Programmatic Ad Platform Predicting TV Impressions

The patent introduces a platform that acts as an intermediary between advertisers and television programming asset sources. This platform uses machine learning models to process third-party data sets and generate predicted performance values, serving as a mediator that translates raw data into actionable advertising decisions. The platform includes components for data ingestion, model training, campaign generation, and performance tracking, all working together to bridge the gap between automated systems and accurate measurement.

The patent changes the parameters used for ad placement from traditional rating estimates to predicted performance values derived from multiple data sources. Instead of relying on single-parameter rating systems, the invention uses multi-dimensional parameters including demographic data, viewing behavior, and contextual relevance. The machine learning models transform these parameters into predictive metrics that guide automated ad buying decisions, allowing the system to optimize based on actual performance indicators rather than estimated ratings.

The patent performs preliminary actions by training machine learning models on historical data and third-party data sets before actual ad campaigns are executed. The system pre-processes data, establishes predictive relationships, and creates baseline performance models in advance. However, unlike traditional methods that lock in ad placements based on preliminary rating estimates, this system uses the preliminary modeling phase to create adaptive algorithms that can be applied to real-time or near-real-time data, allowing campaigns to be optimized closer to airtime without sacrificing planning efficiency.

The patent introduces dynamics into the ad campaign generation process by using machine learning models that can adapt and update predictions based on new data. Rather than static ad placements determined months in advance, the system dynamically adjusts predicted performance values as new information becomes available. The platform can re-evaluate campaign performance and redistribute ad inventory based on actual viewing data and changing market conditions, making the originally static planning process dynamic and responsive.

The patent creates a universal platform that performs multiple functions within a single system. The platform ingests data from various third-party sources, trains machine learning models, generates ad campaigns, distributes ads across multiple television programming asset sources, tracks performance, and provides reporting—all through one integrated system. This multi-functional approach consolidates what would otherwise require multiple separate tools and manual processes, improving productivity while managing complexity through unified architecture rather than disparate systems.

The patent implements self-service capabilities where the system automatically performs tasks that would traditionally require manual intervention. The machine learning models autonomously analyze data, generate predictions, and optimize ad placement decisions without constant human input. The platform can automatically reallocate ad inventory based on performance data, adjust bidding strategies, and generate reports without manual analysis. This self-service automation dramatically improves productivity by eliminating repetitive manual tasks while the modular architecture manages complexity through standardized interfaces and processes.