Patent Publication Number: US-7899871-B1

Title: Methods and systems for e-mail topic classification

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     This application is a continuation-in-part of and claims priority to U.S. patent application Ser. No. 11/657,398, filed Jan. 23, 2007 and entitled “Methods and Systems of Electronic Message Threading and Ranking;” U.S. patent application Ser. No. 11/657,398 is a continuation of U.S. patent application Ser. No. 11/457,317 (now U.S. Pat. No. 7,593,995 issued Sep. 22, 2009), filed Jul. 13, 2006 and entitled “Methods and Systems of Electronic Message Threading and Ranking,” which claims the benefit of U.S. Provisional Application No. 60/761,500, filed Jan. 23, 2006 and entitled “E-Mail Threading, Ranking, Derivation and Topic Classification Methods and Apparatus;” U.S. patent application Ser. No. 11/657,398 also claims the benefit of U.S. Provisional Application No. 60/761,501, filed Jan. 23, 2006 and entitled “Incremental E-Mail Crawling and Indexing Methods and Apparatus,” and U.S. Provisional Application No. 60/761,679, filed Jan. 23, 2006 and entitled “System, Method, and User Interface for Distributed E-Mail Analysis.” 
     The entire disclosures of U.S. patent application Ser. No. 11/657,398, U.S. patent application Ser. No. 11/457,317, U.S. Provisional Application No. 60/761,500, U.S. Provisional Application No. 60/761,501, and U.S. Provisional Application No. 60/761,679 are incorporated herein by reference for all purposes. 
    
