AI-Driven Interactive TV Guide with Blockchain Proximity Detection
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Solution Overview
Problem
Current interactive television systems lack personalized and dynamic content selection and display, failing to effectively utilize data from diverse devices and user interactions to tailor programming guides and advertising to individual users and groups.
Innovation Solution
The implementation of an AI-driven, blockchain-based system that collects and analyzes data from various devices, including IoT, social media, and user activities to customize the electronic programming guide (EPG) in real-time, adjusting content and advertising based on user proximity, preferences, and group dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static programming guides are used, then system simplicity is maintained, but user personalization and engagement are insufficient
Solution Approach 1:
The programming guide transitions from a static display to a dynamic system that automatically updates content based on real-time user proximity detection, device identification, and contextual data from multiple sources including social media and IoT devices, enabling personalized content presentation without manual intervention
Solution Approach 2:
The system performs self-personalization by automatically detecting user presence, identifying user preferences through multiple data sources, and customizing the programming guide content without requiring explicit user input or configuration, making the complexity invisible to the end user
2Measurement precision
If data from multiple devices and sources is collected, then content selection accuracy improves, but data processing complexity increases
Solution Approach 1:
The system merges data from multiple disparate sources including user device data, social media information, IoT device data, and proximity detection data into a unified user profile, enabling comprehensive content personalization while consolidating processing operations into a centralized system
3Productivity
If real-time customization is implemented, then user engagement increases, but processing time and resource usage increase
Solution Approach 1:
The system performs preliminary data collection and user profile creation in advance by continuously gathering data from user devices, social media, and IoT sources before a viewing session begins, so that when users approach the display, personalized content can be quickly delivered without intensive real-time processing
Data Source
AI summary
Developments in interactive programming guides that utilize a confluence of users/viewers from data that may be blockchain and/or data collection from IoT and other devices, proximity sensing of viewership, and both machine and guided learning over a large dataset to produce rules for AI selection of content, format, and features presented to currently active or a nearby set of television users/viewers. Blockchain may be implemented separately for accounting, verification, billing, and/or fees/royalty payments owed to content owners/copyright holders. Playback may be initiated by a remote device and played on a same or yet another or a plurality of remote devices.