Abstract:
While a user is viewing content on a computer display, the universal data relationship inference engine presents related information from disparate data sources. A normalized index is maintained that indexes content to a set of standard taxonomy terms. The inference engine parses content being viewed by the user. If the content includes tags for some of the standard taxonomy terms, then the system may provide the user with the ability to view the related content that is indexed by the normalized index. If there are not taxonomy tags then the system may attempt to recognize non-standard taxonomy terms in the content in order to provide the user with related content. The inference engine may also identify related content by identifying synonyms to the taxonomy terms.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This is a continuation of U.S. application Ser. No. 11/489,962, filed Jul. 20, 2006 (currently pending), the contents of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     It is often a goal of a business to have its employees perform their daily work at a higher level. For example, a business may desire that its sales force, call center representatives, or professional services representatives be more effective in their day-to-day business activities. One way to boost human performance is to provide people the information they need to do their jobs. Such information may include training, access to experts on specific topics, sample work products or other knowledge that is pertinent to helping the employee do his or her job 
     For example, a computer system may allow a call center representative to perform a search on support materials. Or the system may allow the call center representative access to a trouble ticket system for the purpose of discovering how similar problems have been solved in the past. Providing access to support materials is not without its problems. A company may have its content on too many different systems. This then requires a computer user looking for an answer to search each system individually. Or, a company may have its content stored in an unorganized fashion. While an answer may exist, the user may be unable to find the answer among the unorganized materials. Or the user may not know how to properly navigate the computer system to find the information he or she needs or may not know how to formulate an effective query to search for the information. These and other problems can prevent people from performing at a higher level. 
     SUMMARY OF THE INVENTION 
     While a user is viewing content on a computer display, the universal data relationship inference engine may present related information from disparate data sources. A normalized index may be maintained that indexes content to a set of standard taxonomy terms. The inference engine may parse content being viewed by the user. If the content includes tags for some of the standard taxonomy terms, then the system may provide the user with the ability to view the related content that is indexed by the normalized index. If there are not standard taxonomy tags, then the system may attempt to recognize non-standard tags in the content or derive standard tags in order to provide the user with related content. The inference engine may also identify related content by identifying synonyms to the taxonomy terms. 
     It is an objective of the invention to provide computer system users with the ability to leverage content that is relevant to the user&#39;s task at hand. It is another objective of the invention to provide highly relevant related information to a computer user. It is yet another objective of the invention to provide related content in near proximity to the task being handled by a computer user. These and other objectives are realized by embodiments of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flowchart of one embodiment of the present invention. 
         FIG. 2  is a screen shot of a computer application in use by a computer user. 
         FIG. 3  is a schematic diagram illustrating a feature of the embodiment of  FIG. 1 . 
         FIG. 4  is a flowchart of another embodiment of the present invention. 
         FIG. 5  is a schematic diagram of a feature of the present invention that, in the example, is accessible from the computer application of  FIG. 2 . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Aspects of the invention may be embodied in “A Universal Data Relationship Inference Engine” system, which is also referred to as the AUDRIE system for short.  FIG. 1  is a flowchart that maps the major system components in one embodiment of the AUDRIE system  100 . As can be seen in  FIG. 1 , this embodiment of AUDRIE includes three subsystems, namely, the tag normalization subsystem  105 , the index normalization subsystem  110  and the display subsystem  115 . 
       FIG. 2  is a screen shot showing a primary computer application  205  as well as reference support offered by AUDRIE  210  (shown within the dashed lines). The flowchart of  FIG. 1  can be explained through reference to this screen shot. 
     In step  120 , a computer user may view new content. This content may be displayed while the user is accessing a web site, while she is running a software application (such as is shown in  FIG. 2 ), etc. The content being viewed  205  drives what reference support is offered by AUDRIE  210 . In one embodiment of the invention, AUDRIE reacts to three content scenarios. In the first scenario, the displayed content may be identified as content that has already been tagged via a standard taxonomy that is recognizable by the AUDRIE system. In the second scenario, the displayed content may be managed by another application that has its own taxonomy, such taxonomy already having been mapped to AUDRIE&#39;s standard taxonomy. In the third scenario, the viewed content may contain no tags or may be tagged with an unknown taxonomy. In this case, AUDRIE reads the content to discover which of the standard taxonomy terms (or synonyms to terms) are contained in the content. 
     These three scenarios are handled in steps  125 - 145 , which make up AUDRIE&#39;s Tag Normalization subsystem. In the embodiment shown in  FIG. 1 , AUDRIE first determines whether the content is tagged using a recognized, standard taxonomy (step  125 ). If so, then AUDRIE may parse the standard tags in the content (step  130 ). If there are not standard tags, AUDRIE determines whether the content is based on a taxonomy that has been previously mapped (step  135 ). If so, then AUDRIE may read the content&#39;s tags and use a database  150  to convert them to the mapped standard tags (step  140 ). If the content does not follow a standard taxonomy and if the content also does not include tags for which a known mapping exists, then AUDRIE reads the content for standard taxonomy terms and synonyms (step  145 ). Reading screens for content is well known in the art. For example, for web site content, it is well known that a computer program can be designed to parse the underlying HTML code. 
