Conversational Home Control Using Context-Aware Dialog Patterns
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Solution Overview
Problem
Current home automation systems require complex machine learning algorithms and large data sets for conversational interfaces, making them inefficient and difficult to implement for controlling home devices through natural language commands.
Innovation Solution
A conversational interface system using a domain-specific language model, such as ChatScript, that allows users to control home automation devices through natural language commands by matching input text against predefined dialog patterns, resolving ambiguities using context data, and generating follow-up questions to clarify parameters, without the need for complex machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If complex machine learning algorithms and large data sets are used for conversational interfaces, then the system can understand natural language commands, but the implementation becomes inefficient and difficult
Solution Approach 1:
The patent segments the conversational interface into distinct modules: a language model component that handles natural language understanding, a dialog state tracker that manages conversation context, and a controller that executes commands. This modular segmentation allows each component to be developed and trained independently, reducing overall implementation complexity while maintaining effectiveness.
Solution Approach 2:
The patent introduces an intermediary layer (the language model and dialog state tracker) that bridges natural language input and system control commands. This intermediary processes and structures unstructured language input into standardized control signals, making the system easier to implement by decoupling the complexity of natural language understanding from the control logic.
2Reliability
If complex machine learning algorithms are used for conversational interfaces, then natural language understanding improves, but implementation difficulty increases
Solution Approach 1:
The patent performs preliminary action by pre-training the language model on domain-specific data and pre-defining dialog patterns before deployment. The dialog state tracker is pre-configured with expected conversation flows and parameter structures. This preliminary preparation ensures reliable natural language understanding while simplifying implementation, as the heavy lifting is done during the setup phase rather than during runtime or requiring complex real-time processing.
3Measurement precision
If large data sets are used for training conversational interfaces, then conversational accuracy improves, but the system becomes inefficient and difficult to implement
Solution Approach 1:
The patent applies local quality by focusing training data and model specialization on the specific domain of home automation control rather than using general-purpose large language models. The language model is trained on domain-specific dialog patterns and command structures relevant to controlling home devices, achieving high command recognition accuracy for the target application without requiring massive general-purpose datasets.
4Ease of operation
If traditional control panel interfaces are used, then system control is reliable, but user convenience decreases
Solution Approach 1:
The patent substitutes the mechanical control panel interface with a voice-based conversational interface. Instead of requiring physical interaction with buttons, switches, or graphical interfaces, users can control home automation devices through natural speech. The system replaces mechanical input methods with acoustic signal processing and natural language understanding, significantly improving user convenience while maintaining control reliability through structured dialog management.
Data Source
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AI summary
Technologies for conversational control of a home automation system include a computing device in communication with one or more home automation devices. The computing device receives text input indicative of a spoken user request including one or more parameters associated with a home automation command. The computing device matches the text input against a home automation language model including several predefined dialog patterns. The computing device resolves parameters of the command using a context model. The computing device may resolve the location, device, or requested device state of the command. The computing device generates a text response and a home automation command using the language model, and then presents the text response to the user and performs the home automation command using the home automation devices. In some embodiments, the computing device may control an application automation system or a data exploration system. Other embodiments are described and claimed.