Described are systems and methods to determine advertisements to be presented to a user. To determine the advertisements to be presented to the user, the described systems and methods utilize localized contextual information to select the advertisements and the relative positioning of the advertisements to be presented, so as to select and present more relevant advertisements based on the content that is surrounding and proximate to the presentation of the selected advertisements.

The present disclosure in at least one embodiment provides a method of providing an advertisement disclosure for advertisement identification to a content that is displayed in a three-dimensional space by a visual interface, including detecting an advertisement object located within a user sight from the content, inserting the advertisement disclosure in an adjacent position to the advertisement object, and controlling the advertisement disclosure in position or orientation based on a user gaze upon detecting the user gaze.

Various methods, apparatuses/systems, and media for automating sponsored-search data pipelines are disclosed. A processor generates keyword-level metrics data based on received bidder input data that includes cost-per-acquisition (CPA) data and total spending data for each keyword; determines campaign-level CPA threshold data chosen at previous iteration of search campaign and a target CPA data used for current search campaign; calculates, campaign-level metrics data that includes the CPA data and adjusted total spending data; quantifies a final campaign-level reward data based on the calculated campaign-level metrics data, adjusted total spending data, and the target CPA data; updates a distribution corresponding to CPA-threshold data chosen at previous iteration using the final campaign-level reward data; samples CPA-threshold distributions and determines CPA-threshold data chosen at current iteration; executes campaign-level heuristics using the keyword-level metrics data, campaign-level metrics data, and the CPA-threshold data chosen at current iteration; and displaying final heuristic-execution data onto a GUI.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated management of campaigns using scripted rules.

A campaign management system manages campaign datasets, wherein a campaign dataset corresponds to selections and instructions for executing an online campaign. A campaign dataset might be created by a human or a machine, or multiple humans and computers in a crowd-sourcing approach. Campaign datasets can be processed to determine an expected quality metric, a post-run quality metric, and a confidence level. A campaign comparator can compare campaign datasets to identify similar campaign datasets so that an expected quality metric, a post-run quality metric, and/or a confidence level from one campaign dataset can be imputed to another campaign dataset.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

Various systems and methods for aggregating data from disparate sources to determine an optimal package of data items are disclosed. For example, the system described herein can obtain data items from various sources, aggregate and/or organize the data items into an optimal package based on various criteria, and present, via an interactive user interface, the optimal package. Furthermore, the interactive user interface may enable a user to adjust the criteria used to aggregate and/or organize the data items. The system may interactively re-aggregate and re-organize the data items using the adjusted criteria as the user interacts with the package via the user interface. The system and user interface may thus enable the user to optimize the packages of data items based on multiple factors quickly and efficiently.

A technique for dynamically adjusting a digital advertising campaign during an active campaign flight is discussed. Using feedback from a digital survey over an exposed audience of user populations, brand lift may be calculated on a per ad creative and/or per site basis. User characteristics derived from content consumption patterns may be used to optimize ongoing campaigns and formulate target audiences and target creative formats for new campaigns.

The present invention relates to methods for evaluating the effectiveness of all types of communication carried on offline communication media so as to generate data equivalent to the data obtained in online communication media. The present invention uses tangible tools such as fixed-line telephones, cellphones, computers, tablets and any other wearable mobile device to generate that data that will be used in media plans that are much more precise and efficient than the plans currently known. Finally, the present invention relates to said media plan obtained using one of said methods as well as the purchase of optimized media.

Methods, apparatus, systems, and articles of manufacture are disclosed to identify sponsored media in a Document Object Model. An example method includes determining if a first child node in a document object model is a sponsored media node, including to identify if the first child node is an element node, detecting if the first child node is of a valid element node type, and comparing the first child node to one or more geometric thresholds corresponding to the sponsored media node.