Patent Publication Number: US-7587664-B2

Title: Method and system for profiling users based on their relationships with content topics

Description:
CROSS REFERENCE TO RELATED APPLICATION 
   This is a continuation of application Ser. No. 09/401,581, filed Sep. 22, 1999, now U.S. Pat. No. 7,000,194 B1. 

   COPYRIGHT NOTICE 
   A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. 
   RELATED APPLICATIONS 
   This application is related to commonly owned application Ser. No. 09/192,047 titled METHOD AND SYSTEM FOR CONVEYING EXPERTISE BASED ON DOCUMENT USAGE, filed Nov. 13, 1998, now U.S. Pat. No. 6,377,983 B1, which is hereby incorporated by reference into this application. 
   This application is related to commonly owned application Ser. No. 09/191,587 titled METHOD AND SYSTEM FOR SUMMARIZING TOPICS OF DOCUMENTS BROWSED BY A USER, filed Nov. 13, 1998, now U.S. Pat. No. 6,356,898 B2 which is hereby incorporated by reference into this application. 
   BACKGROUND OF THE INVENTION 
   The invention disclosed herein relates to cooperative computing environments and information retrieval and management methods and systems. More particularly, the present invention relates to methods and systems for capturing and generating useful information about a user&#39;s access and use of data on a computer system, such as in the form of documents stored on remote servers, and making such useful information available to others. 
   Most organizations that have grown past a few hundred employees have some form of directory and usually have made some attempt to augment this directory with personal profile attributes such as memberships, position title or project affiliations. Many have attempted to expand each person&#39;s profile by adding skills inventory, educational background or professional accomplishments. Many of these efforts have been successful, but most have not fulfilled their promise. The effort required to update and maintain such a profile and the subjective nature of self description leads to inaccuracies or stale data. 
   Most such “people finder” systems fail due to the lack of timely updates to the expert&#39;s profiles. In addition, many knowledgeable workers do not consider their experience to be valuable to others and may overlook this when manually completing profile forms. The result is that most manually built expertise locator systems are irrelevant or become outdated and eventually fail. 
   There is therefore a need for a system for automatically and dynamically identifying people as having affinity to or being experts in various topics or content and making this information known to others. 
   SUMMARY OF THE INVENTION 
   It is an object of the present invention to solve the problems described above with existing people finder systems. 
   It is another object of the present invention to automate a process of locating people within an organization having some expertise in a topic. 
   Some of the above and other objects are achieved by a method, system and computer program for profiling a user based on the user&#39;s activity. The method involves assigning one or more topics to each of a plurality of documents based at least in part upon content contained in the documents, maintaining an affinity variable associated with the user for each of one or more of the topics assigned to a document attributed to the user, determining whether a first affinity variable for the user for a given topic has reached a threshold, and associating the user with the given topic for the first affinity variable which reaches the threshold. 
   In some embodiments, an affinity generation system according to the present invention analyzes a profiled user&#39;s authorship and document usage within an ‘intranet’ to create a set of affinities between documents and topical classifications used in a hierarchical content catalog. These affinities are weighted depending on the system usage and amount of collected evidence. Once a certain threshold has been reached, the threshold being fixed or dynamically set to achieve a desired policy, the affinities are published into the content catalog. The user looking for specific expertise can then search or browse the content catalog and find both documents and people with strong affinities to the selected content area. 
   The affinity generation system is used to populate and maintain a person&#39;s interest and skills profile. This profile can be a part of a corporate directory system or an independent repository. The profile is used initially as part of a ‘people finder system’ to locate expertise within an organization, but can also be exploited to assist in the creation of ad hoc work teams, review boards, etc. in addition to a general analysis of skills and expertise assets within an organization. 
   The affinity generation system uses automation to create and maintain both the user profiles and published affinities. In addition, the leveraged use of an accepted hierarchical content catalog and the multidimensional content classification scheme insures an accurate set of topical affinities that are easy to relate to the activities of the organization. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The invention is illustrated in the figures of the accompanying drawings which are meant to be exemplary and not limiting, in which like references are intended to refer to like or corresponding parts, and in which: 
       FIG. 1  is a block diagram of an exemplary system for profiling users based upon document usage and updating a content catalog in accordance with one embodiment of the present invention; 
       FIG. 2  is flow chart showing a general process of profiling users performed by the system shown in  FIG. 1 ; 
       FIGS. 3A-3D  contain a flow chart showing the process of profiling users in greater detail in accordance with one embodiment of the present invention; 
       FIG. 4  is an exemplary screen display showing results from a search through the content catalog shown in  FIG. 1  for a selected topic; and 
       FIG. 5  is an exemplary screen display showing a user profile linked to the search results screen shown in  FIG. 4 . 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   The preferred embodiments of a system, method, and article of manufacture containing software programs in accordance with the present invention are described with reference to the drawings in  FIGS. 1-5 . 
