Natural Language Content Dedication Service
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Solution Overview
Problem
Existing voice user interfaces are cumbersome and restrictive, requiring specific command-and-control sequences, leading to user frustration due to inaccurate speech recognition and limitations in engaging users in cooperative dialogues, and fail to integrate information across devices and applications, hindering intuitive and efficient interaction with electronic devices.
Innovation Solution
A natural language content dedication service that detects multi-modal device interactions, identifies content requests from natural language utterances, processes transactions, and customizes content for recipients, enabling seamless interaction across multiple devices through a hybrid processing environment with a virtual router and voice-enabled client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice recognition software is used to simplify user interaction, then ease of operation is improved, but device complexity increases due to the need for sophisticated speech processing and integration across multiple devices and applications
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that sits between the user and the complex device ecosystem. This intermediary translates natural language inputs into device-specific commands and coordinates actions across multiple devices, shielding users from underlying system complexity while enabling seamless cross-device interaction.
Solution Approach 2:
The system creates a universal natural language interface that can handle diverse tasks across different device types and applications. Rather than requiring device-specific commands, the universal interface adapts to various contexts and device capabilities, allowing users to interact with any device through consistent natural language patterns.
2Measurement precision
If existing voice user interfaces require specific command-and-control sequences, then measurement precision of user intent is improved, but ease of operation deteriorates due to the restrictive syntax requirements
Solution Approach 1:
The patent implements dynamic grammar adaptation where the system's understanding framework evolves during conversation. Rather than relying on fixed command sequences, the system dynamically adjusts its interpretation model based on context, user preferences, and conversation history, allowing flexible natural language input while maintaining accurate intent recognition.
Solution Approach 2:
The system incorporates feedback loops where the natural language processing engine continuously learns from user corrections and contextual cues. When users provide feedback on misinterpretations or refine their inputs, the system adjusts its understanding model in real-time, improving both precision and flexibility without requiring rigid command structures.
3Reliability
If existing voice user interfaces are designed for specific applications, then reliability of speech recognition is improved, but adaptability deteriorates due to constraints on information availability across different devices and applications
Solution Approach 1:
The patent creates a universal natural language processing framework that can adapt to various applications and devices while maintaining reliable speech recognition. The system uses context-aware interpretation that leverages information from multiple sources and applications, allowing accurate understanding of user intent across diverse contexts without being limited to specific pre-configured scenarios.
Solution Approach 2:
The system performs preliminary context gathering and device capability assessment before processing natural language requests. By proactively collecting relevant information from multiple devices and applications in advance, the system ensures reliable speech recognition and accurate intent interpretation across diverse contexts, eliminating the need for application-specific configurations.
Data Source
AI summary
The system and method described herein may provide a natural language content dedication service in a voice services environment. In particular, providing the natural language content dedication service may generally include detecting multi-modal device interactions that include requests to dedicate content, identifying the content requested for dedication from natural language utterances included in the multi-modal device interactions, processing transactions for the content requested for dedication, processing natural language to customize the content for recipients of the dedications, and delivering the customized content to the recipients of the dedications.