The invention generally relates to social networks. More particularly, the invention relates to methods and systems for the display and navigation of a social network.
Conventional websites may present a member's connections together with profiles associated with those connections in a list format. In such conventional websites, only a small subset of the member's connections is exposed. Conventional methods for displaying connections do not provide in information or pointers to facilitate further navigation. Some conventional websites provide a network graph that displays the names of a member's connections in concentric circles depending on whether the connection is a friend, or a friend of a friend, etc. and allows a user to see links between connected profiles. Such conventional websites do not present information to the user to facilitate further navigation or allow custom viewing of the network.
Embodiments of the present invention comprise systems and methods that improve social networking. One aspect of one embodiment of the present invention comprises identifying a first profile in a social network, identifying associated profiles associated with the first profile, ranking the associated profiles, wherein ranking is not based exclusively on a degree of separation, and outputting the associated profiles based at least in part on the ranking. Another aspect of one embodiment comprises identifying a user profile, identifying a member profile, determining an association path for the user profile and the member profile, and outputting the association path. Further features and advantages of the present invention are set forth below.
These and other features, aspects, and advantages of the present invention are better understood when the following Detailed Description is read with reference to the accompanying drawings, wherein:
Embodiments of the present invention comprise methods and systems for the display and navigation of a social network. There are multiple embodiments of the present invention. By way of introduction and example, one exemplary embodiment of the present invention, provides a method for ranking and displaying profiles for members of a social network in order to help a user of the social network better visualize connections and relationships between social network members. For example, the user can see a picture of a member of the social network and desire to see that member's network of friends. The user can click on the member and a grid view of the member's friends can then be displayed. The profiles within the grid view can be highlighted to indicate if any of the member's friends are also friends of the user. The profiles can also be arranged in a ranking order based on how popular the profiles are, how active they are on the social network, or on other factors that can help the user decide which are more likely to be of interest.
This introduction is given to introduce the reader to the general subject matter of the application. By no means is the invention limited to such subject matter. Exemplary embodiments are described below.
Various systems in accordance with the present invention may be constructed.
Referring now to the drawings in which like numerals indicate like elements throughout the several figures,
The client devices 102a-n shown in
Client devices 102a-n may also comprise a number of external or internal devices such as a mouse, a CD-ROM, DVD, a keyboard, a display, or other input or output devices. Examples of client devices 102a-n are personal computers, digital assistants, personal digital assistants, cellular phones, mobile phones, smart phones, pagers, digital tablets, laptop computers, Internet appliances, and other processor-based devices. In general, a client device 102a may be any type of processor-based platform that is connected to a network 106 and that interacts with one or more application programs. Client devices 102a-n may operate on any operating system capable of supporting a browser or browser-enabled application, such as Microsoft® Windows® or Linux. The client devices 102a-n can include, for example, personal computers executing a browser application program such as Microsoft Corporation's Internet Explorer™, Netscape Communication Corporation's Netscape Navigator™, and Apple Computer, Inc.'s Safari™.
Through the cheat devices 102a-n, users 112a-n can communicate over the network 106 with each other and with other systems and devices coupled to the network 106. As shown in
The server device 104 shown comprises a server executing a social network engine application program, also known as a social network engine 120. The social network engine 120 allows users, such as user 112a, to interact with and participate in a social network. A social network can refer to a computer network connecting entities, such as people or organizations, by a set of social relationships such as friendship, co-working, or information exchange. Of course, a social network can refer to a computer application or data connecting such entities by such social relationships. Examples of social networks include Orkut.com and Friendster.com.
Social networks can comprise any of a variety of suitable arrangements. An entity or member of a social network can have a profile and that profile can represent the member in the social network. The social network can facilitate interaction between member profiles and allow associations or relationships between member profiles. Associations between member profiles can be one or more of a variety of types, such as friend, co-worker, family member, business associate, common-interest association, and common-geography association. Associations can also include intermediary relationships, such as friend of a friend, and degree of separation relationships, such as three degrees away.
Associations between member profiles can be reciprocal associations. For example, a first member can invite another member to become associated with the first member and the other member can accept or reject the invitation. A member can also categorize or weigh the association with other member profiles, such as, for example, by assigning a level to the association. For example, for a friendship-type association, the member can assign a level, such as acquaintance, friend, good friend, and best friend, to the associations between the member's profile and other member profiles. In one embodiment, the social network engine 120 can determine the typo of association between member profiles, including, in some embodiments, the degree of separation of the association and the corresponding weight or level of the association.
Similar to the client devices 102a-n, the server device 104 shown comprises a processor 116 coupled to a computer-readable memory 118. The server device 104 is in communication with is social network database 130. Server device 104, depicted as a single computer system, may be implemented as a network of computer processors. Examples of a server device 104 are servers, mainframe computers, networked computers, a processor-based device, and similar types of systems and devices. Client processor 110 and the server processor 116 can be any of a number of computer processors, such as processors from Intel Corporation of Santa Clara, Calif. and Motorola Corporation of Schaumburg, Ill.
Each profile within a social network can contain entries, and each entry can comprise information associated with a profile. Examples of entries for a person profile can comprise contact information such as an email addresses, mailing address, IM name, or phone number, personal information such as relationship status, birth date, age, children, ethnicity, religion, political view, sense of humor, sexual orientation, fashion preferences, smoking habits, drinking habits, pets, hometown location, passions, sports, activities, favorite books, music TV, or movie preferences favorite cuisines; professional information such as skills, career, or job description; photographs of a person or other graphics associated with an entity; or any other information describing, identifying, or otherwise associated with a profile. Entries for business profile can comprise industry information such as market sector, customer base, location or supplier information; financial information such as net profits, net worth, number of employees, stock performance; or other types of information associated with the business profile.
The social network engine 120 comprises a path processor 122 and display processor 124. The path processor 122 can determine an association path between profiles. An association path can comprise the fewest number of associated profiles associating a first profile with a second profile. For example, if a profile A is associated with a profile B, and profile B is associated with profile C, then the association path for profiles A and C comprises A, B, C. An association path comprising the fewest number of associated profiles associating a first path and a second path is referred to as a shortest association path. A type-specific association path can also be determined, wherein the fewest number of associations of a specific type are determined by the path processor 122. The path processor 122 can also determine a navigation path. A navigation path is an ordering of profiles viewed by the user 112a to reach a particular profile. For example if user 112a starts by viewing profile A and then views profile B by following a link from profile A, and finally follows another link from profile B to view profile C, the display path can be A, B, C.
