Intelligent derivation of email addresses

A method comprises receiving, by a processor, a query comprising at least one name of a target individual. The method further comprises determining, by the processor, a plurality of email addresses. Each of the determined email addresses has a respective prefix based at least in part upon the at least one name of the target individual, and the determination is based at least in part upon statistical data regarding common syntaxes for email addresses. The method further comprises determining email traffic based at least in part upon the determined email addresses. The method further comprises determining, by the processor, one or more relationships of the target individual based at least in part upon the determined email traffic.

TECHNICAL FIELD OF THE DISCLOSURE

This disclosure relates in general to email management systems and, more particularly, to intelligent derivation of email addresses.

BACKGROUND

As organizations become more complex and diversified, maintaining and organizing outside business contacts becomes increasingly difficult. One part of an organization may not be aware of existing or ongoing relationships developed or maintained in other parts of an organization. As a result, business relationships with persons or entities outside the organization may stagnate. Additionally, a person outside an organization may be contacted by more than one person inside the organization, which may have negative repercussions for the business relationship. As organizations grow, traditional computer and email systems are unable to effectively use email traffic data to identify, organize, and leverage business relationships.

SUMMARY

In accordance with the present disclosure, the disadvantages and problems associated with systems and methods of analyzing email traffic have been substantially reduced or eliminated.

In some embodiments, a method comprises receiving a plurality of emails. Each of the plurality of emails comprises message information and header information. The header information comprises subject information and one or more email addresses. The method further comprises generating filtered email data comprising email addresses by at least filtering message information and subject information from each of the plurality of emails. The method further comprises correlating sender information and recipient information in the filtered email data, the correlation based at least in part on the one or more email addresses. The method further comprises storing in memory one or more email addresses from the filtered email data.

In other embodiments, a method comprises receiving, by a processor, a query comprising at least one name of a target individual. The method further comprises determining, by the processor, a plurality of email addresses. Each of the determined email addresses has a respective prefix based at least in part upon the at least one name of the target individual, and the determination is based at least in part upon statistical data regarding common syntaxes for email addresses. The method further comprises determining email traffic based at least in part upon the determined email addresses. The method further comprises determining, by the processor, one or more relationships of the target individual based at least in part upon the determined email traffic.

In yet other embodiments, a method comprises identifying one or more relationships between a target individual and one or more persons based at least in part on a plurality of emails. The plurality of emails comprise one or more outbound emails, which may comprise emails sent to the target individual from the one or more persons. The plurality of emails further comprise one or more inbound emails, which may comprise emails sent from the target individual to the one or more persons. The method further comprises, for each of the one or more relationships, calculating an amount of outbound emails and an amount of inbound emails. The method further comprises assigning a classification to each of the one or more relationships based at least in part on the amount of outbound emails and the amount of inbound emails. The method further comprises sorting the one or more relationships based at least in part on the classification assigned to each of the one or more relationships. The method may further comprise storing information regarding the sorted relationships.

In yet other embodiments, a method comprises receiving one or more search requests from one or more users, each search request comprising a request for relationship information that is determined using email data, and each search request including at least one of a first name, a last name, and a company name of a target individual. The method further comprises storing search information associated with the one or more search requests. The search information comprises a plurality of fields including a user identification associated with each of the one or more users, a name of the one or more users, a type of search request received, a name of the target individual, an email address associated with the target individual, and a time at which the search request is received. The method may further comprise generating an audit report based at least in part on the stored search information, wherein the audit report comprises at least one of the plurality of fields. The method further comprises storing the audit report.

The present disclosure provides several important technical advantages. One advantage is that a computer system may provide an intelligent display of relationship information based on email traffic to or from an enterprise. Another advantage is that the system may allow for the intelligent collection, filtration, and/or correlation of emails sent to or from an enterprise. If a user desires to identify a network of business relationships for a target individual external to the enterprise, the system may allow the user to input information associated with the target individual such as, for example, a first name, a last name, a middle initial, a company name, and/or a company domain. The system may then generate a list of possible email addresses for the target individual. Using data regarding email traffic, the system may identify and display relationships between one or more individuals and a target individual that may be external to the enterprise. The list may be prioritized based on the strength of each relationship. Thus, without knowing a particular target individual's email address, the system may allow a user to discover information about any existing relationships between one or more individuals and a target individual.

DETAILED DESCRIPTION

FIG. 1illustrates a system10for analyzing email traffic to identify business relationships, according to certain embodiments. System10may generally receive, filter, and correlate email data25to identify a network of business relationships of a target individual80. System10may generate likely email addresses of target individual80based at least in part on a first name, last name, middle name (or initial thereof) and a company name, domain associated with a company, and/or other information associated with target individual80. In some embodiments, system10may prioritize the identified business relationships based on certain characteristics such as, for example, the volume of email traffic sent to and/or from target individual80. System10may audit searches performed by users and may track and display audit trails containing various data attributes. System10may include an email analyzer20, email server30, email repository40, one or more external data sources50, one or more networks60, and one or more clients70.

