Demographic Classification Engine for Media Content Targeting
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Solution Overview
Problem
Current systems lack an effective method to accurately target advertisements to specific demographic groups based on raw data from radio and television broadcasts, which is often in a form that does not facilitate precise ad targeting.
Innovation Solution
An advertising targeting system that includes a learning module and classification engine to infer demographic information from media content, identify relevant content items, and select appropriate advertisements for association with those items based on demographic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If broad audience broadcasting is used, then reach and coverage are improved, but advertising targeting precision deteriorates
Solution Approach 1:
The patent segments the broad audience into specific demographic groups by analyzing media content characteristics (topics, keywords, content themes) and matching them with corresponding demographic profiles. This allows advertisers to target specific segments (e.g., sports fans, news readers, entertainment viewers) rather than treating the audience as a homogeneous mass, thereby resolving the contradiction between broad reach and precise targeting.
Solution Approach 2:
The system changes the parameters of audience characterization from generic demographics to content-based demographic inferences. By analyzing specific content parameters (topics discussed, keywords present, program type) and mapping them to demographic parameters (age groups, gender, interests), the system enables precise targeting while maintaining broad broadcasting capability.
2Quantity of substance
If raw media content data is used directly, then data availability is improved, but demographic inference accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer that analyzes raw media content data through content analysis techniques (topic identification, keyword extraction, thematic classification) to generate structured demographic inferences. This intermediary process transforms unstructured raw data into actionable demographic insights, resolving the contradiction between data availability and inference accuracy.
Solution Approach 2:
The system performs preliminary content analysis on media broadcasts before advertising delivery, pre-processing the raw data to extract demographic-relevant features. By conducting this preliminary action of content classification and demographic mapping in advance, the system prepares accurate demographic information from raw data, enabling precise targeting without sacrificing data availability.
3Reliability
If content analysis is performed to identify demographics, then advertising relevance is improved, but processing complexity deteriorates
Solution Approach 1:
The patent extracts only the essential content features relevant to demographic inference (key topics, dominant keywords, program classification) rather than analyzing all aspects of media content. This selective extraction approach maintains advertising relevance by focusing on demographic-critical information while reducing processing complexity by ignoring irrelevant content details.
Data Source
AI summary
Methods, systems and computer program products for classifying incoming data and identifying relevant demographic information from the incoming data. The incoming data may include media content, and the media content may be classified to determine demographic information such as the audience of the media content associated with the media content related to a topic. A content item presented in another medium on the same topic also may be identified. Subsequently, appropriate advertising based on the demographic information to be associated with the content item may be provided.