a name shown to end-users when conversing with the IVA  102 . Likewise, the avatar  114  is a picture, such as an image of a human face or a cartoon of a human face, shown to the end-user while communicating with the IVA  102 . The knowledge base  116  is a collection of articles from the customer that can be provided as answers to user queries. 
         [0026]      FIG. 1B  is a block diagram  120  of the IVA  102  operatively coupled to a user device  124  over the network  104 . An end-user  122  enters a query into the user device  124  that is forwarded over the network  104  to the IVA  102 . The IVA processes the query  126  by performing a search engine search of the knowledge base and delivering search engine results  128  to the user device  124  over the network  104 . The user device  124  shows the search engine results  128  to the end-user  122 . The end-user  122 , after interacting with the search engine results  128 , sends feedback  130  on the articles in the search engine results to the IVA  122 . The IVA  102  then updates mapping of queries to articles in the knowledge base based on the feedback  130 . Feedback  130  can include metrics such as the end-user  122  selecting (e.g., clicking on) an article, the end-user  122  leaving an article and selecting another article, the end-user  122  manually entering positive or negative feedback about an article, time spent viewing an article, scrolling within an article, or returning to a previously viewed article. 
         [0027]      FIG. 1C  is a block diagram  140  illustrating an example embodiment of the IVA  102  employed in a dialog mode. The IVA  112  in dialog mode maps queries directly to articles, instead of performing a search engine search on the knowledgebase. For example, the end-user  122  enters a query  126  into the user device  124 . The IVA  102 , upon receiving the query  126 , instead of performing a search engine search, directly maps the query  126  in an article or a set of articles, and delivers result(s) based on clusters  142  to the user device  124 . Likewise, the user device then delivers feedback  144  on articles in the results, similar to the feedback  130  described in  FIG. 1B . 
         [0028]      FIG. 2  is a flow diagram  200  illustrating an example embodiment of training the IVA. Training begins by beginning configuration ( 202 ). Then, the user uploads a knowledge base to the IVA ( 204 ). Then, the user can optionally upload an avatar and a corresponding name to the IVA ( 206 ). Then, the configuration is complete and the user can start the IVA for interaction with end-users ( 208 ). 
         [0029]    In one embodiment, the IVA begins with a database of the customer&#39;s content “as is.” The customer is able to create and educate the IVA without help from the vendor. Content can include answers taken from frequently asked questions (FAQs), resolution cards, solutions, etc. The customer&#39;s content “as is” is known to be bad, and embodiments of the systems and methods disclosed herein aim to improve the content in the database. The IVA provides a search experience to end-users using the database of the customer content “as is.” The IVA monitors behavior and final satisfaction of end-users, and modifies the database accordingly to improve future IVA performance. 
         [0030]    In one embodiment, the IVA described herein can be deployed on its host website and exposed to users as soon as the IVA is created. The customer organization is able to immediately provide its users with an IVA user interface (UI) (e.g., a chat window) on its website by adding code (e.g., JavaScript, HTML5, etc.). IVA creation, from the customer perspective, includes selecting a name for the IVA, an avatar for the IVA, and linking the system to its knowledge base. In other embodiments, the IVA can be created without a name and/or avatar. 
         [0031]      FIG. 3A  is a block diagram  300  illustrating an example embodiment of the IVA in an encapsulated search mode. A search engine module  304  within the IVA receives a user query  302 . The search engine module  304  performs a search of the knowledge base, based on the words in the user query  302 , and delivers a presentation to the user  306 . The presentation to the user  306  can include a set of articles that are most closely related to the user query  302  based on the words in the user query  302 . 
         [0032]    Upon deployment, the IVA is in encapsulated search mode, using a search engine on the customer&#39;s knowledge base to provide its response to end-user queries. At this point, the IVA relies on no other data to provide its responses to the query other than words within the customer&#39;s knowledge base. In encapsulated search mode, the behavior of the end-user using the UI of the IVA is recorded. The IVA records the queries of the user, the articles/links the user chooses to click, the time the user spends on each linked page, the user&#39;s satisfaction, either self-reported by use of thumbs up and thumbs down buttons or by implication. Implied user satisfaction can be measured by a user&#39;s leaving an article, or by retaining knowledge of the last article a user visits. 
         [0033]    The IVA, during the encapsulated search phase, sends every user input to its search engine which responds with a series of possible articles. The IVA presents selected top ranked articles to the user as potential responses. The IVA also adds links between the user query and the selected articles with a neutral grade. If an article is already linked with the query, the grade of the article in the IVA system does not change. The IVA links user queries to knowledge base articles (or other answers) based on the grades. The IVA, in discovering the possible links between user queries and knowledge base articles, can then later ask the administrative user of the customer to confirm or reject the links/mapping. 
         [0034]    For example, after the end-user enters query q 1 =“can I send a check?,” the IVA search engine returns four articles: a 123 , a 587 , a 745  and a 457 . At this point, grades of all the articles are zero, as indicated by the following equation (with L(q k ,a n ) being the grade of the link between q k  and a n ): 
         [0000]        L ( q   1   ,a   123 )= L ( q   1   ,a   587 )= L ( q   1   ,a   745 )= L ( q   1   ,a   457 )=0 
         [0035]    From there, the IVA records end-user actions reacting to the presented articles. Each action can assign a number of points to the link between a query and an article (L(q k ,a n )). In one embodiment, an end-user&#39;s clicking a link adds five points to the grade between the article and the query. Similarly, an end-user&#39;s clicking a “thumbs up” adds 10 points to the grade, and the end-user&#39;s clicking a “thumbs down” subtracts 10 points from the grade. For example, if the end-user selects the first article (a 123 ), clicks on “thumbs down” for the same first article (a 123 ), returns to the search screen, click on third one and “thumbs up” and leave gives the following point totals for the links: 
         [0000]        L ( q   1   ,a   123 )=−5
 