Email analyzer20may receive, process, and/or analyze email data25sent to or from enterprise15. In particular embodiments, email data25may include one or more emails sent to or from individuals in enterprise15. For each email, email data may comprise header information, message text, metadata (e.g., time sent, date modified, etc.), and/or any suitable information. Email data25may include entire emails or only portions of emails. In some embodiments, the message body and/or subject information (e.g., the subject line) of an email may be filtered from email data25. In some embodiments, email analyzer20may process email data25to generate correlated and filtered data that may be efficiently searched and/or analyzed.

In some embodiments, email analyzer20may generate, based on received search terms, a list of potential email addresses for target individual80. Target individual80may be any person, group of persons, and/or entity within or without enterprise15. In some embodiments, target individual80is external to and/or not affiliated with enterprise15. Email analyzer20may allow a user to identify a network of business relationships that target individual80has with other people or entities. In particular embodiments, a user may input into system10the first name, last name, and/or middle name (or initial thereof) and/or a company name, a domain associated with a company, and/or other suitable information associated with target individual80. Using rules based on statistical data, email analyzer20may generate a list of potential email addresses associated with target individual80. Email analyzer20may then identify and intelligently display possible relationships that target individual80has with persons in enterprise15and persons outside of, or not belonging to, enterprise15. For example, based on email traffic to and/or from the potential email addresses for target individual80, email analyzer20may group, sort, and/or classify relationships of target individual80. Email analyzer20may then identify and intelligently display possible relationships that target individual80has with persons inside and/or outside of enterprise15. For example, based on email traffic to and from the potential email addresses for target individual80, email analyzer20may group, sort, and/or classify relationships of target individual80.

In some embodiments, email analyzer20may track and display an audit trail for each search conducted by a user of system10. The audit trail for a particular search may comprise various data attributes such as, for example, the name of the user performing the search, the type of search (e.g., company name, individual name, exact email address, etc.), the company and/or individual name in the search, the time of the search, and/or any suitable information. Email analyzer20may be configured to generate periodic audit reports based on predetermined thresholds of usage activity. Email analyzer20may also generate audit reports based on a request from a user of system10.

Email analyzer20may be any suitable computing device comprising a processor and a memory. For example, email analyzer20may comprise one or more workstations, laptops, blade servers, server farms, and/or stand alone servers. Although shown inFIG. 1as comprising a single component, email analyzer20may, in some embodiments, represent functionality provided by several separate physical components. In some embodiments, email analyzer20may represent software operating on one or more servers. AlthoughFIG. 1illustrates, for purposes of example, a single email analyzer20, particular embodiments of system10may include any appropriate number of email analyzers20arranged in any suitable configuration. In embodiments that include multiple email analyzers20, each respective email analyzer20may be operable to communicate, directly or indirectly, with one or more other email analyzers20over network60.

As shown inFIG. 1, email analyzer20may be maintained and/or operated by enterprise15. Enterprise15may represent any organization, business, association, group, and/or entity organized for any suitable purpose(s), such as, for example, commercial, educational, and/or non-profit purpose(s). AlthoughFIG. 1illustrates enterprise15comprising a single location, enterprise15may include any number of locations, and system10may operate across multiple locations or in a single location.

Enterprise15may comprise one or more email servers30. Email server30may send and/or receive emails generated by users within enterprise15and may receive emails from persons outside enterprise15sent to users within enterprise15. In particular embodiments, email server30may represent a Microsoft Exchange email server, a Unix-based server running a sendmail or Simple Mail Transfer Protocol application, and/or other appropriate type of server. Additionally, or alternatively, email server30may maintain a list of organizational contacts for each user or group of users of system10. For example, in particular embodiments, email server30may include contact manager software (e.g., Microsoft Outlook) that organizes and/or stores contact information for persons within and without enterprise15. Email server30may be communicatively coupled, directly or indirectly, to email repository40. Email server30may store, archive, and/or otherwise retain copies of emails on email repository40.

Email repository40may store emails sent, received, and/or processed by email server30. In particular embodiments, email repository40may represent a database connected to email server30and/or a database process executing on email server30. Additionally or alternatively, email repository40may be capable of storing, archiving, and/or otherwise maintaining a database of organizational contacts. As discussed above, email server30may include contact manager software that organizes and/or stores contact information. Email repository40may store a database of contact information utilized by contact manager software. Email repository40may be communicatively coupled, directly or indirectly, to email server30. In particular embodiments, email repository40may communicate with email server30over network60.

Email repository40may be any suitable computing device comprising a processor and a memory. For example, email repository40may comprise one or more workstations, laptops, blade servers, server farms, and/or stand alone servers. Although shown as comprising a single component, email repository40may, in some embodiments, represent functionality provided by several separate physical components. In particular embodiments, email repository40may represent software operating on one or more servers. AlthoughFIG. 1illustrates a single email repository40, particular embodiments may include any appropriate number of email repositories40.