The social network engine 120 further comprises a display processor 124. The display processor 124 can receive an association path from the path processor 122 and configure the association path for presentation to a user, such as user 112a. The display processor 124 can also configure a social network, or a portion of a social network, for presentation. For example, the display processor 124 can determine one or more representative entries in the profile, for example, a name and a picture, and arrange the name and picture associated with the profile so that the user 112a can easily identify the profiles when presented on the client device 102a. Configuring a social network, or a portion of a social network for presentation can further comprise determining a ranking for each of the profiles in the social network, or portion of a social network, and displaying the profiles based at least in part on the ranking for each profile. For example, the display processor 124 can determine a ranking for a profile based on a number of total profiles associated with the profile and can then arrange profiles based at least in part on ranking. For example, higher ranking profiles—in one embodiment, those having relatively greater associated profiles—can be displayed in a central location, or otherwise distinguished from lower ranking profiles when presented to the user 112a.
The display processor 124 can also present profiles in a social network, or a portion or social network, based on entries or associations of each of the profiles. For example, the display processor can display in bold type, or otherwise emphasize or distinguish, profiles that are associated with a member currently viewing the social network such as the user 112a. Thus, for example, when the user 112a is viewing the social network, the display processor 124 can configure a portion of the social network being presented to the user 112a such that each of the names of profiles that represent friends of the user 112a are displayed in bold type. So, if associated profiles A, B, and C are presented to the user 112a, and profiles A and B represent friends of user 112a, as represented by their respective profiles, embodiments of the present invention can display the names associated with profiles A and B in bold type.
Server device 104 also provides access to storage elements, such as a social network storage element, in the example shown in
It should be noted that the present invention may comprise systems having different architecture than that which is shown in
Each member profile can contain entries, and each entry can comprise information associated with a profile. For example, a person's member profile can contain: personal information, such as relationship status, birth date, age, children, ethnicity, religion, political view, sense of humor, sexual orientation, fashion preferences, smoking habits, drinking habits, pets, hometown location, passions, sports, activities, favorite books or music, TV or movie preferences, and favorite cuisines; contact information, such as email addresses, location information, instant messenger name, telephone numbers, and address; professional information, such as job title, employer, and skills; educational information, such as schools attended and degrees obtained, and any other suitable information describing, identifying, or otherwise associated with a person. A business' member profile can, for example, contain a description of the business, and information about its market sector, customer base, location, suppliers, net profits, net worth, number of employees, stock performance, contact information, and other types of suitable information associated with the business.
A member profile can also contain rating information associated with the member. For example, the member can be rated or scored by other members of the social network 200 in specific categories, such as humor, intelligence, fashion, trustworthiness, sexiness, and coolness. A member's category ratings can be contained in the member's profile. In one embodiment of the social network, as member can have fans. Fans can be other members who have indicated that they are “fans” of the member. Rating information can also include the number of fans of a member and identifiers of the fans. Rating information can also include the rate at which a member accumulated ratings or fans and how recently the member has been rated or acquired fans.
A member profile can also contain social network activity data associated with the member. Membership information can include information about a member's login patterns to the social network, such as the frequency that the member logs in to the social network and the member's most recent login to the social network. Membership information can also include information about the rate and frequency that a member profile gains associations to other member profiles. In a social network that comprises advertising or sponsorship, a member profile may contain consumer information. Consumer information may include the frequency, patterns, types, or number of purchases the member makes, or information about which advertisers or sponsors the member has accessed, patronized, or used.
A member profile may comprise data stored in memory. The profile, in addition to comprising data about the member, can also comprise data relating to others. For example, a member profile can contain an identification of associations or virtual links with other member profiles. In one embodiment, a member's social network profile may comprise a hyperlink associated with another member's profile. In one such association, the other member's profile may contain a reciprocal hyperlink associated with the first member's profile. A member's profile may also contain information excerpted from another associated member's profile, such as a thumbnail image of the associated member, his or her age, marital status, and location, as well as an indication of the number of members with which the associated member is associated. In one embodiment, a member's profile may comprise a list of other social network members' profiles with which the member wishes to be associated.
An association may be designated manually or automatically. For example, a member may designate associated members manually by selecting other profiles and indicating an association that can be recorded in the member's profile. According to one embodiment, associations can be established by an invitation and an acceptance of the invitation. For example, as first user can send an invitation to a second user inviting the second user to form an association with the first user. The second user can accept or reject the invitation. According to one embodiment, if the second user rejects the invitation, a one-way association can be formed between the first user and the second user. According to another embodiment, if the second user rejects the association, no association may be formed between the two users. Also, an association between two profiles may comprise an association automatically generated in response to a predetermined number of common entries, aspects, or elements in the two members' profiles. In one embodiment, a member profile may be associated with all of the other member profiles comprising a predetermined number or percentage of common entries, such as interests, hobbies, likes, dislikes, employers and/or habits. Associations designated manually by members of the social network, or associations designated automatically based on data input by one or more members of the social network, can be referred to as user established associations.
Associations between profiles within a social network can be of a single type or can be multiple types and can include, for example, friendship associations, business associations, family associations, community associations, school associations, or any other suitable type of link between profiles. Associations can further be weighted to represent the strength of the association. For example, a friendship association can be weighted more than a school association. Each type of association can have various levels with different weights associated with each level. For example, a friendship association can be classified according to which of a plurality of friendship association levels it belongs to. In one embodiment, a friendship association may be assigned a level by the member from a list of levels comprising: a best friend, a good friend, a regular friend, an acquaintance, and a friend the member has not met.
In
Referring to
Each member represented by the profiles A, B, C, D, E, F, and G comprising the vertices 202, 204, 206, 208, 210, 212, and 214 respectively, for purposes of illustration, comprises a person. Other types of members can be in social network 200. For example, communities, special interest groups, organizations, political parties universities, and legal persons, such as corporations and business partnerships may be members of the social network 200. The associations 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, and 240 illustrated in
Other embodiments of the present invention may comprise directed associations or other types of associations. Directed associations can associate a first profile with a second profile while not requiring the second profile to be associated with the first profile. For example, profile A can be associated by a friendship association with profile B, and profile B can be unassociated with profile A, or profile B can be associated with profile A through a different type of association, such as a business association. Thus a display of profile A's friends can include profile B, but a display of profile B's friends would not include profile A.
