ACR Tuning Data Editing for Conflicting View Credits
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Solution Overview
Problem
Existing automated content recognition (ACR) technologies for collecting tuning data suffer from inaccuracies such as illogical, overlapping, and conflicting view/credit start/end times, leading to incorrect audience measurement and increased processing time due to unclean data.
Innovation Solution
A computing device applies a set of tuning data editing rules to standardize, clean, and edit ACR-collected tuning data, resolving view and credit conflicts, and assigning geographic locations to improve data accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If ACR technologies are used to collect tuning data automatically, then user input is not required and data collection is automated, but data inaccuracies such as illogical, overlapping, and conflicting view/credit start/end times occur
Solution Approach 1:
The patent applies editing rules in advance to preprocess ACR tuning data before further processing. The system automatically detects and corrects illogical, overlapping, and conflicting view/credit start/end times by applying a set of predefined editing rules that resolve data inconsistencies proactively, ensuring data accuracy is maintained throughout the processing pipeline.
2Productivity
If ACR-collected tuning data is processed without editing, then processing speed is maintained, but audience measurement accuracy deteriorates due to data inconsistencies
Solution Approach 1:
The system performs preliminary editing of ACR tuning data by applying a comprehensive set of editing rules that resolve view/credit start/end time conflicts, illogical time sequences, and overlapping data. This preprocessing step ensures that subsequent audience measurement processing operates on clean, consistent data, maintaining both speed and accuracy.
3Quantity of substance
If tuning data with conflicts is processed, then all available data is utilized, but processing time increases due to the need to resolve inconsistencies
Solution Approach 1:
The patent implements a preliminary data editing phase that automatically resolves conflicts in tuning data before main processing begins. By applying editing rules to pre-process the data and eliminate overlapping view/credit times, illogical sequences, and other inconsistencies, the system reduces the computational burden during subsequent processing steps, thereby decreasing overall processing time while maintaining data completeness.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for editing tuning data collected via automated content recognition. Examples include determining whether a time conflict exists between first tuning data corresponding to a first tuning event and second tuning data corresponding to a second tuning event. Examples also include that, in response to determining that the time conflict exists, creating a third tuning event based on the first tuning data, the second tuning data, and one or more criteria. Examples also include that modifying at least one of the first tuning event or the second tuning event based on the third tuning event. Examples also include that crediting a media presentation by the presentation device based on edited tuning data, the edited tuning data including the first modified tuning event, the second modified tuning event, and the third tuning event.