|Publication number||US7181498 B2|
|Application number||US 10/790,332|
|Publication date||Feb 20, 2007|
|Filing date||Mar 1, 2004|
|Priority date||Oct 31, 2003|
|Also published as||US20050097170, WO2005043934A2, WO2005043934A3|
|Publication number||10790332, 790332, US 7181498 B2, US 7181498B2, US-B2-7181498, US7181498 B2, US7181498B2|
|Inventors||Jing Zhu, George Hu|
|Original Assignee||Yahoo! Inc.|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (7), Non-Patent Citations (1), Referenced by (126), Classifications (14), Legal Events (3)|
|External Links: USPTO, USPTO Assignment, Espacenet|
This application claims the benefit of U.S. Provisional Application Ser. No. 60/516,003 filed on Oct. 31, 2003, the benefit of the earlier filing date of which is hereby claimed under 35 U.S.C. § 119 (e) and further incorporated by reference.
The present invention related generally to messaging systems, and more particularly to reducing unsolicited messages from unknown senders.
Mail spam (unsolicited messages) has become a wide spread industrial problem for mail service providers. It not only greatly inconveniences the user but also wastes a mail service provider's resources to maintain and store large amounts of spam message data. In today's world, how well a mail service provider detects and filters spam messages has become a key attribute to the quality of service and ultimately to the economic success of the mail service provider.
As spam filters become more and more aggressive to combat the growing spam problem, the issue of false positives quickly becomes a problem. False positives may be defined as personal and non-spam email that is mistakenly classified as spam by the spam filter. Because of the potential for false positives, a User is often forced to regularly looking into a bulk folder, or the like, to make sure that they don't have any wanted emails misclassified as spam. This may become a time and resource drain for the email users.
Aside from email address forgery, it's an accepted common sense notion that any email sent from a contact in a user's personal address book is wanted by the user and should be given a “green light” to be deposited into a user's inbox folder. In some cases, a spammer could still forge an email address from the personal address book and send unsolicited emails to the user.
Additionally, for many email users, their address books are either empty or not large enough to become useful in the identification of most of the wanted emails. Therefore, there is a need in the industry to enable a more effective approach to managing email delivered to a user's inbox. Thus, it is with respect to these considerations and others that the present invention has been made.
Non-limiting and non-exhaustive embodiments of the present invention are described with reference to the following drawings. In the drawings, like reference numerals refer to like parts throughout the various figures unless otherwise specified. For a better understanding of the present invention, reference will be made to the following Detailed Description of the Invention, which is to be read in association with the accompanying drawings, wherein:
The present invention now will be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific exemplary embodiments by which the invention may be practiced. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Among other things, the present invention may be embodied as methods or devices. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. The following detailed description is, therefore, not to be taken in a limiting sense.
The terms “comprising,” “including,” “containing,” “having,” and “characterized by,” refers to an open-ended or inclusive transitional construct and does not exclude additional, unrecited elements, or method steps. For example, a combination that comprises A and B elements, also reads on a combination of A, B, and C elements.
The meaning of “a,” “an,” and “the” include plural references. The meaning of “in” includes “in” and “on.” Additionally, a reference to the singular includes a reference to the plural unless otherwise stated or is inconsistent with the disclosure herein.
The term “or” is an inclusive “or” operator, and includes the term “and/or,” unless the context clearly dictates otherwise.
The phrase “in one embodiment,” as used herein does not necessarily refer to the same embodiment, although it may.
The term “based on” is not exclusive and provides for being based on additional factors not described, unless the context clearly dictates otherwise.
The term “flow” includes a flow of packets through a network. The term “connection” refers to a flow or flows of messages that typically share a common source and destination.
Briefly stated, the present invention is directed to a method and system for enabling a community-based “green list” (CGL) message filter for mail service providers. The CGL filtering enables trusted messages to be delivered directly into a user's inbox folder so that other messages can be filtered more aggressively in combination to other spam filtering methods. The CGL filtering indicates which messages are sent from those message addresses listed as friends in the user's personal address book. Also, the scope of CGL filtering includes more than those message addresses that the user interacts with directly (one degree of separation). For example, the CGL filtering also evaluates message addresses that are connected more remotely to the user through other nodes (users) in the online community that are located multiple degrees of separation away from the user.
In different embodiments, the CGL filter can be based on different types of message addresses (singly or in combination) including, but not limited to, email addresses and telephone numbers. Also, in other embodiments, the CGL filtering is employed with different types of messages, either singly or in combination, including email, Short Message Service (SMS), Multi-Media Messaging Service (MMS), Instant Message (IM), and the like.
