Real-Time Viewership Anomaly Detection System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional ratings measurements for programs are inadequate in providing fine-grained insights into viewership anomalies, making it difficult for content creators to understand what content attracts or repulses audiences, and to adjust content in real-time to mitigate viewership losses.
Innovation Solution
A system that aggregates viewership data from multiple viewers on a second-by-second basis, using pattern recognition algorithms to identify anomalous events and provide real-time analysis, allowing content creators to modify content during broadcast to address unforeseen viewership changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ratings measurements are used, then program-level ratings can be delivered, but fine-grained insights into viewership anomalies cannot be obtained
Solution Approach 1:
The patent segments viewership data from program-level to minute-level and second-level granularity. The system divides aggregated viewership data into discrete time segments (minutes and seconds) to enable fine-grained analysis of viewership anomalies, allowing identification of specific moments when audiences tune away or show disengagement.
Solution Approach 2:
The patent adds temporal dimensionality to viewership measurement by implementing time-series analysis at multiple granularities (program-level, minute-level, second-level). This multi-dimensional temporal approach transforms traditional single-point ratings into continuous viewership trajectories, enabling detection of anomalies through pattern recognition across different time scales.
2Loss of time
If program-level ratings are delivered after broadcast, then traditional ratings can be provided, but real-time content adjustment cannot be made
Solution Approach 1:
The patent implements preliminary action by providing real-time viewership feedback during broadcast rather than after. The system processes viewership data continuously and delivers minute-level and second-level ratings while the program is still airing, enabling content creators to make adjustments during the broadcast to mitigate viewership losses.
Solution Approach 2:
The patent establishes a feedback loop where real-time viewership data is continuously monitored, analyzed for anomalies, and fed back to content creators during broadcast. This feedback mechanism enables dynamic content adjustment based on actual audience engagement patterns detected through pattern recognition algorithms.
3Measurement precision
If aggregated viewership data is analyzed at program level, then overall ratings can be determined, but specific anomalous events cannot be identified
Solution Approach 1:
The patent segments the analysis process into multiple levels: program-level overall ratings, minute-level intermediate analysis, and second-level detailed anomaly detection. This hierarchical segmentation allows the system to manage processing complexity by breaking down large datasets into manageable time segments that can be analyzed independently for anomalies.
Solution Approach 2:
The patent applies partial action by focusing computational resources on detecting anomalies only in segments where deviations from expected patterns occur, rather than uniformly analyzing every second of every program. The pattern recognition algorithms identify and flag only the anomalous segments for detailed examination.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a method of receiving, by a processing system including a processor, viewership data for a plurality of viewers watching a program; identifying, by the processing system, an anomalous event from the viewership data, wherein the viewership data includes data points per viewer logged on a per second basis, and wherein the processing system identifies the anomalous event using a pattern recognition algorithm; analyzing, by the processing system, the viewership data to determine a reason for the anomalous event; and providing, by the processing system, a user interface that presents a comparison of the anomalous event versus a standard and indicia for the reason for the anomalous event. Other embodiments are disclosed.