   Referring to  FIG. 1 , one embodiment of the system  10  of the present invention includes, among other things a document management system  12  for storing and administrating a plurality of documents  13 , a content catalog  14 , and a user profile repository  16 . For purposes of this description, documents  13  include any types of files containing content such as database files, text documents, graphics, video or audio files, newsgroup files, online bulletin board files, Lotus NOTES documents, e-mail files, chat discussion files, etc., and the document management system  12  is a conventional application program for managing the documents. The system  10  is accessible to a plurality of users  11 , who can create, access, edit, send, and otherwise manipulate or use the documents  13  through the document management system  12 . In some embodiments, the system  10  may be implemented as a server and the users  11  as clients connectable to the server over an intranet, extranet or the Internet. 
   The content catalog  14  is hierarchical taxonomy or collection of content categories or topics with links to the documents  13 . The content catalog includes a user interface for allowing browsing of the hierarchy or directly searching the topics for relevant documents. The content catalog  14  may be of the well-known type, sometimes referred to as a knowledge map, knowledge catalog or content taxonomy. Several public examples of a content catalogs exist on the Internet today, such as YAHOO! and the NETSCAPE Open Directory, and various vendors have toolsets for creating content catalogs for a corporate intranet, including GRAPEVINE, AUTONOMY, and OPENTEXT, etc. 
   A topic generation program  18  serves as an automatic multidimensional classification program to add new documents  13  to the collection in the content catalog  14 . The topic generation program  18  may be of the type employed by web search engines to collect and classify various data. An example of such a program is described in U.S. Pat. No. 5,659,732, issued Aug. 19, 1997, entitled DOCUMENT RETRIEVAL OVER NETWORKS WHEREIN RANKING AND RELEVANCE SCORES ARE COMPUTED AT THE CLIENT FOR MULTIPLE DATABASE DOCUMENTS, which is hereby incorporated by reference into this application. 
   Alternatively, the topic generation program  18  uses a clustering algorithm, such as the k-means algorithm, to identify topics or categories for the documents  13  and compute relatedness or closeness values such as centroid vectors which indicate how relevant a given document is to the topic. Each document may be related in different degrees or strengths to different topics, and each topic may contain a number of documents. Such a program is described for a chat document in co-pending commonly owned U.S. patent application Ser. No. 09/143,075 titled METHOD AND SYSTEM FOR INFORMING USERS OF SUBJECTS OF DISCUSSION IN ON-LINE CHATS filed Aug. 28, 1998, and more generally in U.S. Pat. No. 5,924,105, issued Jul. 13, 1999, titled METHOD AND PRODUCT FOR DETERMINING SALIENT FEATURES FOR USE IN INFORMATION SEARCHING, both of which are hereby incorporated by reference into this application. 
   The user profile repository  16  is a directory containing data about users that is used as an authoritative source of organizational or group membership of an such as a company or otherwise as a repository of user profiles. As is known, a profile is a database record containing data about a user such as the user&#39;s identity, location, phone numbers, email addresses, security credentials, etc. The repository  16  may be a special profile database, such as a Lotus DOMINO database, or it may be an extension of an organization&#39;s existing directory, such as an LDAP directory or Microsoft&#39;s Exchange messaging directory. The system further contains an address directory  20  which receives information from users  11  and which is synchronized with the profile repository  16 . 
   In accordance with the invention, the system  10  contains additional components to support the dynamic assessments of and publication about user affinities to topics. As shown in  FIG. 1 , additional profile extensions  22  are attached to the user profiles in the profile repository  16  to store information about each user&#39;s affinity to content. In one embodiment, the system  10  programmatically extends the design schema of the repository  16  to include affinity results, private and published as described below, and reads the directory to establish a list of target users. External users such as contract workers can be added to the directory for expertise tracking only, if desired. In some embodiments, the extended profile is based on the Lotus DOMINO directory and extensions to the directory&#39;s person record. In Microsoft environments, the DOMINO directory is synchronized with the MS Exchange directory and/or the Win2000 Active Directory, or both. 
   The additional information in the profile extensions include, in one embodiment, a ranked list of the top ten affinities for the user. As described further below, an affinity is a variable such as a number which represents the strength of a user&#39;s connection to a topic as represented by the user&#39;s activities on various of the documents  13 , and may include additional information as described below. Also as explained further below, the user is provided control over maintenance, approval and publication of affinities. 
   The affinities are generated by usage metric software routines  24  which gather document metadata from the document management system  12  and content catalog  14 , stores it in compact form in a relational usage database  26 , and analyzes the stored data in accordance with a desired algorithm. As shown in  FIG. 1 , the document metadata stored in the usage database  26  includes users who have performed activities over the system  10 , documents created by, attributed to, or otherwise used by the users, as determined from the document management system  12 , and topics assigned to those documents as determined from the content catalog  14  as described above. The usage metrics  24  use the stored data to generate affinities between topics and users and under certain conditions store those affinities in the profile extensions  22 . 
   The system further includes an approval agent  28 , which is a software routine that monitors profiles for affinities and requests approval from users to publish their affinities so other users may become aware of them. A publication agent  30  is a software routine which publishes approved affinities to the content catalog  14  by either inserting the user profiles  32  for the approved affinities into the catalog  14  or creating links to the user profiles  32  for the approved affinities to the topics associated with the affinities in the catalog  14 . 