As noted above, system10may comprise one or more external data sources50. External data source50may represent one or more sources of information that facilitate the operation of system10. In particular embodiments, external data source50may represent one or more data servers external to enterprise15that may be communicatively coupled to one or more components of system10. In particular embodiments, external data source50may communicate, directly or indirectly, with email analyzer20over network60. For example, external data source50may include a data feed from a market center, trading exchange, financial information database, contact information database, and/or an electronic directory maintained by a third party (e.g., Hoover's, BoardEx, Big Dough, Bloomberg, Ipreo, and/or Salesforce.com). Access to, or receiving information from, one or more external data sources50may provide email analyzer20with information regarding an external organization's executives, domain name, address, key officers and/or employees, and/or any suitable information pertaining to individuals and/or organizations outside of enterprise15.

External data source50may communicate with system10via network60. Network60may represent any form of communication network supporting circuit-switched, packet-based, and/or any other suitable type of communications. Network60may additionally include any other components of system10capable of transmitting and/or receiving information over a communication network, including email analyzer20, email server30, email repository40, and/or clients70. Although shown inFIG. 1as a single element, network60may represent one or more separate networks (including all or parts of various different networks) that are separated and serve different respective email analyzers20and/or email repositories40. Network60may include routers, hubs, switches, firewalls, content switches, gateways, call controllers, and/or any other suitable components in any suitable form or arrangement. Network60may include, in whole or in part, one or more secured and/or encrypted Virtual Private Networks (VPNs) operable to couple one or more network elements together by operating or communicating over elements of a public or external communication network. In general, network60may comprise any combination of public or private communication equipment such as elements of the public switched telephone network (PSTN), a global computer network such as the Internet, a local area network (LAN), a wide area network (WAN), or other appropriate communication equipment.

Users may interact with system10using clients70. Client70may send and/or receive emails and may generate one or more queries75for target individuals80. Query75may represent an electronic request for relationship information pertaining to a particular target individual80. A user may input query75into client70, which may send query75to email analyzer20. Query75may include one or more characteristics of target individual80. For example, a user may generate query75for target individual80by inputting into client70at least a portion of the name, email address, and/or company name of target individual80. Client70may display the results of query75on a display device.

Clients70may represent general or special-purpose computers operating software applications capable of performing the described operations. For example, clients70may include, but are not limited to, laptop computers, desktop computers, and/or portable data assistants (PDAs). Additionally, in particular embodiments, client70may include one or more processors operable to execute computer logic and/or software encoded on tangible media that perform the described functionality. Client70may include one or more input devices such as a keyboard, trackball, mouse, and/or one or more Graphical User Interfaces (GUIs) through which a user may interact with the logic executing on the processor of client70. Client70may include any appropriate combination of hardware, software, and/or encoded logic suitable to perform the described functionality. Clients70may be communicatively coupled with email analyzer20and/or email server30, directly or indirectly, over network60. AlthoughFIG. 1illustrates, for purposes of example, a particular number and type of clients70, alternative embodiments of system10may include any appropriate number and type of clients70.

In operation, system10may use email data25to identify and leverage relationships that target individual80has with people within and/or without enterprise15. In some embodiments, email analyzer20in system10may receive one or more queries75from users and may generate prioritized relationship data85based on email traffic between one or more persons and one or more target individuals80. In some embodiments, a user at client70generates query75by inputting relevant parameters into a GUI associated with client75such as, for example, a first name, last name, and/or middle name (or initial thereof) and/or a company name, a domain associated with a company, and/or other suitable information associated with target individual80. Client70may transmit query75over network60to email analyzer20.

At appropriate points during operation, email analyzer20may receive email data25from email server30and/or email repository40. Email analyzer20may filter and/or correlate email data25to generate relationship data85based on email traffic. Email analyzer20may use filtered and/or correlated email data25to determine a volume of email traffic sent to and/or from target individual80. Based on the volume of email traffic, email analyzer20may identify and classify relationships between one or more persons and target individual80. For example, email analyzer20may determine that ten or more emails have been sent and/or received between Person A in enterprise15and target individual80. Based on this volume of email traffic, email analyzer20may, for example, classify the relationship between Person A and target individual80as strong. As another example, email analyzer20may determine that between five and ten emails have been sent and/or received between Person B in enterprise15and target individual80. Based on this volume of email traffic, email analyzer20may, for example, classify the relationship between Person B and target individual80as moderate.

After classifying one or more relationships of target individual80specified in query75, email analyzer20may transmit relationship data85to clients70. In particular embodiments, relationship data85may be transmitted over network60. Relationship data85may include data representing one or more attributes of a relationship. For example, relationship data85may include the name of a person with whom target individual80corresponds via email, the source of the person's contact information (e.g., email traffic, contact manager database, and/or other database), and an indicator of the strength of the relationship (e.g. “Knows Well,” “Acquaintance,” etc.). In some embodiments, relationship data85may be displayed on a GUI by client70in a prioritized manner based on the strength of the relationship.

