Media Content Analysis for Advertising Placement Accuracy
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Solution Overview
Problem
Current advertising methods fail to accurately determine correlations and causality between media programs and commercial content and consumer responsiveness, leading to inefficient placement of advertisements, as they do not account for specific elements within programs that appeal to certain viewers and do not update effectively with viewer responses.
Innovation Solution
A method that identifies and stores media and commercial program time occurrence and content information, correlates it with consumer media actions to assign responsiveness probability values, and applies these values to predict consumer responses in a second media program to place advertisements at optimal times and content locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertising placement methods are used based on general program demographics, then the advertising placement process is simple, but the accuracy of targeting specific viewer segments and predicting consumer response is insufficient
Solution Approach 1:
The patent segments the media program content into specific time intervals and content elements, allowing analysis of viewer responses to individual segments rather than treating the entire program uniformly. This enables precise identification of which specific content elements drive advertising effectiveness.
Solution Approach 2:
The system performs preliminary analysis of program content and viewer responses before actual advertising placement occurs. By pre-correlating content information with consumer media actions, the system builds predictive models that guide future advertising decisions without requiring complex real-time processing during broadcast.
2Productivity
If advertising placement is based on general program demographics without specific content analysis, then the implementation is straightforward, but the responsiveness to specific content elements and viewer preferences is not optimized
Solution Approach 1:
The patent incorporates feedback loops where consumer media actions (such as purchases, website visits, or engagement metrics) are correlated with specific program content and timing information. This feedback enables continuous refinement of predictive models to improve advertising response rates over time.
Solution Approach 2:
The system adds temporal and content-specific dimensions to traditional demographic targeting. Instead of only considering general viewer demographics, the analysis incorporates time-based data (specific moments within programs) and content-based data (types of scenes, characters, or moments), creating a multi-dimensional targeting approach.
3Measurement precision
If a comprehensive analysis of program content and viewer responses is implemented, then the accuracy of predicting consumer response to advertising is improved, but the time required for data collection and processing increases
Solution Approach 1:
The system performs data collection and correlation analysis in advance, building comprehensive datasets before actual advertising campaigns launch. By pre-processing program content information and viewer response data, the system reduces real-time processing requirements during actual advertising placement.
Solution Approach 2:
The patent analyzes more comprehensive data than traditionally used, including specific time intervals, content elements, and multiple types of consumer media actions. This excessive data collection initially increases processing time but enables significantly improved prediction accuracy that justifies the investment.
Data Source
AI summary
A method of determining correlations and causality between media program content and consumer responsiveness involves identifying and storing media and commercial program time occurrence and content information and consumer media reviewing actions which occur in connection with the media and commercial program time occurrence and content information. The information is correlated to obtain and assign responsiveness probability values corresponding to type and intensity of consumer response for each of the media and commercial program time occurrence and content information. These responsiveness probability values are then applied to a second media program to place product advertising at a specific time within specific content therein as determined by the responsiveness probability values thus facilitating creation of new ads and modification of existing ones and further, directing placement of those advertisements within any and all broadcast and Internet media programming.