Forecasting Digital Component Distribution Using Futurized Queries
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Solution Overview
Problem
Existing methods struggle to accurately forecast the distribution of digital components over networks in response to electronic requests, especially due to the dynamic nature of the Internet and the difficulty in modeling user behavior and content integration dynamics.
Innovation Solution
A forecasting system that processes historical serving logs to generate futurized queries, determining similarity with incoming requests and using reversible data structures to emulate serving system logic, allowing for efficient reuse and improved accuracy in predicting digital campaign reach and user targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional forecasting methods are used to predict digital component distribution, then the system can provide basic forecasting functionality, but the prediction accuracy is insufficient due to the dynamic nature of the Internet and difficulty in modeling user behavior
Solution Approach 1:
The patent creates a forecasting system that copies the serving system's data structures and logic. The forecasting system uses the same campaign data structures, retrieval library, and serving code organization as the serving system, allowing it to accurately model and predict digital component distribution without requiring separate complex modeling approaches. This copying approach enables the forecasting system to leverage existing proven data structures while maintaining accuracy in predicting user behavior and content integration dynamics.
2Ease of operation
If separate forecasting and serving systems are implemented, then forecasting functionality can be provided, but developer burden increases and maintenance becomes more difficult
Solution Approach 1:
The patent implements a universal system architecture where the serving code is organized to serve dual purposes: actual digital component serving and forecasting. The same campaign data structures, retrieval library, and code organization are used by both the serving system and forecasting system. This multi-functionality approach allows a single codebase to perform both serving and forecasting operations, reducing developer burden and simplifying maintenance while avoiding the complexity of maintaining separate systems.
3Productivity
If the serving code is organized with many global structures and reverse-indexes, then the serving system can efficiently manage digital campaigns, but the code cannot be effectively reused by the forecasting system
Solution Approach 1:
The patent segments the serving code into modular components that can be independently reused by the forecasting system. The campaign data structures are organized as discrete, reusable units that can be instantiated by both serving and forecasting operations. The retrieval library is structured as a separate, callable component that can be invoked by both systems. This segmentation allows the serving code to maintain its efficiency for digital campaign management while enabling the forecasting system to reuse the same data structures and logic without requiring reverse-indexes or complex global structures.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a content platform that receives a request to provide a digital component. The request includes information about users to which the digital component is directed. Futurized queries are obtained from serving logs of a serving system that is configured to execute an existing digital component using serving code that directs digital content to the set of users. The futurized queries are loaded as data structures in memory of a forecasting system. The system uses an instruction set derived from the serving code to determine that similarity between the particular futurized query and the request exceeds a threshold similarity. The system then generates a forecast output as a response to the request based on futurized queries that exceed the threshold similarity. The forecast output includes data describing future provision of the digital component.