The current invention relates to a computer-generated method for optimizing placement of advertising content across multiple different devices. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.

An improved architecture including system and methods for online advertising placement that provide possibly defaulting advertisement tags the opportunity to serve an advertisement ahead of a lower value tag that is guaranteed to fill, resulting in higher CPMs (i.e., Cost Per Mille) for web publishers. The system and methods are configured to deterministically render an advertisement impression from a list of possibly defaulting advertisements in a JavaScript-enabled web browser. The knowledge of the complete outcome of such an “ad chain” at render-time significantly reduces complexity and latency in the supporting ad server. The system and method centers around a novel JavaScript approach to detect when an advertisement has been loaded but not defaulted. Additionally, the system and methods integrate the network and RTB demand channels by looking at all demand sources simultaneously and selecting the buyer from within the user's browser, and address predictive pricing to further enhance the online advertising placement process.

Original concepts obtained from a query may be augmented with additional concepts connected to the original concepts in a concept graph in response to determining that the original concepts did not match a sufficient number of bid functions. The augmented set of concepts may then be evaluated with respect to the bid functions to identify matching ad functions. This process may be repeated until a sufficient number of matching ad functions are found. A bid amount of the matching bid functions may be calculated, such as based on semantic information obtained as a result of the query. The bid amounts may further be based on environmental information. A bid function is selected based on the bid amounts and the content associated with the bid function is provided to the source of the query. The content may be selected based on the semantic information.

The current invention relates to a computer-generated method for optimizing placement of advertising content to consumers' TV's using a programmatic TV bidding model. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.

Systems, devices, and methods are disclosed for predicting potential effectiveness of query-triggered internet advertisements received from different web page publishers using a deep learning neural network language model for clustering queries, and for automatically adjusting bids for advertisements by advertisers based on the predicted potential effectiveness. Using query-clusters rather than queries for adjusting bids for advertisements allows for more accurate and more consistent bidding strategy despite of sparsity in historical advertisement performance data, higher return on investments for the advertisers, and higher revenue for the publishers of the advertisements.

The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A decoupled machine learning workflow generation request is obtained. A set of decoupled tasks specified via the decoupled machine learning workflow generation request is determined, wherein each decoupled task in the set of decoupled tasks is associated with a corresponding class. Dependencies among decoupled tasks in the set of decoupled tasks are determined. A decoupled machine learning workflow structure comprising the set of decoupled tasks and the determined dependencies is generated, wherein the decoupled machine learning workflow structure is executable via a decoupled machine learning workflow controller to produce machine learning results.

A media presentation and distribution system is communicatively coupled to a client device, which handles presentation of overlay-graphic items at the client device. The client device receives a media stream from the media presentation and distribution system via a network. The received media stream includes media content and one or more tags. The client device further identifies in the received media content, the one or more tags, which corresponds to the overlay-graphic items. The client device further identifies candidate time-periods in the media content based on the identified one or more tags in the media content. At least one presentation attribute for the overlay-graphic items is identified based on the identified one or more tags in the media content. The client device further presents the overlay-graphic items at the candidate time-periods om the media content based on the identified at least presentation attribute.

Provided herein are systems, methods and devices for classifying nested content execution loaded by a webpage or an application executed by a client device, comprising a client device executing a webpage or an application loaded from a content server which embed nesting element(s) used for loading nested content from nested content server(s). The webpage/application embeds a host monitoring code executed to collect session data indicative of execution session of the webpage/application including execution of nested content loaded using the nesting element(s), transmitting a signature of the session data to server(s) configured to classify the execution according to at least part of the session data extracted from the signature, and transmitting transaction indicator(s) of execution of the nested content to one or more providers of the nested content which may verify execution of the nested content in the context of the webpage/application based on the classification obtained from the server(s).

Systems, methods, and computer-readable media (transitory and non-transitory) are provided herein for automated submission for solicited application slots. In various embodiments, a digital component source process executing on a first computing system may determine a device identifier associated with an application slot to be populated with digital component(s). The application slot may be solicited by a digital component liaison process executing on the first computing system or a second computing system. The digital component source process may retrieve application slot attainment parameter(s) associated with the device identifier. The application slot attainment parameter(s) may be generated based on location ordinal(s) associated with the device identifier. The digital component source process may determine, based on the retrieved application slot attainment parameters, a submission to populate the application slot with a particular digital component item. The digital component source process may provide the submission to the digital component liaison process.

A personalized advertisement and checkout system and method for generating personalized merchant advertisements to specific device users includes a communications network, an advertisement system, at least one merchant administrator operable by a respective merchant user, at least one partner system and at least one user device operable by a respective device user. Each of the advertisement system, the merchant administrator, the partner system and the user device includes a processor and a memory in communication with the processor. Each of the merchant administrator and the user device includes a display and a user interface, the user interface of the merchant administrator receiving input from the respective merchant user and the user interface of the user device receiving input from the respective device user. The advertisement system ranks merchant campaigns entered by the merchant users and generates a list of merchant offer advertisements that is displayed on the user device.