Ad Opportunity Vector Schema for Cross-Device Targeting
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Solution Overview
Problem
Traditional advertisement management systems lack the ability to effectively address users across multiple devices and channels due to limited data usage, leading to suboptimal decision-making and biases in media buying and selling processes.
Innovation Solution
A system that systematically collects and analyzes data from various sources to create comprehensive user profiles, combining media, location, and time-based information to describe ad opportunities, enabling real-time decision-making and predicting ad performance across devices and channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertisement management systems are used, then device complexity is reduced, but measurement precision of ad performance and user matching capability deteriorates
Solution Approach 1:
The patent segments ad performance measurement into multiple dimensions including user demographics, device characteristics, media channel attributes, and temporal factors. Each dimension is measured and analyzed separately using standardized schemas, allowing comprehensive precision without overwhelming system complexity through modular measurement components.
Solution Approach 2:
The patent introduces standardized data schemas and taxonomy frameworks as intermediary layers between raw ad performance data and analysis systems. These intermediaries structure and normalize data from multiple sources, enabling precise measurement while simplifying system integration through standardized interfaces.
2Loss of information
If comprehensive data collection from multiple sources is implemented, then information completeness for media buying improves, but loss of time in data processing increases
Solution Approach 1:
The patent implements preliminary data normalization and schema validation processes that structure data from multiple sources before analysis. By pre-processing and standardizing data formats in advance, the system achieves comprehensive information collection without excessive processing delays during critical media buying decisions.
Solution Approach 2:
The patent transforms raw data into standardized parameters and metrics that can be efficiently processed and compared across different data sources. By changing data representation to standardized schemas with predefined attributes, the system maintains information completeness while enabling faster processing through consistent data structures.
3Adaptability or versatility
If detailed user profiles across multiple devices are created, then adaptability of advertising strategies improves, but device complexity of data management increases
Solution Approach 1:
The patent creates universal user profiles that function across multiple devices and media channels using standardized identification schemas. These profiles serve multiple purposes including ad targeting, performance tracking, and strategy optimization, reducing data management complexity through multi-functional standardized structures rather than device-specific implementations.
Solution Approach 2:
The patent implements nested profile structures where user demographics, device characteristics, and behavioral data are organized in hierarchical layers. This nesting allows detailed adaptability at multiple levels while managing complexity through structured organization, with each layer building upon standardized foundations from previous layers.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing data and analysis for advertising on networked devices. One of the methods includes creating a vector of identifiers representing an ad opportunity. The method includes linking data attributes that describe the ad opportunity to the identifiers. The method includes expressing the data attributes following predefined scheme of hierarchy. The method includes linking a taxonomy describing data attributes. The method includes obtaining outcome measurements of ad events associated with the ad opportunity. The method also includes associating user interaction events with the ad with at least one of the identifiers or data attributes associated with the identifier.