Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing application program interface calls.

In some examples, a system comprises at least one programmable processor; and a machine-readable medium having instructions stored thereon which, when executed by the at least one programmable processor, cause the at least one programmable processor to execute operations comprising: receiving a first request from at least one user device to execute an instance of an application; transmitting a graphical user interface (GUI) to the at least one user device to be rendered on a display of the at least one user device; receiving a second request, via the GUI, from the at least one user device, to deploy a digital advertisement, the second request including a set of platforms of a plurality of platforms of a multi-platform integration system, a set of settings, a set of parameters, and a set of allocation data; interfacing with each one of the platforms in the set of platforms; and integrating a digital advertisement directly with each one of the platforms in the set of platforms based on the set of settings, the set of parameters and the set of allocation data.

At an advertising server: adding tracking code to advertisements served by the advertising server, wherein the tracking code is configured to cause web browsers displaying the served advertisements to transmit their contents to a security server. At the security server: scanning the received advertisements to detect presence of malicious code, and storing results of the scanning in a database. At the advertising server: prior to serving a new advertisement that has won in RTB, querying the database for scan results associated with the new advertisement. When the scan results indicate a malicious advertisement, preventing a serving of the new advertisement. When the scan results indicate a safe advertisement, allowing a serving the new advertisement. When no scan results are available for the new advertisement, adding the tracking code to the new advertisement and serving it, such that its contents are scanned by the security server.

The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

A computerized method of content verification comprising using a server for receiving a first data from a host monitoring code embedded in a webpage or an application loaded from a content server and executed by a client device, the host monitoring code is executed by the client device during an execution of the webpage or the application which further embeds nesting element(s) for loading nested content from nested content server(s), the first data is indicative of the execution, receiving a second data indicative of the execution from a guest monitoring code embedded in the nested content, combining the first data and second data for compliance verification of the execution with one or more rules associated with the nested content and initiating action(s) according to the verification. Wherein the first data is not available to the guest monitoring code and the second data is not available to the host monitoring code.

Techniques for extracting features of entities and targets that can be applied in a set of applications, such as entity selection prediction, audience expansion, feed relevance, and job recommendation. In one technique, entity interaction data is stored that indicates, for each of multiple entities, one or more targets that are associated with items with which the entity interacted. Token association data is stored that indicates, for each of multiple tokens, one or more targets that are associated with the token. Then, using one or more machine learning techniques, entity embeddings and target embeddings are generated based on the entity interaction data and the token association data. Later, a request for content is received from a particular entity. Based on at least one entity embedding, a content item for the particular entity is identified. The content item is transferred over a computer network and presented to the particular entity.

Aspects of the subject disclosure may include, for example, storing, in a database, a decorating style preference of a user; receiving, from user equipment of the user, one or more images (and/or one or more 2D environment models and/or one or more 3D environment models) depicting an environment in which remodeling is desired; generating, via a machine learning process, a first model to present by the user equipment, the generating the first model being based upon the decorating style preference and the one or more images (and/or the one or more 2D environment models and/or the one or more 3D environment models), the first model comprising a first remodeling proposal for the environment; sending, to the user equipment, the first model, the sending of the first model facilitating display by the user equipment of a first depiction of the environment as proposed by the first remodeling proposal; receiving, from the user equipment, feedback information regarding the first remodeling proposal; generating, via the machine learning process, a second model to present by the user equipment, the generating the second model being based upon the decorating style preference, the one or more images (and/or the one or more 2D environment models and/or the one or more 3D environment models), and the feedback information, the second model comprising a second remodeling proposal for the environment; and sending, to the user equipment, the second model, the sending of the second model facilitating display by the user equipment of a second depiction of the environment as proposed by the second remodeling proposal. Other embodiments are disclosed.

The present disclosure is directed to methods and systems for determining competitive market values for an ad impression on an advertiser exchange. An engine executing on a device may receive a candidate set of inputs associated with ad impressions. The engine may determine competitive market values for an ad impression on an advertiser exchange. The engine may determine candidate clearing prices based on the candidate set of inputs and history of clearing prices on the advertiser exchange. The engine may generate, based on the candidate clearing prices, a competitive market value prediction for the ad impression on the advertiser exchange. The competitive market value prediction may comprise a distribution function of predicted clearing prices on the advertiser exchange. The engine may generate, based on the competitive market value prediction, a fair market value bid for the ad impression in the context of a specific ad campaign.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, from a client device, a first notification of an ad space from a seller, identifying ad conditions corresponding to an ad space inventory to which the ad space belongs comprising a first condition that identifies an allowed creative media type for the ad space inventory, sending a second notification requesting a bid on the ad space to bidders, each bidder representing a respective buyer, receiving bids from the bidders, each bid corresponding to a respective bid price, buyer, and a creative, determining that a first bid of the bids corresponds to a first creative having a media type different from the allowed creative media type, and identifying a preexisting arrangement between the buyer corresponding to the first bid and the seller and, based thereon, allowing the first creative to be served to the ad space.

Methods and apparatus for determining a unique audience exposed to media while reducing memory resources of a computing device are disclosed herein. An example apparatus includes means for logging a plurality of impressions based on impression requests from a plurality of client devices, the plurality of impressions corresponding to media accessed at the client devices; means for obtaining counts, the obtaining means to: obtain a count of demographic impressions logged by a database proprietor; and obtain a count of registered users of the database proprietor exposed to the media; and means for determining a unique audience size by: multiplying a count of the plurality of impressions by a square of the count of the registered users to generate a product; dividing the product by the count of the demographic impressions to generate a quotient; and determining the unique audience size based on a square root of the quotient.