Patent application number | Description | Published |
20120166379 | CLUSTERING COOKIES FOR IDENTIFYING UNIQUE MOBILE DEVICES - Embodiments are directed towards clustering cookies for identifying unique mobile devices for associating activities over a network with a given mobile device. The cookies are clustered based on a Bayes Factor similarity model that is trained from cookie features of known mobile devices. The clusters may be used to determine the number of unique mobile devices that access a website. The clusters may also be used to provide targeted content to each unique mobile device. | 06-28-2012 |
20130097005 | ONLINE TECHNIQUES FOR SELLING GROUP COMBO COUPONS - Techniques for providing group discounts are described. A group discount package is configured by associating a plurality of different items with the package, associating a discount price with each item, and associating a threshold value with at least one item. One or more actions that have corresponding threshold values may also be associated with the package. The group discount package may be offered by enabling users to request to purchase items associated with the package. Each user may request to purchase one or more of the items associated with the package at the associated discount price. Furthermore, the users may be enabled to perform any actions associated with the package. A deal with the package is confirmed when each associated threshold value is met. | 04-18-2013 |
20130159227 | CLUSTERING COOKIES FOR IDENTIFYING UNIQUE MOBILE DEVICES - Embodiments are directed towards clustering cookies for identifying unique mobile devices for associating activities over a network with a given mobile device. The cookies are clustered based on a Bayes Factor similarity model that is trained from cookie features of known mobile devices. The clusters may be used to determine the number of unique mobile devices that access a website. The clusters may also be used to provide targeted content to each unique mobile device. | 06-20-2013 |
20140237597 | AUTOMATIC SIGNATURE GENERATION FOR MALICIOUS PDF FILES - In some embodiments, automatic signature generation for malicious PDF files includes: parsing a PDF file to extract script stream data embedded in the PDF file; determining whether the extracted script stream data within the PDF file is malicious; and automatically generating a signature for the PDF file. | 08-21-2014 |
20140358826 | SYSTEMS AND METHODS FOR CONTENT RESPONSE PREDICTION - Techniques for predicting a user response to content are described. According to various embodiments, a configuration file is accessed, where the configuration file includes a user-specification of raw data accessible via external data sources and raw data encoding rules. In some embodiments, the raw data includes raw member data associated with a particular member and raw content data associated with a particular content item. Thereafter, source modules encode the raw data from the external data sources into feature vectors, based on the raw data encoding rules. An assembler module assembles one or more of the feature vectors into an assembled feature vector, based on user-specified assembly rules included in the configuration file. A prediction module performs a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item. | 12-04-2014 |
20150088788 | SYSTEMS AND METHODS FOR CONTENT RESPONSE PREDICTION - Techniques for predicting a user response to content are described. According to various embodiments, a configuration file is accessed, where the configuration file includes a user-specification of raw data accessible via external data sources and raw data encoding rules. In some embodiments, the raw data includes raw member data associated with a particular member and raw content data associated with a particular content item. Thereafter, source modules encode the raw data from the external data sources into feature vectors, based on the raw data encoding rules. An assembler module assembles one or more of the feature vectors into an assembled feature vector, based on user-specified assembly rules included in the configuration file. A prediction module performs a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item. | 03-26-2015 |
20150278962 | SUBSET MULTI-OBJECTIVE OPTIMIZATION IN A SOCIAL NETWORK - This disclosure relates to systems and methods that include a member activity database including data indicative of interactions with content items on a social network by a population of users of the social network. A processor is configured to obtain an optimization criterion based on at least two constraints related to a performance of the social network, obtain, for a subset of the population of users, at least some of the data indicative of interactions with content items from the member activity database, determine, based on the at least some of the data as obtained, an operating condition for the social network that is estimated to meet the optimization criterion, and provide, to at least some of the user devices via the network interface, the social network based, at least in part, on the operating condition. | 10-01-2015 |
20160132781 | SYSTEMS AND METHODS FOR CONTENT RESPONSE PREDICTION - Techniques for predicting a user response to content are described. According to various embodiments, a configuration file is accessed, where the configuration file includes a user-specification of raw data accessible via external data sources and raw data encoding rules. In some embodiments, the raw data includes raw member data associated with a particular member and raw content data associated with a particular content item. Thereafter, source modules encode the raw data from the external data sources into feature vectors, based on the raw data encoding rules. An assembler module assembles one or more of the feature vectors into an assembled feature vector, based on user-specified assembly rules included in the configuration file. A prediction module performs a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item. | 05-12-2016 |