Interactive Living Room Content With Real-Time AI Responses
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Solution Overview
Problem
Current interactive television systems lack effective two-way conversational capabilities, struggle with dynamic content adaptation, and fail to provide multi-lingual support, leading to disjointed user experiences and reduced accessibility for diverse audiences.
Innovation Solution
A system and method that utilizes AI-driven prompt generation and real-time interaction to identify user attributes, generate multilingual responses, and adapt to user preferences, incorporating advanced assistant functions for enhanced engagement and accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If basic chatbots or assistants are integrated into media devices, then user engagement and interactivity are improved, but language support remains limited and functionality is restricted to basic IoT control and media playback
Solution Approach 1:
The virtual assistant is designed to perform multiple functions beyond basic media control, including translating on-screen content into multiple languages (Spanish, French, German, etc.), providing content summaries, answering queries about displayed content, and controlling smart home devices. This multi-functional approach resolves the contradiction by expanding both engagement capabilities and language support simultaneously.
Solution Approach 2:
The system introduces an AI-based translation layer that acts as an intermediary between the original content and the user. This translation intermediary enables communication in multiple languages without requiring separate chatbot systems for each language, thus improving language support while maintaining user engagement through a unified interface.
2Ease of operation
If conventional ITV systems provide basic interaction features, then ease of operation is improved, but the systems fail to comprehend and respond to diverse queries and comments in real time
Solution Approach 1:
The patent replaces conventional rule-based interaction systems with an AI-based virtual assistant that uses natural language processing and large language models. This substitution enables the system to comprehend and respond to diverse user queries in real-time, transforming the mechanical, pre-programmed interaction model into an intelligent, adaptive one that maintains ease of operation while dramatically improving response capability.
Solution Approach 2:
The system dynamically adjusts its response parameters based on the type of query received. For simple commands, it provides immediate responses; for complex queries requiring translation or content analysis, it adjusts processing parameters to handle the additional complexity while maintaining real-time performance through optimized AI model inference.
3Adaptability or versatility
If ITV systems attempt to handle diverse and dynamic content, then content adaptability is improved, but the systems struggle to keep pace with rapid information flow and adjust responses accordingly
Solution Approach 1:
The virtual assistant pre-loads and analyzes metadata about upcoming content segments, preparing translation models and response templates in advance. This preliminary action allows the system to rapidly adapt to diverse content types without sacrificing response speed, as the heavy processing work is performed before the content actually plays.
Solution Approach 2:
The system dynamically adjusts its processing depth and translation quality based on content urgency and type. For time-sensitive news content, it uses faster, simplified translation models; for less time-critical entertainment content, it employs more comprehensive analysis and translation, thereby maintaining overall response speed while handling diverse content adaptively.
4Ease of operation
If voice recognition systems are used for ITV interaction, then ease of operation is improved, but accent support is lacking leading to misinterpretation and user frustration
Solution Approach 1:
The AI-based virtual assistant serves as an intermediary layer between voice input and command execution. It uses advanced speech-to-text models trained on diverse accents and languages to accurately transcribe and interpret user commands, then translates the intent into appropriate actions. This intermediary processing significantly improves accent recognition accuracy while maintaining the ease of voice-based operation.
Data Source
AI summary
System and method for generating one or more interactive responses for a computing device, said system comprising at least one processing unit connected to a memory. The method comprises generating one or more prompts to be displayed at the computing device. Thereafter, the method comprises receiving in real time one or more input queries from the at least one computing device. The method further comprises identifying one or more interactive attributes based on the one or more input queries and the one or more prompts. Lastly, one or more responses are generated to the one or more input queries and displayed at the display unit of the at least one computing device.