Context-Aware Ad Transitions

Developed an AI-powered system that:

1. Analyzes the last few seconds of visual and audio content using multiple AI technologies:
   • Visual Analysis: CNNs and RNNs for object detection, scene recognition, and activity recognition
   • Audio Analysis: Speech recognition, NLP for themes and sentiments, sound classification
   • Textual Analysis: Subtitle analysis and OCR for on-screen text
2. Generates contextually relevant ad transitions using:
   • Machine Learning Models: CNNs, NLP Transformers (BERT, GPT), Audio Models, and GANs
   • Ad Matching algorithms: Contextual matching, user profile matching, and reinforcement learning
3. Implements real-time processing with latency optimization and robust privacy measures
4. Utilizes innovative measurement techniques, including device accelerometer data analysis for non-intrusive engagement tracking

While this is a conceptual project, the projected outcomes include:

• Increased ad retention rates (target >80%)
• Higher user engagement with ads (target >10% interaction rate)
• Improved conversion rates (target >5%)
• Enhanced user satisfaction with the advertising experience
• Potential annual revenue of $13,200,000 with an ROI of 1220% in the first year