Device Mapping Accuracy Scoring with Media Viewing Data
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Solution Overview
Problem
Existing device mapping systems struggle with assessing the accuracy of device associations, leading to potential inaccuracies in categorizing devices based on media content playback.
Innovation Solution
A method and system that utilizes automated content recognition (ACR) to process media content, determine viewing behaviors, and calculate an accuracy score by comparing device categories to viewing data, using statistical tests to evaluate the randomness of device mappings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device mapping systems assign devices to categories based on assumed associations, then device categorization can be performed, but the accuracy of these mappings cannot be reliably assessed
Solution Approach 1:
The patent introduces media content viewing data as an intermediary indicator to assess device mapping accuracy. Instead of directly measuring mapping accuracy, the system uses ACR-generated viewing behavior data as a proxy metric. Devices in the same category should exhibit similar viewing patterns, providing an indirect but measurable way to evaluate mapping quality without requiring complex direct accuracy measurement mechanisms.
2Reliability
If device mappings are created without verification, then device categorization can be quickly established, but the mappings may be random or inaccurate
Solution Approach 1:
The system performs self-verification by automatically collecting media content viewing data from devices and using this data to assess whether devices within the same category exhibit consistent viewing behaviors. The device mapping system validates its own accuracy through automated content recognition and statistical analysis of viewing patterns, eliminating the need for external manual verification while ensuring mapping reliability.
Solution Approach 2:
The patent implements a feedback mechanism where device mapping accuracy is continuously assessed based on viewing behavior data. The system collects viewing data, compares it against expected patterns for devices in the same category, and uses this feedback to identify and correct inaccurate mappings. This closed-loop feedback ensures mappings remain reliable without requiring continuous manual intervention.
3Measurement precision
If detailed viewing data collection is implemented to verify device mappings, then mapping accuracy can be improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential viewing behavior data needed for accuracy assessment using automated content recognition. Instead of collecting and processing all possible device data, the system selectively extracts media content viewing information through ACR technology. This extraction approach captures the critical viewing patterns necessary for evaluating device mappings while avoiding the complexity of processing comprehensive device datasets.
Data Source
AI summary
Provided are methods, devices, and computer-program products for determining an accuracy score for a device mapping system. In some examples, the accuracy score can be based on a device map of the device mapping system and viewing data from an automated content recognition component. In such examples, the accuracy score can indicate whether the device mapping system is assigning similar categories to devices that have similar player of media content. In some examples, a device map can be determined to be random, indicating that the device mapping system is inaccurate. In contrast, if the device map is determined to have a sufficiently low probability of being merely random in nature, the device mapping system can be determined to be accurate.