Product Placement Database Using Audio-Visual Filters
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Solution Overview
Problem
Current methods for evaluating the value of product placement in advertising lack efficiency in tracking and quantifying the impact of product appearances in both video and audio content across various media programs, especially for legacy programs where placements may occur years after initial release.
Innovation Solution
A system and method for constructing a product placement database that automatically identifies and rates product placements in video and audio content by using image and audio processing filters to generate entries with context descriptors, allowing for the calculation of an advertising value based on the frequency and quality of these placements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image and audio processing filters are used to identify product placements, then productivity and measurement precision are improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of product placement identification into separate image processing filters and audio processing filters. Each filter handles specific aspects (visual product detection, audio product mention detection) independently, then results are integrated to generate comprehensive database entries. This segmentation improves productivity by allowing parallel processing while managing complexity through modular design.
Solution Approach 2:
The patent introduces a product placement database as an intermediary structure that stores and organizes identification results from multiple filters. The database acts as a mediator between the complex processing filters and the final advertising value calculation, enabling efficient querying and analysis without requiring direct complex interactions between all processing components.
2Measurement precision
If comprehensive context descriptors are generated for each product placement, then measurement precision and advertising value accuracy are improved, but loss of time and processing resources increase
Solution Approach 1:
The system generates context descriptors with varying levels of detail based on the specific placement situation. Not every product placement receives the same level of comprehensive analysis - the system applies processing intensity selectively, focusing detailed context descriptor generation on placements that contribute most to advertising value, thereby balancing precision with time efficiency.
Solution Approach 2:
The patent changes parameters of the analysis process dynamically - adjusting the depth of context descriptor generation, the strictness of matching criteria, and the level of detail in database entries based on factors such as product prominence, placement duration, and media program type. This allows the system to maintain measurement precision for critical placements while reducing processing time for less significant ones.
Data Source
AI summary
A system that incorporates the subject disclosure may include, for example, a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations comprise obtaining product image data and comparing image data of a media program with the product image data. A product placement in the media program is determined based on the comparison, and an entry in a product placement database is generated. The entry comprises identifiers of the media program, a product descriptor descriptive of the product, and a context descriptor descriptive of a presentation scheme used for the first product placement. A rating is assigned to the media program with respect to the product in accordance with the entry of the product placement in the product placement database and other entries of other product placements in the product placement database. Other embodiments are disclosed.