    
     BACKGROUND OF THE INVENTION 
     Embodiments of the present invention generally relate to techniques for processing electronic messages. More particularly, the present invention relates to methods and systems for e-mail topic classification. 
     Collaboration using electronic messaging, such as e-mail and instant messaging, is becoming increasingly ubiquitous. Many users and organizations have transitioned to “paperless” offices, where information and documents are communicated almost exclusively using electronic messaging. As a result, users and organizations are also now expending time and money to sort and archive increasing volumes of digital documents and data. 
     At the same time, state and federal regulators such as the Federal Energy Regulatory Commission (FERC), the Securities and Exchange Commission (SEC), and the Food and Drug Administration (FDA) have become increasingly aggressive in enforcing regulations requiring storage, analysis, and reporting of information based on electronic messages. Additionally, criminal cases and civil litigation frequently employ electronic discovery techniques, in addition to traditional discovery methods, to discover information from electronic documents and messages. 
     One problem is that complying with these disclosure and/or reporting requirements is difficult because of the large amounts of electronic messages that accumulate. As broadband connections to the Internet are common in most homes and businesses, e-mails frequently include one or more multi-megabyte attachments. Moreover, these e-mails and attachments are increasingly of diverse and propriety formats, making later access to data difficult without the required software. 
     Another problem is that the disclosure and/or reporting requirements do not simply require that the electronic message be preserved and then disclosed. Often, the disclosure and/or reporting requirements are more focused toward information about the electronic message, such as who had access to sensitive data referred to in the contents of a particular electronic message. Some companies have teams of employees spending days and weeks reviewing e-mails in order to respond to regulatory audits and investigations. For these reasons, the inventors believe that users and organizations need electronic message analysis solutions to help lower costs in disclosing and/or reporting information related to electronic messaging. 
     In light of the above, there is a need for techniques for processing electronic messages that address some of the problems in the prior art. 
     BRIEF SUMMARY OF THE INVENTION 
     Embodiments of the present invention generally relate to techniques for processing electronic messages. More particularly, the present invention relates to methods and systems for e-mail topic classification. 
     In various embodiments, a method for processing e-mails includes receiving a plurality of e-mails. For each e-mail in a plurality of e-mails, a feature representation is generated for an e-mail based on a set of noun phrases associated with the e-mail. A set of topics associated with the plurality of e-mails is then generated based on the feature representation for each e-mail. 
     In some embodiments, generating the feature representation for the e-mail based on the set of noun phrases associated with the e-mail includes determining sentence structure associated with the e-mail. Parts of speech associated with the e-mail are determined, wherein the parts of speech include the set of noun phrases associated with the e-mail. In one embodiment, generating the set of topics associated with the plurality of e-mails based on the feature representation of each e-mail includes determining a centroid associated with a cluster of feature representations of one or more e-mails in the plurality of e-mails. A topic in the set of topics is determined based on the centroid. 
     A name associated with a topic in the set of topics may be determined based on the feature representation of one or more e-mails in the plurality of e-mails. In some embodiments, input indicative of a concept of interest is received. A topic is identified as relevant to the concept of interest. One or more e-mails in the plurality of e-mails associated with the identified topic are displayed as relevant to the concept of interest. Each topic may be ranked in the set of topics. A hierarchy may be determined between a first topic in the set of topics and a second topic in the set of topics. 
     In some embodiments, a computer program product includes a computer-readable medium storing a set of code modules which when executed by a processor of a computer system cause the processor to process e-mails. The computer program product includes code for receiving a plurality of e-mails, code for generating a feature representation for an e-mail based on a set of noun phrases associated with the e-mail for each e-mail in a plurality of e-mails, and code for generating a set of topics associated with the plurality of e-mails based on the feature representation for each e-mail. 
     In various embodiments, a system for processing e-mails includes a processor and a memory. The memory stores a set of instructions which when executed by the processor cause the processor to receive a plurality of e-mails, generate a feature representation for an e-mail based on a set of noun phrases associated with the e-mail for each e-mail in a plurality of e-mails, and generate a set of topics associated with the plurality of e-mails based on the feature representation for each e-mail. 
     A further understanding of the nature and the advantages of the inventions disclosed herein may be realized by reference of the remaining portions of the specification and the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order to more fully understand the present invention, reference is made to the accompanying drawings. Understanding that these drawings are not to be considered limitations in the scope of the invention, the presently described embodiments and the presently understood best mode of the invention are described with additional detail through use of the accompanying drawings. 
         FIG. 1  is a block diagram of an electronic message processing system in one embodiment according to the present invention. 
         FIG. 2  is a block diagram of software components for processing electronic messages in one embodiment according to the present invention. 
         FIG. 3  is a block diagram of a computer system that may be used for processing electronic messages in one embodiment according to the present invention. 
         FIG. 4  is a block diagram of an exemplary e-mail message. 
         FIG. 5  is a block diagram illustrating an exemplary processing flow of e-mail messages in one embodiment according to the present invention. 
         FIG. 6  is a block diagram illustrating an exemplary message sequence chart related to an e-mail thread. 
         FIG. 7  is a flowchart for processing e-mail messages to determine derived e-mail messages in one embodiment according to the present invention. 
         FIG. 8A  and  FIG. 8B  are a flowchart for determining an e-mail rank associated with an e-mail message in one embodiment according to the present invention. 
         FIG. 9  is a flowchart for processing e-mail messages for placement in an e-mail thread in one embodiment according to the present invention. 
         FIG. 10A  and  FIG. 10B  are a flowchart for organizing an e-mail message in an e-mail thread in one embodiment according to the present invention. 
         FIG. 11  is a flowchart for ordering e-mail threads in one embodiment according to the present invention. 
         FIG. 12  is a screenshot of an exemplary dashboard displaying information related to processing of e-mail messages in one embodiment according to the present invention. 
         FIG. 13  is a screenshot of an exemplary search dialog displaying information related to e-mail messages in one embodiment according to the present invention. 
         FIG. 14  is a simplified flowchart of a method for classifying e-mails into topics in one embodiment according to the present invention. 
         FIG. 15  is a flowchart of a method for identifying topics based on feature vectors associated with e-mails in one embodiment according to the present invention. 
         FIG. 16  is a block diagram illustrating tokenization of an e-mail in one embodiment according to the present invention. 
         FIG. 17  is a block diagram illustrating feature vector extraction in one embodiment according to the present invention. 
         FIG. 18  is a block diagram illustrating automatic categorization in one embodiment according to the present invention. 
         FIG. 19  is a block diagram illustrating topic creation in one embodiment according to the present invention. 
         FIG. 20  is a block diagram illustrating manual topic classification in one embodiment according to the present invention. 
         FIG. 21  is a block diagram illustrating automatic topic classification in one embodiment according to the present invention. 
         FIG. 22  is a block diagram illustrating updating a topic classification in one embodiment according to the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Embodiments of the present invention generally relate to techniques for processing electronic messages. More particularly, the present invention relates to methods and systems for e-mail topic classification. 
     The embodiments discussed herein are illustrative of one or more examples of the present invention. As these embodiments of the present invention are described with reference to illustrations, various modifications or adaptations of the methods and/or specific structures described may become apparent to those skilled in the art. All such modifications, adaptations, or variations that rely upon the teachings of the present invention, and through which these teachings have advanced the art, are considered to be within the scope of the present invention. Hence, the present descriptions and drawings should not be considered in a limiting sense, as it is understood that the present invention is in no way limited to only the embodiments illustrated. 
     Processing of Electronic Messages 
       FIG. 1  is a block diagram of an electronic message processing system  100  in one embodiment according to the present invention. Electronic message processing system  100  includes master index  105 , messaging applications programming interface (MAPI) module  110 , e-mail servers  115 , duplicate eliminator  120 , buffer manager  125 , indexer  130 , thread analyzer  135 , topic classifier  140 , analytics extraction, transformation, and loading (ETL) module  145 , directory interface  150 , and directory servers  155 . Master index  105  includes e-mail tables  160 , e-mail full text index  165 , topic tables  170 , cluster full text index  175 , distribution list full text index  180 , dimension tables  185 , participant tables  190 , and fact tables  195 . E-mail servers  115  include one or more mail servers  117 . Directory servers  155  include one or more directory servers  157 . 
     Master index  105  includes hardware and/or software elements that provide storage and retrieval of information associated with electronic messages, such as e-mail, instant messaging (IM) messages, Short Message Service (SMS) messages, Multimedia Message Service (MMS), and the like. Some examples of master index  105  are flat files, databases, data marts, data warehouses, and other repositories of data. Although the disclosure references electronic messages as e-mail messages, the disclosure should not be considered as limited to only e-mail message formats. The disclosure may also apply to other types of electronic messages, such as IM, SMS, MMC messages, and the like. 
     In various embodiments, e-mail tables  160  store information associated with e-mail messages processed by the system  100 . E-mail full text index  165  stores an inverted index that enables fast searching of contents (e.g., headers and body), metadata, and attachments of e-mail messages processed by the system  100 . Topic tables  170  store relationships between categories or topics and e-mail messages processed by the system  100 . Cluster full text index  175  stores an index of e-mail messages that have a close relationship, such as relationships based on statistical analysis of noun phrases, and the like. The e-mail messages having close relationships are then associated with topics in the topic tables  170 . Distribution list full text index  180  stores the full text of e-mail messages associated with a distribution or mailing list. Participant tables  190  store information related to participants of a distribution or mailing list (e.g., To-recipients, CC-recipients, BCC-recipients, etc.). Dimension tables  185  and fact tables  195  store information related to data warehouse processing of e-mail messages. 
     MAPI module  110  is linked to the e-mail servers  115  and to the duplicate eliminator  120 . In this example, the e-mail servers  115  include one or more mail servers  117 . MAPI module  110  includes hardware and/or software elements that communicate with the e-mail servers  115 . E-mail servers  115  include hardware and/or software elements that provide electronic messaging services, such as e-mail transport, storage, and retrieval. One example of the mail servers  117  is a computer system running Microsoft Exchange Server 2000 from Microsoft Corporation of Redmond, Wash. In other examples, the e-mail servers  117  may include operating systems, such as Microsoft Windows 2000/XP/2003, UNIX, and Linux, and mail transport agents, mail user agents, and the like. E-mail messages may be stored on the mail servers  117  in a file, such as an Outlook PST file, and the like. 
     Duplicate eliminator  120  includes hardware and/or software elements that detect and eliminate redundant and/or duplicative information retrieved by the MAPI module  110 . Buffer manager  125  is linked to the duplicate eliminator  120  and the indexer  130 . Buffer manager  125  includes hardware and/or software elements that manage data communications between the duplicate eliminator  120  and the indexer  130 . 
     Indexer  130  is linked to the master index  105 . Indexer  130  includes hardware and/or software elements that process electronic messages to determine message content and generate metadata associated with the electronic messages. For example, the index  130  may process an e-mail message to parse header and body fields to retrieve message content and generate metadata associated with the e-mail message. 
     Thread analyzer  135  is linked to the indexer  130  and the master index  105 . Thread analyzer  135  includes hardware and/or software elements that organize e-mail messages into one or more e-mail threads. An e-mail thread is a series or sequence of one or more e-mail messages that form a logical “discussion” or “communication.” Some examples of e-mail messages within an e-mail thread are e-mail messages related by sender address, recipient address, topic, and time. Another example of e-mail messages within an e-mail thread are e-mail messages with forwarding replies, CC-recipients, BCC-recipients, and the like. In this example, the thread analyzer  135  determines the position of an e-mail message in an e-mail thread in response to message content and metadata of the e-mail message. 
     Topic classifier  140  is linked to the master index  105 . Topic classifier  140  includes hardware and/or software elements that determine one or more topics or categories in response to e-mail message content and metadata. The topic classifier  140  may determine the topic of an e-mail message based on the subject header or in response to the content of the body of an e-mail message. The topic classifier  140  may also associate an e-mail message with a given topic, classifier, and/or category. 
     Analytics ETL module  145  is linked to the master index  105 . Analytics ETL module  145  includes hardware and/or software elements that provide an interface accessing content and metadata processed by the system  100 . In one example, the analytics ETL module  145  provides an interface for extracting data from the master index  105  and/or external data sources; an interface for transforming the data, which includes cleansing, aggregation, summarization, integration, as well as basic transformation; and an interface for loading the data into some form of data warehouse for further analysis and processing. 
     Directory interface  150  is linked to the master index  105  and the directory servers  155 . Directory interface  150  includes hardware and/or software elements that access information stored in a directory. A directory is any database of information associated with objects, such as users or computer hosts. In various embodiments, the directory servers  155  include one or more directory servers  157  running Active Directory by Microsoft Corporation of Redmond, Wash. In other embodiments, other types of directory servers and/or services may be used such as Lightweight Directory Access Protocol (LDAP) servers, Identity Management servers, and the like. In various embodiments, examples of information stored in the directory servers  155  include “organizational” or “corporate” data, such as department identifiers associated with a user or computer host, a group identifier associated with a user, a corporate or departmental title associated with a user, telephone and address information, and security information. 