     Through these three scenarios, AUDRIE&#39;s Tag Normalization subsystem  105  compiles a list of normalized tags representing the type of content being displayed to the user. AUDRIE&#39;s Display subsystem  115  handles retrieving reference materials related to the content and presenting these reference materials to the user. At step  155 , AUDRIE searches a normalized index for the normalized tags for the current content to determine which reference materials may be related to the content that the user is viewing. Although AUDRIE could present all of these reference materials to the user, in one embodiment, AUDRIE deduces which subset of the materials might be most relevant to the displayed content and displays it in standard categories (step  160 ). Then only this most relevant reference material is made available to the user. 
     Element  210  of  FIG. 2  shows one embodiment of a toolbar provided for by AUDRIE. As shown, AUDRIE provides the user access to various types of relevant content. Based on the content being viewed  205 , a set of relevant tasks may be presented  215  (such as wanting to set up a new customer, wanting to complete a skill assessment, etc.). Relevant knowledge reference materials  230  may also be available. AUDRIE may also enable access to relevant collaboration materials  235  or relevant experts  240 . The user may have access to relevant training  245  or to materials that support a relevant business process  250 . Other relevant computer applications  255  as well as business intelligence  260  may be made available through AUDRIE. Of course, these types of reference materials are some of the many types that can be supported by AUDRIE. 
     The AUDRIE toolbar  210  may be configured to collapse and expand categories of reference materials to save screen real estate. In the example shown in  FIG. 2 , reference categories for which AUDRIE has determined relevant content may have blinking indicators  220  while blank categories have non-blinking indicators  225 . By clicking on one of the blinking indicators  220 , the user is able to expand the category from just the heading to the actual content within. For example, under the Experts category,  FIG. 2  shows that AUDRIE has determined that James Smith, Sarah Martin and Erica Bayou can be contacted for expertise regarding the type of content currently being displayed  205 . AUDRIE may enable additional reference materials to be presented or searched. In  FIG. 2 , each category expanded with content includes a “More” link. For example, in the Experts category, the user may click on this “More” link to access a search feature shown in  FIG. 5 . This enables the user to find reference materials that are outside of the short list of items displayed in the AUDRIE toolbar. For example, here the user is able to search for experts based on specific qualifications, such as a company or sales process. 
     As has now been explained, AUDRIE&#39;s Tag Normalization subsystem  105  determines the type of content being displayed to the user based on certain tags. AUDRIE&#39;s Display subsystem  115  searches for the tags in a normalized index in order to present the most relevant reference materials to the user. AUDRIE&#39;s Index Normalization subsystem  110 , which builds and maintains this normalized index will now be described. 
     Reference materials may be stored in a series of content sources  180 . For example, as  FIG. 3  explains, in order to populate the Experts category  240 , AUDRIE may rely on such content sources  180  as a company&#39;s HR employee directory  305 , a database of CVs/resumes for the employees  310  and a skills database  315 . In the embodiment shown in  FIG. 1 , AUDRIE creates an index  175  for each of these content sources. The index may be based on keywords or tags in the content sources. AUDRIE may have a background process  170  that builds these indexes  175 . Much like identifying the type of content being displayed to the user, to build the indices this background process  170  may search the content sources  180  for standard tagging or for tagging that has been mapped to the standard tags. The process  170  may also look for standard taxonomy terms or synonyms of these terms in the content sources. Each content source index  175  may be combined to form a consolidated index  165 . It is this consolidated index that AUDRIE&#39;s Display subsystem  115  may search to determine the relevant reference materials to make available to the user. 
       FIG. 3  also illustrates the concept of mapping non-standard taxonomies.  FIG. 3  illustrates that taxonomies exist outside of AUDRIE. For example, a company may rely on a pre-existing expertise taxonomy to structure data about its employees. AUDRIE may map this expertise taxonomy  320  to its standard taxonomy to interface with the various HR content sources  325 . 
     The foregoing description addresses embodiments encompassing the principles of the present invention. The embodiments may be changed, modified and/or implemented using various types of arrangements. Those skilled in the art will readily recognize various modifications and changes that may be made to the invention without strictly following the exemplary embodiments and applications illustrated and described herein, and without departing from the scope of the invention, which is set forth in the following claims. For example, the AUDRIE system described above provides for analyzing content being viewed by the user in three ways, namely, looking for standard tags in the content, looking for non-standard tags that have been mapped, and then looking for recognized terms and synonyms. One skilled in the art will recognize that the AUDRIE system can be developed in differing embodiments. For example,  FIG. 4  shows a non-tagged version of AUDRIE that is also within the scope of the present invention.