According to another embodiment, a directed or single direction association can be formed when one member indicates an association with a second member but the second member does not reciprocate the association. For example, a member associated with profile A can indicate that he is a friend of a member associated with profile B. The member associated with profile B in this example can decide not to indicate that the member associated with profile A is a friend. According to one embodiment, profile B would not be displayed with profile A's friends nor would profile A be displayed with profile B's friends as the association was not reciprocated by profile B. Similarly, profile B may not be included, for example, within a listing of friends of profile A for purposes of determining degrees of separation, for example. Thus, in such an embodiment, the displaying of profile B can be controlled by the associations the member associated with profile B assents to.
Within a social network, a degree of separation can be determined for associated profiles. In one embodiment, a degree of separation between two profiles can be determined by the fewest number of edges of a certain type separating the associated profiles. In another embodiment, a type-specific degree of separation may be determined. A type-specific degree of separation comprises a degree of separation determined based on one particular type of association. For example, a profile A has a friend association degree of separation of two from profile E. The fewest number of friendship associations between profile A and profile E is two—the friendship association comprising edge 222 between profiles A and D and the friendship association comprising edge 234 between profiles D and E. Thus, for the associated profiles A and E the degree of friendship separation, determined according to one aspect of one embodiment of the present invention, is two.
Another type-specific degree of separation can also be determined for profiles A and E. For example, a common characteristic degree of separation can be determined by determining the fewest number of common characteristic associations separating profile A and profile E. According to the embodiment depicted in
According to other aspects of certain embodiments of the present invention, the degree of separation may be determined by use of a weighting factor assigned to each association. For example, close friendships can be weighted higher than more distant friendships. According to certain aspects of embodiments using a weighting factor, a higher weighting factor for an association can reduce the degree of separation between profiles and lower weighting factors can increase the degree of separation. This can be accomplished, for example, by establishing an inverse relationship between each association and a corresponding weighting factor prior to summing the associations. Thus, highly weighted associations would contribute less to the resulting sum than lower weighted associations.
The association path 306 can comprise one or more association paths determined by the path processor 122 including, for example, a shortest association path, a type-specific association path, or a navigation path. The association path 306 can display a name associated with each profile in the association path. For example, in the embodiment depicted in
The display grid 308 can further comprise graphics associated with a plurality of associated profiles, names associated with each of the profiles within the plurality of profiles, and a number representing a total number of associations for the associated profile. For example, a graphic above “Kerah” in
Various methods in accordance with the present invention may be carried out One exemplary method according to the present invention comprises a first profile in a social network, identifying associated profiles associated with the first profile, ranking the associated profiles, wherein ranking is not based exclusively on a degree of separation, and outputting the associated profiles based at least in part on the ranking.
According to another embodiment the ranking can be based at least in part on social network activity data. The social network activity data can comprise one or more of login frequency, number of rights, date of last profile update, existence of an email address, existence of a picture, and instant messaging availability status. According to another embodiment the ranking can be based at least in part on biography data. The biography data can comprise one or more of age, name, interests, and geographical location. According to another embodiment the ranking can be based at least in part on popularity data. The popularity data can comprise one or more of a number of profiles associated with a first profile, feedback, and rating data. According to another embodiment the ranking can be based at least in part on data indicating relatedness. The data indicating relatedness can comprise one or more of an amount of common entries, a degree of separation, and an amount of contacts between profiles. According to another embodiment the ranking can be based on custom settings entered by a user.
Another exemplary method of one embodiment of the present invention comprises identifying a user profile, identifying a member profile, determining an association path for the user profile and the member profile, and outputting the association path. According to another embodiment, the association path can comprise a shortest path, type specific path, or navigation path. According to another embodiment the profiles can be displayed in a grid format and can be positioned based on ranking order.
Once a member profile is determined, the method 400 proceeds to block 406, wherein the social network search engine 120 identities associated profiles associated with the member profile determined in block 404. The associated profile can comprise, for example, profiles associated by a specific type of association, such as a friendship association, or profiles associated by any or all other types of associations within a social network. By determining associated profiles by a certain type of association, the method 400 can cause the display of a type-specific social network. By selecting profiles associated by each and every type of association in a social network, the method 400 can cause the display of type-neutral social network.
Once associated profiles are identified by the network search engine 120, the method 400 proceeds to block 408, wherein the display processor 124 determines a ranking for each associated profile. Ranking associated profiles can help the user 112a find profiles more likely to be of interest and otherwise facilitate navigation of the social network. For example, some users of a social network may initially set up an account but access the account infrequently or cease using it altogether. Such users may not be as desirable of associations as users who frequently access the social network. The display processor 124 can rank profiles to help users easily identify, for example, which profiles are more active than others.
There are many ways in which the associated profiles can be ranked. The ranking for an associated profile can be determined, for example, based on social network activity data, biography data, popularity data, relatedness data, or other suitable types of data tending to distinguish profiles.
Ranking profiles based on social network activity can comprise, for example, ranking users based on how frequently or how often a member of the social network logs on to the social network, how often or how recently a member has updated his profile information, when a member joined the social network, whether a member is currently available for instant messaging, whether a member has an available email address, a picture, or other identifiers associated with his profile, or other information tending to indicate that a member is actively using or available on the social network.
Ranking profiles based on biography data associated with a profile can comprise, for example, ranking profiles based on age, name, geographical location, interests, or other suitable information associated with the profile. For privacy concerns, the display processor 124 can require a permission indication for sharing biography information prior to ranking profiles based on such information. As an example of ranking based on biography date, the display processor 124 can identify an age associated with the user's 112a profile and identify ages for associated profiles. The display processor 124 can then rank the profiles based on closeness of age and output the profiles such that profiles closer in age to the user 112a are emphasized over other profiles, for example. As other examples of ranking based on biography data, the display processor 124 can cause the profiles to be ranked alphabetically to facilitate locating known profiles, or ranked based on geographical location. For example, the user's 112a profile can indicate a geographical location of California, and associated profiles can be ranked according to the closeness of their geographical to California. Thus, for example, a profile indicating a geographical location of New York can be ranked lower than a profile indicating a geographical location of Arizona, and thus the user 112a can more readily identify local users.