In some embodiments, a node can be identified as a super node or determined to be one because the node has entries linked to relatively large numbers of other message addresses. The CGL filtering can employ various message filtering approaches based on a super node relationship to a user. For example, CGL filtering may be configured to allow messages from a super node to be delivered to the user's inbox, but not messages that are at least two degrees of separation away from the user, where a super node is the only first degree node separating the user and the sender. Consider the example where super node X is one degree of separation from the user, and nodes Y and Z are each two degrees of separation from the user. Node Y may be separated from the user by some other node W. However, node Z is separated from the user only through super node X. Then, CGL filtering may be configured to enable delivery of messages from super node X, nodes W and Y, but not node Z. Thus, CGL filtering based on a relationship with a super node may be used to exclude a message from the user's inbox or treat the message with less trust than another message from another node's relationship, even though they may be both the same degree of separation away from the user.
An example of a super node might be an email address that provides information for a popular online company, such as email@example.com. Another example of a super node might be a popular mailing list subscribed by thousands and/or tens of thousands people.
Illustrative Operating Environment
Although not shown, the LANs and WANs of network 102 are typically interconnected by routers and/or switches. Also, communication links within the LANs and WANS can include twisted pair, fiber optics, or coaxial cable, while communication links between networks may utilize analog telephone lines, full or fractional dedicated digital lines including T1, T2, T3, and T4, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links, and the like. Furthermore, the number of WANs, and LANs in
The media used to transmit information in communication links as described above illustrates one type of computer-readable media, namely communication media. Generally, computer-readable media includes any media that can be accessed by a computing device. Computer-readable media may include computer storage media, communication media, or any combination thereof.
Additionally, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The terms “modulated data signal,” and “carrier-wave signal” includes a signal that has one or more of its characteristics set or changed in such a manner as to encode information, instructions, data, and the like, in the signal. By way of example, communication media includes wired media such as twisted pair, coaxial cable, fiber optics, wave guides, and other wired media and wireless media such as acoustic, RF, infrared, and other wireless media.
Mail server 110 and green list server 108 includes, among other components, a central processing unit (processor), a memory for storing instructions, and a network interface unit connected via a bus. The network interface unit includes the necessary circuitry for connecting mail server 110 and green list server 108 to network 102, and is constructed for use with various communication protocols including the TCP/IP and UDP/IP protocols. The network interface unit may include or interface with circuitry and components for communicating information, such as graphical displays, advertiser data, and the like over a wired and/or wireless communications medium. Network interface unit is sometimes referred to as a transceiver.
Illustrative Server Environment
Computer server 200 includes processing unit 212, video display adapter 214, and a mass memory, all in communication with each other via bus 222. The mass memory generally includes RAM 216, ROM 232, and one or more permanent mass storage devices, such as hard disk drive 228, tape drive, optical drive, and/or floppy disk drive. The mass memory stores operating system 220 for controlling the operation of server 102. Any general-purpose operating system may be employed. Basic input/output system (“BIOS”) 218 is also provided for controlling the low-level operation of server 102. As illustrated in
The mass memory as described above illustrates another type of computer-readable media, namely computer storage media. Computer storage media may include volatile, nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computing device.
The mass memory also stores program code and data. One or more applications 250 are loaded into mass memory and run on operating system 220. Examples of application programs include mail programs, schedulers, calendars, database programs, word processing programs, HTTP programs, HDML programs, SMS programs, spreadsheet programs, and so forth. Mass storage may further include applications such as mail server 252, message store 254, and community-based green list (CGL) server 256.
Although illustrated in
Computer server 200 may also include an SMTP handler application for transmitting and receiving e-mail, an HTTP handler application for receiving and handing HTTP requests, and an HTTPS handler application for handling secure connections. The HTTPS handler application may initiate communication with an external application in a secure fashion.
Computer server 200 also includes input/output interface 224 for communicating with external devices, such as a mouse, keyboard, scanner, or other input devices not shown in
Community Based Green List
Mirroring the interaction of individuals in the real world, email addresses can form a community based on various online activities such as adding into the personal address book, saving emails to the personal folders, sending or replying to emails, and the like. In
In the common green list implementation, email address firstname.lastname@example.org is in john's green list and emails sent by email@example.com will be filtered directly into john's inbox folder. However this filtering mechanism based on the personal address book can have limited capability since other email addresses in the network shown above are not participating in the filtering.
An enhanced green list can be constructed from the email addresses connected to the email address of interest with multiple degrees of separation. This enhanced green list not only can include the email addresses with first degree of separation such as those found in a messenger buddy list, personal address book and saved messages in a personal folder; but also can include the email addresses in the network with two or more degrees of separation. For example, in the example shown above, first degree email addresses like firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, second degree email addresses like Helen@msn.com, email@example.com, firstname.lastname@example.org, and third degree email addresses like Kevin@cisco.com all become part of the enhanced green list. Since this enhanced green list takes the community connectivity of the email address into account, it represents a “community-based green list (CGL)”.
