Multi-Assistant NLP Orchestrator for Distinct Voice Synthesis
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Solution Overview
Problem
Current natural language processing systems lack the ability to seamlessly integrate multiple assistants with distinct voices, editorial content, and skill system capabilities, limiting user experience and flexibility in interacting with various devices and contexts.
Innovation Solution
A system that implements multiple assistants by using unique TTS configurations, allowing each assistant to have a distinct voice, editorial content, and skill system capabilities, which are determined based on device type, wakewords, user identifiers, and context, enabling personalized interactions across different devices and scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple assistants with distinct voices and capabilities are integrated into a single natural language processing system, then user experience and flexibility are improved, but system complexity increases
Solution Approach 1:
The system segments the assistant selection process into distinct functional components: wakeword detection module, user identifier recognition module, context analysis module, and TTS configuration selection module. Each component handles a specific aspect of assistant determination, reducing overall system complexity while enabling multiple assistants with distinct voices and capabilities
Solution Approach 2:
The patent introduces an intermediary orchestrator component that coordinates between the natural language processing system and multiple assistants. This intermediary manages the complexity by centralizing the logic for selecting appropriate TTS configurations and assistant identities, allowing the system to handle multiple assistants without proportionally increasing complexity throughout the entire architecture
2Ease of operation
If assistant selection is based on multiple factors including device type, wakewords, and user identifiers, then interaction personalization is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring assistant identities and TTS voice profiles in advance. When a natural language input is received, the system can quickly match the input against pre-established criteria (wakewords, user identifiers, device types) without performing complex real-time analysis, thus reducing processing time while maintaining personalized interaction
Solution Approach 2:
The patent applies local quality by making assistant selection context-dependent rather than uniform across all inputs. Different selection criteria (wakeword recognition, user identifier matching, device type detection) are applied locally based on the specific characteristics of each input, allowing fast processing paths for simple cases while maintaining personalization for complex scenarios
Data Source
AI summary
Techniques for a natural language processing (NLP) system to implement more than one assistant are described. The NLP system may receive a natural language input from a device. The NLP system may also receive one or more signals representing one or more assistants to be implemented with respect to the natural language input. The NLP system may intelligently select an assistant to be invoked with respect to the natural language input. Once the assistant is selected, the NLP system may cause content, output to a user, to have characteristics specific to the assistant.