One or more embodiments of system10may provide important technical advantages. For example, system10may provide an intelligent display of relationship data85based on email traffic to or from enterprise15. In some embodiments, system10facilitates the intelligent collection, filtration, and/or correlation of emails sent to or from enterprise15. If a user desires to identify a network of business relationships for target individual80inside or outside of enterprise15, system10may allow the user to input information associated with the target individual80, such as a first name, a last name, a middle name (or initial thereof), a company name, and/or a domain associated with a company. System10may then generate a list of possible email addresses for target individual80. Using email data25, system10may identify and display relationships of target individual80. The relationships may be prioritized based on the strength of each relationship. Thus, without knowing the actual email address of target individual80, system10may allow a user to discover information about any existing relationships between individuals inside or outside an enterprise and target individual80. Particular embodiments of system10may provide some, none, or all of the foregoing advantages.

FIG. 2illustrates email analyzer20in system10, according to certain embodiments. Email analyzer20may generally receive, process, sort, analyze, and/or intelligently display information associated with emails sent to or from enterprise15. In particular embodiments, email analyzer20may audit users of system10and/or may audit queries75associated with the use of email analyzer20. Email analyzer20may include a processor210, memory220, interface module230, preprocessing module240, derivation module250, classification module260, and auditing module270.

Processor210may represent and/or include any form of processing component, including general purpose computers, dedicated microprocessors, or other processing devices capable of processing electronic information. Examples of processor210include digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and any other suitable specific or general purpose processors. AlthoughFIG. 2illustrates a single processor210in email analyzer20, email analyzer20may include any suitable number of processors210. In embodiments of system10in which email analyzer20represents software executing on other components of system10, processor210may represent a processor utilized in performing operations associated with the particular component on which email analyzer20is operating.

Processor210may be communicatively coupled to memory220. Memory220may store processor instructions, queries75, results of queries75, email data25, audit results, and/or any suitable information. Memory220may comprise any collection and arrangement of volatile and/or non-volatile components suitable for storing data. For example, memory220may comprise random access memory (RAM) devices, read only memory (ROM) devices, magnetic storage devices, optical storage devices, and/or any other suitable data storage devices. In particular embodiments, memory220may represent, in part, computer-readable storage media on which computer instructions and/or logic are encoded. In such embodiments, some or all the described functionality of email analyzer20may be provided by processor210executing the instructions encoded on the described media. Although shown inFIG. 2as a single component, memory220may represent any number of memory components within, local to, and/or accessible by email analyzer20. Although shown inFIG. 2as being located internal to email analyzer20, memory220may represent storage components remote from email analyzer20such as, for example, components of a Network Attached Storage (NAS), Storage Area Network (SAN), or any other type of remote storage system.

Processor210may be communicatively coupled to one or more interface modules230in email analyzer20. Interface module230may facilitate communication between email analyzer20, email server30, email repository40, clients70, and/or other components of system10. For example, email analyzer20may receive query75from client70through interface module230. Through interface module230, email analyzer20may receive email data25from email server30and/or email repository40. In particular embodiments, interface module230may include or represent one or more interface cards suitable for communication over network60. AlthoughFIG. 2illustrates a single interface module230in email analyzer20, email analyzer20may include any suitable number of interface modules230.

Email analyzer20may comprise one or more preprocessing modules240. Preprocessing module240may generally receive, filter, and/or correlate raw email data25from email repository40. Processor210may use the filtered and/or correlated email data25output by preprocessing module240to determine results of queries75from users. Preprocessing module240may receive email data25from email repository40in real-time and/or in periodic batches. For example, preprocessing module240may receive and process email data25once every month, once every week, and/or according to any appropriate schedule. Once raw email data25is received, whether in real-time or in batches, preprocessing module240may process the raw email data25to generate correlated and/or filtered email data25that may be efficiently searched. For example, in particular embodiments, preprocessing module240may isolate header information from emails in raw email data25. Preprocessing module240may filter subject information (e.g., the subject line of an email) and/or message text from the emails in the raw email data25. That is, preprocessing module240may isolate message text from relevant emails and store and/or process all or only header information. Preprocessing module240may, additionally or alternatively, correlate redundant instances of emails from email data25(e.g., redundant emails may result from distribution lists and/or from emails sent to multiple recipients). Some and/or all of the functions of preprocessing module240described above may be performed by email server30.

In some embodiments, preprocessing module240may correlate emails in email data25from email repository40based on senders and recipients. For example, preprocessing module240may group together an email sent from Person A to Person B and a reply from Person B to Person A. Preprocessing module240may capture email addresses from the header information associated with emails. For example, preprocessing module240may identify and store email addresses from the “To” field, the “From” field, the “Cc” field, and/or the “Bcc” field of emails. Preprocessing module240may, additionally or alternatively, link information in email data25to electronic directories. For example, preprocessing module240may identify an email address in the header information of email data25and may retrieve additional information corresponding to the email address (e.g., a name, telephone number, address, etc.) from one or more additional data sources such as, for example, a contacts database, a human resources database, etc. In particular embodiments, preprocessing module240may receive information from one or more external data sources50and may determine whether emails sent to or from individuals or organizations identified by external data sources50correspond to emails in email data25from email server30or email repository40.

