Patents
12 Patents Issued
Building a social graph based on links received and selected by receiving users of the open web
Issued: Jun 10, 2014
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails.
Building a social graph using sharing activity of users of the open web by creating an edge representing category type
Issued: Nov 18, 2014
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. Building of the social graph can include creating an edge in the social graph that is representative of a particular category type.
Building a social graph based on links received and selected by recipients and using graph to personalize content for delivery
Issued: Aug 4, 2015
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. A technique can include receiving activity information when a recipient accesses a first link; when a first node representative of a sender of the first link is not identified in a social graph and after receiving the activity information, creating a second node to represent the sender in the social graph; and using to the social graph to personalize content for delivery.
Updating weights of edges of a social graph based on sharing activity of users of the open web
Issued: Aug 18, 2015
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. The system includes receiving activity information, identifying nodes in a social graph related to the activity information, and updating weights of edges based on the activity information.
Building a social graph based on links received and selected by recipients
Issued: Aug 25, 2015
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. A technique can include a sender sending a first link to at least one recipient. When a first node representative of the sender in social graph is not identified and after receiving the activity information, creating a second node to represent the sender in the social graph.
Building a social graph using sharing activity of users of the open web by identifying nodes in the social graph and adjusting weights associated with edges
Issued: Sep 15, 2015
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. Building of the social graph can include creating an edge in the social graph that is representative of a particular category type. This includes identifying nodes in a social graph corresponding to sharing activity and adjusting weights associated with edges.
Building a social graph using sharing activity of users of the open web by creating an edge representing a category type
Issued: Sep 29, 2015
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. For example, the sharing activity can include receiving first activity information for a sender of a message to a recipient by a collection resource at a Web site, the collection resource adding a link to the message, and receiving second activity information when the recipient accesses the link. Building of the social graph can include creating an edge in the social graph that is representative of a particular category type.
Building a social graph with sharing activity between users of the open web and inferring interest of a node based on edges between first-degree and second-degree nodes
Issued: Apr 19, 2016
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. Interest of a node can be inferred based on edges between first-degree and second-degree nodes.
Building a social graph using sharing activity of users of the open web by updating an edge representing a category type
Issued: Jul 12, 2016
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Personally identifiable information is not collected. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. For example, the sharing activity can include receiving first activity information for a sender of a message to a recipient by a collection resource at a Web site, the collection resource adding a link to the message, and receiving second activity information when the recipient accesses the link. Building of the social graph can include creating or updating an edge in the social graph that is representative of a particular category type.
Delivering personalized content based on a social graph of sharing activity of users of the open web
Issued: Aug 30, 2016
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Personally identifiable information is not collected. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. Building of the social graph can include creating an edge in the social graph that is representative of a particular category type. Based on the social graph, personalized digital content can be selected.
Using fingerprinting to identify a node in a social graph of sharing activity of users of the open web as representing a particular person
Issued: Oct 3, 2017
A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Personally identifiable information is not collected. The sharing activity can include receiving first activity information for a sender of a message to a recipient by a collection resource at a Web site, the collection resource adding a link to the message, and receiving second activity information when the recipient accesses the link. The first or second activity information can include a cookie, which can be used to identify a node in a social graph as being representative of a particular person or user. When a match is not found, a fingerprinting approach can be performed using attributes, such as device identifiers; IP addresses; operating systems; browsers types; browser versions; or user navigational, geo-temporal, or behavioral patterns.
User activity analysis using word clouds
Issued: Jun 25, 2019
A method, device, and system for determining attributes of at least one user from words of at least one document. A user profile vector based at least on the at least one document experienced by the at least one user is provided. At least one weight is determined for at least one word in the at least one document, the at least one weight being based at least on the time that the at least one document was experienced by the user and the frequency with which the user experienced the at least one document; and a word cloud associated with the user is generated, the word cloud including the at least one word from the at least one document if the corresponding at least one weight of the at least one word at least meets a threshold.