   An exemplary process performed by the system of  FIG. 1  is described generally with reference to  FIG. 2  and then more particularly with reference to  FIGS. 3A-3D . If a user creates a new document, step  40 , one or more topics are generated for the document, step  42 . If a user manipulates an existing document, the one or more previously assigned topics contained in the content catalog are retrieved, step  46 . In some embodiments, certain users, such as system administrators who access many documents, may be identified as nonaffinity generating and thus their activities would be ignored. The affinity strength representing the relationship between each of the topics assigned to the document is updated to reflect the user&#39;s increased affinity to the topics, step  46 . In a simple embodiment, the affinity strength is represented an integer which is incremented for each document created or used by the user. More complex embodiments, such as the one described below, consider a variety of additional factors including the document&#39;s closeness to each topic, the frequency with which the user accesses documents in this topic, and the level of affinity by other users to the same topic. That is, the closer the document is to the topic; the more frequently the user accesses documents in the topic; and the lower the general level of affinity by others—the greater the user&#39;s increase in affinity strength will be. 
   If the affinity strength reaches or exceeds a threshold, step  50 , which threshold may be predefined or set dynamically depending upon the circumstances, the affinity is added to the user&#39;s extended profile, step  52 . If the user consents to publication of the affinity, step  54 , the affinity is published to the content catalog, step  56 . Other users who perform searches for the topic through the content catalog are then informed of this user&#39;s affinity to and potential status as an expert in the topic. If the user does not approve publication of the affinity, the affinity is stored in the user&#39;s extended profile, step  58 , for use in identifying the affinity as nonapproved to thereby prevent continuous requests for approval or to serve as a basis for comparison as the affinity strength increases to support an additional request for approval. 
   Referring now to  FIG. 3A , a more detailed description of the process starts with users of the system registering their profile information in the address directory, step  70 . The profile repository is synchronized with the address directory, step  72 , to establish a reliable, authoritative source of user profile information. The profile repository reflects the community of expertise that the system  10  will create affinities for, and may include people outside of an organization who are not in the directory. 
   If a user creates a new document, step  74 , the topic generator generates one or more topics and closeness values, such as centroid vectors, between each topic and the document, step  76 . It further updates the content catalog with the new topic/document relationships, step  78 . 
   In addition to authorship, the system  10  can provide a deeper, more expansive and more accurate set of affinities by profiling the user&#39;s use of other data sources such as email, discussion databases, and other collaborative tools by which content may be publicly accessed. Typically, this requires the user&#39;s permission and privacy control mechanisms. Once permitted, the system examines authorship, document actions such as filing, forwarding, deletion, etc., document reaction, e.g., responses/replies, content reuse, e.g., incorporation into new documents, and citations, e.g., ‘bookmarking’, created links to the source, etc., and builds a refined set of document valuations. When analyzed against the catalog topics, this additional evidence provides the user and the system with a detailed affinity set. 
   Thus, if a user accesses or manipulates an existing document, step  80 , the system determines whether the topic(s) associated with the documents are to be updated as a result, e.g., the document is edited, step  82 . If so, the topic generation process is initiated, step  76  and content catalog updated accordingly, step  78 . As a result, the content catalog contains the most up-to-date representations of the topics associated with the affected document. 
   The usage metrics routines query the catalog for the topical categories that the document has been classified under, step  84 , and uses the result to build or modify the association table or usage database of profiled authors and the documents that they have created or accessed, step  86 . As explained above, a document may be ‘soft classified’ under multiple categories or topics depending on the range of content in the document. As documents are processed by the usage metrics component, typically after indexing and classification in the content catalog, the association table gets populated. 
   At a scheduled interval, step  88 , the usage metrics component analyzes the associations for each profiled user and computes the strength of the association. Thus, the usage metrics routines open the usage database, step  90  ( FIG. 3B ), and, for each user in the usage table and for each topic associated with the user, finds the number of documents associated with the topic, step  92 . A topic affinity count is incremented, step  94 , which represents the number of affinities associated with the topic across all users, for use as explained below. For each document listed as associated with the topic, the closeness value between the document and topic is retrieved from the content catalog, step  96 , and metadata about the document, including dates and times of creation and other access by the user is retrieved, step  98 , from the document management system. This information is retrieved for each document until no documents remain for the topic, step  100 . The usage metric routine then computes several numbers for the set of documents, including an average closeness value among documents in the topic, a decay time derived from the average amount of time that has passed since the documents were created/accessed, and a frequency by which the documents in the topic were created/accessed, step  102 . 
   This process is repeated for each topic associated with the user until none remain, step  104 , and for each user in the usage table until all users have been processed, step  106 . An affinity density is then computed for each topic, step  108  ( FIG. 3C ), which represents the total instances of affinity across all users for the topic as a ratio of all topical affinities. This number helps indicate whether the topic will generate too many affinities and is thus too general to be of practical use to other users. 
   For each user and topic, the usage metrics routine computes an affinity strength, step  110 . In one exemplary embodiment, this computation involves the variable retrieved and calculated above and is performed according to the following equation:
 