Email analyzer20may comprise one or more derivation modules250. Derivation module250may generate, based on received query terms, a list of potential email addresses for target individual80. Thus, a user of system10, without knowing the actual email address of target individual80, may input a first name, last name, middle name (or initial thereof), company name, and/or domain associated with a company of target individual80, and derivation module250may generate a list of potential email addresses. Derivation module250may generate the list of possible email addresses using rules based on statistical data regarding common structures and syntax for prefixes and/or domains of email addresses. Using statistical data may allow derivation module250to generate likely email addresses of target individual80instead of merely random permutations of character data. Each possible email address may be derived from different permutations of the initials, the first name, the last name, the middle name, the company name, the company domain, and/or one or more other characters associated with target individual80. For example, a user of system10may wish to identify persons who know John Q. Public of Company1 Company. The user may formulate a query by inputting “John Q. Public” as the name and “Company1 Company” as the company of target individual80. Derivation module250may then use statistical data to generate a list of potential email addresses (e.g., j.public@company1.com, j.q.public@company1.com, j-public@company1.com, etc.). In some embodiments, email analyzer20may filter one or more numeric characters from prefixes of email addresses in conjunction with searching for relationships and/or analyzing email traffic.

Derivation module250may rank the potential email addresses based at least in part on the frequency at which the associated prefix and/or domain structure (e.g., firstinitial.lastname, firstname.middleinitial.lastname, etc.) appear in archived email data25. Based on the potential email addresses generated by derivation module250, email analyzer20may search email data25output by preprocessor module240to identify relationships between target individual80and other people. The relationships may be identified based on email traffic between the potential email addresses generated by derivation module250and other email addresses.

Email analyzer20may comprise one or more classification modules260. Classification module260may identify and intelligently display information regarding relationships between individuals within or without enterprise15. As discussed above, email analyzer20may identify relationships based on email traffic between potential email addresses of target individual80(as generated by derivation module250) and other email addresses. Classification module260may then intelligently sort the relationships according to the type of each relationship. For example, if contact classification module260finds ten or more emails from target individual80to Person A and only two emails from Person A to target individual80, then classification module260may classify the relationship with Person A as “High Inbound/Low Outbound.” In another example, if classification module260finds five or fewer emails from target individual80to Person A and five emails from Person A to target individual80, classification module260may classify the relationship as “Moderate Inbound/Moderate Outbound.” The volume of emails corresponding to each particular threshold and the number of thresholds used may be configurable according to the needs of the user and/or enterprise15and the overall capabilities of system10. In some embodiments, classification module260may classify the relationship for each person with whom target individual80corresponds. Classification module260may present an intelligent display that sorts the relationships according to type. For example, in particular embodiments, relationships classified as “High Inbound/High Outbound” may be displayed first, while relationships classified as “Low Inbound/Low Outbound” may be displayed last. The particular types of relationships displayed, as well as the order in which they are displayed, may be configurable by a user of system10.

In particular embodiments, classification module260may classify relationships based, at least in part, on factors other than email traffic. For example, the classification may be based, at least in part, on email directories and/or contact lists. Classification module260may be capable of receiving contact information from email server30and/or email repository40. For example, email server30and/or email repository40may store or maintain a contacts database using contact management software such as, for example, Microsoft Outlook and/or Lotus Notes. In particular embodiments, each user in system10maintains a respective directory of contacts. A user's directory of contacts may be stored on that user's computer and/or in memory in email server30and/or email repository40. A particular user may use contact management software to designate certain contacts in the contacts database as being strong or weak. For example, particular contacts may be marked as “Knows Well,” as “Knows a Little,” as “Acquaintance,” etc. Based on the strength of the relationship, classification module260may give a higher or lower priority to a particular relationship in the display of relationship data85. Stronger relationships may be listed above weaker relationships.

In some embodiments, classification module260may identify and de-prioritize spam emails and/or may mask certain data (e.g., employee names, employee identification numbers, etc.) depending on the security clearance of the user. The amount and/or type of information presented in a display of relationship data85may be configurable based on the security clearance of the user that generated the particular query75. For example, user names, identifying information, and/or email addresses may be hidden or revealed based on the role of the user within enterprise15. Thus, classification module260may provide an intelligent display of relationships based on email traffic and/or other information sources.

Email analyzer20may comprise one or more auditing modules270. Auditing module270may generally determine, maintain, and/or display an audit trail for each query75conducted by a user of system10. The audit trail for a particular query75may comprise various data attributes such as, for example, the name of the user performing the search, the type of search (e.g., company name, individual name, email address, etc.), the company and/or individual name in query75, the time of query75, etc. In particular embodiments, if the volume and/or frequency of queries75by a particular user of system10exceeds a configurable threshold, auditing module270may notify an administrator or other operator of system10. In particular embodiments, auditing module270may send an email that includes a report regarding the search history of the particular user. Additionally or alternatively, auditing module270may provide a real-time audit log of queries75generated by users of system10and/or executed by email analyzer20. Thus, auditing capabilities provided by auditing module270may allow system10to detect and prevent misuse of particular features of system10.

