AI Brand Detection and Ad Frequency Control Across IoT Platforms
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Solution Overview
Problem
The increasing number of Internet-connected devices has led to challenges in adapting advertising strategies, with users experiencing suboptimal ad consumption due to the lack of effective optimization, personalization, and integration across various advertising platforms, resulting in a degraded user experience.
Innovation Solution
The development of systems and methods for programmatic generation of training data, entity detection using artificial intelligence, and advanced frequency management, which involve a training data generation engine, model training engine, deep learning model service, voting engine, and frequency management service to optimize ad delivery by identifying suitable ad inventory, analyzing media items, and managing ad frequency based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advertising networks expand to include diverse IoT devices and platforms, then the inventory of advertising platforms increases dramatically, but the user experience becomes fractured and suboptimal
Solution Approach 1:
The patent implements a universal frequency management system that operates across multiple advertising platforms and devices (smart TVs, set-top boxes, streaming devices, mobile devices). The system uses a common frequency identifier and centralized management approach to provide consistent ad frequency control universally across diverse platforms, preventing fractured user experience while maintaining platform diversity.
Solution Approach 2:
The system employs feedback mechanisms by tracking ad impressions across devices and platforms, storing frequency data in databases, and using this information to make real-time decisions about ad serving. The frequency management service continuously monitors and adjusts ad delivery based on accumulated impression data, ensuring optimal user experience across the expanded platform inventory.
2Device complexity
If legacy advertising systems are used, then system simplicity is maintained, but they are not capable of addressing new challenges and opportunities in IoT advertising
Solution Approach 1:
The patent segments the advertising system into distinct functional components: frequency management service, impression tracking service, database storage, and platform-specific adapters. This modular architecture allows legacy systems to maintain simplicity while new IoT-capable components are added independently, enabling gradual adaptation without complete system replacement.
Solution Approach 2:
The frequency management service acts as an intermediary layer between legacy advertising systems and new IoT platforms. It translates between different platform protocols and the centralized frequency control mechanism, allowing legacy systems to communicate with diverse IoT devices without requiring complex modifications to either side.
3Productivity
If ad frequency is increased to maximize advertising reach, then advertising effectiveness improves, but user experience degrades due to inundation with advertisements
Solution Approach 1:
The system dynamically changes the frequency parameter based on multiple factors including device type, user engagement history, time of day, and platform characteristics. Instead of using a fixed frequency threshold, the frequency management service adjusts parameters in real-time to optimize the balance between advertising reach and user experience, serving ads more frequently on engaged users and less frequently on saturated devices.
Data Source
AI summary
Systems and methods for entity detection using artificial intelligence, including: a deep learning model service configured to: select and analyze a set of frames from a media item to determine a set of candidate brand-probability pairs; a voting engine configured to: determining that a first brand-probability pair of a set of candidate brand-probability pairs based on at least one obtained hyperparameter value does not meet a threshold for determining whether candidate brand-probability pairs are to be included in a result set; excluding the first brand-probability pair from the result set based on the determination; sorting the result set; and selecting at least one final brand-probability pair from the result set; and an offline transcoding service configured to: store the final brand-probability pair in a repository with a relation to an identifier of the media item.