     In operation of the electronic message processing system  100 , the MAPI module  110  retrieves e-mail messages from the e-mail servers  115  (e.g., from one of the mail servers  117 ). For example, the system  100  may “crawl” the e-mail servers  115  requesting e-mail messages through the MAPI module  110 . The duplicate eliminator  120  filters redundant and/or duplicate e-mail messages received from the e-mail servers  115 . 
     The indexer  130  receives the e-mail messages from the duplicate eliminator  120  through the buffer manager  125 . The indexer  130  processes the e-mail messages to determine the contents of the e-mail messages and metadata associated with each e-mail message. The indexer  130  stores a full text index of the e-mail messages and the metadata in the master index  105 . For example, the indexer  130  stores sender and recipient information associated with an e-mail message in the e-mail tables  160 ; the indexer  130  stores an inverted word list of the full text of the e-mail message in the e-mail full text index  165 ; etc. 
     The thread analyzer  135  processes the contents of the e-mail messages and the metadata in the master index  105  to organize the e-mail messages into e-mail threads. In general, the thread analyzer  135  organizes the e-mail messages into e-mail threads that form a discussion or communication of a topic or concept. One example of operation of the thread analyzer  135  is described below with respect to  FIGS. 9 ,  10 A, and  10 B. The topic classifier  140  processes the contents of the e-mail messages and the metadata in the master index  105  to determine topics or categories associated with the e-mail messages. The topic classifier  140  stores the categories or topics in the topics tables  170 , and stores a full text index of e-mail messages belonging to and/or associated with the same topic in the cluster full text index  175 . 
     The directory interface  150  retrieves directory or organizational information from the directory servers  155  (e.g., from one of the directory servers  157 ) related to the e-mail messages. The indexer  130  or the thread analyzer  135  may use the organizational information during, processing, indexing, and/or threading of the e-mail message. In this example, the organizational data is stored in the participant tables  190  and the distribution list full text  180 . 
     A user or computer process connects to the analytics ETL module  145  to retrieve information associated with the e-mail messages processed by the system  100 . Advantageously, the electronic message processing system  100  provides a user or organization with access to e-mail messages, and other information, such as header information, message contents, message attributes, metadata, and the like, to assist in reporting requirements or gathering information for the purposes of electronic discovery. After “crawling” e-mail repositories (e.g., one of the mail servers  117 ) to retrieve e-mail messages, the system  100  processes and indexes the retrieved e-mail messages and stores metadata related to the processed e-mail messages in the master index  105 . The system  100  allows the user or organization to search and query the processed e-mail messages and the metadata to quickly extract and process relevant information. The system  100  further provides threading and topic classification of e-mail messages to enhance the discovery and presentation of relevant information to the user or organization. 
     In various embodiments, after an initial crawl of a data or e-mail repository, such as the e-mail servers  115 , the system  100  may incrementally process newly arriving e-mail messages on a daily bases, an hourly basis, or the like. As described above, the new e-mail messages may be incorporated into the master index  105 . 
       FIG. 2  is a block diagram of software components  200  for processing e-mail messages in one embodiment according to the present invention. Software components  200  include portal  202 , indexer  204 , crawler  206 , distributed services  208 , and administration interface  210 . Portal  202  is linked to the indexer  204 , which is linked to the crawler  206 . Distributed services  208  and administration interface  210  are linked to each of the portal  202 , the indexer  204 , and the crawler  206 . 
     Portal  202  includes software elements for accessing and presenting information provided by the indexer  204 . In this example, the portal  202  includes web applications  212  communicatively coupled to information gathering and presentation resources, such as a Java Server Page (JSP) module  214 , a query engine  216 , a query optimization module  218 , an analytics module  220 , and a domain templates module  222 . 
     Indexer  204  includes software elements for processing and storing e-mail messages. The indexer  204  includes metadata  224 , full text indices  226 , thread analysis  228 , group effects  230 , and topics  232 . 
     Crawler  206  includes software elements for retrieving e-mail messages from an e-mail repository. Some examples of an e-mail repository are an e-mail server (e.g., one of the mail servers  117  of  FIG. 1 ), a Post Office Protocol (POP) enabled computer server, an Internet Message Access Protocol (IMAP) enabled computer server, and files, such as PST files, UNIX style maildirs/mbox formats, and the like. In this example, the crawler  206  includes Reference Information Storage System (RISS) module  234 , Enterprise Vault Software (EV) module  236 , MAPI module  238 , PST module  240 , Directory Services (ADS) module  242 , and a Microsoft Exchange Server Mailbox Merge Wizard (ExMerge) module  244 . 
       FIG. 3  is a block diagram of a computer system  300  in one embodiment according to the present invention. In this example, computer system  300  includes a monitor  310 , computer  320 , a keyboard  330 , a user input device  340 , one or more computer interfaces  350 , and the like. In the present embodiment, the user input device  340  is typically embodied as a computer mouse, a trackball, a track pad, a joystick, wireless remote, drawing tablet, voice command system, eye tracking system, and the like. The user input device  340  typically allows a user to select objects, icons, text and the like that appear on the monitor  310  via a command such as a click of a button or the like. 
     Embodiments of the computer interfaces  350  typically include an Ethernet card, a modem (telephone, satellite, cable, ISDN), (asynchronous) digital subscriber line (DSL) unit, FireWire interface, USB interface, and the like. For example, the computer interfaces  350  may be coupled to a computer network  355 , to a FireWire bus, or the like. In other embodiments, the computer interfaces  350  may be physically integrated on the motherboard of the computer  320 , may be a software program, such as soft DSL, or the like. 
     In various embodiments, the computer  320  typically includes familiar computer components such as a processor  360 , and memory storage devices, such as a random access memory (RAM)  370 , disk drives  380 , and system bus  390  interconnecting the above components. 
     The RAM  370  and disk drive  380  are examples of tangible media configured to store data such as embodiments of the present invention, including executable computer code, human readable code, or the like. Other types of tangible media include floppy disks, removable hard disks, optical storage media such as CD-ROMS, DVDs and bar codes, semiconductor memories such as flash memories, read-only-memories (ROMS), battery-backed volatile memories, networked storage devices, and the like. 
     In various embodiments, computer system  300  may also include software that enables communications over a network such as the HTTP, TCP/IP, RTP/RTSP protocols, and the like. In alternative embodiments of the present invention, other communications software and transfer protocols may also be used, for example IPX, UDP or the like. 
     It will be readily apparent to one of ordinary skill in the art that many other hardware and software configurations are suitable for use with the present invention. For example, the computer may be a desktop, portable, rack-mounted or tablet configuration. Additionally, the computer may be a series of networked computers. Further, the use of other micro processors are contemplated, such as Pentium™ or Core™ microprocessors from Intel; Sempron™ or Athlon64™ microprocessors from Advanced Micro Devices, Inc.; and the like. Further, other types of operating systems are contemplated, such as Windows®, WindowsXP®, WindowsNT®, or the like from Microsoft Corporation, Solaris from Sun Microsystems, LINUX, UNIX, and the like. In still other embodiments, the techniques described above may be implemented upon a chip or an auxiliary processing board (e.g. a programmable logic device or a graphics processor unit). 
     In operation, computer system  300  receives electronic messages, such as e-mail messages, from electronic messaging repositories. Computer system  300  processes an e-mail message to determine message attribute data associated with the e-mail messages. Message attribute data is information related to an attribute or content of an electronic message. Some examples of message attribute data are sender e-mail address or sender identifiers, recipient identifiers, names associated with sender/recipient identifiers, attachment data, in-line text, body content, routing information, header information, and the like. The message attribute data allows computer system  300  to provide users and organizations with access to message content, relationships between e-mail messages, topics, rankings, and the like. 
       FIG. 4  is a block diagram of an exemplary e-mail message  400 . The e-mail message  400  can be any message transmitted over a communications network, such as the Internet. In one example, the e-mail message  400  is a message communicated using one of the protocols adapted for communication using the Transport Control Protocol/Internet Protocol (TCP/IP) suite of protocols used over the Internet, such as the Simple Mail Transfer Protocol (SMTP). The e-mail message  400  may be communicated by using dedicated messaging client, such as Outlook and the like, and a web browser, such as Mozilla Firefox and Microsoft Internet Explorer and the like using a web-mail interface. 
     E-mail message  400  includes e-mail header  410  and e-mail body  420 . In this example, e-mail header  410  generally includes message attribute data related to header information, such as routing information, spam/virus scanning information, a subject, a sender identifier (e.g., the originating or sending e-mail address), one or more recipient identifiers (e.g., To-recipients, CC-recipients, and BCC-recipients, and distribution list e-mail addresses), priority, and the like. As the e-mail message  400  travels to its destination, information about the path or network hosts through which the e-mail message  400  passed may be appended to the e-mail header  410  in the routing information. 
     E-mail header  410  may also contain information about the e-mail client from which the e-mail message  400  was sent. Additionally, the e-mail header  410  may include information related to the format or encoding used to communicate the e-mail body  420 . 
     The e-mail message  400  is typically encoded in ASCII (American Standard Code for Information Interchange) text. The e-mail message  400  includes message attribute data related to portions (e.g., headers, body, etc.) of the e-mail message  400 . In various embodiments, the e-mail body  420  includes non-text data, such as graphic images and sound files and the like, in-line with text and as attachments. Some examples of the contents of the e-mail body  420  are plain text, base-64 encoded text, an encoded binary file, a portion of an e-mail message, an attached Portable Document Format (PDF) file, an attached or in-line Microsoft Word document file, and the like. 
     In various embodiments, e-mail body  420  of the e-mail message  400  also includes a quoted message  430 . The quoted message  430  itself includes quoted message header  440  and quoted message body  450 . In general, quoted message  430  is a portion of an e-mail message or an entire e-mail message. Portions of e-mail messages are often included in-line with other text in the e-mail body  420 . For example, the e-mail message  400  may be a reply to an initial or earlier e-mail message that is included in the e-mail body  420  as the quoted message  430 . Entire or complete e-mail messages are often included in-line or as an attachment to the e-mail message  400 . In other embodiments, quoted message  430  may be a forwarded messages, etc. 
     Quoted message header  430  comprises information, such as sender and recipient identifiers, much like the e-mail header  410 . Often, the quoted message header  430  includes at least a sender identifier, one or more recipient identifiers, a subject, a timestamp, and the like. Quoted message body  450  may be plain text, html, encoded text, and the like. The quoted text body  450  also may include portions of other e-mail messages and attachments. 
       FIG. 5  is a block diagram illustrating an exemplary processing flow of electronic messages in one embodiment according to the present invention. In crawler box  505 , computer system  300  retrieves e-mail messages from e-mail repositories, such as an e-mail server or a file containing e-mail messages, and sends the e-mail messages to a buffer manager. In buffer manager box  510 , computer system  300  buffers or otherwise manages production and consumption of the e-mail messages retrieved while computer system  300  is “crawling” the e-mail repositories. In e-mail provider box  515 , computer system  300  creates batches of e-mail messages. In this example, batching the e-mail messages allows computer system  300  to apply batch-processing techniques to message attribute data associated with a batch of e-mail messages. For example, computer system  300  may create batches of 10, 50, or 100 e-mail messages. 
     In duplicate eliminator box  520 , computer system  300  processes the e-mail messages in the e-mail message batches to determine duplicates or redundant e-mail messages. For example, a user A of the mail server  117  ( FIG. 1 ) may have sent an e-mail message addressed to user B and to user C. When computer system  300  retrieves e-mail messages from mailboxes on the mail server  117  for users A, B, and C, user A&#39;s mailbox contains the e-mail message as sent to user B and user C. Additionally, both user B&#39;s and user C&#39;s mailbox contains the respective user&#39;s copy of the e-mail message as received from user A. In this example, computer system  300  receives possibly three copies of the e-mail message in the duplicate eliminator box  520 . 
     Computer system  300  determines which of the three copies of the e-mail message to further process. In one example, computer system  300  determines two MD5 checksums for each e-mail message to “identify” an e-mail message. A first strict MD5 checksum is computed to be unique and represents an exact match of a previously processed e-mail message. A second “relaxed” MD5 checksum is computer to be non-unique or semi-unique. 
     When computer system  300  receives a new e-mail, computer system  300  processes the new e-mail message (e.g., address normalization and cleansing) and computes a strict MD5 checksum for the new e-mail message and compares the strict MD5 checksum to previously computed strict MD5 checksums to determine whether the new e-mail message is unique. In one example of operation, computer system  300  computes the strict MD5 checksum in response to message attribute data associated with an e-mail message using the sender e-mail address or sender identifier, sorted To-recipient e-mail addresses or To-recipient identifiers, sent time, alpha-numeric contents of subject, and the body text (e.g., body text size, contents of the body text, etc.). 
     Computer system  300  then computes a relaxed MD5 checksum using a portion of the message attribute data used to compute the strict MD5 checksum. Other information not included in the e-mail message but associated with the message attribute data may be used to compute the strict and relaxed MD5 checksums. Other types of integrity, detection, and authenticity algorithms, such as cyclical redundancy checks (CRCs), hashes, and the like, may be used in addition to or in the alternative to the MD5 checksum. 
     In this example, if the strict MD5 checksum for the new e-mail message is different, computer system  300  computes a relaxed MD5 checksum for the new e-mail message and compares the relaxed MD5 checksum to previously computed relaxed MD5 checksums. If the relaxed MD5 checksum for the new e-mail message is different, then the new-e-mail address is not a duplicate. If the relaxed MD5 checksum for the new e-mail message is the same as one or more previously computed relaxed MD5 checksums, computer system  300  applies rules or policies to eliminate possible duplicate e-mail messages that may occur due to time differences, header processing, and the like, and also the addition of trailing content, such as disclaimers, names of attachment files, and the like. 
     In surface processor box  525 , computer system  300  processes the e-mail messages (e.g., to populate the master index  105  of  FIG. 1  with information related to the message attribute data). Some examples of surface processing are whether text in a body of an e-mail message is text included in another e-mail message (e.g., as a response to the e-mail message), identity information of senders, and identity information of recipients. In attachment processor box  530 , computer system  300  processes the e-mail message for attachments. If an e-mail message includes an attachment, computer system  300  further processes the attachment in stellent processing box  535 . In this example, computer system  300  processes the attachment according to content management and searching solutions from Stellent, Inc. of Eden Prairie, Minn. In attachment full text index box  540 , computer system  300  stores an inverted index of the extracted text of an attachment, if any (e.g., in the master index  105 ). 