Each of processor210, memory220, interface module230, preprocessing module240, derivation module250, classification module260, and auditing module270may represent any appropriate combination of hardware and/or software suitable to perform the described functions. Elements of email analyzer20may include logic encoded on tangible, computer-readable media and executed on processor210and/or other computer hardware suitable to provide the described functions of email analyzer20. Additionally, any two or more of the described modules may represent or include common components.

FIG. 3illustrates an example Graphical User Interface (“GUI”)300that assists a user in formulating query75for information regarding target individual80, according to certain embodiments. Client70may display GUI300to a user. By interacting with GUI300, the user may generate queries75for target individuals80and may view information regarding relationships of target individual80. GUI300may include input boxes302a,302b, and302cthat allow the user to formulate query75. Input box302amay receive user input for a first name of target individual80. Input box302bmay receiver user input for a last name of target individual80. Input box302cmay receive user input for a company name and/or domain associated with a company of target individual80. In particular embodiments, a user may formulate query75by inputting information into one or more of input boxes302. In some embodiments, GUI300may include additional input boxes302whereby a user may input additional information including, for example, an email address, a work address, a telephone number, etc.

In some embodiments, GUI300includes a search type selector304that allows a user to designate the type of query to be performed. Search type selector304may include options for “Enterprise Only,” “Email Relationships Only,” and “All Relationships.” Selecting “Enterprise Only” may enable queries75for contacts within a contacts database maintain by system10and/or external information sources50. Selecting “Email Relationships Only” may enable queries for relationships between target individual80and persons in enterprise15(e.g., by searching email data25for emails between target individual80and a person in enterprise15). Selecting “All Relationships” may enable searching for relationships between target individual80and persons in enterprise15(e.g., by searching a contacts database, external information sources50, and/or email data25for emails associated with target individual80).

In some embodiments, GUI300comprises a sort box306that allows a user to select a preference for the order in which results are displayed. For example, a user may wish to see results of a particular query75in the order of last name, first name, organization, etc. Thus, sort box306may be a drop-down box that contains one or more of these or other sorting options.

In some embodiments, GUI300comprises a search results box308that displays the results of query75for target individual80. For example, as shown inFIG. 3, a user has input query75for target individual80named “John Doe.” The user has selected “All Relationships” in search type selector304. As discussed above, email analyzer20may generate a list of potential email addresses for target individual80. Email analyzer20may then search email data25received from email server30and/or email repository40for one or more of the potential email addresses. As shown in the illustrated example, search results box308may display one or more potential matching names for the particular target individual80. Once displayed, a user may select a particular name to obtain more information about target individual80.

In some embodiments, GUI300may comprise a display option box310that may filter names displayed in search results box308. In particular embodiments, by selecting various options, a user may be able to display only names found in a contacts database, found through email data25, and/or found through any other relevant source. Conversely, by unselecting one or more options, a user may be able to hide names found in a contacts database, through email data25, and/or through any other relevant source.

FIG. 4illustrates a table400comprising example email permutations generated by email analyzer20, according to certain embodiments. As discussed above, email analyzer20may generate, based on received query terms, a list of potential email addresses for target individual80. Thus, email analyzer20may allow a user to search email data25for relationships with target individual80without necessarily knowing the actual email address or addresses of target individual80. Accordingly, a user of system10, without knowing the actual email address of a person inside or outside of enterprise15, may input the person's first name, last name, middle name (or initial), company name, and/or company domain. Email analyzer20may then generate a list of likely email addresses. In the illustrated example, email analyzer20received query75for target individual80named “John Q. Public” who works at “Company1 Company.” Email analyzer20may generate likely email addresses based on one or more of (in whole or in part) a first name, a middle name, a last name, a company name, and/or one or more additional characters. Email analyzer20may use all or part of the first name, middle name, and/or last name to generate one or more likely email prefixes. Email analyzer20may use all or part of the company name to generate one or more likely email domains. The term prefix may refer to the portion of an email address before the “@” symbol, and the term domain may refer to the portion of an email address after the “@” symbol. Email analyzer20may determine various prefixes and domains based at least in part on statistical data regarding common syntaxes and structures of email addresses. AlthoughFIG. 4illustrates for purposes of example a particular list of possible email addresses, numerous other possible addresses may be generated by email analyzer20.