However, as the degrees of separation get bigger, the association strength of email addresses to the email address of interest becomes weaker. The existence of these email addresses with big degrees of separation in the CGL can be expected to become less useful in the filtering of emails. Thus, for some purposes a cut-off limit can be set for the maximum number of degrees of separation from a user. In this way, email addresses beyond this cut-off limit probably could not be included in the community-based green list without contributing to a degradation in the effectiveness of CGL filtering.
The CGL filtering is typically employed as an anti-spam method, not a “friends” list. Thus, although many friends' email addresses would be included in this community, not all email addresses in the CGL would be considered or trusted as friends in the more traditional sense.
Email Inclusive Filtering
Emails coming from email addresses in the CGL could bypass most other spam filters and be deposited into inbox folder directly. In one embodiment, direct blocking of particular email addresses could still override the CGI filtering. Similarly, in another embodiment, a blocked list for email addresses that is generated by the user as part of a “this is spam” click (selection) could also override the CGI filter. Typically, any emails that were not from the CGL would continue to be subject of spam filtering by other methods. However, since a relatively large percentage of non-spam emails could be protected by the CGL filter, other anti-spam filter settings for other emails could be turned on relatively high without too much concern for false positives.
Email Exclusive Filtering
In one embodiment, most emails that did not originate from the email addresses in the CGL would be filtered and deposited into another folder (not the inbox folder) such as a Bulk folder or another folder whose unread emails are deleted after a period of time. The rate of false positives for non-spam email that is not part of the CGL would be relatively less due to the high coverage of emails for the user and the overall net effect of the CGL filtering on emails from the online community.
A community-based green list (CGL) filter offers several benefits including the following: (1) significantly reduce the false positive rate caused by other spam filtering techniques; (2) automatic maintenance by a service provider so that users don't have to manage this filtering system other than to perform normal activities; and (3) the CGL filter can be more effective than a green list that is only based on a user's personal address book due in part to the coverage and network effect of the CGL. Also, in one embodiment, if anyone is added into a friends' green-list, they will also be automatically included in a user's green-list.
Establishing a First Degree Green-List
The email addresses in a first degree green list can be collected from many sources, including, but not limited to:
(1) personal address book;
(2) messenger buddy list;
(3) saved email messages in personal folders;
(4) Sent, replied, and forwarded emails;
(5) “This is not spam” emails;
(6) relatively small active groups that the user has participated in (optional);
(7) Received email that wasn't classified as spam (the opposite of item 4 because the recipient will be the first degree of the sender, but not necessarily vise versa); and
(8) Any other feeds that may establish relationship between two email addresses (such as social networking, etc.).
Not all of these sources should be treated as equal. It is likely that one or more actions may not be sufficient to establish a first degree of separation between A and B. Instead, multiple actions from the same or different sources may be required to establish the first degree for CGL purposes. For example, we may want to consider A sending email to B not sufficient as the condition for B in A's first degree of separation. Rather, A might have to send two or three emails within a certain time frame (e.g. three months) before a first degree relationship is established. Alternatively, A could send B one email AND A and B belongs to the same group or mailing list with a size of less than 100 would qualify B to be in A's first degree of separation group.
The first degree green-list can be bi-directional. For example, if B is in A's first degree green-list, A may not be in B's green-list.
The first degree link may be broken or removed through certain user actions as well. For example, a “This is spam” complaint from A to B will remove B from A's first degree green-list.
Building a Community-based Green List
The email addresses in these sources form A's first degree addresses in the CGL, then first degree contacts of these first degree addresses that are not already A's first degree shall be added as the second degree contacts for A. Similarly, the third degree of A can be composed of the first degrees of A's second degrees that are not already A's first and second degree email addresses.
Certain emails addresses may need to be treated specially to maintain the quality of the green-list. For example, some email addresses in the CGL may be connected to a large number of email addresses, such as the email list. A super node may be identified as a node that has more than a certain number of first degree links. The absolute number may be adjusted by real example scenarios. The existence of these super nodes in the CGL may degrade the effectiveness of the CGL. These super nodes could be removed in the CGL as part of multi-degree consideration.
Updating a Community-based Green List
Individual updates to these sources shall be propagated to the CGL. The update doesn't have to be real time and can be performed in a batch mode. The exception to this may be the first degree update. It is relatively straightforward to update the first degree links. Therefore, it may be worthwhile to update the first degree in real time while the >1 degrees can be updated in a batch mode.
When a new email address is registered, its personal CGL can be very small. Initially, the majority of the emails received will be from email addresses that are outside of its first degree link or even not connected. As this email address interacts more with the community, its first degree can grow quickly along with its second degrees. More and more emails will be coming from the first and second degrees. Also, after a certain period of time, the CGL may reach a semi-stable state, growing very slowly if growing at all, reflecting the fact that the community of the friends will reach a semi-stable state after some duration of social engagement. The effective cut-off radius may be infinity when the address is a new born (none or few first degree links). However, the cut-off radius should quickly decrease as the mailbox matures, making the CGL system more effective for the user.
