Unified Ad Selection API for Context-Based Delivery
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Solution Overview
Problem
Current advertising systems face challenges in selecting and delivering relevant advertisements to users across diverse media channels, as they often rely on multiple APIs for different types of advertisements and lack efficient methods for user context-based ad selection and performance tracking.
Innovation Solution
A data processing system that determines user context, retrieves candidate advertisements from databases, and selects and sends them to publishers using a unified API, incorporating user search data, historical performance data, and business rules to optimize ad relevance and delivery, while allowing for pay-per-call and subscription advertisements to be managed within a single platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple APIs are used for different types of advertisements, then advertisers can access diverse ad types, but the system complexity increases and unified management becomes difficult
Solution Approach 1:
The patent combines multiple advertisement types (search ads, display ads, video ads, app ads) into a single unified API structure. The advertising platform uses a common interface to retrieve, manage, and deliver all ad types, eliminating the need for separate APIs for each ad type and simplifying the overall system architecture while maintaining versatility.
Solution Approach 2:
The unified API is designed to handle multiple advertisement types through a single interface. The system can retrieve and manage search ads, display ads, video ads, and app ads using the same API calls, making the interface universal and adaptable to different ad formats without requiring type-specific implementation details.
2Measurement precision
If user context-based ad selection is implemented, then ad relevance improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user context data and ad criteria before actual ad retrieval. User profiles, historical data, and contextual information are analyzed in advance to establish selection criteria, so that during ad retrieval the system only needs to match pre-determined criteria rather than performing complex analysis in real-time.
Solution Approach 2:
The system incorporates feedback mechanisms that use historical performance data to continuously refine ad selection algorithms. By analyzing past ad performance and user interactions, the system learns optimal selection criteria and can make faster, more accurate ad recommendations over time without requiring excessive computational resources for each individual ad retrieval operation.
3Productivity
If performance tracking is integrated into the unified platform, then revenue optimization improves, but the data processing complexity increases
Solution Approach 1:
The performance tracking system is segmented into modular components that handle different aspects of data processing independently. The system separates ad retrieval, performance monitoring, data analysis, and revenue optimization into distinct modules, making the overall data processing complexity manageable while maintaining the ability to optimize revenue across the entire advertising platform.
Data Source
AI summary
An advertising system determines the context of a user accessing a publication media (e.g., an online web site). The advertising system retrieves candidate advertisements from one or more databases based on the user's context (e.g., a user search request). The advertising system selects particular advertisements and then sends them to the user (e.g. for display on the user's terminal or device).