Affinity strength=(Doc.#*Freq.*Decay* CV )/ TAD  
 
where:
         Doc. # is a value representing the number of authored documents that are classified in the topic category (the more documents, the greater the affinity should be);   Freq. or Frequency is a value representing how often documents are authored by the user in this topic (greater frequency implies greater affinity);   Decay is a value derived inversely from the average age of the authorship of the documents (older documents having less value in the support of affinities);   CV or Centroid Vector is a value representing the closeness of the document content to the topical catalog category, as generated by the topic generation program (greater closeness values imply greater affinity); and   TAD or Topic Affinity Density is a value representing the number of affinities published for each category across all users over the total number of affinities.       

   As one skilled in the art will recognize, a given affinity generation system may consider more or less factors and may weight them differently to reflect a desired goal within the set of users. For example, as described herein, the system preferably considers creation of and changes made to documents as affinity generating events. However, certain systems such as library systems or the world wide web may place greater emphasis on accessing or downloading documents and the time spent doing so rather than attempting to change documents. Such access may be the basis for computing affinity values. Similarly, in an electronic commerce system, events such as purchases, co-purchases, viewing or interacting with ads, or product inquiries may be used to compute affinity values. 
   If the affinity strength so computed is greater than a threshold, which may be dynamically determined by the system or set via a policy document, step  1112 , an affinity is written into a private section of the user&#39;s profile, step  114 , so as to be accessible only by the user himself and not other users. The process of computing affinity strengths and determining their status is repeated for each topic, step  116 , and each user, step  118 . 
   At periodic intervals, the approval agent runs to identify and process new ‘proposed’ affinities in user profiles extensions, step  120 . An affinity is determined to be new by the usage metrics routine by comparison with existing affinities stored in the user&#39;s profile, and a flag may be set to indicate its status as new. The agent sends the profiled user a request for approval of the affinity such as by email notification, step  122 . The user may open his profile and decide to either publish this affinity or to suppress it, step  124 . If approved for publication, the approval agent moves the affinity into a public section of the user&#39;s profile, step  126 , so the affinity will be viewable and searchable in the user&#39;s profile. This process is repeated by the approval agent for all profiles having new affinities, step  128 . 
   Periodically the catalog publication agent identifies and collects new ‘published’ affinities, step  130  ( FIG. 3D ) and creates link documents in the content catalog for the person which will provide a link to the person&#39;s profile, step  132 . This is repeated for all profiles with new published affinities, step  134 . 
   If the user chooses to suppress the affinity, it is kept in the profile and used as a filter to avoid repeated affinity proposals. In one implementation of the system, users are allowed to declare specific affinities. This is done through the content catalog interface, which directly writes the affinity in the ‘published’ state to the user&#39;s profile. System administrators can also designate affinities through the content catalog&#39;s taxonomy editor interface, which also writes the affinity to the user&#39;s profile. The process of approving and publishing affinities can be controlled from a policy document, where timeouts can be set to override user&#39;s who do not respond to the affinity notification. Until the affinities are approved and published by the user or via a policy proxy, they are kept in a coded numerical form for privacy within a restricted section of the profile document. The same treatment is applied to the affinity suppression list. 
   Until the affinity is published by the user, the affinity is stored in the user&#39;s profile in a numerical, tuple form to avoid detection by full text searches. The tuple contains a topic ID, a unique catalog ID for the topic (e.g., from DB/2 catalog); a source code; the affinity strength (e.g., a number from 0 to 1); a heuristic mask (a set of flags for privacy, publishing, etc.). A sample affinity tuple format is shown below is Table I: 
   
