Ad Pod Detection via Viewer Drop Analysis
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Solution Overview
Problem
Traditional methods fail to accurately determine the number of viewers for non-scheduled content, such as live programming and content accessed through digital video recorders, due to unpredictable schedules and flexible viewing options, making it difficult for content providers to assess viewership and advertisement effectiveness.
Innovation Solution
A system and method that analyzes tune data from various devices, including set-top boxes and digital video recorders, to track viewer behavior and detect ad pods by aggregating data across multiple platforms, identifying significant viewer drops to determine ad pod presence and generate reports for advertisers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional viewing data analysis methods are used for non-scheduled content, then data collection is simple, but measurement precision of viewership and advertisement effectiveness deteriorates
Solution Approach 1:
The patent segments the viewing data analysis process into distinct components: tune data collection from multiple devices, ad pod detection through viewer drop analysis, and separate reporting mechanisms. This segmentation allows the system to handle non-scheduled content by breaking down the complex measurement task into manageable segments that can be processed independently, improving measurement precision without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes tune data between collection and final reporting. This intermediary component detects ad pods by analyzing viewer retention patterns and generates standardized reports that bridge the gap between raw data and actionable insights, enabling accurate measurement of non-scheduled content without requiring direct complex processing at every system level
2Adaptability or versatility
If flexible programming and unpredictable schedules are implemented, then adaptability of content delivery improves, but reliability of viewership measurement deteriorates
Solution Approach 1:
The patent employs dynamic analysis methods that adapt to unpredictable schedules by continuously monitoring viewer retention patterns in real-time. Instead of relying on fixed scheduled time slots, the system dynamically detects ad pod boundaries based on actual viewer behavior changes, allowing reliable measurement regardless of programming flexibility or schedule variations
Solution Approach 2:
The system implements feedback mechanisms where detected ad pod information is used to refine subsequent viewership measurements. By continuously analyzing viewer retention data and comparing it against detected ad pod patterns, the system learns and adapts to different programming formats, maintaining measurement reliability across diverse and flexible content schedules
3Ease of operation
If digital video recorders and flexible viewing options are provided, then ease of operation for viewers improves, but difficulty of detecting and measuring viewership deteriorates
Solution Approach 1:
The patent enables self-service viewership measurement by analyzing tune data that is automatically generated by viewing devices themselves. The system leverages the devices' inherent ability to track tune events and viewer retention patterns, converting the devices' operational data into viewership measurements without requiring additional complex tracking hardware or manual intervention, thus maintaining ease of operation while enabling accurate measurement
Data Source
AI summary
Systems and methods for analyzing viewing behavior of users viewing video content and advertisements are provided. A system may periodically or continuously receive tune data reflecting the viewing behavior of users, analyze the viewing behavior to determine a location and time period of advertisement blocks in a viewed segment, and determine parameters reflecting a comparison between a number of viewers that viewed the advertisement blocks and a number of viewers that viewed non-advertised content. The system may also deliver a report of the analysis to advertisers, agencies, media sellers, or other parties that are interested in measuring the effectiveness of advertisements on users. The analysis by the system can be done over multiple different types of content-distribution platforms.