Ranking based on popularity data can comprise, for example, ranking profiles based on a number of profiles to which the profile is associated, ranking profiles based on feedback or ratings from social network members, or raking profiles based on other suitable indications of popularity for a user profile. For example, the display processor 124 can determine how many profiles are associated with each profile and rank profiles associated with a greater number of profiles higher than those associated with fewer profiles. In determining how many profiles are associated with a given profile, the display processor 124 can identify how many profiles are within a certain degree of separation from the given profile. For example, the display processor 124 can identify how many profiles are within one degree of separation and thus determine a popularity based, for example on how many “friends” a profile has and not on how many “friends of a friend” the profile has. Alternatively, other degrees of separation could be used. Additionally, the display processor 124 can determine a rating score recursively factoring in a number of profiles associated in turn with each associated profile.
Ranking based on popularity can also comprise receiving feedback or ratings from members of the social network. For example, the social network engine 120 can allow members of the social network to enter ratings for a given member of the social network and store the ratings in association with the given member. Ratings can include, for example, indications of how friendly, attractive, cool, trustworthy, etc. the given member is perceived to be. The ratings can be compiled by the display processor 124 to determine a ranking for the profiles of the social network thus enabling a user to easily identify which profiles are perceived to be more popular.
Rankings based on relatedness data can comprise, for example, ranking based on an amount of common entries between profiles, a degree of separation between profiles, an amount of contacts such as instant messages or emails between contacts, or other suitable data indicating how closely related two profiles are. For example, the display processor 124 can identify that the user 112a has sent and received a high number of emails from a certain member of the social network. The display processor 124 can thus determine a higher ranking for the certain member of the social network than for an other members with whom the user 112a has had little contact.
The ranking for each associated profile can be specific to the user 112a viewing the social network, or non-user specific. For example, if the user 112a has indicated a strong association with the associated profile receiving a ranking, the associated profile can receive a higher ranking. If another user, for example user 112b, has indicated a dislike of the same associated profile, the associated profile can receive a lower ranking when the social network is being viewed by user 112b. Alternatively, each associated profile can receive the same ranking based on factors independent of which user is viewing the social network.
Once a ranking for each associated profile is determined, the method 400 proceeds to block 410, wherein associations with the user profile are determined. The associations with the user profile can comprise associations between an associated profile and the profile of the user 112a. For example, in the social network depicted in
The display processor 124 can then configure the social network so that the name “Kerah” is highlighted in some fashion such as with a distinguishing color, font, or typeface. For example, in
Once associations are determined between the associated profile and the user profile, the method 400 proceeds to block 342 wherein the display processor 124 causes the display of the member profile. The display processor 124 can cause the member profile to be displayed, for example, similar to the display illustrated in
Once the display processor 124 causes the display of the member profile, the method 400 proceeds to block 414, wherein the display processor 124 causes the display of the associated profiles. The associated profiles can be displayed, for example, in a display grid similar to the display grid 308 illustrated in
Once a member profile is identified, the method 500 proceeds to block 506, wherein the patch processor 122 determines an association path for the user profile and the member profile determined in blocks 502 and 504. During block 506, the path processor 122 can determine any suitable type of association path including a shortest association path or a type-specific association path. An association path can be determined by determining the fewest number of edges separating associated profiles and then determining the profiles comprising the vertices joined by the edges. For example, in
Another method of determining an association path is to determine the fewest number of edges of a certain type separating the associated profiles and to then determine the profiles comprising the vertices joined by the edges. This method of determining an association path can produce a type-specific association path. A type-specific association path is an association path determined based on one particular type of association. For example, in
Another type-specific association path can also be determined for profiles A and E. For example, a common characteristic association path can be determined by determining the fewest number of common characteristic associations separating profile A and profile E. According to the embodiment depicted in
Once the display processor 124 receives the association path, the method 500 proceeds to block 508, wherein the display processor 124 causes the association path to be presented. The association path can be presented by displaying the association path on a social network display such as the social network display depicted in
While the above description contains many specifics, these specifics should not be construed as limitations on the scope of the invention, but merely as exemplifications of the disclosed embodiments. Those skilled in the art will envision an other possible variations that are within the scope of the invention.
This application is a continuation of U.S. application Ser. No. 11/195,790, filed on Aug. 1, 2011, entitled “Methods and Systems for the Display and Navigation of a Social Network,” which is a continuation of U.S. Pat. No. 8,015,119, filed on Aug. 26, 2004, entitled “Methods and Systems for the Display and Navigation of a Social Network,” which claims the benefit of U.S. Provisional Application No. 60/538,035, entitled “Methods and Systems for the Display and Navigation of a Social Network” filed Jan. 21, 2004, each of which are hereby incorporated by this reference.
