Digital Content Selection Using Triangular Bernoulli Approximation
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Solution Overview
Problem
Existing methods for selecting digital content, such as advertisements, are inefficient due to the use of numerical integration for calculating valuations, which is computationally slow and unstable with large datasets and few successful results, leading to latency and high power consumption.
Innovation Solution
The system employs triangular approximation of the Bernoulli distribution to calculate valuations, clustering historical samples into homogenous groups based on device attributes, and selecting digital content from groups with similar attributes to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerical integration is used for calculating valuations, then measurement precision is improved, but productivity deteriorates due to computational slowness
Solution Approach 1:
The patent replaces the expensive, time-consuming numerical integration method with a cheap, fast triangular approximation method. The triangular approximation uses simple geometric calculations (area of triangle = 0.5 × base × height) instead of iterative numerical integration, providing sufficient valuation accuracy at a fraction of the computational cost and time.
Solution Approach 2:
The patent changes the calculation approach from numerical integration (complex, iterative) to triangular approximation (simple, direct). This parameter change in the mathematical method transforms the valuation calculation from a computationally intensive process to a lightweight operation that maintains acceptable precision while dramatically improving speed.
2Measurement precision
If numerical integration is used for calculating valuations, then measurement precision is improved, but use of energy worsens due to high power consumption
Solution Approach 1:
The patent substitutes the energy-intensive numerical integration process with the energy-efficient triangular approximation. The simple geometric calculation requires minimal computational resources and power, making it suitable for mobile and energy-constrained devices while maintaining adequate valuation precision.
Solution Approach 2:
The patent replaces the complex mechanical-like iterative process of numerical integration with a simple geometric calculation approach. The triangular approximation uses basic mathematical operations that consume significantly less energy compared to the iterative numerical methods, reducing power consumption for valuation calculations.
3Measurement precision
If numerical integration is used for calculating valuations, then measurement precision is improved, but loss of time worsens due to latency
Solution Approach 1:
The patent uses the triangular approximation as a lightweight, fast alternative to numerical integration. The valuation calculation is completed almost instantaneously using simple triangle area formulas, eliminating the latency associated with iterative numerical integration while providing sufficient accuracy for real-time advertising decisions.
Solution Approach 2:
The patent skips the time-consuming iterative steps of numerical integration and directly applies the triangular approximation formula. This allows the system to rush through the valuation calculation process quickly, providing timely results for real-time ad selection without sacrificing essential accuracy.
4Measurement precision
If clustering samples into homogenous groups is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the historical samples into homogenous groups based on device attributes (e.g., device type, operating system, screen resolution). This segmentation allows the system to select ads from groups with similar characteristics, improving selection accuracy while using simple attribute-based clustering that does not significantly increase system complexity.
Solution Approach 2:
The patent applies local quality by treating different device groups differently. Instead of using a single valuation approach for all devices, the system tailors the ad selection to specific device groups based on their attributes. This improves precision for each device type while maintaining overall system simplicity through attribute-based categorization.
Data Source
AI summary
A computer-implemented method for selecting a digital content, comprising: receiving a plurality of samples, each comprises a request having a plurality of values of a plurality of attributes, and associated with a success value for a Bernoulli distributed event having a campaign and a bid rate (BR) of the campaign; clustering the plurality of samples in a plurality of homogenous nodes according to respective plurality of values; identifying a group campaign with a highest valuation for each one of the plurality of nodes using triangular approximation of the Bernoulli distribution of events in the node; receiving a query from a device including a plurality of other values of the plurality of attributes; selecting one of the plurality of nodes; selecting a digital content of the group campaign with highest valuation identified for the selected node; and generating a response to the query including the selected content.