         [0000]        L ( q   1   ,a   587 )=0 
         [0000]        L ( q   1   ,a   745 )=+15 
         [0000]        L ( q   1   ,a   457 )=0 
         [0036]    Other user input, such as scrolling the article, time spent on the article, or leaving the article, can also be recorded and modify the grade accordingly. The IVA monitors user input and adjusts the grades of the links for every end-user query. 
         [0037]      FIG. 3B  is a block diagram  350  illustrating example embodiment of the IVA employed in dialog mode. In dialog mode, a direct answer lookup module  352  receives the user query  32 . The direct answer lookup module  352  employs data in the IVA based on previously received user feedback to link the user query  302  directly to an article or a set of articles and delivers the articles in a presentation to the user  354 , if such a link is found. On the other hand, if the link is not found, the direct answer lookup module relays the user query to the search engine module  304 , which performs a search as described in  FIG. 3A . In relation to  FIG. 3B , the search engine module  304  then delivers the presentation  306  to the end-user as described in relation to  FIG. 3A . This can be referred to as a “search fallback,” where. in the event that the IVA does not successfully find a helpful article for the end-user, the IVA can offer a search of the knowledge base/database based on the user query. 
         [0038]    The IVA switches to dialog mode after a threshold of volume of traffic to the IVA. The IVA UI does not change from the perspective of the user in dialog mode. However, instead of querying the search engine to provide the responses, the IVA can map a user input directly to an intent and to a response or article. If the IVA cannot map the input to an intent, or if the IVA cannot map the intent to a response, then the IVA performs a search of the knowledge base, as described above. In the dialog mode, the behavior of the end-user continues to be recorded, as described above, in order to further refine the IVA&#39;s knowledge of the end-user&#39;s intents. 
         [0039]      FIG. 4A  is a flow diagram  400  illustrating example embodiment of the IVA employed in encapsulated search mode. First, the IVA begins the encapsulated search mode ( 402 ). The IVA receives a user query ( 404 ). Then, the IVA performs a search engine search of the knowledge base with the words in the user query ( 406 ). The IVA then presents the end-user with articles found from the search ( 408 ). The articles found in the search can be ranked according to matches with the words in the query or another metric. Then, the IVA receives user feedback on the search results ( 410 ). The IVA, based on the user feedback, adjusts scores of the articles in relation to the query received for future mapping of the query to each article in the search results. Then the IVA determines whether enough feedback has been received to deliver directly mapped answers to the user query ( 412 ). 
         [0040]    If enough feedback has been received, the IVA begins dialog mode ( 414 ). Dialog mode is further described in relation to  FIG. 4B . In relation to  FIG. 4A , if the IVA determines that there is not enough feedback received to deliver directly mapped answers to the user ( 412 ). The IVA continues the encapsulated search mode ( 416 ), and then receives a subsequent user query ( 404 ). 
         [0041]      FIG. 4B  is a flow diagram  450  illustrating an example embodiment of dialog mode employed by the IVA. The IVA begins dialog mode ( 414 ) after the encapsulated search mode has been complete ( FIG. 4A ). In relation to  FIG. 4B , the IVA then receives the user query ( 452 ). Then, the IVA determines whether there is a direct map of a query to an article ( 454 ). If there is a direct map to an article ( 454 ), then the IVA maps the query to the article or articles ( 456 ). Then, the IVA presents the articles to the end-user ( 458 ). The IVA receives user feedback on the search results to update the links further between queries and articles, even though encapsulated search mode is over ( 460 ). Then, the IVA receives another user query ( 452 ). 
         [0042]    On the other hand, if there is no direct map of a query to an article ( 454 ), the IVA performs a search engine search of the knowledge base ( 462 ). Then, the IVA presents the articles to the end-user ( 458 ). 
         [0043]      FIG. 5  is a block diagram  500  illustrating an example embodiment of clusters mapping to a knowledge base  508 . In the background, on a regular basis, the IVA reviews all the end-user queries it receives. The IVA clusters the queries by grouping them by their linguistic proximity. The IVA clusters end-user queries with the same meaning together. Clustering is further described in “Semantic Clustering” by Jean-Marie Larchavêque et al., filed as U.S. application Ser. No. 12/758,091 on Mar. 26, 2010 and “Semantic Clustering and Conversational Agents” by Jean-Marie Larchavêque et al., filed as U.S. application Ser. No. 12/748,2010 on Mar. 26, 2010, both of which are incorporated herein by reference in their entirety. 
         [0044]    If d(q i ,q j ) is the linguistic distance between q i  and q j  and Q is the entire query space, linguistic clusters can be defined as follows: 
         [0000]      ∀ qεQ,∃cCQ,∀ ( q   1   ,q   2 )ε c   2   ,d ( q   1   ,q   2 )&lt;ε and  qεc  
 
