IoT Automation Command Disambiguation Using Context Signals
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Solution Overview
Problem
Existing IoT automation systems require users to manually build automation rules through graphical user interfaces, which can be technically challenging, and natural language interfaces for provisioning these rules often result in ambiguous commands due to indefinite terms.
Innovation Solution
A computing system uses context information, such as network signaling, ambient audio, and user input, to disambiguate ambiguous natural language automation-configuration commands by resolving references to multiple devices in an IoT ecosystem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language interface is used for provisioning IoT automation rules, then ease of operation is improved, but ambiguity in commands increases due to indefinite terms
Solution Approach 1:
The system performs preliminary actions by proactively presenting multiple candidate devices with contextual information before the user finalizes their selection. This allows the user to confirm or correct the system's interpretation, resolving ambiguity while maintaining natural language input benefits.
Solution Approach 2:
The system implements feedback by presenting candidate device interpretations to the user and receiving confirmation or correction. This feedback loop ensures accurate device identification while maintaining the ease of natural language interaction.
2Measurement precision
If manual GUI configuration is used for automation rules, then command precision is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system acts as an intermediary by processing natural language commands through NLP to extract intended device references, then presenting candidate matches to the user for confirmation. This mediates between the simplicity of natural language and the precision needed for accurate device control.
Solution Approach 2:
The system replaces the mechanical interaction of manual GUI configuration with natural language processing. Instead of requiring users to navigate complex graphical interfaces, the system uses NLP to interpret spoken or typed commands and presents structured candidate selections for confirmation.
3Speed
If natural language commands are processed without context, then processing speed is improved, but measurement precision of device identification worsens
Solution Approach 1:
The system performs preliminary processing by quickly parsing the natural language command to identify key elements and potential device references, then uses context to refine candidate selections before presenting them to the user for final confirmation.
Solution Approach 2:
The system uses feedback by presenting context-based candidate device interpretations to the user and receiving confirmation or correction, ensuring accurate device identification while maintaining efficient processing through pre-filtering candidates using contextual information.
Data Source
AI summary
A method and system for use of context information to disambiguate an automation-configuration command. In an example method, a computing system receives a voice command uttered by a user into a voice-interface device, the voice command describing an Internet-of-Things (IoT) automation. Further, in response to receiving the voice command, the computing system determines, based on context information not specified by the voice command, which of multiple IoT devices should be a subject of an IoT rule that implements the described IoT automation, and provisions the IoT rule with the determined IoT device as the subject of the IoT rule. In example implementations, the context information could be based on network signaling between devices, ambient audio in the user's environment, and/or one or more other factors.