     In e-mail processing box  545 , after attachment processing or if no attachment exists in an e-mail message, computer system  300  operates on the batch of e-mail messages to parse or extract further information associated with message attribute data from the e-mail messages. In NP extraction box  550 , for example, computer system  300  processes subject and body content of the e-mail messages, such as to extract noun phrases, and the like. Computer system  300  then normalizes the extracted noun phrases into a feature vector that represents topical information associated with the e-mail messages. 
     In batch committer box  555 , computer system  300  commits the processed e-mail messages in the batch to storage. In one example, computer system  300  populates the master index  105  with information parsed or indexed in the e-mail processor box  545 . In e-mail full text index box  560  of this example, computer system  300  stores a full text index of the e-mail messages (e.g., in the e-mail full text index  165  of  FIG. 1 ). In SQL tables box  565 , computer system  300  prepares Structured Query Language (SQL) tables allowing the e-mail messages and message attribute data associated with the e-mail messages in the batch to be searched using SQL statements. 
     In thread analyzer box  570 , computer system  300  processes the e-mail messages to determine e-mail threads in response to message attribute data of the e-mail messages. In thread full text index box  575 , computer system  300  stores a full text index of e-mail threads (e.g., in the master index  105 ). Further operations of computer system  300  in the thread analyzer box  570  are described further with respect to  FIGS. 8A ,  8 B,  9 ,  10 A, and  10 B. 
       FIG. 6  is a block diagram illustrating an exemplary message sequence chart related to an e-mail thread. An e-mail thread is a series or sequence of one or more e-mail messages that form a logical “discussion” or “communication.” E-mail messages can be related by thread criteria, such as time, sender, topic, etc. An e-mail thread also can provide an indication of user interactions to an earlier or original e-mail message that initiated a discussion or communication formed by a series of e-mail messages. Typically, the e-mail that initiated the subsequent user interactions or communications is called a thread origin (e.g., e-mail message  605 ). 
     Referring to  FIG. 6 , a thread criterion defines an initial time starting indicative of when an e-mail message  605  was sent. As a result, the e-mail thread includes e-mail messages, transmitted during a time interval, that satisfy another thread criterion. In this example, computer system  300  determines e-mail messages that satisfy the thread criterion are e-mail messages related to the e-mail message  605  or otherwise transmitted in response to the e-mail message  605 . 
     In this example, user (Sender) A composes e-mail message  605  to three users. The e-mail message  605  may be considered the origin of the e-mail thread illustrated in  FIG. 6 . User B receives e-mail message  610  as a carbon copy (CC) recipient. User C receives e-mail message  615  as a To-recipient. User D receives an e-mail message  620  as a CC-recipient. 
     In response to the e-mail message  610 , the user B composes an e-message to users A and D. The user A receives e-mail message  625  as a To-recipient, and the user D receives e-mail message  630  as a CC-recipient. The user B may have forwarded or replied to the e-mail message  610  such that the e-mail messages  625  and  630  included the body text of the e-mail message  610  (in other words the original e-mail message  605 ). The e-mail messages  625  and  630  may also include the e-mail message  610  as an attachment, and include a similar subject as the e-mail message  610 . 
     Next in the e-mail thread of  FIG. 6 , in response to the e-mail message  615 , the user C composes an e-mail message to user A. The user A receives e-mail message  635  as a To-recipient. Subsequently, again in response to the e-mail message  615 , the user C composes an e-mail to users A, B, and D. The user A receives e-mail message  640  as a To-recipient. The user B receives e-mail message  645  as a To-recipient. The user D receives e-mail message  650  as a CC-recipient. 
     After receiving the e-mail message  640 , the user A composes an e-mail message to users B, C, and D in response to the e-mail message  625 . The user B receives e-mail message  655  as a CC-recipient. The user C receives e-mail message  660  as a To-recipient. The user D receives e-mail message  665  as a CC-recipient. 
     Subsequently, in response to the e-mail message  640 , the user A composes an e-mail message to users B, C, and D. The user B receives e-mail message  670  as a CC-recipient. The user C receives e-mail message  675  as a To-recipient. The user D receives e-mail message  680  as a CC-recipient. 
     Advantageously, computer system  300  allows a user or organization to discover information in e-mail messages that relates to discussions or communications about specific topics. Computer system  300  organizes the information, such as e-mail messages, into a thread and generates one or more topics in response to message attribute data associated with e-mail messages. Computer system  300  allows the user or organization to analyze the information to drive better business performance and/or comply with regulatory requirements. 
     Furthermore, computer system  300  allows the users and organizations to analyze properties of e-mail (such as recipients, replies, forwards, subject header, etc.), and combine the properties with organizational or corporate data to derive discussions and communication patterns within an organization or corporation. Computer system  300  provides access to electronic messages and message attribute data associated with the electronic messages. This allows users and organizations to quickly extract, analyze, and report information. 
     Derived Electronic Messages 
     As a result of user interactions in response to an e-mail message, subsequent e-mail messages may include quoted text from prior e-mail messages or include prior e-mail messages as attachments. Computer system  300  ( FIG. 3 ) allows users or organizations to retrieve transactional e-mail messages from local e-mail repositories. Transactional e-mail messages are electronic messages that are received from and/or stored on an e-mail server or in a file (e.g., one of the mail servers  117  of  FIG. 1 ). A transactional message may include quoted text or attachments. 
     In various embodiments, computer system  300  processes the transactional e-mail messages to determine derived e-mail messages. Derived e-mail messages are electronic messages sent by electronic messaging services, where the electronic messages are included within other electronic messages. As described with respect to  FIG. 4 , some examples of derived e-mail messages are quoted text in forwarded or replied to e-mail messages, and e-mail messages included as attachments. For example, a transactional e-mail message may include a derived e-mail message. A benefit provided by various embodiments is that computer system  300  allows users and organizations to capture information in derived e-mail messages that otherwise may not have been retrieved from the local e-mail repositories as transactional e-mail messages. 
     As the use of electronic messaging proliferates, e-mails are often received from outside of organizations that initiate discussions or communications within the organization. Computer system  300  provides the users or organizations the ability to determine from derived e-mail messages whether discussions or communication originated from outside the organization. Additionally, computer system  300  allows the users or organizations to track whether topic discussion left or went outside the organization during a series of e-mail messages and later returned to an internal discussion within the organization. 
     In general, a system for processing e-mail messages (e.g., computer system  300  of  FIG. 3 ) includes a processor and a communications interface. The communications interface receives, from an e-mail repository, a transactional e-mail message comprising message attribute data. The processor is coupled to the communications interface. As described above, the processor places the transactional e-mail message in an e-mail thread in response to the message attribute data of the transaction e-mail message. The processor then determines whether there is a derived e-mail message included in the transactional e-mail message. For example, if a derived e-mail messages such as a reply or forwarded message, is included in the transactional e-mail message, the processor determines derived message attribute data of the derived e-mail message. The processor then places the derived e-mail message in the e-mail thread in response to the derived message attribute data of the derived e-mail message. 
       FIG. 7  is a flowchart for processing e-mail messages to determine derived e-mail messages in one embodiment according to the present invention.  FIG. 7  begins in step  700 . In step  705 , computer system  300  ( FIG. 3 ) receives a transactional e-mail message from an e-mail repository. A transactional e-mail message is an e-mail message directly retrieved from an e-mail message repository, such as an e-mail server or an e-mail storage file (e.g., a PST file), as opposed to a derived e-mail message which generally is determined from a transactional e-mail message. 
     In step  710 , computer system  300  determines message attribute data of the transactional e-mail message. Some examples of message attribute data are message content body, quoted text sections, attachments, signature sections, and message header information—such as a sender identifier, one or more recipient identifiers, the number of recipients, routing information, a subject line, a time stamp, and the like. In step  715 , computer system  300  determines an e-mail rank associated with the transactional e-mail message. An e-mail rank is any quantitative value, symbol, or indicator associated with an e-mail message that is used to provide an indication of qualitative value, relevance, standing, degree, or position of the e-mail message. 
     In this example, computer system  300  determines a numerical value in response to the message attribute data for the e-mail rank associated with the transactional e-mail message. Computer system  300  may also assign the transactional e-mail message 3 or 4 stars out of 5 stars. Additionally, computer system  300  may also use external sources of information to determine the e-mail rank of e-mail messages. One exemplary method of determining an e-mail rank associated with e-mail messages is described with respect to  FIGS. 8A and 8B . 
     In step  720 , computer system  300  places the transactional e-mail message in an e-mail thread in response to the message attribute data. In step  725 , computer system  300  determines whether a derived e-mail message is included in the transactional e-mail message. The derived e-mail message may be included in the transactional e-mail message as quoted text and as an attachment. 
     In step  730 , if a derived e-mail message does not exist or is not included in the transactional e-mail message, the flowchart ends in step  750 . Alternatively, if a derived e-mail message does exist or is included in the transactional e-mail message, the flowchart continues in step  735 . 
     In step  735 , computer system  300  determines derived message attribute data of the derived e-mail message. Some examples of derived message attributed data are a relationship with the transaction e-mail message (e.g., in-line, attached, forwarded, replied, etc.), derived message content body, quoted text sections, attachments, signature sections, and derived message header information—such as a sender identifier, one or more recipient identifiers, the number of recipients, routing information, a subject line, a time stamp, and the like. 
     In step  740 , computer system  300  determines an e-mail rank associated with the derived e-mail message. In this example, computer system  300  determines the e-mail rank of the derived e-mail message in response to the derived message attribute data and the e-mail rank of the transactional e-mail message. In step  745 , computer system  300  places the derived e-mail message in the e-mail thread, along with the transactional e-mail message, in response to the derived e-mail message data.  FIG. 7  ends in step  750 . 
     Ranking Electronic Messages 
     In various embodiments, computer system  300  processes e-mail messages (e.g., transactional e-mail messages and derived e-mail messages) to determine an e-mail rank associated with the e-mail message. A benefit provided by various embodiments is that computer system  300  allows users and organizations to sort, analyze, and process captured information in transactional and derived e-mail messages in response to e-mail ranks. 
     In general, a system (e.g., computer system  300  of  FIG. 3 ) for ranking electronic messages includes a processor. The processor receives an e-mail message and determines a sender identifier associated with the e-mail message. The processor may also determine message attribute data in response to the e-mail message. The processor then determines an e-mail rank associated with the e-mail message in response to the sender identifier. The processor may determine the e-mail rank based on the message attribute data. 
       FIG. 8A  and  FIG. 8B  are a flowchart for determining an e-mail rank associated with an e-mail message in one embodiment according to the present invention.  FIG. 8A  begins in step  800 . In step  805 , computer system  300  receives message attribute data of the e-mail message. In this example, the e-mail message can be a transactional e-mail message or a derived e-mail message. 
     In step  810 , computer system  300  determines a sender identifier related to the e-mail message based on the message attribute data. Some examples of sender identifiers are Internet e-mail address (such as To, CC, and BCC), usernames, hostnames, last names, and first names. In step  815 , computer system  300  determines a sender value for the e-mail message in response to the sender identifier. 
     In step  820 , computer system  300  receives organizational data related to the sender identifier. For example, computer system  300  may determine that the sender identified is the CEO, CIO, CTO, President, and the like. In various embodiments, computer system  300  retrieves the organizational data from organizational or corporate directories, corporate organization charts, and the like. In step  825 , computer system  300  determines a sender weight for the e-mail message in response to the organizational data related to the sender identifier. In step  830 , computer system  300  determines a sender portion of the e-mail rank in response to the sender value and the sender weight. 
     In step  835 , computer system  300  determines a recipient identifier related to the e-mail message based on the message attribute data. In step  840 , computer system  300  determines a recipient value for the e-mail message in response to the recipient identifier. 
     Referring to  FIG. 8B , in step  845 , computer system  300  receives organizational data related to the recipient identifier. In step  850 , computer system  300  determines a recipient weight for the e-mail message in response to the organizational data related to the recipient identifier. In step  855 , computer system  300  determines a recipient portion of the e-mail rank in response to the recipient value and the recipient weight. 
     In step  860 , if multiple recipients of the e-mail exist, the flowchart returns to step  835  to determine another recipient identifier. If no more recipient identifiers exist or recipient processing is otherwise terminated, the flowchart continues in step  870 . 
     In step  870 , computer system  300  determines a sender text value for the e-mail message based on text contributed by the sender. In one example, the entire e-mail message comprises original text contributed by the sender. In another example, the e-mail message comprises answers contributed by the sender and questions included in a previously received e-mail message. If no text is contributed, computer system  300  may determine the sender text value to be zero (0). For the more original text contributed, computer system  300  determines a larger sender text value. 
     In step  875 , computer system  300  calculates the e-mail rank for the e-mail message in response to the sender portion, the recipient portion of one or more recipients, and the sender text value. Computer system  300  maps or otherwise associates the e-mail rank to the e-mail message.  FIG. 8B  ends in step  880 . 
     In one embodiment, computer system  300  determines the e-mail message rank as a weighted average of a SenderValue, ToValue, and CCValue. For example, computer system  300  determines the e-mail rank (e.g., MessageRank) according to the following equation: 
     
