Video Optimizer Using Bayesian Probability for Content Relationships
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Solution Overview
Problem
Media networks face challenges in determining which media content drives viewership to other content, making it difficult to optimize advertising and content placement effectively.
Innovation Solution
A system comprising data storage and processing servers and reporting servers that analyze event data from media devices to determine relationships between viewed media content, using Bayesian probability to identify correlations and create a video optimizer for improving advertising strategies and content placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertisement campaign theories are used to design media content placement, then advertising strategies can be formulated based on general viewer preferences, but the system cannot accurately determine which specific content drives viewership to other content
Solution Approach 1:
The patent segments the overall viewership data into individual event records, each containing specific content identifiers and timestamps. This segmentation allows the system to analyze relationships between specific content pieces rather than treating viewership as aggregate data, enabling precise measurement of which content drives viewership to other content.
Solution Approach 2:
The system implements feedback by continuously collecting event data from media devices, analyzing the relationships between viewed content and subsequently viewed content, and using these insights to optimize advertising and content placement decisions. This closed-loop feedback mechanism enables the system to accurately determine content-driven viewership relationships over time.
2Productivity
If event data from media devices and server logs are collected and analyzed using Bayesian probability, then the system can accurately determine relationships between media content and drive optimized advertising strategies, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent creates a multi-functional system where the data storage and processing servers perform multiple functions: collecting event data from various media devices, storing diverse content identifiers and timestamps, analyzing relationships using Bayesian probability, and generating optimization insights for both advertising and content placement. This universal system consolidates multiple functions into a single platform, managing complexity through integration rather than separate specialized systems.
Solution Approach 2:
The system manages complexity by focusing on key parameters such as content identifiers, timestamps, and Bayesian probability calculations. By identifying and analyzing only the most relevant parameters from the vast amount of event data, the system achieves accurate relationship determination without requiring equally complex processing infrastructure for all possible data attributes.
Data Source
AI summary
There is provided a system and method for a video optimizer for determining relationships between events. The method comprises receiving a total number of events for a plurality of contents over a period of time, receiving a first number of events of a first content of the plurality of contents over the period of time, receiving a second number of events of the plurality of contents over the period of time, and determining a relationship between the first content and the second content based on the first number of events, the second number of events, and the total number of events. A second relationship may be determined by utilizing a first time of event by a user of the first content, a second time of event by the user of the second content, and a weighed correspondence.