Application-Aware Digital Assistant Control for Rapid Command Learning
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Solution Overview
Problem
Digital assistants face challenges in efficiently interacting with new applications and commands, requiring cumbersome and time-intensive training processes, which hinder developer integration and user access to diverse tasks.
Innovation Solution
An electronic device with a digital assistant system that associates spoken commands with application metadata, allowing quick learning of new commands and interactions without lengthy registration processes, using lightweight natural language models to determine user intents and conserving battery power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional training processes are used to enable digital assistant to interact with new applications, then the digital assistant can learn new commands, but the process is cumbersome and time-intensive
Solution Approach 1:
The system performs preliminary actions by automatically generating training data from application metadata and performing pre-training before the digital assistant needs to interact with new applications. This eliminates the need for time-intensive manual training processes when new applications are introduced, as the assistant is already prepared with foundational knowledge from metadata-based pre-training.
Solution Approach 2:
The digital assistant performs self-service by automatically learning new commands through metadata analysis without requiring external training intervention. The system autonomously processes application metadata, generates training examples, and updates its command recognition capabilities, eliminating the need for manual training procedures and reducing time loss.
2Adaptability or versatility
If traditional training processes are used to enable digital assistant to interact with new applications, then the digital assistant can learn new commands, but the process is cumbersome
Solution Approach 1:
The system enables self-service by automatically extracting training data from application metadata and performing autonomous learning without requiring manual intervention. Developers simply need to provide application metadata, and the system handles the entire training process automatically, making integration straightforward and easy to operate.
Solution Approach 2:
Application metadata serves as an intermediary that bridges the gap between new applications and the digital assistant. Instead of requiring direct manual training interactions, the metadata automatically translates application functionality into training examples, simplifying the integration process and improving ease of operation.
3Power
If lightweight natural language models are used to determine user intents, then processing power requirements are reduced, but model accuracy may be affected
Solution Approach 1:
The system extracts only the essential features and patterns needed for user intent recognition from large datasets during the pre-training phase. By extracting key training signals from metadata and condensed training examples, the lightweight model receives only the most relevant information, maintaining accuracy while reducing processing requirements.
Solution Approach 2:
The approach changes the parameters of training data by using metadata-derived examples instead of full-scale conversational datasets. This parameter change allows the lightweight model to achieve comparable performance with reduced computational complexity, as the metadata-based training focuses on essential command-pattern relationships rather than exhaustive conversation scenarios.
Data Source
AI summary
Systems and processes for operating a digital assistant are provided. An example method includes, at an electronic device with one or more processors and memory, while an application is open on the electronic device: receiving a spoken input including a command, determining whether the command matches at least a portion of a metadata associated with an action of the application, and in accordance with a determination that the command matches at least the portion of the metadata associated with the action of the application, associating the command with the action, storing the association of the command with the action for subsequent use with the application by the digital assistant, and executing the action with the application.