AOT Tuning Data Classification Using AV Operating State Correlation
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Solution Overview
Problem
The increasing complexity of audio-visual devices in households complicates media rating accuracy by generating significant All-Other-Tuning (AOT) data, which dilutes the accuracy of media consumption metrics.
Innovation Solution
Utilizing information from AV network protocols like HDMI-CEC to identify operating states of AV devices, performing forward and backward passes on reference databases to classify AOT data, and reducing it by correlating with identified media data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AV devices are monitored to determine media consumption, then media ratings accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary system consisting of a meter and central facility that mediates between AV devices and media rating calculations. The meter collects operating state data from AV devices through standardized protocols (HDMI-CEC, SCART) and transmits it to the central facility for processing, separating the complexity of device monitoring from the media rating calculation process.
Solution Approach 2:
The monitoring system is segmented into distinct functional components: AV devices generate operating state data, the meter collects and transmits this data, and the central facility performs classification and media rating calculations. This segmentation allows each component to be optimized independently while working together to achieve accurate media ratings despite AV device complexity.
2Quantity of substance
If AOT data is collected to capture all viewing activity, then completeness of viewing data is improved, but accuracy of media ratings deteriorates
Solution Approach 1:
The system applies different quality standards to different types of viewing data. AOT data is collected with basic completeness (device was on and tuned), but then differentiated from identified media content through local quality filtering. The classification process assigns different weights and processing levels to AOT data versus identified media data, allowing both to be captured while maintaining rating accuracy through selective processing.
Solution Approach 2:
The central facility receives AOT data from the meter and provides feedback through the classification process. By analyzing operating state characteristics and comparing them against reference databases, the system identifies which AOT data represents actual media consumption versus idle device state, creating a feedback loop that refines the media ratings calculation.
3Measurement precision
If operating state data is queried to differentiate active devices, then measurement precision is improved, but data processing complexity increases
Solution Approach 1:
The meter performs preliminary action by collecting and storing operating state data from AV devices in advance, organizing it in a structured format with timestamps and device identifiers. This pre-processing at the meter level reduces the complexity of data processing at the central facility, as the raw data is already organized and ready for classification rather than requiring comprehensive collection and organization at the central facility.
Solution Approach 2:
The system transforms operating state data into different parameter representations for analysis. Instead of processing raw device signals, the system converts them into standardized operating state characteristics (power status, tuning information, device identifiers) that can be efficiently processed and compared against reference databases to determine media consumption patterns.
Data Source
AI summary
An example apparatus includes a record updater to initialize a variable to be equal to a first time of a first record classified as AOT data or to be equal to a last time of a last record classified as AOT data, an operating state identifier to access a first channel of the first record and last channel of the last record, a comparator to compare the first channel with a second channel or a third channel with the last channel, the second channel corresponds to a previously classified record of the first record and the third channel corresponds to a previously classified record of the last record, and a duplicator to, in response to the first channel matching the second channel, or the last channel matching the third channel, create a replica of the second channel or the third channel to store in place of the AOT data.