Abstract: The disclosed systems and methods provide a cloud-based framework for the creation and/or enhancement of high quality media content for dissemination over a computerized network. The disclosed framework is configured to operate in accordance with at least one of a content creation, content enhancement and/or rendering template. Such templates can function as rules engines that call specific types of ML or AI algorithms or routines that modify the input data according to the type of template being used. Thus, the disclosed framework can be utilized for creating new high-quality media, improving upon existing media and/or reformatting media for rendering upon dissemination.

Abstract: The disclosed systems and methods provide a novel framework that enables cost-effective, accurate and scalable detection and recognition of key events in sporting or live events. The framework functions by creating a domain-specific video dataset with frame level annotations (i.e., deep domain datasets) and then training a lightweight camera view classifier to detect camera views for a given video. The disclosed framework uses pre-trained pose estimation and panoptic segmentation models along with geometric rules as labeling functions to define scene types and derive frame level classification training data. According to some embodiments, disclosed frameworks may be used to identify key persons or events, select a thumbnail corresponding to a key person or event, generate personalized highlights to enhance user experience and social media promotions for a team, sport or players, and predict and select the best camera view sequence for automatic highlights generation.

Abstract: One or more computing devices, systems, and/or methods are provided. A request for content associated with a device and/or a set of request information associated with the request for content may be received. A content item may be transmitted to the device. A set of client information associated with the device may be received. The set of client information may be analyzed to determine a fraudulence label associated with the request for content. Fraud detection information generated based upon the set of request information, the set of client information and/or the fraudulence label may be stored in a fraud detection database. A second request for content associated with a second device and/or a second set of request information associated with the second request for content may be received. A second fraudulence label may be determined based upon the second set of request information and/or the fraud detection database.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: Disclosed are methods and devices for converting mobile notifications to content using a local content exchange. In one embodiment, the method comprises receiving a request for content from a mobile application; querying a data store of available content, the data store of available content populated with one or more notifications received by other mobile applications installed on the mobile device; identifying a responsive notification for the request for content based on the querying, the content item identified based on bid parameters associated with the one or more notifications; and transmitting the responsive notification to the mobile application.

Abstract: The present teaching relates to tracking an event at a plurality of distributed servers. In one example, an event to be tracked is determined. A user associated with the event is identified. A script is generated to be embedded in a web page. The script triggers an event message when the user performs an online behavior related to the web page in accordance with the event. The event message triggered by the script is received. A tracing flag is determined from the event message. An instruction is provided to the plurality of distributed servers for executing one or more applications based on the event and the tracing flag.

Abstract: One or more computing devices, systems, and/or methods for determining whether requests for content are fraudulent are provided. A request for content may be received from a first device. A first user profile associated with the first device may be identified. The first user profile may comprise activity information associated with the first device, demographic information associated with the first device and/or interest information associated with the first device. A user profile database may be analyzed to identify a set of user profiles similar to the first user profile. A relevance score associated with the request for content may be generated based upon the resource, the set of user profiles and/or the first user profile. The relevance score may be compared with a threshold relevance to determine whether the request for content is fraudulent.

Abstract: Methods, systems, and computer-readable media are disclosed for utilizing unused network capacity for prefetch requests. One method includes: receiving, over a network, network traffic information from a network provider of the network; determining a threshold value for prefetch request fulfillment based on the received network traffic information; receiving, over the network, a plurality of prefetch requests from an application running on a mobile device connected to the network of the network provider; determining, for each prefetch request of the plurality of prefetch requests, a score for the prefetch request based on the received plurality of prefetch requests; and responding to, for each prefetch request of the plurality of prefetch requests, the prefetch request based on the determined threshold value and the determined score for the prefetch request.

Abstract: One or more computing devices, systems, and/or methods are provided. Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.

Abstract: Systems, apparatuses, and methods are provided for determining a bid value for placing an advertisement onto advertising space available through an electronic marketplace. A method is used for calculating the option value of maintaining the advertisement in the advertising space during one or more periods of time. The option value may be based on expected profits and the estimated future value of maintaining the advertisement. The option value may then be used to calculate the bid price for placing the advertisement.

Abstract: Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Abstract: The present disclosure relates to systems and methods customizing electronic communications. A future event associated with a first user may be determined, and a second user that is associated with the first user may be identified. A plurality of communications involving the first user and the second user may be analyzed. A selection rule may be applied based on the analyzed plurality of communications, the selection rule identifying content from the database. Content from the database may be selected based on the application of the selection rule. An electronic message may be provided to the first user identifying the future event, and the selected content may be provided to the first user.

