ACR Tuning Data Editing for View and Credit Time Conflicts
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Solution Overview
Problem
Existing automated content recognition (ACR) technologies face issues with unidentified and misidentified media, illogical or overlapping view/credit start/end times, and missing detailed content metadata, leading to inaccurate audience measurement and increased processing time and power consumption.
Innovation Solution
A computing device applies a set of tuning data editing rules to standardize, clean, and edit ACR-collected data, including view and credit conflict resolution, to ensure accurate media crediting and audience measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If ACR technology is used to automatically monitor and detect media being displayed, then user input is not required and media identification can be performed automatically, but the system produces unidentified and misidentified media, illogical or overlapping view/credit start/end times, and missing detailed content metadata
Solution Approach 1:
The patent introduces an intermediary processing system between the ACR automatic detection and the final media identification results. This intermediary applies editing rules to detect and correct errors in ACR data, such as misidentified media, illogical timestamps, and missing metadata, thereby maintaining automation while improving accuracy through a mediating validation layer
Solution Approach 2:
The system implements feedback mechanisms where ACR data is continuously monitored and validated against established editing rules. When discrepancies are detected (such as overlapping timestamps or misidentified content), the system provides feedback to correct these errors, enabling continuous improvement of identification accuracy while maintaining automated operation
2Productivity
If ACR data is collected without editing rules, then data collection is simple and fast, but the data contains errors requiring manual review and increasing processing time and power consumption
Solution Approach 1:
The patent applies editing rules as a preliminary automated action during the data collection process itself, rather than as a subsequent manual review step. This preliminary processing corrects errors in ACR data (such as illogical timestamps and misidentifications) at the source, preventing error propagation and eliminating the need for time-consuming manual review while maintaining high data collection efficiency
Solution Approach 2:
The system enables self-service by implementing automated editing rules that allow the ACR data collection system to correct its own errors without external intervention. The editing rules autonomously detect and fix issues such as overlapping view/credit timestamps and misidentified media, making the system self-correcting and eliminating the need for manual processing time
3Device complexity
If ACR data is collected without editing rules, then the collection process is simple, but the data contains illogical or overlapping view/credit start/end times leading to inaccurate audience measurement
Solution Approach 1:
The patent introduces an intermediary editing rule system that sits between simple ACR data collection and final audience measurement. This intermediary layer automatically detects and corrects illogical or overlapping timestamps in the collected data, ensuring measurement reliability without significantly increasing process complexity by using rule-based automated validation
Solution Approach 2:
The system replaces complex manual validation mechanisms with automated editing rules that use logical algorithms to detect and correct timestamp errors. This substitution maintains simplicity by using programmable rule-based systems rather than manual review processes, while simultaneously improving reliability through consistent automated validation of view/credit timestamps
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.