     
       
         
             
           
             
               TABLE I 
             
           
          
             
                 
             
             
               AFFINITY TUPLES 
             
          
         
         
             
             
          
             
               Component 
               Description 
             
             
                 
             
             
               Topic ID 
               Unique topic ID 
             
             
               Source Code 
               Integer: 
             
             
                 
                0 = Computed by usage metrics 
             
             
                 
                1 = User declared 
             
             
                 
                2 = Management designated 
             
             
                 
                3-6 = reserved 
             
             
                 
                7-9 = for third party use 
             
             
               Affinity strength 
               Float from 0 to 1, where: 
             
             
                 
                0 = no affinity 
             
             
                 
                1 = highest affinity (universal expert) 
             
             
               Heuristic mask 
               An extensible integer mask, where: 
             
             
                 
                First digit = Suppress state (1 = Keep private) 
             
             
                 
                Second digit = Review state (1 = reviewed or 
             
             
                 
                overwritten by policy) 
             
             
                 
                Third digit = Publish state (1 = Publish to catalog) 
             
             
                 
               For example: 
             
             
                 
                000 = new, not reviewed or publishable 
             
             
                 
                010 = reviewed, but not publishable 
             
             
                 
                001 = published (used for declared affinity) 
             
             
                 
                1XX = suppressed, but stored. 
             
             
                 
             
          
         
       
     
   
   This is the basic affinity of one embodiment. It is written into the ‘Proposed Affinities’ field of an extended user profile, one affinity per line, comma delimited, after the module checks to see that the topic ID isn&#39;t already present in the published or suppressed fields. The proposed field is hidden from public view where affinities are held pending user review. The usage metrics module does all the work and error checking. 
   From the content catalog a user can find people and documents by either browsing or searching. In either case, people will be represented in a manner similar to documents, either tersely (e.g., name, title, email, etc.) or verbosely (name, title, phone, email, location, Lotus SAMETIME status, etc.) An exemplary screen display showing documents and users associated in the content catalog with the topic “JavaScript” and located as a result of search through the catalog is shown in  FIG. 4 . As shown in  FIG. 4 , a document listing  150  is generated and displayed including closeness values expressed as percentages, and a user affinity or expert list  160  is generated and displayed with users ranked by affinity strength and identified in this embodiment only by name. 
   The names in the user expert list  160  are hyperlinked to user profiles, so that clicking on a selected user name would bring up the profile in a separate window, either a business card or full profile depending on context, with full profiles being brought up in embodiments with a more verbose listing of experts. An exemplary user profile screen display is shown in  FIG. 5 . Additionally, graphical hooks  180  for chat and email are integrated as shown in  FIG. 5  by implementing links or icons wherever a person is represented in the content catalog, and an availability icon  190  is displayed indicating the person&#39;s availability. For example, the email addresses will be computed into a mailto: URL. The chat, email and availability tools may be supported by various existing software collaboration and messaging products such as Lotus SAMETIME or the like. 
   While the invention has been described and illustrated in connection with preferred embodiments, many variations and modifications as will be evident to those skilled in this art may be made without departing from the spirit and scope of the invention, and the invention is thus not to be limited to the precise details of methodology or construction set forth above as such variations and modification are intended to be included within the scope of the invention.