Media Name Normalization for Cross-Platform Content Matching
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Solution Overview
Problem
Inaccurate cross-platform audience measurement due to differing media content names across various platforms, leading to duplicated content labeling and inconsistent reporting of media consumption.
Innovation Solution
A system and method for media name matching and normalization that includes data extraction, cleaning, and pattern matching using fuzzy logic and machine learning to identify and standardize media content across platforms, enabling unduplicated total audience measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If media content is tracked across different platforms using their respective platform names, then platform-specific audience measurement is maintained, but cross-platform accuracy deteriorates due to name inconsistencies
Solution Approach 1:
The patent introduces a name normalization service as an intermediary component that mediates between different media platforms. This service receives media names from various platforms, normalizes them to a standard format, and enables consistent cross-platform matching. The intermediary resolves the contradiction by adding a dedicated layer that handles name inconsistencies without requiring changes to the underlying platform-specific systems.
Solution Approach 2:
The system performs preliminary name normalization and standardization before conducting audience measurement across platforms. By pre-processing media names to convert them into a consistent format, the system eliminates name inconsistencies prior to the measurement process, thereby improving cross-platform accuracy without complicating the core measurement functionality.
2Reliability
If media names are standardized across platforms, then cross-platform measurement accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing function into a separate name normalization service that operates independently from the audience measurement system. This segmentation allows the normalization logic to be isolated, tested, and maintained separately, reducing the complexity burden on the core measurement processing while ensuring reliable cross-platform data standardization.
3Measurement precision
If episode-level identification is implemented, then content duplication is reduced, but system complexity increases
Solution Approach 1:
The name normalization service is designed with multi-functionality to handle various media types and platforms universally. It can process different media names from television, streaming, cable, and other platforms, applying the same normalization logic across all inputs. This universal approach reduces system complexity by using a single versatile service rather than multiple specialized identification systems.
Data Source
AI summary
Methods and apparatus to facilitate matching of names for same media content are disclosed. Example methods include analyzing first data associated with first media content and, when a program name/identifier and/or episode name/identifier is not identified in the first data, supplementing the data with second data to form third data and processing the third data with respect to fourth data associated with second media content and calculating a composite match score including a program match score and an episode match score based on processing the third data with respect to the fourth data. When the first media content is determined to match the second media content based on the processing of the third data with respect to the fourth data and a comparison of the composite match score to a threshold is satisfied, a normalized media name is generated for the first media content and the second media content.