       
         
           
             MessageRank 
             = 
             
               
                 
                   
                     
                       
                         ( 
                         
                           SenderValue 
                           * 
                           SenderWeight 
                         
                         ) 
                       
                       + 
                     
                   
                 
                 
                   
                     
                       
                         ( 
                         
                           ToValue 
                           * 
                           ToWeight 
                         
                         ) 
                       
                       + 
                       
                         ( 
                         
                           CCValue 
                           * 
                           CCWeight 
                         
                         ) 
                       
                     
                   
                 
               
               
                 SenderWeight 
                 + 
                 ToWeight 
                 + 
                 CCWeight 
               
             
           
         
       
     
     In this example, computer system  300  derives the SenderValue component using a weighted average of the sender&#39;s role (e.g., organizational role or corporate office) and the text contributed by the sender. Computer system  300  determines the value of the text contributed by the sender based on word frequency. Another example of determining e-mail rank is described in U.S. Provisional Application No. 60/761,500, filed Jan. 23, 2006 and entitled “E-Mail Threading, Ranking, Derivation and Topic Classification Methods and Apparatus.” 
     Electronic Message Threading 
     In various embodiments, computer system  300  processes e-mail messages (e.g., transactional e-mail messages and derived e-mail messages) to determine “discussions” or “communications.” These discussions or communications may be found in a series or sequence of e-mail messages. A benefit provided by various embodiments is that computer system  300  allows users and organizations to sort, analyze, and process captured information in transactional and derived e-mail messages into logical discussions or communications. 
     In general, a system (e.g., computer system  300  of  FIG. 3 ) for threading of electronic messages includes a processor. The processor receives an e-mail message and determines message attribute data in response to the e-mail message. The processor then determines the position of the e-mail in an e-mail thread in response to the message attribute data. 
       FIG. 9  is a flowchart for processing e-mail messages for placement in an e-mail thread in one embodiment according to the present invention.  FIG. 9  begins in step  900 . In step  910 , computer system  300  receives an e-mail message. In step  920 , computer system  300  determines message attribute data of the e-mail message. In step  930 , computer system  300  processes the message attribute data to determine a location of the e-mail message in an e-mail thread. 
     In step  940 , computer system  300  determines whether the e-mail message is similar to an existing e-mail message in the e-mail thread. If the e-mail message is not similar to an existing e-mail message in the e-mail thread,  FIG. 9  ends in step  980 . Alternatively, if the e-mail message is similar to an existing e-mail message in the e-mail thread, the computer system  300  determines whether the existing e-mail message is a transactional e-mail message in step  950 . In this example, computer system  300  provides deference to transactional e-mail messages, as opposed to derived e-mail messages whose message texts may have been altered during a reply or forward operation. 
     In step  960 , computer system  300  marks the e-mail message as a duplicate. In step  970 , computer system  300  deletes the duplicate e-mail message.  FIG. 9  ends in step  980 . 
       FIG. 10A  and  FIG. 10B  are a flowchart for organizing an e-mail message, such as the e-mail message  640  from user C of  FIG. 6 , in an e-mail thread in one embodiment according to the present invention.  FIG. 10A  begins in step  1000 . In step  1005 , computer system  300  receives the e-mail message  640  (e.g., from one of the mail servers  117  of  FIG. 1 ). In step  1010 , computer system  300  determines message attribute data of the e-mail message  640 , such as sender identifier, recipient identifier, subject, timestamps, and the like. 
     In step  1015 , computer system  300  determines whether the subject of the e-mail message  640  (e.g., from the message attribute data) is substantially similar to the subject of an e-mail thread. If the subjects are not similar, in step  1020 , computer system  300  determines whether the e-mail message  640  includes quoted text from a chronologically earlier e-mail message in the e-mail thread (e.g., text from e-mail messages  615 ). If the e-mail message  640  does not include quoted text, computer system  300  determines whether the e-mail message  640  is included as an attachment of an existing e-mail message in the e-mail thread (e.g., in e-mail messages  670 ,  675 , or  680 ) in step  1025 . 
     If the e-mail message  640  is not included as an attachment, in step  1030 , computer system  300  determines whether a relationship exists between a sender of the e-mail message  640  and a recipient of an existing e-mail message in the e-mail thread (e.g., with the e-mail message  615 ,  670 ,  675 , and  680 ). If computer system  300  makes a negative determination in each of the steps  1015 ,  1020 ,  1025 , and  1030 , computer system  300  creates a new e-mail thread with the subject of the e-mail message  640  in step  1035 . If computer system  300  makes a positive determination in any of the steps  1015 ,  1020 ,  1025 , and  1030 , computer system  300  proceeds to determine the position of the e-mail message  640  within the corresponding e-mail thread in step  1045 . 
     Referring to  FIG. 10B , in step  1050 , computer system  300  determines whether a portion of the e-mail message  640  is included in a chronologically later e-mail message in the e-mail thread, for example in the e-mail messages  670 ,  675 , or  680 . If a positive determination is made, computer system  300  determines whether there is no quoted text or attachments (such as would indicate an earlier derived e-mail message) in the e-mail message  640  in step  1055 . If another positive determination is made, computer system  300  determines whether multiple existing e-mail messages in the e-mail thread refer to the e-mail message  640  in step  1060 . 
     If computer system  300  makes a positive determination in the steps  1050 ,  1055 , and  1060 , computer system  300  marks the e-mail message  640  as an origin of the e-mail thread. In general, the origin of an e-mail thread is an e-mail message that initiated the sending of subsequent e-mail messages forming a logical discussion or conversation. Typically, the subsequent e-mail messages have similar subjects as the origin e-mail address or refer to the origin e-mail message. The subsequent e-mail messages may also include all or a portion of the original e-mail address as quoted text or as an attachment. 
     In step  1070 , computer system  300  places the e-mail message  640  in the e-mail thread in response to the message data. If the computer system makes a negative determination in the steps  1050 ,  1055 , and  1060 , computer system  300  places the e-mail message  640  in the e-mail thread in response to the message attribute data. If the computer system makes a positive determination in the steps  1050 ,  1055 , and  1060 , computer system  300  places the e-mail message as the origin of the e-mail thread. 
     In this example, computer system  300  places the e-mail message  640  chronologically before the e-mail messages  670 ,  675 , and  680 . Computer system  300  identifies the relationships between the e-mail messages  640 ,  670 ,  675 , and  680 , such as between sender and recipient, quoted text, attachments, and the like. Computer system  300  places the e-mail message  640  chronologically after the e-mail message  615 . Computer system  300  identifies the relationships between the e-mail messages  615  and  640 . 
     In various embodiments, as computer system  300  incrementally receives e-mail messages, the e-mail message may not be received in chronological order, or any order for that matter. In response, computer system  300  may continuously “promote” or “demote” processed e-mail messages as the origin of an e-mail thread. Computer system  300  may continuously organize the e-mail thread in response to processed e-mail messages, altering relationships and updating the positions of e-mail messages in the thread in response to message attribute data of the e-mail messages.  FIG. 10B  ends in step  1075 . 
     Ordering of Electronic Message Threads 
     In various embodiments, computer system  300  processes e-mail threads (e.g., transactional e-mail messages and derived e-mail messages) to determine an ordering associated with the e-mail threads. A benefit provided by various embodiments is that computer system  300  allows users and organizations to sort, analyze, and process captured information in transactional and derived e-mail messages into e-mail threads that may be ordered based on different criteria, such as time, topic, rank, and relevance. 
     In general, a system (e.g., computer system  300  of  FIG. 3 ) for ranking electronic messages includes a processor. The processor receives a plurality of e-mail messages and determines a plurality of e-mail threads in response to the plurality of e-mail messages. The processor determines an e-mail rank associated with each e-mail message in the plurality of e-mail threads. The processor determines an e-mail rank associated with an e-mail message in response to a sender identifier related to the e-mail message. 
     The processor determines a thread rank for each e-mail thread in the plurality of e-mail threads. The processor determines a thread rank associated with an e-mail thread in response to e-mail ranks of each e-mail message associated with each respective e-mail thread. The processor then determines an ordering of the plurality of e-mail threads in response to the thread rank associated with each e-mail thread in the plurality of e-mail threads. 
       FIG. 11  is a flowchart for ordering e-mail threads in one embodiment according to the present invention.  FIG. 11  beings in step  1100 . In step  1110 , computer system  300  receives a plurality of e-mail messages. In step  1120 , computer system  300  determines the e-mail rank associated with each e-mail message in the plurality of e-mail messages. 
     In step  1130 , computer system  300  determines e-mail threads in response to the plurality of e-mail messages. In step  1140 , computer system  300  determines a thread rank associated with each e-mail thread in response to e-mail ranks of each e-mail message associated with each respective e-mail thread. In one example, the thread rank is a weighted average of the e-mail ranks associated with the e-mail message in an e-mail thread. 
     In step  1150 , computer system  300  determines an ordering of the e-mail threads in response to the thread ranks of each e-mail thread. Computer system  300  then may display the ordering to a user or generate a report containing the ordering. The ordering of e-mail threads allows a user or organization to determine which communications or conversations embodied in e-mail threads are most active or most relevant to a topic or other search criteria.  FIG. 11  ends in step  1160 . 
     Advantageously, computer system  300  can display the ordering of the e-mail thread to a user. For example, computer system  300  can provide the user with an ordering of e-mail threads based on a search performed for discussions or communications related to organization trade secrets. In another example, computer system  300  displays an ordering of the most active or highly discussed topics or categories in an organization. 
       FIG. 12  is a screenshot  1200  of an exemplary dashboard  1205  displaying information related to processing of e-mail messages in one embodiment according to the present invention. The dashboard  1205  includes a search box  1210 , a search submit button  1215 , an advanced search button  1220 , a time span interface  1225 , a personalized topic display portion  1230 , a group display portion  1235 , a current status display portion  1240 , a participants/topic display portion  1245 , a total content display portion  1250 , and a message/topic display portion  1255 . 
     In this example, the search box  1210  allows a user to enter search criteria and click the search submit button  1215  to search information processed by the system  100  ( FIG. 1 ). The user may click the advanced search button  1220  to enter a dialog (not show) provided additional search function features. The retrieved search results may be displayed in the dashboard  1205  or in a new display window. Additionally, the time span interface  1225  allows the user to enter day and time information to restrict the information displayed by the dashboard  1205 . 
     The personalized topic display portion  1230  depicts topics of interest to the user. The topic may be arranged in the personalized topic display portion  1230  according to topic alphabetical order, topic status, the number of discussions related to a particular topic, and the number of messages related to a particular topic, as illustrated. The group display portion  1235  depicts groups in an organization and the top or most active topics, discussions, and key individuals associated with a topic, as illustrated. 
     The current status display portion  1240  depicts the current status of the system  100 , such as the number of e-mail messages and attachments in the master index  105 , and the number of messages retrieved from an e-mail server, an archive, and a PST file, as illustrated. The participants/topic display portion  1245  depicts the number of participants for a particular topic. For example, the top five topics and the number of participants associated with each of the top five topics may be displayed by a pie chart. Each topic may be displayed using a different color, as illustrated. 
     The total content display portion  1250  depicts the number of e-mail messages, the number of attachments, the number of topics, the number of discussions, the number of individuals, the number of groups, and the number of annotations in the master index  105 , as illustrated. A unique or descriptive icon may represent each portion of the content in the master index. The message/topic display portion depicts the message count associated with a particular topic. In this example, the screenshot  1200  depicts a bar chart for the message count of five topics. In some embodiments, the dashboard  1205  includes links for printing or downloading information presented on the dashboard. 
       FIG. 13  is a screenshot  1300  of an exemplary search dialog  1310  displaying information related to e-mail messages in one embodiment according to the present invention. The e-mail message search dialog  1310  includes a search box  1320 , a search button  1330 , and a search listing  1340 . The search box  1320  allows a user to enter search terms to search information processed by the system  100 . The search button  1330  submits the search terms to the e-mail processing system (e.g., the analytics ETL  116 ). 
     The search listing  1340  displays the information retrieved from the master index  105 , as illustrated. In this example, the search listing  1340  displays whether an e-mail message includes an attachment, a time stamp, a sender identifier (“From”), a recipient identifier (“To”), a location or folder identifier, and a subject. In some embodiments, the search listing  1340  displays the e-mail messages sorted by a subject, a sender identifier, and/or an e-mail rank, as illustrated, as well as by other information desired by the user. 
     Topic Classification 
     In various embodiments, topic classification (e.g., topic classifier  140  of  FIG. 1 ) provides a way to classify threads and e-mails (along with attachments) into topics (or categories of interest), to facilitate navigation, discovery, and usage of e-mail content. By classifying e-mails into topics, like e-mails may be grouped together. In some embodiments, manual specification of a taxonomy for classification is provided. Accordingly, a user or administrator may create and augment the taxonomy with auto-clustering and other techniques. 
     In general, a topic is a convenient way to classify e-mail and text documents. Topics represent an abstraction that represents a set of documents. These documents are viewed as a collection, sharing certain similar properties, such as semantically related subject matter and the like. Topics may be organized into a hierarchy, with parent-level topics representing a higher level conceptual organization. Flat topics are just one level grouping of documents. One property of a hierarchy is that all documents/e-mails belonging to a leaf-level (or child-level) also belong to the parent-level. 
     Typically, an e-mail can belong to several topics. The quoted text of an e-mail participates in the topic identification, along with the new text of the e-mail. A thread can also belong to multiple topics. Topics can also be created based on a role, using customizable topic templates. Static topic creation is a model where determining the topic to which an e-mail document belongs is done at indexing time (e.g., indexer  130  of  FIG. 1 ). Dynamic topic creation is at query time. 
     In various embodiments, a topic may be created manually. Manual topic creation involves configuring a certain set of topics, along with specifying properties of each topic. Specification of these properties will depend on the manner chosen for classifying documents into topics. In some embodiments, topics are classified using auto-categorization, followed by user re-arrangement, and then by user specification of topics. 
       FIG. 14  is a simplified flowchart of a method for classifying e-mails into topics in one embodiment according to the present invention. The processing depicted in  FIG. 14  may be performed by software modules (e.g., instructions or code) executed by a processor of a computer system, by hardware modules of the computer system, or combinations thereof.  FIG. 14  begins in step  1400 . 
     In step  1410 , a plurality of e-mails is received. For example, computer system  300  may receive a plurality of e-mails from an e-mail message store, such as an Exchange Server, an IMAP server, a PST file, and the like. In step  1420 , for each e-mail in the plurality of e-mails, computer system  300  generates a feature representation for an e-mail based on a set of noun phrases (NPs) associated with the e-mail. In general, a feature representation is any set, collection, fingerprint, vector, and the like that represents one or more features or properties associated an e-mail. In various embodiments, a feature representation includes a feature vector that represents a scoring of noun phrases contained in the e-mail document. 
     In step  1430 , computer system  300  generates a set of topics associated with the plurality of e-mails based on the feature representation for each e-mail. For example, computer system  300  may determine a cluster of e-mails based on similarities in the feature representations of the e-mails. The cluster itself may represent a topic, or a concentrated portion of the cluster, or a centroid may be used to identify the topic from the cluster of common or similar noun phrases.  FIG. 14  ends in step  1440 . 
       FIG. 15  is a flowchart of a method for identifying topics based on feature vectors associated with e-mails in one embodiment according to the present invention. The processing depicted in  FIG. 15  may be performed by software modules (e.g., instructions or code) executed by a processor of a computer system, by hardware modules of the computer system, or combinations thereof.  FIG. 15  begins in step  1500 . 
     In step  1510 , computer system  300  receives a plurality of e-mails. In step  1520 , computer system  300  selects an e-mail from a plurality of e-mails. In step  1530 , computer system  100  determines the sentence structure of the e-mail. In some embodiments, computer system  300  performs linguistic analysis to determine the sentence structure. 
     In step  1540 , computer system  300  determines parts of speech of the e-mail, including noun phrases. For example, computer system  300  may perform linguistic and/or statistical analysis on the e-mail to decompose the e-mail into noun phrases. In step  1550 , computer system  300  generates a feature vector of the e-mail based on the noun phrases. 
     In step  1560 , computer system  300  determines whether there are any e-mails remaining in the plurality of e-mails. If there are e-mails remaining, processing continues at step  1520  where computer system  300  selects another e-mail from the plurality of e-mails. 
     If there are no more e-mails remaining, in step  1570 , computer system  300  determines centroids associated with clusters of feature vectors of one or more e-mails. A centroid may include an entire cluster. A centroid may further include a number of similar feature vectors that satisfy or exceed a limit or threshold. 
     In step  1580 , computer system  300  identifies a set of topics based on the centroids. For example, each centroid may represent a single topic. In some embodiments, a hierarchy of topics is determined.  FIG. 15  ends in step  1590 . 
       FIG. 16  is a block diagram illustrating tokenization of an e-mail in one embodiment according to the present invention. In general, an e-mail or document is processed from a stream of bytes into a “bag-of-words” representation. The process typically contains phrase extraction, stemming, and other pre-processing. 
     In this example, an e-mail document  1610  first flows through a filter/tokenizer component  1620 , which provides a list of tokens. Entity extraction component  1630  converts word-tokens into entities. Some examples of entities are Social Security Numbers, Phone Numbers, Contract Numbers, Normal words, and the like. Entity extraction is typically through a set of pre-programmed entity extraction rules defined in Entity descriptors component  1640 . In some embodiments, a regular-expression parser may be employed to extract entities, although enterprises may have a need to express entities that are specific to their enterprise (such as contract numbers) using entity descriptors  1640 . 
     Noun-Phrase extraction component  1650  uses the entities fed to it by entity extraction component  1630 , and tags words with their noun-phases. In some embodiments, all or some of a part-of-speech may also be tagged. Dictionary  1660  provides standard phrases, grammar, spelling, and the like to determine parts-of-speech, such as noun phrases. Stemmer  1670  determines root word forms and word stems. The result of this phase of analysis is a set of words/phrases  1680  (e.g., a “Bag-of-words”), tagged appropriately. 
       FIG. 17  is a block diagram illustrating feature vector extraction in one embodiment according to the present invention. In this phase of the analysis, the bag-of-words is processed, and “core features” are extracted from the e-mail or document. 
     The first step with the “Bag-of-words” (e.g., set of words/phrases  1710 ) is to perform an insertion of the e-mail document using Full-Text Indexer  1720  into the Full Text Index  1730  (e.g., E-mail Full-Text Index  165  of  FIG. 1 ). Following this, Thread Analysis  1740  inserts the e-mail document in Thread Index  1750  (e.g., Cluster Full Text Index  175  of  FIG. 1 ). 
     Feature Extractor  1760  then determines a “Feature vector” for the e-mail document. In general, Feature Extractor  1760  is responsible for deriving the appropriate “feature vector” that represents the unique characteristics of that e-mail document. A feature vector is any representation of the parts-of-speech that form a document, including noun phrases. 
     In various embodiments, Feature Extractor  1760  includes one or more feature vector extraction rules. Some examples of feature vector extraction rules are words with high tf-idf score, words that are marked as “important” for this role (as in role-based search), words that appear in the subject of the document, words that appear in the “New Text”, words that are present in the “Quoted Text” that are in the feature vector for the original e-mail referred to in the “Quoted Text,” and the like. 
     In some embodiments, feature vectors are extracted and scores are assigned to these entries. In one embodiment, the number of feature vector words is limited to between 10-20 entries. Each entry may be associated with a score, which is computed as follows: 
               s   ij     =     Round   ⁡     (     10   *       1   +     log   ⁡     (     tf   ij     )           1   +     log   ⁡     (     l   j     )             )             
where (tf ij ) is the term frequency of term i in document j, (l 1 ) is the length of the document j, and the term frequency is normalized by the length of the document.
 
