Abstract: Techniques are disclosed for an AdVM (Advertising Virtual Machine) system, modules, components and methods that provide multiple layers of ad security for end-users. AdVM browsers isolate, monitor and restrict ads in sandboxes. AdVM browsers are configurable to monitor, report abuse and restrict ad performance based on configurable parameters such as system usage, security, privacy, inadvertent clicks, required ad ratings, permissions (whitelisting) and denials (blacklisting). AdVM browser abuse reports are used to generate profiles, whitelists and blacklists for ads, advertisers and other ad participants, which AdVM browsers use to allow or deny ad performances. Publishers assist AdVM browsers with ad detection by declaring ads in content. Ad security is improved by participation of advertisers, ad networks and an ad quality authority in creating trusted or rated ads that can be selected and verified over untrusted or unrated ads.

Abstract: A method and system for selecting web advertisements to optimize revenue. The method includes signing a plurality of deals with one or more advertisers. An index of a plurality of web advertisements for the plurality of deals is created based on a plurality of parameters. A group of web advertisements is selected from the index for a group of deals based on corresponding index values. The group of web advertisements is displayed for the group of deals. A predetermined number of user sign-ups are obtained within a predefined time period from one or more users for each deal of the group of deals. Payment is received upon activation of each deal. The system includes one or more electronic devices, a communication interface, a memory that stores instructions, and a processor that is responsive to the instructions.

Abstract: The subject matter disclosed herein relates to utilizing one or more premium rules to associate a premium markup to a base bid for online advertising.

Abstract: A machine learning based automated online form-filling technique provides for automatically completing user input controls based on previously stored information. An associative parser is used to identify and associate characteristics related to form controls with the corresponding form controls. The characteristics of the user input controls are input into a machine learning based semantic inference engine that was trained for the purpose of identifying the type of information that is supposed to be input into various user input controls. The semantic inference engine operates to label the controls in a manner that describes the meaning of the control, i.e., the type of information that should be automatically input into the corresponding controls. Consequently, the user input controls can be automatically filled in with previously stored user profile information associated with the corresponding labels.