Personalized Video Frame Editing for Tailored Streaming Ads
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Solution Overview
Problem
Video streaming services lack personalized video advertising tailored to individual viewers, limiting premium revenue generation opportunities.
Innovation Solution
A system and method for incorporating personalized elements into video streams by capturing frames, applying personalization elements, and generating design files to embed personalized data within video frames, using tools like scroll, drawing, and time stamps, and adjusting positions and effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video streaming services add more video advertising to generate additional revenue, then revenue streams are improved, but the advertisements are not tailored to individual customers which limits premium revenue generation
Solution Approach 1:
The advertisement system is segmented into multiple components: base video content, personalization data layers, and dynamic insertion points. This allows the video stream to be divided into segments where personalized elements can be inserted without replacing the entire advertisement, enabling both high-volume advertising and individualized content delivery.
Solution Approach 2:
Personalization data is collected and prepared in advance before the video stream is delivered to the viewer. Viewer profiles, preferences, and demographic information are pre-processed into structured formats that can be quickly matched with appropriate advertisement content during stream delivery, enabling real-time personalization without delaying ad insertion.
2Adaptability or versatility
If personalized video content is generated for each individual viewer, then viewer engagement and premium revenue are improved, but system complexity and processing requirements increase
Solution Approach 1:
The personalization system uses universal data structures and templates that can handle multiple types of viewer data (demographics, preferences, viewing history) through a single processing framework. The same infrastructure processes different personalization requirements for various video content types, reducing overall system complexity while maintaining high adaptability.
Solution Approach 2:
An intermediary personalization engine acts as a mediator between the video streaming platform and the advertisement delivery system. This intermediate layer handles the complex processing of personalization data, matching viewers with appropriate content, and managing the insertion of personalized elements, thereby isolating the complexity from both the core streaming system and the advertisement platform.
3Ease of operation
If personalization elements are applied to video frames in real-time, then viewer experience is improved, but processing time and computational resources increase
Solution Approach 1:
Personalization elements such as text overlays, graphical modifications, and targeted advertisement content are pre-rendered and stored in a ready-to-insert format. During real-time streaming, these pre-prepared elements are simply inserted at designated points in the video frame rather than being generated from scratch, dramatically reducing processing time while maintaining high-quality personalized output.
Solution Approach 2:
Instead of reprocessing entire video frames for personalization, the system applies modifications only to specific local regions of the frame where personalization elements need to appear. This localized approach minimizes computational overhead by focusing processing resources only on the portions of the video that require personalization, preserving overall processing speed while enhancing viewer experience.
Data Source
AI summary
A system is described. The system includes at least one physical memory device to store a content editor and one or more processors coupled with the at least one physical memory device to execute the content editor to receive a selection of a video frame of a background video file displayed within in a graphical user interface (GUI), generate one or more personalization elements indicating personalized data that is to appear in a reference video associated with the background video file, apply the one or more personalization elements to the selected video frame and generate a design file including the one or more personalization elements.