An example apparatus includes processor circuitry to: access first input data from meters, the meters to monitor media devices associated with a plurality of panelists, the first input data including media source data and panel data; reduce a dimensionality of the first input data to generate second input data of reduced dimensionality relative to the first input data, the dimensionality of the first input data to be reduced based on a prior probability of an audience rating associated with the plurality of panelists and an approximation of a dependency of the audience rating on at least one of the media source data and the panel data; and decode the second input data of reduced dimensionality to output a probability model parameter for a multivariate probability model, the multivariate probability model having dimensions corresponding to the first input data, the multivariate probability model to label census data.

A system is provided that determines reserve inventory units required for each promotional campaign. Based on one of input parameters to meet defined parameters for defined amount of inventory units for one or more specified durations until end of a specified upcoming time-frame, inventory units are allocated from defined amount of inventory units among each inventory utilization type. Incremental value of revenue from each inventory utilization type is optimized and ratings for previously allocated inventory units assigned to a promotion inventory utilization type is increased. Previously allocated inventory units are periodically adjusted and re-distributed among each inventory utilization type based on difference in demand value of an estimated inventory units forecasted for upcoming specified duration and actual value of the inventory units for current duration. Based on remaining inventory units and each inventory utilization type, schedule of a channel is communicated to a user device, via a network.

Techniques for joint optimization of user segments and delivery channels are described. In one aspect, a method, includes obtaining activity data from a user device associated with a user, selecting, using a selector of a machine learning model, a user segment for the user based on the activity data, mapping, using a mapping function of the machine learning model, activity data for the user segment to features defined by multiple media channels, each media channel assigned a resource component, generating, using an objective predictor of the machine learning model, an objective prediction for the user segment based on the features and resource components of the media channels, the objective prediction identifying a media channel from the multiple media channels with a composite scalar metric above a defined threshold, and providing content to the user device via the media channel. Other embodiments are described and claimed.

Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for demand-side platforms (DSPs) to effectively plan, launch, optimize and monitor the performance of content campaigns over a network on network resources. The disclosed framework operates to perform strategic and data-driven processes for DSP initiatives that can define campaign parameters, and in real-time, monitor the effectiveness of campaigns such that their modifications and/or alterations can be dynamically performed so as to adapt to the changing landscapes of how the campaign is being disseminated over a network and received by users. The framework can implement AI/ML and/or LLM models and functionality to provide DSPs with comprehensive tools for managing, curating and analyzing content related to content campaigns for optimal and accurate performance and impact.

A method, apparatus, and computer program product are disclosed to improve generation of electronic communications. The method may provide a plurality of content slots each configured to receive content, the content comprising at least one of promotion content or non-promotion content. The method may also include maintaining a database comprising a plurality of promotion content generators and non-promotion content generators, and determining, using a processor, one of the plurality of promotion content generators or non-promotion content generators for respectively supplying corresponding promotion content or non-promotion content to each of the plurality of content slots. The determining the one of the plurality of promotion content generators or non-promotion content generators may include determining selection parameters, and scoring the plurality of promotion content generators and non-promotion content generators based at least in part on the selection parameters.

Systems and methods for redeeming a reward held by an individual are described. A method for redeeming a reward includes determining threshold criteria for provision of a targeted reward redemption offer, identifying at least one individual based upon the threshold criteria, determining the targeted reward redemption offer, and providing the targeted reward redemption offer to the identified individual. A response to the provided targeted reward redemption offer may be received, and an account of the identified individual may be adjusted in accordance with the targeted reward redemption offer and the received response.

A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.

An example computing device includes: a processor; and a system memory, the system memory including instructions which, when executed by the processor, cause the computing device to: identify a financial account associated with debit card use; parse financial transactions associated with the financial account to identify debit card transactions; categorize the debit card transactions into categories based upon types of merchants; map the categories to incentives associated with a credit card; and present potential savings associated with the incentives.

The present disclosure provides a computer system (112). The computer system (112) performs a method for encoding user count with a low memory footprint. The method includes a first step of receiving real-time and adaptive frequency of user visibility. Further, the method includes another step of receiving a user device (106) id associated with one or more users (104). Furthermore, the method includes yet another step of encoding the user visibility count. The frequency of the user visibility is the number of times the computer system receives a request from a user device (106). The user device (106) id is a unique string of numbers and letters. The unique string of numbers and letters identifies the user device (106) associated with one or more user (104). The user visibility count is encoded by using one or more data structures and one or more algorithms.

Systems and methods for determining whether and how to present digital animations in a user interface are disclosed. According to certain embodiments, the systems and methods may facilitate the identification of a set of products or services purchased by an individual, and the determination of a reward level associated with the set of products or services. The systems and methods may select a digital animation, from a set of digital animations that is predetermined based on a set of probabilities, corresponding to the reward level, and present the digital animation in a user interface.