Implementation also pays attention to the following issues:
(1) Email aliases: user with email aliases has the same address book, buddy list and mail folders, so all email aliases shall be treated as one node.
(2) Disposable email address: the email address network built around the disposable email address may contain spammers so this network would be separated out from the CGL built around the primary email address or aliases.
(3) Forged email address: spammer may forge the friend's email address and send emails to the user, CGL may not be as effective without some form of email address verification mechanism. However, since it would be relatively difficult for a spammer to get into everybody's green list, the email forgery issue should not pose a relatively large issue for the deployment of CGL.
The effectiveness of the CGL depends at least in part on coverage. If A's first degree are also hosted by the same ISP as A, then A will have a “complete” second degree list as well. Similarly, if all A's second degree are also with the same ISP, then A will have a complete third degree list too. However, most likely, only a portion of A's first degree will be hosted by the same ISP, and therefore, this ISP will only have a portion of A's second degree contact. If that portion is small or even zero, then the CGL might not be as effective.
The percentage of A's first degree or second degree that should be in the same network for sufficient effectiveness is determinable by modeling and real examples. 100% would be the most effective. 1% would most likely not be enough for most applications.
Some aspects of the embodiments discussed above are presented in the flow charts shown in
Each block of the flowchart illustrations discussed below, and combinations of blocks in the flowchart illustrations below, can be implemented by computer program instructions. These program instructions may be provided to a processor to produce a machine, such that the instructions, which execute on the processor, create means for implementing the actions specified in the flowchart block or blocks. The computer program instructions may be executed by a processor to cause a series of operational steps to be performed by the processor to produce a computer-implemented process such that the instructions, which execute on the processor, provide steps for implementing the actions specified in the flowchart block or blocks.
Accordingly, blocks of the flowchart illustrations support combinations of means for performing the specified actions, combinations of steps for performing the specified actions and program instruction means for performing the specified actions. It will also be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by special purpose hardware-based systems, which perform the specified actions or steps, or combinations of special purpose hardware and computer instructions.
At decision block 404, a determination is made as to whether the message's origination address is part of the CGL associated with node A. If true, the process advances to block 406, where the received message is placed in at least one trusted folder associated with node A. Next, the process returns to a calling process to process other actions.
Alternatively, if the determination at decision block 404 had been false (message's origination address is not in node A's CGL), then the process moves to block 408. At block 408, an anti-spam filtering mechanism is applied to the received message. The anti-spam filter mechanism could include evaluating a pre-determined black list of message addresses that are known to send spam, evaluating a white list of message addresses that are known to not send spam, content filtering to identify messages related to unsavory topics such as pornography, and the like. However, the present invention is not so limited, and virtually any anti-spam filtering mechanism may be employed to evaluate the message and/or the message address B, and the like.
From block 408, the process flows to decision block 410, where a determination is made whether the received message is determined to be a spam message. If the message is determined to be a spam message, the process flows to block 412; otherwise, the process branches to block 406 described above.
At block 412, the message is placed in a folder based on the results of the anti-spam filtering. For example, messages identified as spam could be automatically rejected, dropped, placed in a bulk folder, or the like. Messages left in the bulk folder could be automatically deleted after a period time. Next, the process returns to processing other actions.
In any event, process 500 begins, after a start block, at decision block 502, where a determination is made as to whether a new relationship is to be added to the CGL database for node A from node B. For ease of illustration, process 500 describes the new relationship to be for node A from node B. If no, the process loops until a new relationship is received (identified). However, if the determination at decision block 502 is affirmative, the process steps to decision block 504 where another determination is made as to whether node B is a new node. If node B is a new node, processing branches to block 510; otherwise, processing proceeds to decision block 506. At block 510, the new node, node B, is added to the CGL filter database. Processing then proceeds to decision block 506.
At decision block 506 a determination is made whether a first degree of separation associated with either node A, node B, or both nodes A and B exceeds a predetermined maximum for a super node. If a first degree of separation associated with any of the nodes of interest exceeds the predetermined maximum, processing branches to block 512; otherwise processing branches to block 508. The predetermined maximum may be set at virtually any value, be based on an algorithm, or the like. In one embodiment, the predetermined maximum is set at 1000. At block 512, the node(s) identified with a first degree of separation that exceeds the predetermined maximum degrees of separation is classified as a super node. The super node designation could be used by the CGL filter to not trust and/or exclude some if not all of the relationships associated with the super node. For example, for the relationship between the current node and the super node, degree of separation that are greater than two may be excluded, identified with a lower of trust, or the like. In any event, processing then continues to block 508, where the link (relationship) between nodes A and B are added to the CGL filter database. Upon completion of block 508, process 500 returns to a calling process to perform other actions.