         [0045]    The operation creates a set of disjoint clusters mapping the entire query space Q. The clusters can be assigned links to the articles, the grade of the links being a function of the grades of the links of the cluster&#39;s contained queries, as described by the following relationship. 
         [0000]      Λ( c   n   ,a   m )= f ( L ( q   i   ,a   m ), q   j   εc   n )
 
         [0046]    The above function accounts for links not existing exist between some queries in the cluster and a particular article. As a result, each cluster has a preferred article which is the article whose link has the highest grade, noted as a(c n ). The IVA, during the second pass of clustering, groups linguistic clusters that have the same preferred article. The clusters can also be referred to as intent. 
         [0000]        C ( a   k )={ q,qεc   n  and  a ( c   n )= a   k } 
         [0047]    Links and grades on the dual clusters are defined the same as links and grades for linguistic clusters. Although dual clusters can be defined by a preferred article, the clusters are linked to multiple lower ranked articles, and the Λ function (a→Λ(C,a)) is defined for the lower ranked articles in addition to the preferred article. 
         [0048]    Dual approach clustering combines groups queries by both linguistic aspects of the query but also by the end-user&#39;s intent (as reflected by the grades of the links). Dual approach clustering combines two ways of ascertaining the meaning of an incoming query: (1) its local linguistic data, and (2) what the end-user previously indicated is an appropriate response. 
         [0049]    A first cluster  502 , second cluster  504 , and third cluster  506  include a plurality of queries  516   a - e ,  518   a - e , and  520   a - e . Each of the clusters  502 ,  504  and  506  map to articles in the knowledge base  508 . For example, the first cluster  502  maps, via a mapping link  522 , to a second article  512  in the knowledge base  508 . The mapping link  522  is based on feedback of the queries  516   a - e  within the first cluster  502  that indicate the second article  512  is most relevant to the topic of the queries  516   a - e  stored in first cluster  502 . 
         [0050]    Further, the third cluster  506  includes a mapping link  524  also to the second article  512 . The mapping link  524 , like the mapping link  522 , is based on the queries  520   a - e  within the third cluster  506 . The IVA, based on the first cluster  502  and third cluster  506  both mapping to the second article  512 , can suggest to merge the first cluster  502  and third cluster  506 . The IVA suggests merging the first cluster  502  and third clustering  506  because the respective queries  516   a - e  and  520   a - e  of both clusters indicates that the second article  512  is an answer to both. This implies that the first cluster  502  and the queries  516   a - e  therein and the third cluster  506  and the queries  520   a - e  therein includes similar queries that may be simply worded differently and not graded same by a linguistic search, but are about the same content. 
         [0051]    The second cluster  504 , which includes queries  518   a - e , is mapped to a third article  514  via mapping link  526 . The second cluster  504  is not a candidate for merging because no other cluster maps to the same third article  514 . The knowledge base  508  also includes first article  510  to which no cluster has a mapping link. 
         [0052]    Each cluster  502 ,  504  and  506  can map to multiple articles in addition to mapping to one article. A cluster mapping to one article is shown in  FIG. 5  for simplicity. Each cluster can map to multiple articles, having a top-ranked article and other lower-ranked articles to display to the user. Further, a cluster can map to no article if the queries have not indicated to which article the queries map. 
         [0053]    Further, the IVA can issue other suggestions to the customer to improve the end-user experience. For example, the IVA can suggest that the customer improve content by creating or deleting answers. The IVA can further suggest creating refining questions to hone in on an end-user query. The IVA can also map intents of a query directly to a known solution. The IVA can additionally offer suggestions to the organization, such as the rewriting of an article, merging of two similar articles (as described above), and splitting of an answer into two different articles. 
         [0054]    The IVA can include an administrative tool for the customer&#39;s use, which can prompt an administrative user of the customer to take one or more actions to improve the IVA&#39;s content. These actions are presented simply to the administrative user, for example, by requiring only one click to perform the action. 