       FIG. 18  is a block diagram illustrating automatic categorization in one embodiment according to the present invention. In this example, a Feature vector  1810  for each e-mail document is input into Categorization Engine  1820 . Categorization Engine  1820  then performs a search into a Category Feature Vector Search Index  1830 . Category Feature Vector Search Index  1830  maps the 10-20 feature vector words of the e-mail document to a set of Potential Categories  1840 . 
     Potential Category  1840  makes a selection based on the relevance score exceeding a certain threshold, so between 5-10 potential categories are determined. The feature vector of each category is extracted in Potential Category Vectors  1850 . Similarity/Distance Estimator  1860  computes a similarity distance between the feature vector of the document and the feature vector of the category. If the similarity score is high enough, the category is assigned in Actual Category  1870  for the e-mail document. If the document does not match any category, the e-mail document is assigned as un-categorized. 
       FIG. 19  is a block diagram illustrating topic creation in one embodiment according to the present invention. Based on a periodic schedule, un-categorized documents  1910  may be examined to determine if it is necessary to create new categories. The feature vector of uncategorized documents is fed into Category Generation Engine  1920 . Category Generation Engine  1920  uses categorization rules from Category Definition Rules  1930 , and creates new categories along with feature vectors for these new categories in New Category Names and Feature Vectors  1940 . Typically, each feature vector for a category is restricted to be between 20-50 words, which provides just enough words to discriminate the category from other categories. In some embodiments, scores are maintained for the feature vectors, so an ordered list of vector entries is always available. 
     In general, the top five entries of a feature vector should be enough to classify an e-mail document into its category. The other entries are maintained so that, when the category does not discriminate well enough causing too many documents to appear in one category, the remaining entries of the feature vector can be used to split the category into sub-categories. In some embodiments, when an e-mail document is added to a category, the category feature vector is updated, with additional scores from the e-mail document that was added. Statistics may be maintained, such as the number of documents in the category, which may be used to determine if there is a need to split the category. 
     In various embodiments, the merging of a new document&#39;s feature vector with the feature vector of a cluster of e-mail documents is per the following formula:
 
TF m,i =TF s,i +TF d,i   (1)
 
DF m,i =DF s,i +DF d,i   (2)
 
 N   m   =N   i   +N   j   +N   ij   (3)
 
 L   m =√{square root over ( N   m )}  (4)
 
 S   m,i =( k ·√{square root over (TF m,i )}+ l ·√{square root over (DF m,i )})/ L   m   (5)
 
FV=Sort( Si ) i=1,N     m     (6)
 
     In the above equations, merging feature vectors of a collection of documents with the feature vector of cluster according to equation (1) is provided where the Term Frequency (TF) of each term in the source collection (Document Feature Vector) is combined with the term in the destination (Cluster Feature Vector). In some embodiments, this combining is a weighted addition, with the weight for the new document downward-adjusted by the Document Frequency (DF) for the term. 
     The second step in the merge, according to equation (2), is a combination of the document frequency of the term in the destination cluster with the document frequency in the source document, DF s,i . 
     The third step in the merge, according to equation (3), is to collect the total number of terms, which is the sum of the number of terms uniquely present in the Cluster Feature Vector, the number of terms uniquely present in the Document Collection, and the number of terms common to both. 
     The fourth step in the merge, according to equation (4), is to calculate a Feature Vector Length Normalization Factor, so that clusters with very few terms are comparable with another cluster with a larger number of terms. 
     The fifth step in the merge, according to equation (5), is to compute the score for a term, using the Term Frequency of a term, its Document Frequency and a combined score. This combination is weighted using two constant factors, k and l, based on the importance of term frequency and document frequency. 
     The final step in the merge, according to equation (6), is to sort the newly formed terms in descending order. If the new number of terms is larger than a threshold (for example, predetermined or defined as 100), the lowest ranking terms are dropped. 
     Statistics may be maintained, such as the number of documents in the category, which may be used to determine if there is a need to split the category. 
     In various embodiments, an important consideration in defining Document Clusters is the time it takes to process a very large number of documents, and the number of iterations needed for the Cluster Centroid to converge. To facilitate very fast topic classification, some embodiments employ the presence of a Full Text Engine, for fast searching capability. In particular, these embodiments take the Document Noun Phrases and build an inverted index of the following type: 
     [NP-1]—Document1, Document2, Document3 . . . . DocumentN 
     [NP-2]—Document2, Document5 . . . DocumentM 
     . . . 
     [NP-M]—DocumentX, DocumentY . . . DocumentZ 
     When processing a new document, a Feature Vector associated with the document is computed. Then, a search may be performed against the inverted index to identify a set of potential cluster members. These potential members are collected in a Hit Vector, and ordered based on Search Relevance Ranking, such as using the following formula. 
     In one example, for a collection of documents D, a specific document d, and a set of noun phrases q containing t i  individual noun phrases, the score for the document may be determined according to the following: 
               s   ⁡     (     q   ,   d     )       =       ∑     t   ∈   q       ⁢           tf     t   ,   q       *     idf   t         norm   q       *         tf     t   ,   d       *     idf   t         norm   d       *     coord     q   ,   d       *     weight   t               
where
 
 tf   t,X=√{square root over (freq (t,X))} 
 
               idf   t     =     1   +     log   ⁢          D          freq   ⁡     (     t   ,   D     )                           norm   d     =          d                        coord     q   ,   d       =            q   ⋂   d               q                
and
 
     
       
         
           
             
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     The above formulas, in this example, are based on the Apache Lucene Search Engine query evaluation provided by the Apache Software Foundation of Forest Hill, Md. 
     To facilitate this evaluation, in various embodiments, the ranking noun phrases are inserted from the feature vector of the document into a special Lucene Text Index region called TopicNounPhrases. Given all the documents and their Lucene regions, the above score is computed, to identify potential cluster members. 
     Once potential cluster members are identified, the process of determining whether they belong in a Text Cluster is based on one of the two similarity measures a) Cosine distance measurement and b) Log similarity measurement. In some embodiments, a configurable option is provided for a user or administrator of the system to select between the two measurements, based on efficacy of the topic clustering results. In general, the Cosine distance measurement produces good results, but for noun phrases that are distributed in skewed or uneven document distributions, the log similarity measurement produces better results. 
     Cosine Similarity Measurement 
     In various embodiments, for two Feature Vectors P and Q, that have Noun Phrases of frequency TF p  and TF q  respectively for the common noun phrases between the two vectors, the Cosine Similarity Measurement may be determined as follows: 
               S   ⁡     (     p   ,   q     )       =           ∑     t   ∈     (     P   ⋂   Q     )         ⁢       TF   p     *     TF   q                   ∑     t   ∈   P       ⁢     TF   p         *         ∑     t   ∈   Q       ⁢     TF   q                   
Lot Similarity Measurement
 