FIG. 5illustrates an example GUI500displaying the results of query75, according to certain embodiments. GUI500may comprise a classification matrix510, one or more priority rules520, and at least one results table530. Matrix510may visually depict logic for classifying the strength of relationships associated with target individual80. In particular, matrix510may depict a sorting algorithm for classifying email traffic sent to and/or received from target individual80. For example, matrix510may comprise rows that represent the volume of outbound email sent from Person A to Person B. Person A and/or Person B may be within or without enterprise15. The columns in matrix510may represent the volume of inbound email sent from Person B to Person A. For each of the vertical and horizontal sets of data, “None” may indicate that no email was sent or received. “L” may indicate that a relatively low amount of emails were sent or received. Thus, “L” may represent an amount less than first threshold number of emails sent and/or received between Person A and Person B. “M” may indicate that a moderate amount of emails were sent or received. “M” may represent an amount between a first threshold and a second threshold. “H” may indicate that a high volume of emails were sent or received. “H” may represent an amount equal to or greater than the second threshold. Each of the respective first and second thresholds may be configurable based on the total number of emails received, the strength of the business relationships, and/or any other relevant considerations, factors, and/or variables. An intersection of “H” inbound emails and “H” outbound emails may indicate a strong (or “A”) relationship between Person A and Person B. In the example shown, any combination of “H-H” or “H-M” may be designated as an “A” relationship. Similarly, “B” relationships may include any combination of “L-H” and/or “L-M.” Any combination including a designation of “None” may indicate that there is no meaningful relationship (i.e., a “C” relationship). Based at least in part on the particular designation (“A,” “B,” or “C,”), email analyzer20may prioritize relationships when presenting a list of relationships to a user.

In some embodiments, priority rules520may define a particular preference order for sorting and/or displaying relationships. For example, based at least in part on priority rules520, email analyzer20may sort and/or display relationships in a particular order, including but not limited to: (1) relationships between board members of enterprise15and an outside organization; (2) relationships where at least one correspondent is designated in a contacts database as “Know Well”; (3) email traffic in which the relationship between correspondents is designated as an “A” relationship as described above; (4) email traffic in which the relationship between correspondents is designated as a “B” relationship as described above; (5) relationships where at least one correspondent is designated in a Microsoft Outlook contacts database as “Know A Little”; and (6) email traffic in which the relationship between correspondents is designated as a “C” relationship. The particular order specified by priority rules520may be configurable in accordance with the wishes or needs of a particular user of system10.

Results table530may display one or more relationships identified by email analyzer20in response to query75from a user. In the illustrated example, results table530displays various relationships of target individual80named “Jane R. Doe.” Results table530may optionally display (1) the parties to the relationship (e.g., a person within enterprise15that has a relationship with target individual80); (2) the role (e.g., job title) of each party; (3) the volume of email between the two parties (either inbound, outbound, or both); (4) whether target individual80appears in the contacts database of a party; and/or (5) whether target individual80is designated as “Knows Well” or “Knows A Little” in a contacts database. Results table530may display a prioritized list of relationships in accordance with the preference order specified by priority rules520. Depending on a user's level of access, GUI500may display an actual number of emails sent to and/or from target individual80, or GUI500may display a general description of the number of emails sent and/or received (e.g., high, medium, or low).

FIG. 6illustrates GUI600showing an example audit report generated by email analyzer20, according to certain embodiments. As discussed above, email analyzer20may determine and display an audit trail for queries75conducted by each user of system10. An administrator of system10may request email analyzer20to generate an audit trail for one or more users. GUI600may include at least one table610that includes a plurality of fields for each user audited. Table610may display one or more user identifiers, a last name, a first name, a role within enterprise15, a region, a number of queries generated by each user during particular months, and the total number of queries generated by a user during a configurable time period (e.g., over the last year). GUI600may allow the administrator to select a particular user to request additional information relating to a particular audit trail. A particular audit trail may comprise various data attributes such as, for example, the name of the user performing the search, the type of search (e.g., company name, individual name, exact email address, etc.), the company and/or individual name in the query75, the time of the search, and/or any other appropriate attributes. Email analyzer20may be configured to generate periodic reports based at least in part on predetermined thresholds of usage activity. Further, as discussed above, an audit report containing one or more tables610may be automatically generated and sent to an administrator based on a predetermined configuration. For example, an audit report may be generated and/or sent periodically (e.g., once every month, once every six months, etc.) or may be generated upon request by an administrator. In some embodiments, an audit report may be generated and/or sent based on the volume and/or frequency of queries75by a particular user exceeding a configurable threshold.

FIG. 7is a flow diagram illustrating an operation in accordance with a particular embodiment of system10. In the illustrated example, operation begins at step700with email analyzer20receiving one or more emails from email server30. In particular embodiments, email analyzer20may receive email data25from email server30and/or email repository40. Email analyzer20may receive email data25in real-time or in periodic batches. For example, email analyzer20may receive email data25once every month, once every week, and/or according to any appropriate schedule. Email analyzer20may be capable of receiving email data25in response to query75or command by a user of system10.

At step702, email analyzer20may process, correlate, and/or filter emails in email data25. As discussed above, email analyzer20may process raw email data25to generate correlated and filtered email data25that may be efficiently searched. For example, email analyzer20may correlate redundant instances of emails (e.g., redundant emails may result from distribution lists or from emails sent to multiple recipients). Email analyzer20may correlate emails based on senders and recipients. For example, email analyzer20may group together an email sent from Person A to Person B and a reply from Person B to Person A. Email analyzer20may group together an email sent from Person A to Person B and a forwarded email from Person B to Person C. In general, emails may be associated in any appropriate manner suitable to facilitate efficient searching and/or processing in system10.