Although the invention is described in terms of communication between a user and a network device, the invention is not so limited. For example, the communication may be between virtually any resource, including but not limited to multiple users, multiple servers, and any other device, without departing from the scope of the invention.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US6023723 *||Dec 22, 1997||Feb 8, 2000||Accepted Marketing, Inc.||Method and system for filtering unwanted junk e-mail utilizing a plurality of filtering mechanisms|
|US6269369 *||Nov 2, 1997||Jul 31, 2001||Amazon.Com Holdings, Inc.||Networked personal contact manager|
|US6654787 *||Dec 31, 1998||Nov 25, 2003||Brightmail, Incorporated||Method and apparatus for filtering e-mail|
|US6691156 *||Mar 10, 2000||Feb 10, 2004||International Business Machines Corporation||Method for restricting delivery of unsolicited E-mail|
|US20030167311 *||Apr 3, 2003||Sep 4, 2003||Kirsch Steven T.||Method and system for selectively blocking delivery of electronic mail|
|US20040177110 *||Mar 3, 2003||Sep 9, 2004||Rounthwaite Robert L.||Feedback loop for spam prevention|
|US20040255122 *||Jun 12, 2003||Dec 16, 2004||Aleksandr Ingerman||Categorizing electronic messages based on trust between electronic messaging entities|
|1||Jordan et al., "The Augmented Social Network: Building identity and trust into the next-generation internet", first monday Peer-Reviewed Journal on the Internet, www.firstmonday.dk/issues/issue8<SUB>-</SUB>8/jordan/, pp. 1-64.|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US7548544 *||May 5, 2006||Jun 16, 2009||Ironport Systems, Inc.||Method of determining network addresses of senders of electronic mail messages|
|US7552230 *||Jun 15, 2005||Jun 23, 2009||International Business Machines Corporation||Method and apparatus for reducing spam on peer-to-peer networks|
|US7603417||Dec 30, 2003||Oct 13, 2009||Aol Llc||Identifying and using identities deemed to be known to a user|
|US7613776||Dec 30, 2003||Nov 3, 2009||Aol Llc||Identifying and using identities deemed to be known to a user|
|US7636756||Oct 31, 2007||Dec 22, 2009||Buyerleverage E-Mail Solutions Llc||System and method for granting deposit-contingent e-mailing rights|
|US7653693||Jan 26, 2010||Aol Llc||Method and system for capturing instant messages|
|US7653695||Feb 17, 2005||Jan 26, 2010||Ironport Systems, Inc.||Collecting, aggregating, and managing information relating to electronic messages|
|US7693945 *||Jun 30, 2004||Apr 6, 2010||Google Inc.||System for reclassification of electronic messages in a spam filtering system|
|US7711786||Jan 22, 2008||May 4, 2010||Zhu Yunzhou||Systems and methods for preventing spam|
|US7712136||Dec 7, 2006||May 4, 2010||Ironport Systems, Inc.||Controlling a message quarantine|
|US7716287||Dec 20, 2004||May 11, 2010||Aol Inc.||Organizing entries in participant lists based on communications strengths|
|US7725546 *||Jan 19, 2005||May 25, 2010||Buyerleverage||System and method for granting deposit-contingent e-mailing rights|
|US7748038||Dec 6, 2004||Jun 29, 2010||Ironport Systems, Inc.||Method and apparatus for managing computer virus outbreaks|
|US7756930 *||May 28, 2004||Jul 13, 2010||Ironport Systems, Inc.||Techniques for determining the reputation of a message sender|
|US7836133||May 5, 2006||Nov 16, 2010||Ironport Systems, Inc.||Detecting unwanted electronic mail messages based on probabilistic analysis of referenced resources|
|US7849142||May 27, 2005||Dec 7, 2010||Ironport Systems, Inc.||Managing connections, messages, and directory harvest attacks at a server|
|US7854007||May 5, 2006||Dec 14, 2010||Ironport Systems, Inc.||Identifying threats in electronic messages|
|US7870200||May 27, 2005||Jan 11, 2011||Ironport Systems, Inc.||Monitoring the flow of messages received at a server|
|US7873695||May 27, 2005||Jan 18, 2011||Ironport Systems, Inc.||Managing connections and messages at a server by associating different actions for both different senders and different recipients|
|US7877493||May 5, 2006||Jan 25, 2011||Ironport Systems, Inc.||Method of validating requests for sender reputation information|
|US7917588||May 26, 2005||Mar 29, 2011||Ironport Systems, Inc.||Managing delivery of electronic messages using bounce profiles|