         [0055]    One action provided by the administrative tool is “Map.” The administrative tool presents the administrative user with a list of end-user inputs and a knowledge base article. The administrative tool asks the administrative user to enter whether each input on the list should trigger this article as a response. 
         [0056]    The administrative tool can also ask the administrative user to merge two similar articles, split an article into two if queries with two or more different subjects commonly result in the same article, rewrite an article, create an article if a cluster is mapped to no article or no article with a rank above a certain threshold, or delete an article that is mapped to no cluster or has no rank for any cluster or query above a certain threshold. 
         [0057]    The IVA can also detect the need for clarification between two articles. If two articles need clarification, the administrative tool can ask the administrative user to create a clarifying question to be presented to the user. The clarifying question should elicit the user&#39;s intent to discriminate between these two articles. Therefore, the IVA can evolve into more than a question answering system by being able to handle more complex dialog guided by detecting the need for clarification and adapting the dialog with the administrator-generated clarifying question. 
         [0058]    In one embodiment, when the customer needs to take an action, the administrative tool presents example user inputs to the administrative user that the IVA detected before proposing the action. This presents the decision for the administrative user as whether the administrative wants the IVA responding to the user query as shown, as opposed to making decisions abstractly. For example, the administrative tool can ask the administrative user to determine whether “Question A” should map to “Article X,” and the administrative user can answer with a “yes” or “no.” 
         [0059]    As soon as the IVA has initiated dual clustering, the IVA reviews the clusters and their respective links to articles to propose enhancement in the knowledge of the agent to the customer. For every cluster C, the IVA employs the A function (a→Λ(C,a)), and the distribution of its results are the signals to select which (if any) suggestion to make. A good solution for a cluster C and a corresponding article a is determined if its grade Λ(C,a) is greater than a threshold (t), where G c  is the set of the good solutions for cluster C. 
         [0000]        G   c   ={a,Λ ( C,a )&lt; t}   
         [0060]    Let also /G c / be the cardinal (i.e., indicate the number of articles it contains) of this set. 
         [0000]    
       
         
               
               
               
             
           
               
                   
               
               
                   
                 Suggestions 
                 Conditions to trigger suggestion 
               
               
                   
               
             
             
               
                 Map 
                 Connect a cluster to  
                 /G c / = 1 
               
               
                   
                 an article. 
                   
               
               
                 Create 
                 Write a new article. 
                 C ≠  and /G c / = 0 
               
               
                 Delete 
                 Delete a useless  
                 C (a) =  
               
               
                   
                 article. 
                   
               
               
                 Clarify 
                 Connect a cluster to  
                 1 &lt; /G c / &lt; 4 
               
               
                   
                 a clarifying question. 
                   
               
               
                 Merge 
                 Merge two articles. 
                 1 &lt; /G c / &lt; 4 
               
               
                 Split 
                 Split one article into  
                 C(a) is composed of at least two linguistic 
               
               
                   
                 two new articles. 
                 clusters that are too distant from each other. 
               
               
                 Rewrite 
                 Rewrite an article. 
                 C(a) is composed of at least two linguistic 
               
               
                   
                   
                 clusters that are too distant from each other. 
               