At step704, email analyzer20may isolate message text information from header information in email data25. For example, in particular embodiments, email analyzer20may isolate header information in email data25and filter the message text from the email data25. That is, email analyzer20may strip message text from relevant emails and store and/or process only header information. In some embodiments, email analyzer20may filter subject information from the header information in email data25. Email analyzer20may capture email addresses from header information in email data25. For example, email analyzer20may analyze header information in email data25and store one or more email addresses contained in the header information (e.g., email addresses contained in the “To” field, the “From” field, the “Cc” field, or the “Bcc” field). Email analyzer20may additionally link data in emails to electronic directories. As discussed above, in particular embodiments, email analyzer20may receive information from one or more external data sources50and may determine whether emails sent to or from individuals or organizations identified by external data sources50correspond to email data25received from email server30or email repository40. In particular embodiments, email analyzer20may store the correlated and filtered email data25in a memory associated with email analyzer20.

At step706, a user of system10may enter one or more query terms. In particular embodiments, a user of system10may query whether persons within or without enterprise15have relationships with target individual80. Using a GUI at client70, a user may query system10for information about target individual80. By interacting with a GUI, a user of system10may be able to generate queries75for target individuals80and receive responses for potential relationships that may match the relevant information entered.

At step708, email analyzer20may generate possible email addresses for a given query75. As discussed above, email analyzer20may generate, based on received query terms, a list of potential email addresses for target individual80. In general, email analyzer20may allow a user to search email data25for relationships with target individual80without necessarily knowing the exact email address or addresses of target individual80.

At step710, email analyzer20may search one or more databases for information matching the queried target individual80. In particular embodiments, email analyzer20may search stored and/or correlated email data25received in step704for email addresses that match one or more of the email addresses generated in step708. Additionally, email analyzer20may search a contacts database for a name, email address, company name, and/or other information that matches target individual80queried in step706. Email analyzer, in particular embodiments, may search one or more external information sources50for a name, email address, company, and/or other information that matches target individual80queried in step706.

At step712, email analyzer20displays search results to a user. As discussed above with respect toFIG. 3, email analyzer20may display search results for a particular target individual80in a search results box in a GUI associated with client70. Search results may be grouped in order of relevance and/or in accordance with the particular source from which a particular result was found. For example, email analyzer20may search for information in one or more databases, including external information sources50, correlated and/or stored email data25, and a contacts database. As discussed above, email analyzer20may generate a list of potential email addresses for the name and/or organization entered and may search email information received from email server30and/or email repository40for one or more of the generated email address.

At step714, email analyzer20may display relationship data85to a user. In particular embodiments, email analyzer20may transmit relationship data85to a particular client70to be displayed on a GUI. Relationship information may include information pertaining to individuals within or without enterprise15that have a relationship with target individual80, an indication of the strength of each such relationship, information regarding the volume of emails between the person and target individual80, and/or the particular database in which target individual80was found by email analyzer20. Email analyzer20may classify the relationships for each person with whom target individual80corresponds. For example, if email analyzer20finds ten or more emails from target individual80to Person A and only two emails from Person A to the target individual, then the relationship with Person A may be classified as “High Inbound/Low Outbound.” In another example, if there are five or fewer emails from target individual80to Person A and five emails from Person A to the target individual, email analyzer20may classify the relationship as “Moderate Inbound/Moderate Outbound.”

The volume of emails corresponding to each particular threshold, and the number of thresholds use may be configurable according to the needs of enterprise15and the overall capabilities of system10. Based on the relevant classifications and/or identification of potential relationships, email analyzer20may present an intelligent display that sorts the relationships according to type. For example, in particular embodiments, relationships classified as “High Inbound/High Outbound” may be displayed first, while relationships classified as “Low Inbound/Low Outbound” may be displayed last. Additionally, the particular types of relationships displayed, as well as the order in which they are displayed may be configurable by an operator of system10. In particular embodiments, email analyzer20may base a classification of a relationship on factors other than emails between target individual80and one or more persons in enterprise15. For example, the classification may be based, at least in part, on email directories and/or contact databases. Email analyzer20may be capable of receiving contact information from email server30and/or email repository40. For example, email server30and/or email repository40may store and/or maintain a contacts database used by contact management software. Contacts that are marked as “Knows Well” may be given higher priority in the display of relationship information. In some embodiments, email analyzer20may identify and de-prioritize spam emails and/or may mask certain data (e.g., employee names, employee identification numbers, passwords, etc.) depending on the security clearance of the user. Thus, email analyzer20may provide an intelligent display of relationships based on email traffic and/or other information sources. Once presented with an ordered and intelligently displayed list of relationships, a user of system10may leverage the relationships for further business interactions.

The steps illustrated inFIG. 7may be combined, modified, or deleted where appropriate, and additional steps may also be added to those shown. Additionally, the steps may be performed in any suitable order without departing from the scope of the present disclosure.

Although the present disclosure has been described with several embodiments, numerous changes, variations, alterations, transformations, and modifications may be suggested to one skilled in the art, and it is intended that the present disclosure encompass such changes, variations, alterations, transformations, and modifications as fall within the scope of the appended claims.