|US7945674 *||Dec 29, 2003||May 17, 2011||Aol Inc.||Degrees of separation for handling communications|
|US7949759 *||Dec 29, 2003||May 24, 2011||AOL, Inc.||Degrees of separation for handling communications|
|US7962643||Jun 27, 2008||Jun 14, 2011||International Business Machines Corporation||Method and apparatus for reducing spam on peer-to-peer networks|
|US8037150||May 18, 2004||Oct 11, 2011||Aol Inc.||System and methods for providing multiple personas in a communications environment|
|US8060566||Nov 30, 2005||Nov 15, 2011||Aol Inc.||Automatically enabling the forwarding of instant messages|
|US8117265 *||Dec 30, 2003||Feb 14, 2012||Aol Inc.||Identifying and using identities deemed to be known to a user|
|US8166310||May 26, 2005||Apr 24, 2012||Ironport Systems, Inc.||Method and apparatus for providing temporary access to a network device|
|US8185638 *||Apr 25, 2011||May 22, 2012||Aol Inc.||Degrees of separation for handling communications|
|US8224902 *||Feb 3, 2005||Jul 17, 2012||At&T Intellectual Property Ii, L.P.||Method and apparatus for selective email processing|
|US8250144||Dec 22, 2009||Aug 21, 2012||Blattner Patrick D||Multiple avatar personalities|
|US8402378||Nov 7, 2008||Mar 19, 2013||Microsoft Corporation||Reactive avatars|
|US8452849||May 28, 2013||Facebook, Inc.||Host-based intelligent results related to a character stream|
|US8473855||Nov 16, 2007||Jun 25, 2013||Microsoft Corporation||Enhanced search results|
|US8538895||Mar 15, 2005||Sep 17, 2013||Aol Inc.||Sharing social network information|
|US8560706 *||May 16, 2012||Oct 15, 2013||Facebook, Inc.||Degrees of separation for handling communications|
|US8577972||Jan 19, 2010||Nov 5, 2013||Facebook, Inc.||Methods and systems for capturing and managing instant messages|
|US8595146||Mar 15, 2005||Nov 26, 2013||Aol Inc.||Social networking permissions|
|US8621020||Jun 19, 2012||Dec 31, 2013||At&T Intellectual Property Ii, L.P.||Method and apparatus for selective E-mail processing|
|US8627215||Feb 25, 2011||Jan 7, 2014||Microsoft Corporation||Applying access controls to communications with avatars|
|US8635273||Dec 20, 2004||Jan 21, 2014||Aol Inc.||Announcing new users of an electronic communications system to existing users|
|US8701014||Nov 18, 2003||Apr 15, 2014||Facebook, Inc.||Account linking|
|US8775560||Jan 31, 2013||Jul 8, 2014||Facebook, Inc.||Host-based intelligent results related to a character stream|
|US8775950||Sep 15, 2012||Jul 8, 2014||Facebook, Inc.||Automatic categorization of entries in a contact list|
|US8782781 *||Apr 5, 2010||Jul 15, 2014||Google Inc.||System for reclassification of electronic messages in a spam filtering system|
|US8812407||Aug 14, 2013||Aug 19, 2014||Aol Inc.||Sharing social network information|
|US8819176||Sep 13, 2012||Aug 26, 2014||Facebook, Inc.||Intelligent map results related to a character stream|
|US8874672||Feb 13, 2012||Oct 28, 2014||Facebook, Inc.||Identifying and using identities deemed to be known to a user|
|US8898239||Dec 20, 2004||Nov 25, 2014||Aol Inc.||Passively populating a participant list with known contacts|
|US8910056||Aug 9, 2010||Dec 9, 2014||Facebook, Inc.||Automatic categorization of entries in a contact list|
|US8918460||Apr 22, 2010||Dec 23, 2014||Facebook, Inc.||Organizing entries in participant lists based on communications strengths|
|US8930480 *||Oct 8, 2013||Jan 6, 2015||Facebook, Inc.||Degrees of separation for filtering communications|
|US8949261 *||Dec 22, 2010||Feb 3, 2015||Facebook, Inc.||Clarifying search results using social-networking information|
|US8954530||Sep 13, 2012||Feb 10, 2015||Facebook, Inc.||Intelligent results related to a character stream|
|US8954531||Sep 13, 2012||Feb 10, 2015||Facebook, Inc.||Intelligent messaging label results related to a character stream|
|US8954534||Jan 4, 2013||Feb 10, 2015||Facebook, Inc.||Host-based intelligent results related to a character stream|
|US8959159||Apr 1, 2010||Feb 17, 2015||Microsoft Corporation||Personalized email interactions applied to global filtering|
|US8965964||Dec 29, 2004||Feb 24, 2015||Facebook, Inc.||Managing forwarded electronic messages|
|US9002949||Dec 21, 2004||Apr 7, 2015||Google Inc.||Automatically enabling the forwarding of instant messages|