Abstract: Systems and methods are for identifying online advertisements to display to a user based on historical user data collected across the user's use of a plurality of Internet devices. One method includes receiving, over a network, a request for an advertisement to display at a first device of the user, the request including a unique identifier stored on the user's first device; accessing, in a database, a demographic or browsing history information generated from the user's use of a second device, the demographic or browsing history information being stored in the database in relation to the unique identifier; and identifying, based on the demographic or browsing history information, an advertisement to display at the user's first device. The demographic or browsing history information is synchronized based on the unique identifier being stored on the user's first device and the user's second device.

Abstract: Systems and methods are disclosed for protecting consumer privacy in an online advertising environment. A request may be received from a browser for a webpage along with a unique browser identifier. The browser may be provided a first portion of the webpage that is locally available. The unique browser identifier may be provided to at least one advertising entity, wherein the advertising entity determines an advertisement based, at least in part, on the unique browser identifier. The advertisement may be received from the advertising entity, and provided to the browser as a second portion of the webpage.

Abstract: Systems, apparatuses, and methods are provided for determining a bid value for placing an advertisement onto advertising space available through an electronic marketplace. A method is used for calculating the option value of maintaining the advertisement in the advertising space during one or more periods of time. The option value may be based on expected profits and the estimated future value of maintaining the advertisement. The option value may then be used to calculate the bid price for placing the advertisement.

Abstract: In an example, a first content item is provided for display on a first client device. A first feedback signal is received. The first feedback signal is indicative of one or more first user reactions to display of the first content item on the first client device. The first content item is provided for display on a second client device. A second feedback signal is received. A first popularity score associated with the first content item is determined based upon the first feedback signal and the second feedback signal. A first popularity label is assigned to the first content item based upon the first popularity score. An enhanced content presentation interface including the first content item is generated based upon the first popularity label assigned to the first content item. The enhanced content presentation interface including the first content item is presented on a third client device.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for performing automatic detection of surfaces in video frames resulting in the creation of a seamless in-video ad experience for viewing users. The disclosed framework operates by leveraging available surfaces in videos to show advertisements in compliance with publisher protection, compliance and policy in a fully automatic, end-to-end solution. The disclosed framework evidences a streamlined, automatic and computationally efficient process(es) that modifies digital content at the surface level within the frames of the content in compliance with the digital rights of the owners of the content being merged via the disclosed augmentation.

Abstract: Systems, methods, and computer-readable media are disclosed for augmenting real-time bidding data with proprietary data. One method includes: receiving, at a server over an electronic communications network from a real-time impression bidder, a bid request or a request to augment a bid request with proprietary data; accessing, by the server from an internal database, proprietary data of a data augmenting service based on a user identifier of the bid request; determining, by the server, proprietary data to include in an augmented bid request based on at least one of the received bid request and the user identifier; formatting, by the server, the augmented bid request into a standardized, augmented bid request; and transmitting, by the server over the electronic communications network, the standardized, augmented bid request to the real-time impression bidder.

Abstract: Systems and methods are disclosed for processing electronic content, such as text, videos, and images. According to certain embodiments, user interactions with electronic content may be tracked over a plurality of modalities, such as web pages, email, mobile applications, and social media. The tracked user interactions may include copy/paste events, explicit user highlighting, social sharing, and user voting. Key passages of electronic content may be identified based on the tracked user interactions and ranked against one another. Ranking of passages may be based, for example, on a raw or normalized score for the identified key passages. Alternatively, the ranking of a passage may be based on a ratio of user interactions with the passage to total views of the electronic text containing the passage. One or more of the identified key passages (e.g., the highest ranked passages) may be published to one or more applications.

Abstract: The present teaching generally relates to identifying data bucket overlap with online experiments. In a non-limiting embodiment, first data representing a first set of identifiers associated with a first data bucket of a first online experiment may be obtained. Second data representing a second set of identifiers associated with a second data bucket of the first online experiment may be obtained. Based on the first data and the second data, a first number of identifiers that are associated with the first data bucket and the second data bucket may be determined. In response to determining that the first number exceeds a threshold, a data flag indicating that results associated with the first online experiment are inconsistent may be generated.