     In various embodiments, given an existing vector P and a new vector Q the log similarity measure may be determined as follows: 
     
       
         
           
             
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     In the above equations, the values S p  and S q  are scores for the common terms between the two vectors P and Q. The values DF p  and DF q  represent the document frequency of the term, whose log value is used to normalize the sum of the scores. After these are computed, the exponential ratio term is further normalized by the ratio of the sum of the TF t  the term frequency of common terms to a similar sum of the TF t . 
     When a category reaches a certain critical mass (e.g., in terms of number of documents, or in terms of its feature vector composition), the category may be split into multiple categories. As this occurs, a parent category may also be created. The parent category&#39;s feature vector may be derived from the common feature vectors from all the children&#39;s feature vectors. It is possible for some of the e-mail documents to only match a Parent Category and not any of the Children Categories. This Category Hierarchy Generation may be an infrequently scheduled task—perhaps once every million e-mail documents indexed, or once every week, etc. 
     In various embodiments, a default naming policy is provided to name a category. This can be renamed later by an administrator or user. In general, the default naming policy takes one or two category feature vector words with the highest scores, and uses those words as the name for the category. 
     In some embodiments, manual topic/category creation and integration are based on users defining topics using certain terms. A graphical user interface (GUI) for manual topic generation may include a dialog box with fields for entering the name of a topic, a set of terms/words representing the topic, a topic category, and the like. When topics are defined, the definition is maintained in a database table, such as shown below: 
     
       
         
           
               
               
               
               
               
               
               
             
               
                   
               
               
                   
                   
                   
                   
                   
                   
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                 TopicId 
                 UserId 
                 TopicName 
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                 1 
                 &lt;user-name&gt; 
                 Apple 
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                 iPod 
                 10 
                   
               
               
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                 Apple 
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                 Steve Jobs 
                  9 
                   
               
               
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                 AppleFruit 
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                 “fruit and 
                 10 
                   
               
               
                   
                   
                   
                   
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     Typically, topic definitions may be edited through the GUI such as using add/modify/delete operations. In one embodiment, topic definitions may be maintained on a per-user level. Upon completion of an add/modify/delete event associated with the GUI, an evaluation of the changes occurs which results in one or more of the following assessments. The GUI change is a cosmetic change such as the name of the topic. Such a change results in the topic name changing, without a re-classification. The GUI change has a topic classification change and is inconsistent with existing topics that are already defined. The GUI change has a topic classification change and needs to result in a re-classification of documents. Thus, a range of e-mail documents may be re-classified per the current topic definition. Typically, to perform this re-classification, the e-mails document is not re-crawled. Instead, the e-mail document&#39;s noun phrases (NPs) are stored, and are used for applying the queries. 
     After the GUI has set up all topics, a “Done with all topics” GUI action invokes an indexer thread that is responsible for querying a topic configuration database for all new topic configurations; building a new Query-Net (if query based topic definition is part of the feature set); building a new NP Full-Text Index that contains the NPs of all the new topic definitions; searching these NPs from the Doc-NPs to identify all documents with a hit (this query may be limited to a time range); iterating through the docs from the above hit list and ensuring that the doc matches the selection criteria and applying the topic index; ensuring that, if a document belongs to multiple topics, all topics are properly identified; creating a score for the document; capturing the [document, topic, score] for ranking the topics of a document; updating topic statistics for the topic; updating global indexer statistics; and persisting the document-to-topic map and topic-to-document map; etc. 
     In various embodiments, to enable integration of manual topics and to allow for post-crawl re-classification, topic creation may be performed in two phases. In phase  1 , all the NPs and entities are extracted from e-mail documents and stored in a Full-Text Index similar to the Cluster-FTI (e.g., Cluster Full-Text Index  175  of  FIG. 1 ). In phase  2 , all the Cluster-FTI documents are processed, running through and applying the Cluster rules (either manual or auto). 
     The first stage data flow is primarily concerned with identification of subject and body regions of an e-mail document, and creation of noun phrases (NPs) from these regions. These NPs are added to the Topic Full-Text index and the E-mail Full-Text index. The topic classification first stage is invoked in the context of the e-mail processing (e.g., indexer  130  of  FIG. 1 ), after completing the surface analysis, and preliminary e-mail Full-Text processing. Thread Analysis is not yet completed at this point. The subject and body regions are then reprocessed, and a Token Reader is added to the Topic Full-Text index. Topic Full-Text index stores the noun phrases as a searchable region for each document. In various embodiments, a text feature vector is constructed and stored in the E-mail Full-Text index. 
     The second stage of topic classification takes the results from the first stage and generates categories, as well as assigns e-mail documents to topics. In one embodiment, topic classification gives preference to manual topics, on the assumption that user-defined topics have a higher degree of fidelity to topic definitions. As part of the initial setup, manual topics are processed into a text feature vector for the topic. The text feature vectors are then populated or indexed into a Cluster Full-Text index (e.g., Cluster Full-Text index  175  of  FIG. 1 ). 
       FIG. 20  is a block diagram illustrating manual topic classification in one embodiment according to the present invention. In this example, Topic Full-Text index  2002  (TopicFT) is searched using search filter  2004 . Additionally, a MAXDOC from TopicMembers  2006  is applied to determine the range of e-mail documents to process. The documents from TopicFT  2002  are processed in order, based on hits. 
     For each ESA DocID  2008 , the NP feature vector is extracted in NP TermVector  2010  (e.g., using the Lucene Field TermVector property). The extracted feature vector is then used to construct a query with all the NPs, and searched against Cluster Full-Text index  2012  (ClusterFT). Manual topics  2014  are received to generate Text Feature Vectors  2016  stored in the ClusterFT  2012 . 
     If there are search hits  2020 , the hits are processed against a similarity measure in Cosine/Log Similarity  2022 . This measure is either a Cosine or Log function of the cluster&#39;s text feature vector and the e-mail document&#39;s text feature vector. If the similarity measure is greater than a specified threshold, one or more topic clusters  2024  are identified. The document is then added to the topic&#39;s document set  2026 . 
     Upon a Flush Criterion  2028 , (for example, after every 1000 documents processed), the topic document set is written to a topics table  2032  having topics members  2034  and  2036  (e.g., Topic Tables  170  of  FIG. 1 ) through update functions within the TopicsManager  2030 . After topic clusters are written out, an e-mail Full-Text index  2038  TopicIDs table  2040  is also updated. The cluster may remain in memory after the flush, in anticipation of more documents entering the Topic Clusters  2024 . If manual topic classification/identification does not produce hits, the e-mail document is processed for auto-categorization. 
       FIG. 21  is a block diagram illustrating automatic topic classification in one embodiment according to the present invention. In general, the first step in auto-categorization  2105  is creation of uncategorized clusters. When certain conditions are met, the uncategorized clusters are moved to promoted topics  2155 . These auto-generated topics are then persisted on database tables. 
     In one embodiment, when a document is presented to auto-categorize  2105 , a NP Query  2110  is created using NPS of the feature vector associated with the e-mail document. NP Query  2110  is then used to search TopicFT  2115  to identify document hits. For example, the NP Query  2110  searches for other noun phrase regions. If there are no hits from the Topic Full-Text index search, a new uncategorized cluster  2120  is created. In general, a new cluster is created, the document&#39;s information is added to the new cluster&#39;s set of documents, the feature vector of the new cluster is set to that of the first e-mail document, and the new cluster is added to the e-mail document&#39;s list of clusters. Note that the name of the cluster has not been created yet. In addition, since the cluster is brand new, it is not ready to be promoted to a Topic Cluster. 
     In this example, Doc Hits  2125  are other documents that share noun phrases similar to those found in the currently processed document. If there are hits against the Topic Full-Text index, those hits that exceed certain selection criteria are selected. A DocClusterMap  2130  is consulted to identify the list of clusters to which the document hits belong. Cluster List  2135  contains potential clusters to which this document may belong. 
     Membership of the document is confirmed in a cluster, by Cosine or Log similarity  2140 . If the document belongs to an existing cluster  2145 , the document is then added to the cluster&#39;s document set. The text feature vector of the document is also merged with the cluster&#39;s text feature vector. The top 100 NPs of the cluster are also maintained. The cluster is added to the e-mail document&#39;s cluster map. Additionally, if the document causes the cluster to become ready to be promoted, the cluster is promoted to a topic. 
     In general, when documents enter an uncategorized cluster, the document stays in that state until a promotion event. Cluster Promotion  2150  is triggered, for example, once every predetermined number of documents are processed. Promotion  2155  may be viewed as a guarded way to create a topic, and criteria that delays promotion is likely to result in a topic of a higher fidelity. At the same time, a delay in promotion means that the in-memory uncategorized cluster map grows larger, and new clusters also are generated unnecessarily. 
       FIG. 22  is a block diagram illustrating updating a topic classification in one embodiment according to the present invention. In this example, when clusters are promoted during Promotion  2210 , the cluster in Uncategorized Cluster Map  2222  becomes ready to be persisted in Topic Cluster Map  2230 . After receiving a name in Create Name  2240 , during a flush operation  2250 , TopicsManager  2260  tracks those clusters that were promoted and adds the clusters to the Topic Table  2270 . TopicsManager  2260  creates Topic Table  2270  entries (e.g., Topic Members and Topic Terms), with an indication of whether the topic is Auto or Manual, and Last Modified Time. Topic Members are the list of documents in the Topic Cluster. Topic Terms are the NPs for the promoted cluster. 
     In some embodiments, once a cluster is promoted, no new NPs are added to that topic cluster. However, the cluster continues to stay in the promoted cluster map. Any change to the cluster&#39;s document list changes the entry to a dirty-entry and the next Flush will process the dirty entries and update the Topic Members, and the E-mail Full-Text index TopicID region  2280 . 
     Once a cluster is promoted, it stays as a promoted Topic Cluster in ClusterFT  2290 . The cluster may be removed by performing a “cleanup” operation, and removing the cluster from ClusterID, removing Noun Phrases associated with the cluster, and removing the association of the cluster to one or more e-mail documents. 
     In some embodiments, when new e-mail documents are crawled and indexed, the second stage of auto-categorization picks up these new documents on the next scheduled classification cycle. This document addition step can occur in one of the following ways—the new documents only populate manual topic clusters, the new documents populate either a manual topic cluster or an existing auto-cluster, the new document populates an existing uncategorized auto-cluster, or the new document creates a new cluster. 
     Manual topic definition supports both term type topics as well as query type topics. In general, term topics are very similar to auto-topics in that the specified terms are processed into a feature vector, and a cosine dot product is computed from the manual topic definition and the feature vector of the document. Given that the feature vector of the document is based on noun phrases, this cosine similarity evaluates the distance of the document against the noun phrase specification and, if the distance is small, the document is included in the topic. 
     Query topics are those that are based on a pure search into the E-mail Full-Text index. In one form of query, the topic is defined as a comma or new-line separated sequence of search terms. These terms are treated as an OR query against the Subject, NewText, and QuotedText regions of E-mail FT Documents, and a Lucene MultiFieldQuery is issued. If the Hit Score is greater than a threshold, the document is included in the topic. For example, “company information, intellectual property, IP” does a Lucene BooleanOrClause query of the two phrases and the term and checks if there is a hit in Subject, NewText, and QuotedText region. 
     In another form of query, the topic is defined as a comma or new-line separated sequence of region and term within the region as follows: (region1 term1) (region2 term2) . . . (regionN termN). The query constructs an OR&#39;ed query of all of them and applies a hit score threshold. For example: “(subject ken lay) (body stock sale)” collects all documents where “ken lay” is in the subject or “stock sale” is in the body. 
     In yet another form of query, the topic is defined as a pure Lucene Query, as per the following syntax. The “lucene:” keyword is an indicator that the rest of the text is a pure Lucene query. This is a Lucene Slop Query. Any string that is parseable may be included using Lucene&#39;s StandardTextTokenizer. For example (lucene: body:\“ken lay\”˜2body:\“stock sale) collects all documents where “ken lay” is within two words of “stock sale” within the body and puts them into a topic. 
     In some embodiments, a topic cleanup operation will remove all actions of auto-categorization. These are: delete all TopicTable entries” that are of type ‘auto’, remove the Topic Members, Topic Terms associated with topics, delete all ClusterFT entries for the topic, and delete all stored fields in EmailTopicIDs in EmailFT. 
     The following are some sample e-mail observations: 
     Subject Analysis 
     Sample Subject: 
     CALME Disclosure Schedule Information 
                                     Text   Baseform   Phrase syntax and part-of-speech                  CALME   Calme   premodifier, proper noun, noun phrase begins       Disclosure    disclosure   premodifier, noun, noun phrase continues       Schedule   schedule   premodifier, noun, noun phrase continues       Information   information   nominal head, noun, noun phrase ends,                sentence boundary                    
Main Body Analysis:
 
