On-Device AI Voice Assistant for Enterprise Contextual Control
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Solution Overview
Problem
Commercially available voice assistants are not well-suited for interacting with specialized enterprise and industry-specific applications, as they lack support for product-specific commands and are not customizable for context-based interactions.
Innovation Solution
A custom AI voice assistant system that includes a self-contained artificial intelligence model trained to map text inputs to intents and a knowledge base that maps intents to application-specific actions, allowing for context-based interactions without requiring network communication, enabling voice-based control of applications even when offline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic voice assistant is used, then it can service common use cases, but it cannot support product-specific commands or enterprise applications
Solution Approach 1:
The system segments the voice assistant functionality into separate components: a custom AI model for intent recognition, a knowledge base for domain-specific information, and an action execution module. This segmentation allows each component to be optimized independently while maintaining overall system adaptability to product-specific use cases.
Solution Approach 2:
The patent implements local quality by allowing different parts of the system to have specialized characteristics - the AI model is trained on product-specific data, the knowledge base contains domain-specific information, and the action module executes application-specific commands. This enables the system to be highly adaptive in specific domains without requiring complete redesign.
2Reliability
If network communication is required for voice processing, then cloud-based AI can be used, but voice assistance cannot function offline
Solution Approach 1:
The system achieves multi-functionality by designing the AI model and knowledge base to operate in both online and offline modes. The same on-device components can process voice inputs whether network connectivity is available or not, providing universal functionality across different operational contexts.
Solution Approach 2:
The patent introduces an on-device AI model as an intermediary that can process voice inputs locally without requiring direct network communication. This intermediary capability allows the system to function autonomously offline while still maintaining the ability to connect to cloud services when needed.
3Object-affected harmful factors
If voice data is transmitted over the network, then cloud processing can be utilized, but data privacy and security are compromised
Solution Approach 1:
The patent extracts the voice processing capability from cloud-based services and places it directly on the user device. By taking out the AI model and knowledge base from remote servers and installing them locally, the system protects sensitive voice data from network transmission while maintaining full processing capability.
Solution Approach 2:
The system implements self-service by enabling the device to process and analyze voice inputs independently using on-device AI models and local knowledge bases. This self-sufficient approach eliminates the need to transmit voice data externally, thereby protecting data privacy while maintaining robust processing capabilities.
Data Source
AI summary
An intent-based voice assistant system comprising an intent-based voice assistant, a self-contained artificial intelligence model trained to map input text to a plurality of intents and a knowledge base that maps the plurality of intents to commands supported by an application. The system includes instructions executable for: running a speech-to-text converter to convert a voice input to a text string; running the self-contained artificial intelligence model to process the text string to map the text string to an intent; mapping the intent output by the self-contained artificial intelligence model for the text string to an application action; and executing the application action using the application.