|US9043405 *||Feb 26, 2007||May 26, 2015||Linkedin Corporation||Method of leveraging social networking with a messaging client|
|US9047364||Jan 16, 2013||Jun 2, 2015||Facebook, Inc.||Intelligent client capability-based results related to a character stream|
|US9049569||May 11, 2010||Jun 2, 2015||Google Inc.||Prohibiting mobile forwarding|
|US9053173||Jan 28, 2013||Jun 9, 2015||Facebook, Inc.||Intelligent results related to a portion of a search query|
|US9053174||Jan 30, 2013||Jun 9, 2015||Facebook, Inc.||Intelligent vendor results related to a character stream|
|US9053175||Jan 30, 2013||Jun 9, 2015||Facebook, Inc.||Intelligent results using a spelling correction agent|
|US9070118||Sep 14, 2012||Jun 30, 2015||Facebook, Inc.||Methods for capturing electronic messages based on capture rules relating to user actions regarding received electronic messages|
|US9075867||Jan 31, 2013||Jul 7, 2015||Facebook, Inc.||Intelligent results using an assistant|
|US9075868||Feb 13, 2013||Jul 7, 2015||Facebook, Inc.||Intelligent results based on database queries|
|US9088879||Feb 19, 2013||Jul 21, 2015||Google Inc.||Automatically enabling the forwarding of instant messages|
|US9171064||Jan 31, 2013||Oct 27, 2015||Facebook, Inc.||Intelligent community based results related to a character stream|
|US9185067||Nov 4, 2008||Nov 10, 2015||Facebook, Inc.||System and method for analyzing communications|
|US9203647||Sep 15, 2012||Dec 1, 2015||Facebook, Inc.||Dynamic online and geographic location of a user|
|US9203794||Sep 14, 2012||Dec 1, 2015||Facebook, Inc.||Systems and methods for reconfiguring electronic messages|
|US9203879||Sep 14, 2012||Dec 1, 2015||Facebook, Inc.||Offline alerts mechanism|
|US9215095||Oct 7, 2011||Dec 15, 2015||Microsoft Technology Licensing, Llc||Multiple personalities|
|US9246975||Sep 14, 2012||Jan 26, 2016||Facebook, Inc.||State change alerts mechanism|
|US9253136||Sep 14, 2012||Feb 2, 2016||Facebook, Inc.||Electronic message delivery based on presence information|
|US20040193684 *||Dec 30, 2003||Sep 30, 2004||Roy Ben-Yoseph||Identifying and using identities deemed to be known to a user|
|US20040205126 *||Dec 30, 2003||Oct 14, 2004||Roy Ben-Yoseph||Identifying and using identities deemed to be known to a user|
|US20040205127 *||Dec 30, 2003||Oct 14, 2004||Roy Ben-Yoseph||Identifying and using identities deemed to be known to a user|
|US20040210639 *||Dec 30, 2003||Oct 21, 2004||Roy Ben-Yoseph||Identifying and using identities deemed to be known to a user|
|US20050055416 *||Apr 16, 2004||Mar 10, 2005||Heikes Brian Dean||Managing instant messages|
|US20050076240 *||Dec 29, 2003||Apr 7, 2005||Barry Appleman||Degrees of separation for handling communications|
|US20050076241 *||Dec 29, 2003||Apr 7, 2005||Barry Appelman||Degrees of separation for handling communications|
|US20050108329 *||May 18, 2004||May 19, 2005||Andrew Weaver||Multiple personalities|
|US20050193076 *||Feb 17, 2005||Sep 1, 2005||Andrew Flury||Collecting, aggregating, and managing information relating to electronic messages|
|US20050198131 *||Dec 20, 2004||Sep 8, 2005||Barry Appelman||Passively populating a participant list with known contacts|
|US20050198172 *||Dec 20, 2004||Sep 8, 2005||Barry Appelman||Organizing entries in participant lists based on communications strengths|
|US20050198176 *||Jan 19, 2005||Sep 8, 2005||Buyerleverage Email Solutions Llc||System and method for granting deposit-contingent e-mailing rights|
|US20050201535 *||Apr 4, 2004||Sep 15, 2005||Robert LaLonde||Classification of wanted e-mail via web of relationship utilization of Public Key Infrastructure (PKI)|
|US20050216300 *||Mar 15, 2005||Sep 29, 2005||Barry Appelman||Sharing social network information|
|US20050265319 *||May 26, 2005||Dec 1, 2005||Clegg Paul J||Method and apparatus for destination domain-based bounce profiles|
|US20050268345 *||May 26, 2005||Dec 1, 2005||Harrison Robert B||Method and apparatus for providing temporary access to a network device|
|US20050283837 *||Dec 6, 2004||Dec 22, 2005||Michael Olivier||Method and apparatus for managing computer virus outbreaks|
|US20060004896 *||Jun 16, 2004||Jan 5, 2006||International Business Machines Corporation||Managing unwanted/unsolicited e-mail protection using sender identity|
