Neural Network Media Object Replacement for Contextual Advertising
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Solution Overview
Problem
Conventional digital advertising methods are ineffective in providing personalized and contextually relevant ads within digital data streams, as they lack the ability to dynamically replace product images based on user attributes and surroundings, resulting in non-natural and non-targeted advertising experiences.
Innovation Solution
A system utilizing neural networks to identify and replace product images within digital data streams, processing contextual information from user attributes and surrounding pixels to insert personalized and contextually suitable ads, ensuring the replaced images appear natural and relevant to the viewer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional advertising methods are used, then the advertising system is simple and easy to implement, but the advertising is not personalized and contextually relevant
Solution Approach 1:
The patent introduces neural networks as intermediary components that process user attributes and surrounding pixel data to determine contextual relevance. These neural networks act as mediators between the raw data and the advertising decision, enabling personalized ad placement without requiring complex manual configuration. The system uses intermediaries to bridge the gap between simplicity and personalization capability.
Solution Approach 2:
The patent replaces conventional mechanical advertising systems with neural network-based automated decision-making. Instead of manual ad placement rules, the system uses trained neural networks to automatically analyze user attributes and contextual information, substituting complex mechanical processes with intelligent algorithms that can adapt to various scenarios.
2Reliability
If product images are replaced based on user attributes, then advertising becomes targeted and relevant, but the replaced images may not appear natural within the data stream
Solution Approach 1:
The patent applies local quality by analyzing surrounding pixels specifically at the location where the product image will be replaced. The neural networks examine the local visual context (surrounding pixels) to determine appropriate replacement images that match the immediate environment, ensuring visual naturalness while maintaining relevance to the user's attributes.
Solution Approach 2:
The system changes parameters by adjusting image characteristics based on both user attributes and surrounding pixel analysis. The neural networks modify visual parameters such as color, lighting, and composition of replacement images to match the local context, ensuring that ads appear natural while remaining targeted to individual viewers.
3Adaptability or versatility
If dynamic replacement of product images is implemented, then advertising becomes contextually relevant, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-training neural networks on large datasets of images and contextual information before actual ad placement. This pre-processing allows the models to make rapid predictions during live data stream processing, reducing real-time computational requirements while maintaining high contextual relevance through the preliminary learning phase.
Data Source
AI summary
A method may include receiving frames associated with a video stream, identifying a first object image included in at least some of the frames and masking a region, in the at least some of the frames, associated with the first object image. The method may also include receiving information identifying at least one attribute associated with a user and identifying, based on the received information, a second object image to replace the first object image. The method may further include replacing pixel values in the masked region with contextually suitable pixel values associated with the second object image and outputting the video stream with the second object image replacing the first object image in the at least some of the frames.