Number | Name | Date | Kind |
---|---|---|---|
5287498 | Perelman et al. | Feb 1994 | A |
5537586 | Amram et al. | Jul 1996 | A |
5950200 | Sudai et al. | Sep 1999 | A |
5963951 | Collins | Oct 1999 | A |
6041311 | Chislenko et al. | Mar 2000 | A |
6049777 | Sheena et al. | Apr 2000 | A |
6052122 | Sutcliffe et al. | Apr 2000 | A |
6061681 | Collins | May 2000 | A |
6073105 | Sutcliffe et al. | Jun 2000 | A |
6073138 | de l'Etraz et al. | Jun 2000 | A |
6092049 | Chislenko et al. | Jul 2000 | A |
6130938 | Erb | Oct 2000 | A |
6185559 | Brin et al. | Feb 2001 | B1 |
6192119 | Wilson | Feb 2001 | B1 |
6256648 | Hill et al. | Jul 2001 | B1 |
6285999 | Page | Sep 2001 | B1 |
6327590 | Chidlovskii et al. | Dec 2001 | B1 |
6366962 | Teibel | Apr 2002 | B1 |
6389372 | Glance et al. | May 2002 | B1 |
6526440 | Bharat | Feb 2003 | B1 |
6529903 | Smith et al. | Mar 2003 | B2 |
6594673 | Smith et al. | Jul 2003 | B1 |
6615209 | Gomes et al. | Sep 2003 | B1 |
6658423 | Pugh et al. | Dec 2003 | B1 |
6665715 | Houri | Dec 2003 | B1 |
6678681 | Brin | Jan 2004 | B1 |
6697478 | Meldrum et al. | Feb 2004 | B1 |
6725259 | Bharat | Apr 2004 | B1 |
6754322 | Bushnell | Jun 2004 | B1 |
6754873 | Law et al. | Jun 2004 | B1 |
6834195 | Brandenberg et al. | Dec 2004 | B2 |
6837436 | Swartz et al. | Jan 2005 | B2 |
6865546 | Song | Mar 2005 | B1 |
6871186 | Tuzhilin et al. | Mar 2005 | B1 |
6895406 | Fables et al. | May 2005 | B2 |
6912505 | Linden et al. | Jun 2005 | B2 |
7013292 | Hsu et al. | Mar 2006 | B1 |
7069308 | Abrams | Jun 2006 | B2 |
7080117 | de Pinto et al. | Jul 2006 | B2 |
7092821 | Mizrahi et al. | Aug 2006 | B2 |
7106848 | Barlow et al. | Sep 2006 | B1 |
7113917 | Jacobi et al. | Sep 2006 | B2 |
7117254 | Lunt et al. | Oct 2006 | B2 |
7118153 | Kitayama et al. | Oct 2006 | B2 |
7130777 | Garg et al. | Oct 2006 | B2 |
7130844 | Elder et al. | Oct 2006 | B2 |
7139252 | Babu et al. | Nov 2006 | B2 |
7234117 | Zaner et al. | Jun 2007 | B2 |
7269590 | Hull et al. | Sep 2007 | B2 |
7275068 | Huberman et al. | Sep 2007 | B2 |
7318037 | Solari | Jan 2008 | B2 |
7343335 | Olliphant | Mar 2008 | B1 |
7360080 | Camnisch et al. | Apr 2008 | B2 |
7366990 | Pitroda | Apr 2008 | B2 |
7383258 | Harik et al. | Jun 2008 | B2 |
7433832 | Bezos et al. | Oct 2008 | B1 |
7433876 | Spivack et al. | Oct 2008 | B2 |
7472110 | Achlioptas | Dec 2008 | B2 |
7478078 | Lunt et al. | Jan 2009 | B2 |
7539697 | Akella et al. | May 2009 | B1 |
7555110 | Dolan et al. | Jun 2009 | B2 |
7610287 | Dean et al. | Oct 2009 | B1 |
7742468 | Vagelos | Jun 2010 | B2 |
20010039500 | Johnson | Nov 2001 | A1 |
20020023230 | Bolnick et al. | Feb 2002 | A1 |
20020042791 | Smith et al. | Apr 2002 | A1 |
20020059130 | Cheng et al. | May 2002 | A1 |
20020059201 | Work | May 2002 | A1 |
20020103682 | Stemmer et al. | Aug 2002 | A1 |
20020116458 | Bricklin et al. | Aug 2002 | A1 |
20020116466 | Trevithick et al. | Aug 2002 | A1 |
20020123988 | Dean et al. | Sep 2002 | A1 |
20020124053 | Adams et al. | Sep 2002 | A1 |
20020133365 | Grey et al. | Sep 2002 | A1 |
20020133481 | Smith et al. | Sep 2002 | A1 |
20020137490 | Gallant | Sep 2002 | A1 |
20020143874 | Marquette et al. | Oct 2002 | A1 |
20020143944 | Traversat et al. | Oct 2002 | A1 |
20020169782 | Lehmann et al. | Nov 2002 | A1 |
20020169835 | Paul et al. | Nov 2002 | A1 |
20020174073 | Nordman et al. | Nov 2002 | A1 |
20020194112 | dePinto et al. | Dec 2002 | A1 |
20030050977 | Puthenkulam et al. | Mar 2003 | A1 |
20030063072 | Brandenberg et al. | Apr 2003 | A1 |
20030069749 | Shear et al. | Apr 2003 | A1 |
20030083898 | Wick et al. | May 2003 | A1 |
20030101227 | Fink | May 2003 | A1 |
20030154213 | Ahn | Aug 2003 | A1 |
20030163530 | Ribak et al. | Aug 2003 | A1 |
20030233650 | Zaner et al. | Dec 2003 | A1 |
20040042599 | Zaner et al. | Mar 2004 | A1 |
20040059708 | Dean et al. | Mar 2004 | A1 |
20040088325 | Elder et al. | May 2004 | A1 |
20040093224 | Vanska et al. | May 2004 | A1 |
20040093324 | Marappan | May 2004 | A1 |
20040119740 | Chang et al. | Jun 2004 | A1 |
20040122681 | Ruvolo et al. | Jun 2004 | A1 |
20040122803 | Dom et al. | Jun 2004 | A1 |
20040122811 | Page | Jun 2004 | A1 |
20040162830 | Shirwadkar et al. | Aug 2004 | A1 |
20040167794 | Shostack et al. | Aug 2004 | A1 |
20040172378 | Shanahan et al. | Sep 2004 | A1 |
20040193684 | Ben-Yoseph | Sep 2004 | A1 |
20040215793 | Ryan et al. | Oct 2004 | A1 |
20040221309 | Zaner et al. | Nov 2004 | A1 |
20040249811 | Shostack et al. | Dec 2004 | A1 |
20040258220 | Levine et al. | Dec 2004 | A1 |
20040260781 | Shostack et al. | Dec 2004 | A1 |
20050015432 | Cohen | Jan 2005 | A1 |
20050050158 | Solari | Mar 2005 | A1 |
20050120084 | Hu et al. | Jun 2005 | A1 |
20050152521 | Liljestrand | Jul 2005 | A1 |
20050159998 | Buyukkokten et al. | Jul 2005 | A1 |
20050165785 | Malkin et al. | Jul 2005 | A1 |
20050171832 | Hull et al. | Aug 2005 | A1 |
20050171954 | Hull et al. | Aug 2005 | A1 |
20050193054 | Wilson et al. | Sep 2005 | A1 |
20050197846 | Pezaris et al. | Sep 2005 | A1 |
20050209999 | Jou | Sep 2005 | A1 |
20050210409 | Jou | Sep 2005 | A1 |
20050216300 | Appelman et al. | Sep 2005 | A1 |
20050246420 | Little | Nov 2005 | A1 |
20050256866 | Lu et al. | Nov 2005 | A1 |