|US20060010215 *||May 27, 2005||Jan 12, 2006||Clegg Paul J||Managing connections and messages at a server by associating different actions for both different senders and different recipients|
|US20060031314 *||May 28, 2004||Feb 9, 2006||Robert Brahms||Techniques for determining the reputation of a message sender|
|US20060031359 *||May 27, 2005||Feb 9, 2006||Clegg Paul J||Managing connections, messages, and directory harvest attacks at a server|
|US20060059238 *||May 27, 2005||Mar 16, 2006||Slater Charles S||Monitoring the flow of messages received at a server|
|US20070011347 *||Jun 15, 2005||Jan 11, 2007||George David A||Method and apparatus for reducing spam on peer-to-peer networks|
|US20070070921 *||May 5, 2006||Mar 29, 2007||Daniel Quinlan||Method of determining network addresses of senders of electronic mail messages|
|US20070073660 *||May 5, 2006||Mar 29, 2007||Daniel Quinlan||Method of validating requests for sender reputation information|
|US20070078936 *||May 5, 2006||Apr 5, 2007||Daniel Quinlan||Detecting unwanted electronic mail messages based on probabilistic analysis of referenced resources|
|US20070079379 *||May 5, 2006||Apr 5, 2007||Craig Sprosts||Identifying threats in electronic messages|
|US20070083929 *||Dec 7, 2006||Apr 12, 2007||Craig Sprosts||Controlling a message quarantine|
|US20070220607 *||Dec 7, 2006||Sep 20, 2007||Craig Sprosts||Determining whether to quarantine a message|
|US20070245245 *||Feb 12, 2007||Oct 18, 2007||Allen Blue||Searching and reference checking within social networks|
|US20070250566 *||Dec 20, 2004||Oct 25, 2007||Barry Appelman||Announcing new users of an electronic communications system to existing users|
|US20070250585 *||Feb 26, 2007||Oct 25, 2007||Eric Ly||Method of leveraging social networking with a messaging client|
|US20080071876 *||Oct 31, 2007||Mar 20, 2008||Buyerleverage E-Mail Solutions Llc||System and method for granting deposit-contingent e-mailing rights|
|US20080082512 *||Nov 16, 2007||Apr 3, 2008||Aol Llc||Enhanced Search Results|
|US20080263202 *||Jun 27, 2008||Oct 23, 2008||George David A||Method and apparatus for reducing spam on peer-to-peer networks|
|US20090044013 *||Jan 22, 2008||Feb 12, 2009||Zhu Yunzhou||Systems and Methods for Preventing Spam|
|US20090158184 *||Nov 7, 2008||Jun 18, 2009||Aol Llc, A Delaware Limited Liability Company (Formerly Known As Ameria Online, Inc.)||Reactive avatars|
|US20090234922 *||Nov 30, 2005||Sep 17, 2009||Aol Llc||Automatically Enabling the Forwarding of Instant Messages|
|US20100169801 *||Dec 22, 2009||Jul 1, 2010||Aol Llc||Multiple avatar personalities|
|US20100263045 *||Oct 14, 2010||Daniel Wesley Dulitz||System for reclassification of electronic messages in a spam filtering system|
|US20100325113 *||Aug 9, 2010||Dec 23, 2010||Aol Inc.||Automatic categorization of entries in a contact list|
|US20110093498 *||Apr 21, 2011||Facebook, Inc.||Clarifying Search Results Using Social-Networking Information|
|US20110148916 *||Jun 23, 2011||Aol Inc.||Modifying avatar behavior based on user action or mood|
|US20110196939 *||Aug 11, 2011||Aol Inc.||Degrees of separation for handling communications|
|US20110209198 *||Aug 25, 2011||Aol Inc.||Applying access controls to communications with avatars|
|US20120317212 *||May 16, 2012||Dec 13, 2012||Aol Inc.||Degrees of separation for handling communications|
|US20140108571 *||Oct 8, 2013||Apr 17, 2014||Facebook, Inc.||Degrees of separation for handling communications|
|US20140325007 *||Jul 9, 2014||Oct 30, 2014||Google Inc.||System for reclassification of electronic messages in a spam filtering system|
|USRE45254||May 31, 2013||Nov 18, 2014||Facebook, Inc.||Implicit population of access control lists|
|U.S. Classification||709/206, 709/238, 709/249, 709/207, 709/242|
|International Classification||H04Q, G06F15/16, G06F15/173|
|Cooperative Classification||G06Q10/107, H04L51/12, H04L12/585|
|European Classification||G06Q10/107, H04L51/12, H04L12/58F|
|Mar 1, 2004||AS||Assignment|
Owner name: YAHOO! INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ZHU, JING;HU, GEORGE;REEL/FRAME:015039/0904
Effective date: 20040226
|Jul 21, 2010||FPAY||Fee payment|
Year of fee payment: 4
|Jul 23, 2014||FPAY||Fee payment|
Year of fee payment: 8