Content Series Interest Detection With Targeted Viewer Reengagement
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Solution Overview
Problem
Existing systems fail to effectively determine and address a viewer's reduction of interest in a content series, leading to potential loss of investment and cancellation of television programs.
Innovation Solution
A computer-implemented method using processing circuitry to monitor consumption patterns, identify changes indicative of reduced interest, and provide targeted interventions such as spoilers based on contextual information and machine learning models to reengage viewers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional viewership monitoring methods are used, then the system is simple to operate, but it cannot effectively detect reduction of interest in real-time
Solution Approach 1:
The patent segments the viewership data analysis into multiple dimensions: consumption frequency, consumption duration, consumption timing, and contextual factors. Each dimension is monitored separately and then integrated to form a comprehensive interest reduction detection system, enabling precise measurement without overwhelming system complexity
Solution Approach 2:
The system implements continuous feedback loops where consumption data is constantly monitored, patterns are analyzed, and adjustments are made to detection algorithms. This feedback mechanism enables real-time detection of interest reduction while maintaining system adaptability and precision
2Reliability
If no intervention is provided when interest reduces, then the system remains simple, but production investments are lost and programs may be canceled
Solution Approach 1:
The system performs preliminary actions by identifying interest reduction trends before they lead to program cancellation. By detecting changes in consumption patterns early and implementing interventions such as targeted content recommendations or viewer engagement campaigns, the system prevents negative outcomes while maintaining reasonable operational complexity
Solution Approach 2:
The system enables self-service mechanisms where the platform automatically responds to detected interest reduction through algorithmic content recommendations and personalized viewer experiences, reducing the need for manual intervention while ensuring program continuity
3Loss of information
If detailed consumption monitoring is implemented, then the reason for interest reduction can be identified, but the system requires more data processing resources
Solution Approach 1:
The patent extracts only the most relevant features from consumption data for analysis, such as key timing patterns, frequency changes, and contextual correlations. By selecting and extracting only essential information rather than processing all raw data, the system identifies reduction reasons efficiently while minimizing energy consumption
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on content type, viewer history, and contextual factors. By changing the granularity and depth of data collection according to specific conditions, the system maintains comprehensive information gathering while optimizing data processing energy usage
Data Source
AI summary
Systems and methods are provided herein for determining reduction of interest in a content series and to increasing the interest upon such determination. This may be accomplished by a device monitoring consumption of a content series to determine a pattern of consumption. The device may identify a change in the pattern of consumption indicative of a reduction of interest and determine a reason for the reduction in interest. Based on the reason for the reduction of interest, the device may provide an operation, such as a spoiler, to increase interest in the content series.