20050267940 | Galbreath et al. | Dec 2005 | A1 |
20060026288 | Acharya et al. | Feb 2006 | A1 |
20060077957 | Reddy et al. | Apr 2006 | A1 |
20060085259 | Nicholas et al. | Apr 2006 | A1 |
20060136419 | Brydon et al. | Jun 2006 | A1 |
20060184997 | La Rotonda et al. | Aug 2006 | A1 |
20060206604 | O'Neil et al. | Sep 2006 | A1 |
20070127631 | Difiglia | Jun 2007 | A1 |
20070171898 | Salva | Jul 2007 | A1 |
20070173236 | Vishwanathan et al. | Jul 2007 | A1 |
20070248077 | Mahle, Jr. et al. | Oct 2007 | A1 |
20080004941 | Calabria | Jan 2008 | A1 |
20080056475 | Brannick et al. | Mar 2008 | A1 |
20080133716 | Rao et al. | Jun 2008 | A1 |
20080192656 | Vagelos | Aug 2008 | A1 |
20090013386 | Puthenkulam et al. | Jan 2009 | A1 |
20110098156 | Ng et al. | Apr 2011 | A1 |
Number | Date | Country |
---|---|---|
11265369 | Sep 1999 | JP |
02132604 | May 2002 | JP |
WO 01084266 | Nov 2001 | WO |
WO 02079984 | Oct 2002 | WO |
WO 0068860 | Jul 2003 | WO |
WO 05015470 | Feb 2005 | WO |
Entry |
---|
Adamic et al., “A Social Network Caught in the Web,” Internet Journal, First Monday, Jun. 2, 2003, vol. 8, No. 6, pp. 1-22. |
Agarwal et al., “Enabling Real-Time User Interests for Next Generation Activity-Oriented Social Networks,” Thesis submitted to the Indian Institute of Technology Delhi, Department of Computer Science & Engineering, 2005, 70 pgs. |
Anwar et al., “Leveraging 'Social-Network' Infrastructure to Improve Peer-to Peer Overlay Performance: Results from Orkut,” University of Illinois at Urbana-Champaign USA, 2005, 9 pgs. |
AT&T Personal Reach Service: Benefits and Features, Mar. 29, 2010, 7 pgs. |
AT&T Personal Reach Service: Personal Reach Service, Mar. 29, 2010, 2 pgs. |
Baird et al., “Neomillennial User Experience Design Strategies: Utilizing Social Networking Media to Support “Always on” Learning Styles,” J. Educational Technology Systems, vol. 34(1), 2005-2006, Baywood Publishing Co., Inc., pp. 5-32. |
Boyd, et al., “Social Network Sites: Definition, History, and Scholarship,” Journal of Computer-Mediated Communication, International Communication Association, 2008, pp. 210-230. |
Churchill et al., “Social Networks and Social Networking,” IEEE Computer Society, Sep.-Oct. 2005, pp. 14-19. |
Cohen et al., “Social Networks for Creative Collaboration,” C&C '05, Apr. 12-15, 2005, London, United Kingdom, pp. 252-255. |
Decker et al., “The Social Semantic Desktop,” Digital Enterprise Research Institute, DERI Galway, Ireland, DERI Innsbruck, Austria, DERI Technical Report, May 2, 2004, 7 pgs. |
Dukes-Schlossberg et al., “Battlefield Awareness and Data Dissemination Intelligent Information Dissemination Server,” Air Force Research Laboratory, Rome Research Site, Rome, NY, Nov. 1, 1999, 31 pgs. |
Eagle et al., “Social Serendipity: Proximity Sensing and Cueing,” MIT Media Laboratory Technical Note 580, May 2004, 18 pgs. |
Erickson et al., “Social Translucence: Using Minimalist Visualizations of Social Activity to Support Collective Interaction,” Designing Information Spaces: The Social Navigation Approach, Springer-verlag: London, 2003, pp. 1-19. |
Gross et al., “Information Revelation and Privacy in Online Social Networks,” WPES '05, Alexandria, Virginia, Nov. 7, 2005, pp. 71-80. |
Hammond et al., “Social Bookmarking Tools (I),” D-Lib Magazine, Apr. 2005, vol. II, No. 4, ISSN 1082-9873, 23 pgs. |
Heer et al., “Vizster: Visualizing Online Social Networks,” University of California, Berkeley, 8 pgs. |
International Search Report, International Application No. PCT/US2008/005118, dated Sep. 30, 2008, 2 pgs. |
Leonard, “You Are Who You Know,” Internet, retrieved at http://www.salon.com, Jun. 15, 2004, 15 pgs. |
LiveJournal, “FAQ #163: How Do I Find a Syndicated Account?” Last Updated: thebubba, Jan. 6, 2004, 2 pgs. |
Marwick, “Selling Your Self: Online Identity in the Age of a Commodified Internet,” University of Washington, 2005, 192 pgs. |
MediaSift Ltd., DataSift: Realtime Social Data Mining Platform, Curate and Data Mine the Real Time Web with DataSift, Dedipower, Managed Hosting, [Retrieved on May 13, 2011], 1 pg. |
Metcalf et al., “Spatial Dynamics of Social Network Evolution,” 23rd International Conference of the System Dynamics Society, Jul. 19, 2005, pp. 1-13. |
Mori et al., “Real-world Oriented Information Sharing Using Social Networks,” Group '05, Sanibel Island, Florida, USA, Nov. 6-9, 2005, pp. 81-84. |
Murchu et al., “Online Social and Business Networking Communities,” Digital Enterprise Research Institute DERI Technical Report, National University of Ireland, Aug. 11, 2004, 22 pgs. |
Nardi et al., “Blogging as Social Activity, or, Would You Let 900 Million People Read Your Diary?” CSCW'04, Nov. 6-10, 2004, vol. 6, Issue 3, Chicago, Illinois, pp. 222-231. |
Neumann et al., “Semantic social network portal for collaborative online communities,” Journal of European Industrial Training, 2005, Emerald Group Publishing, Limited, vol. 29, No. 6, pp. 472-487. |
Ring Central, Inc., Internet, retrieved at http://www.ringcentral.com, Apr. 19, 2007, 1 pg. |
Singh et al., “CINEMA: Columbia InterNet Extensible Multimedia Architecture,” Department of Computer Science, Columbia University, pp. 1-83. |