               
                   
               
             
          
         
       
     
         [0061]    The IVA annotates links between clusters of queries and articles based on the actions taken by the customer. The IVA then uses those annotations in dialog mode. Once enough of the query space is mapped to articles or clarifying questions the agent can be switched into dialog mode. In this mode, when a user inputs a query, the IVA attempts to determine to which cluster C the end-user&#39;s query belongs. There are three possibilities:
       1) The IVA finds a cluster and the cluster is mapped to an article or a clarifying question, and the IVA responds the article or clarifying question. If the user is not happy with the provided response, the user has the option of using the search engine, for example, via a “see more answers button.”   2) The IVA finds a cluster that is not mapped to an article or clarifying question. The IVA responds with articles contained in G c .   3) The IVA cannot find a cluster, so the IVA employs the search engine to find an article.       
 
         [0065]    In this mode, the IVA continues to employ behavior monitoring, clustering and guided evolution in the background so that the IVA is continuously improved. 
         [0066]      FIG. 6  illustrates a computer network or similar digital processing environment in which the present invention may be implemented. 
         [0067]    Client computer(s)/devices  50  and server computer(s)  60  provide processing, storage, and input/output devices executing application programs and the like. Client computer(s)/devices  50  can also be linked through communications network  70  to other computing devices, including other client devices/processes  50  and server computer(s)  60 . Communications network  70  can be part of a remote access network, a global network (e.g., the Internet), a worldwide collection of computers, Local area or Wide area networks, and gateways that currently use respective protocols (TCP/IP, Bluetooth, etc.) to communicate with one another. Other electronic device/computer network architectures are suitable. 
         [0068]      FIG. 7  is a diagram of the internal structure of a computer (e.g., client processor/device  50  or server computers  60 ) in the computer system of  FIG. 6 . Each computer  50 ,  60  contains system bus  79 , where a bus is a set of hardware lines used for data transfer among the components of a computer or processing system. Bus  79  is essentially a shared conduit that connects different elements of a computer system (e.g., processor, disk storage, memory, input/output ports, network ports, etc.) that enables the transfer of information between the elements. Attached to system bus  79  is I/O device interface  82  for connecting various input and output devices (e.g., keyboard, mouse, displays, printers, speakers, etc.) to the computer  50 ,  60 . Network interface  86  allows the computer to connect to various other devices attached to a network (e.g., network  70  of  FIG. 6 ). Memory  90  provides volatile storage for computer software instructions  92  and data  94  used to implement an embodiment of the present invention (e.g., IVA implementing code detailed above). Disk storage  95  provides non-volatile storage for computer software instructions  92  and data  94  used to implement an embodiment of the present invention. Central processor unit  84  is also attached to system bus  79  and provides for the execution of computer instructions. 
         [0069]    In one embodiment, the processor routines  92  and data  94  are a computer program product (generally referenced  92 ), including a computer readable medium (e.g., a removable storage medium such as one or more DVD-ROM&#39;s, CD-ROM&#39;s, diskettes, tapes, etc.) that provides at least a portion of the software instructions for the invention system. Computer program product  92  can be installed by any suitable software installation procedure, as is well known in the art. In another embodiment, at least a portion of the software instructions may also be downloaded over a cable, communication and/or wireless connection. In other embodiments, the invention programs are a computer program propagated signal product  107  embodied on a propagated signal on a propagation medium (e.g., a radio wave, an infrared wave, a laser wave, a sound wave, or an electrical wave propagated over a global network such as the Internet, or other network(s)). Such carrier medium or signals provide at least a portion of the software instructions for the present invention routines/program  92 . 
         [0070]    In alternate embodiments, the propagated signal is an analog carrier wave or digital signal carried on the propagated medium. For example, the propagated signal may be a digitized signal propagated over a global network (e.g., the Internet), a telecommunications network, or other network. In one embodiment, the propagated signal is a signal that is transmitted over the propagation medium over a period of time, such as the instructions for a software application sent in packets over a network over a period of milliseconds, seconds, minutes, or longer. In another embodiment, the computer readable medium of computer program product  92  is a propagation medium that the computer system  50  may receive and read, such as by receiving the propagation medium and identifying a propagated signal embodied in the propagation medium, as described above for computer program propagated signal product. 
         [0071]    Generally speaking, the term “carrier medium” or transient carrier encompasses the foregoing transient signals, propagated signals, propagated medium, storage medium and the like. 
         [0072]    While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.

Technology Classification (CPC): 6