     Body Text:
         I am sending you the EOL model on Greg Piper&#39;s request. The model reflects accurately the traded volumes on EOL for 2000. Projections for 2001-2011 were built based on feedback from traders, investment bank and research institutes B2B forecasts and, common sense. These can be manipulated by changing the assumptions in the assumption sheet.   On the financial side the balance sheet and income statement have not been completely modelled since no specific deal structure has been defined. Though, they include the main revenue and expense components for EOL.   If you have any doubts with respect to the functionality of the model, please let me know. I am available to clarify them at your convenience.   Marc Eichmann       

     
       
         
           
               
               
               
             
               
                   
               
               
                 Text 
                 Baseform 
                 Phrase syntax and part-of-speech 
               
               
                   
               
             
            
               
                 I 
                 I 
                 nominal head, pro-nominal 
               
               
                 am 
                 be 
                 auxiliary verb, indicative present 
               
               
                 sending 
                 send 
                 main verb, participle progressive 
               
               
                 you 
                 you 
                 nominal head, pro-nominal 
               
               
                 the 
                 the 
                 premodifier, determiner 
               
               
                 EOL 
                 EOL 
                 premodifier, proper noun, noun phrase begins 
               
               
                 model 
                 model 
                 nominal head, noun, noun phrase ends 
               
               
                 on 
                 on 
                 preposed marker, preposition 
               
               
                 Greg 
                 Greg 
                 premodifier, proper noun, noun phrase begins 
               
               
                 Piper&#39;s 
                 Piper 
                 premodifier, proper noun, noun phrase 
               
               
                   
                   
                 continues 
               
               
                 request 
                 request 
                 nominal head, noun, noun phrase ends 
               
               
                 . 
                 . 
                 sentence boundary 
               
               
                 The 
                 the 
                 premodifier, determiner 
               
               
                 model 
                 model 
                 nominal head, noun, single-word noun phrase 
               
               
                 reflects 
                 reflect 
                 main verb, indicative present 
               
               
                 accurately 
                 accurately 
                 adverbial head, adverb 
               
               
                 the 
                 the 
                 premodifier, determiner 
               
               
                 traded 
                 traded 
                 premodifier, adjective, noun phrase begins 
               
               
                 volumes 
                 volume 
                 nominal head, plural noun, noun phrase  
               
               
                   
                   
                 continues 
               
               
                 on 
                 on 
                 postmodifier, preposition, noun phrase 
               
               
                   
                   
                 continues 
               
               
                 EOL 
                 EOL 
                 nominal head, proper noun, noun phrase ends 
               
               
                 for 
                 for 
                 postmodifier, preposition 
               
               
                 2000 
                 2000 
                 nominal head, numeral 
               
               
                 . 
                 . 
                 sentence boundary 
               
               
                 Projections 
                 projection 
                 nominal head, plural noun, single-word  
               
               
                   
                   
                 noun phrase 
               
               
                 for 
                 for 
                 postmodifier, preposition 
               
               
                 2001 
                 2001 
                 nominal head, numeral 
               
               
                 — 
                 — 
                   
               
               
                 2011 
                 2011 
                 nominal head, numeral 
               
               
                 were 
                 be 
                 auxiliary verb, indicative past 
               
               
                 built 
                 build 
                 main verb, participle perfect 
               
               
                 based 
                 base 
                 main verb, participle perfect 
               
               
                 on 
                 on 
                 preposed marker, preposition 
               
               
                 feedback 
                 feed back 
                 nominal head, noun, noun phrase begins 
               
               
                 from 
                 from 
                 postmodifier, preposition, noun phrase  
               
               
                   
                   
                 continues 
               
               
                 traders 
                 trader 
                 nominal head, plural noun, noun phrase ends 
               
               
                 , 
                 , 
                   
               
               
                 investment  
                 investment 
                 premodifier, noun, noun phrase begins 
               
               
                 bank 
                 bank 
                 nominal head, noun, noun phrase ends 
               
               
                 and 
                 and 
                 coordination marker 
               
               
                 research 
                 research 
                 premodifier, noun, noun phrase begins 
               
               
                 institutes  
                 institute 
                 premodifier, plural noun, noun phrase  
               
               
                   
                   
                 continues 
               
               
                 B2B 
                 b2b 
                 premodifier, noun, noun phrase continues 
               
               
                 forecasts 
                 forecast 
                 nominal head, plural noun, noun phrase ends 
               
               
                 and 
                 and 
                 coordination marker 
               
               
                 , 
                 , 
                   
               
               
                 common 
                 common 
                 premodifier, adjective, noun phrase begins 
               
               
                 sense 
                 sense 
                 nominal head, noun, noun phrase ends 
               
               
                 . 
                 . 
                 sentence boundary 
               
               
                 These 
                 this 
                 nominal head, pro-nominal 
               
               
                 can 
                 can 
                 auxiliary verb, indicative present 
               
               
                 be 
                 be 
                 auxiliary verb, infinitive 
               
               
                 manipulated 
                 manipulate 
                 main verb, participle perfect 
               
               
                 by 
                 by 
                 preposed marker, preposition 
               
               
                 changing 
                 change 
                 main verb, participle progressive 
               
               
                 the 
                 the 
                 premodifier, determiner 
               
               
                 assumptions 
                 assumption 
                 nominal head, plural noun, single-word  
               
               
                   
                   
                 noun phrase 
               
               
                 in 
                 in 
                 preposed marker, preposition 
               
               
                 the 
                 the 
                 premodifier, determiner 
               
               
                 assumption 
                 assumption 
                 premodifier, noun, noun phrase begins 
               
               
                 sheet 
                 sheet 
                 nominal head, noun, noun phrase ends 
               
               
                 . 
                 . 
                 sentence boundary 
               
               
                 On 
                 on 
                 preposed marker, preposition 
               
               
                 the 
                 the 
                 premodifier, determiner 
               
               
                 financial 
                 financial 
                 nominal head, adjective 
               
               
                 side 
                 side 
                 main verb, indicative present 
               
               
                 the 
                 the 
                 premodifier, determiner 
               
               
                 balance 
                 balance 
                 premodifier, noun, noun phrase begins 
               
               
                 sheet 
                 sheet 
                 nominal head, noun, noun phrase ends 
               
               
                 and 
                 and 
                 coordination marker 
               
               
                 income 
                 income 
                 premodifier, noun, noun phrase begins 
               
               
                 statement 
                 statement 
                 nominal head, noun, noun phrase ends 
               
               
                 have 
                 have 
                 auxiliary verb, indicative present 
               
               
                 not 
                 not 
                 adverbial head, adverb 
               
               
                 been 
                 be 
                 auxiliary verb, participle perfect 
               
               
                 completely 
                 completely 
                 adverbial head, adverb 
               
               
                 modelled 
                 model 
                 main verb, participle perfect 
               
               
                 since 
                 since 
                 preposed marker, clause marker 
               
               
                   
                 since 
                 preposed marker, preposition 
               
               
                 no 
                 no 
                 premodifier, pro-nominal 
               
               
                 specific 
                 specific 
                 premodifier, adjective, noun phrase begins 
               
               
                 deal 
                 deal 
                 premodifier, noun, noun phrase continues 
               
               
                 structure 
                 structure 
                 nominal head, noun, noun phrase ends 
               
               
                 has 
                 have 
                 auxiliary verb, indicative present 
               
               
                 been 
                 be 
                 auxiliary verb, participle perfect 
               
               
                 defined 
                 define 
                 main verb, participle perfect 
               
               
                 . 
                 . 
                 sentence boundary 
               
               
                 Though 
                 though 
                 adverbial head, adverb 
               
               
                 , 
                 , 
                   
               
               
                 they 
                 they 
                 nominal head, pro-nominal 
               
               
                 include 
                 include 
                 main verb, indicative present 
               
               
                 the 
                 the 
                 premodifier, determiner 
               
               
                 main 
                 main 
                 premodifier, adjective, noun phrase begins 
               
               
                 revenue 
                 revenue 
                 nominal head, noun, noun phrase ends 
               
               
                 and 
                 and 
                 coordination marker 
               
               
                 expense 
                 expense 
                 premodifier, noun, noun phrase begins 
               
               
                 components 
                 component 
                 nominal head, plural noun, noun phrase  
               
               
                   
                   
                 continues 
               
               
                 for 
                 for 
                 postmodifier, preposition, noun phrase  
               
               
                   
                   
                 continues 
               
               
                 EOL 
                 EOL 
                 nominal head, proper noun, noun phrase ends 
               
               
                 . 
                 . 
                 sentence boundary 
               
               
                   
               
            
           
         
       
     
     In this example, there are 12 candidate noun phrases, all with possible topics. However, all noun phrases that have some matches with the Subject Line have a better chance of being more accurate representatives of the topics in question. The target topics for this e-mail would be: 
     EOL Model 
     Balance sheet and income statement 
     B2B forecasts. 
     Revenue and Expense components 
     Trade Volumes for &lt;year&gt; 
     Projections for &lt;year&gt; 
     The present invention can be implemented in the form of control logic in software or hardware or a combination of both. The control logic may be stored in an information storage medium as a plurality of instructions adapted to direct an information-processing device to perform a set of steps disclosed in embodiments of the present invention. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the present invention. 
     The above description is illustrative but not restrictive. Many variations of the invention will become apparent to those skilled in the art upon review of the disclosure. The scope of the invention should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the pending claims along with their full scope or equivalents.