Steen et al., “Development of we-centric, context-aware, adaptive mobile services requires empathy and dialogue,” Freeband FRUX, Oct. 17, 2005, Internet Journal, Netherlands, pp. 1-4. |
Superfeedr Track, Internet, retrieved at http://blog.superfeedr.com/track/filter/xmpp/pubsubhubbub/track, May 13, 2011, 8 pgs. |
Twitter Blog: Tracking Twitter, Internet, retrieved at http://blog.twitter.com/2007/09/tracking-twitter.html, May 13, 2011, 2 pgs. |
Twitter Announces Fire Hose Marketplace: Up to 10K Keyword Filters for 30 Cents, Internet, retrieved at http://www.readywriteweb.com/archives/twitter—announces—fire—hose—marketplace—up—to—10k.php, May 13, 2011, 7 pgs. |
Van Eijk et al., “We-centric, context-aware, adaptive mobile service bundles,” Freeband, Telematica Instituut, TNO telecom, Nov. 30, 2004, 48 pgs. |
Wenger et al., “Technology for Communities,” CEFRIO Book Chapter v 5.2, Jan. 18, 2005, pp. 1-15. |
Amazon.com, “Feedback FAQ,” 3 pgs, [online] [Retrieved on Jul. 29, 2004] Retrieved from the internet <URL:http://pages.amazon.com/execlobidos/tg/browse/-/1161284/qid=1 09111 0289/sr=1-1/002-2>. |
Ebay.com, “What is eBay?” 2 pgs, [online] [Retrieved on Jul. 29, 2004] Retrieved from the internet <URL:http://pages.ebay.com/help/welcome/questions/about-ebay.html>. |
Ebay.com, “How to Bid,” 2 pgs, [online] [Retrieved on Jul. 29, 2004] Retrieved from the internet <URL:http://pages.ebay.com/help/welcome/bid.html>. |
Ebay.com, “How to Sell,” 2 pgs, [online] [Retrieved on Jul. 29, 2004] Retrieved from the internet <URL:http://pages.ebay.com/help/welcome/sell.html. |
Amazon.com, “Selling at Amazon Marketplace,” 2 pgs, [online] [Retrieved on Jul. 29, 2004] Retrieved from the internet <URL:http://pages.amazon.com/execlobidos/tg/browse/-/1161234/ref=hp—hp—Is—4—2/002-283572>. |
Amazon.com, “New Seller FAQ,” 3 pgs, [online][Retrieved on Jul. 29, 2004]Retrieved from the internet <URL:http://pages.amazon.com/execlobidos/tg/browse/-/1161274/002-2835726-551622>. |
Amazon.com, “How to Get a Great Feedback Score,” 3 pgs, [online] [Retrieved on Jul. 29, 2004] Retrieved from the internet <URL:http://pages.amazon.com/execlobidos/tg/browse/-/131074 71/qid=1091110289/sr=1-5/002>. |
Ebay.com, “Frequently Asked Questions,” 4 pgs, [online] [Retrieved on Jul. 29, 2004] Retrieved from the internet <URL:http://pages.ebay.com/help/basics/faq.html as available via the internet and printed on Jul. 29, 2004>. |
Amazon.com, “Amazon.com Friends and Favorites,” 6 pgs, [online] [Retrieved on Feb. 27, 2004] Retrieved from the internet <URL:http://www.amazon.com/exec/obidos/subst/community/community-home.html/ref=pd—ys—. . . >. |
Amazon.com, “Wish Lists,” 4 pgs, [online] [Retrieved on Feb. 18, 2004] Retrieved from the internet <URL:http://www.amazon.com/exec/obidos/tg/browse/-/897204/ref=ya—hp—reg—1/002-9880811- . . . >. |
Amazon.com. “Purchase Circles,” 2 pgs, [online] [Retrieved on Feb. 27, 2004] Retrieved from the internet <URL: http://www.amazon.com/exec/obidos/tg/browse/468604/ref=cm—pc—faq/002-0759267-82 . . . >. |
Iyer, S., “Accounts Website,” 7 pgs, [online] [Retrieved on Jul. 29, 2004] Retrieved from the internet <URL:http://www.cs.rice.edu/-ssiyer/accounts/>. |
Doctorow, copy, “Running Notes From Revenge of the User: Lessons from Creator/User Battles,” web page at http://craphound.com/danahetcon04.txt, as available via the Internet and printed Jul. 28, 2004. |
Microsoft Corporation, “Is Friendster the ‘Next Big Thing’?” web page at http://mobilemomentum.msn.comlarticle.aspx?aid=4, as available via the Internet and printed Jul. 29, 2004, 2 pages. |
Multiply, “Multiply Privacy Policy,” web page at http://multiply.com/info/privacy, as available via the Internet and printed on May 3, 2004, 4 pages. |
Multiply, “Multiply Terms of Service,” web page at http://multiply.com/info/tos, as available via the Internet and printed on printed May 3, 2004, 6 pages. |
Multiply, “Help,” web page at http://multiply.comlinfo/help, as available via the Internet and printed May 3, 2004. |
Multiply, “About Multiply,” web page at http://multiply.com/info/about, as available via the Internet and printed May 3, 2004. |
Sullivan, Danny, “Is It Really Personalized Search?” http://searchengine watch.com, printed May 13, 2004. |
International Search Report and Written Opinion, International Application No. PCT/US2005/001544, dated Apr. 29, 2005, 9 pgs. |
International Search Report and Written Opinion, International Application No. PCT/US2005/002240, dated Sep. 26, 2006, 5 pgs. |
Ebay.com, “Star,” 2 pgs, [online] [Retrieved on Jul. 1, 2008] Retrieved from the internet <URL:http://pages.ebay.com/help/basics/g-stars.html>. |
Kamvar, S., et al., “The EigenTrust Algorithm for Reputation Management in P2P Networks.” International World Wide Web Conference Proceedings of the 12th Proceedings of the 12th ICWWW 2003, pp. 640-651. |
“PlanetAll,” From Internet Archive Wayback Machine on Nov. 1997, 19 pgs, [online] [Retrieved on Mar. 17, 2004] Retrieved from the internet <URL: Internet Archive Wayback Machine: www.archive.org/www/planetall.com>. |
Jensen, C., et al., “Finding Others Online: Reputation Systems for Social Online Spaces,” Group Spaces, CHI 2002, Apr. 20-25, 2002, pp. 447-454, vol. 4, Iss. 1. |
Balabanovic, M., et al., “Content-Based, Collaborative Recommendation,” Mar. 1997, pp. 66-72, vol. 40, No. 3. |
Choi, J., “Netflix Prize for the best collaborative filtering algorithm,” Data mining and parallelization, CGL Blog, Jul. 16, 2008, [online] [Retrieved on May 13, 2009] Retrieved from the internet <URL: http://jychoi-report-cgl.blogspot.com/2008/07/netflix-prize-for-best-collaborative.html>. |
Glance, N., et al., “Knowledge Pump: Supporting the Flow and Use of Knowledge,” Information Technology for Knowledge Management, 1998, 22 pgs. |
Kautz, H., et al., “ReferralWeb: Combining Social Networks and Collaborative Filtering,” Communications of the ACM, Mar. 1997, 4 pgs, vol. 40, No. 3. |
“Collaborative filtering,” Wikipedia, Last modified Oct. 17, 2007, [online] [Retrieved on May 13, 2009] Retrieved from the internet <URL:http://web.archive.org/web/20071020061658/http:/en.wikipedia.org/wiki/Collaborative—filtering>. |
Resnick, P., et al., “Recommender Systems,” Communications of the ACM, Mar. 1997, pp. 56-58, vol. 40, No. 3. |
Rucker, J., et al., “Personalized Navigation for the Web,” Communications of the ACM, Mar. 1997, pp. 73-75, vol. 40, No. 3. |
“Mufin.com: content-based recommendations,” Net, Blogs and Rock'n'Roll, Oct. 8, 2008, [online] [Retrieved on May 13, 2009] Retrieved from the internet <URL: http://www.netblogsrocknroll.com/2008/10/mufin-music-fin.html>. |
“Recommender system,” Wikipedia, Last modified Jul. 27, 2009, [online] [Retrieved on Aug. 6, 2009] Retrieved from the internet <URL:http://en.wikipedia.org/wiki/Recommendation—system>. |
NomadNet, “Nomad Net News,” web page at http://www.netnomad.com/, as available via the Internet and printed Dec. 1, 2004. |
Ofoto, “Ofoto Share Albums,” web page at htt˜:/Iwww.ofoto.com/ShareOverview.isg?UV=36308566308678428514107, as available via the Internet and printed Dec. 1, 2004. |
Online Business Network, “Social Networking Site Guide-Ryze,” web page at http://www.onlinebusinessnetworks.com/online-social-networks-guide/ryzEtQbp, as available via the Internet and printed Dec. 1, 2004. |
PC World, “PCWorld-ICQ Builds a Social Network,” web page at http://www.gcworld.com/news/article/O,aid,115084, OO.asg, as available via the Internet and printed Dec. 1, 2004. |
PictureDot, “CactusVision WebCam from PictureDot.com-Broadcast your live webcam now, FREE!” web page at http://www.picturedot.com/CactusVision WebCam Info.asp, as available via the Internet and printed Dec. 1, 2004. |
SAE International, “Why should I register to use the SAE website?” web page at http://my.sae.org/whyregister.htm, as available via the Internet and printed Dec. 1, 2004. |
Theme your Desktop, “Free Webcam Thumbnails on your Desktop—ANY webcam.com,” web page at http://themes.anywebcam.com/desktop/desktop.html, as available via the Internet and printed Dec. 1, 2004. |
Westlaw, “WestClip”, 2004, web page at <URL:http://west.thomson.com/westlaw/westclip>, as available via the Internet and printed Jul. 28, 2004, 3 pgs. |
Yahoo!, “Introducing RSS Headlines,” web page at http://e.my.yahoo.com/config/promo—content?.module=content, as available via the Internet and printed Jun. 18, 2004. |
Yahoo!, “Yahoo! Chat,” web page at http://chat.yahoo.com/, as available via the Internet and printed Dec. 1, 2004. |
Yahoo!, “Yahoo! Help-Yahoo! GeoCities Tour,” web page at http://help.yahoo.com/help/us/geo/tour/tour-O1.html, as available via the Internet and printed Dec. 1, 2004. |
Yahoo! Groups, “Customize LostDrive-In,” web page at http://groups.yahoo.com/group/lostdrivein/conwiz, as available via the Internet and printed Jun. 2, 2004. |
Lance, G.N. et al., “Computer programs for hierarchical polythetic classification (similarity analyses:),” The Computer Journal, C.S.I.R.O. Computing Research Section, 1967, vol. 9, pp. 60-64. |
Lance, G.N. et al., “A Generalized Sorting Strategy for Computer Classifications,” Nature, Oct. 8, 1966, vol. 212, p. 218. |
Lance, G.N., et al., “A General Theory of Classificatory Sorting Strategies 1. Hierarchical Systems,” The Computer Journal, C.S.I.R.O. Computing Research Section, 1967, vol. 9, No. 4, pp. 373-380. |
Lance, G.N., et al., “Mixed-data classificatory programs. I. Agglomerative Systems,” Austral. Comput. J., 1967, vol. 1, pp. 15-20. |
Milligan, G., “Ultrametric Hierarchical Clustering Algorithms,” Psychometrika, Sep. 1979, vol. 44, No. 3, pp. 343-346. |
Tan, P.N., et al., “Cluster Analysis: Basic Concepts and Algorithms,” Introduction to Data Mining, Chapter 8 (Section 8.3.3—The Lance-Williams Formula for Cluster Proximity, p. 524), 2006, pp. 487-568. |
Tribe.net, “Listings Directory.” web page at http://www.tribe.netltribe/servlet|template/pub,Listings.vm, as available via the Internet and printed Jun. 28, 2004. |
Petersen's Photographic, “My Photos at Photographic,” web page at http://myphotos.photographic.com/, as available via the Internet and printed Dec. 1, 2004. |
Examination Report for European Patent Application No. 05706067.5, dated Jun. 17, 2010, 5 pages. |
Number | Date | Country | |
---|---|---|---|
20130227002 A1 | Aug 2013 | US |
Number | Date | Country | |
---|---|---|---|
60538035 | Jan 2004 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 13195790 | Aug 2011 | US |
Child | 13850790 | US | |
Parent | 10928654 | Aug 2004 | US |
Child | 13195790 | US |