Crowdsourced Command Datasets for Privacy-Aware Digital Assistants
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Solution Overview
Problem
Conventional digital assistants face issues with privacy concerns, misinterpretations of spoken commands, and the requirement for users to structure their commands in a specific dialect, leading to user frustration and limited functionality.
Innovation Solution
A crowd-sourced digital assistant system that learns from user interactions to create an evolving library of dialects and commands, allowing it to understand a variety of user inputs and reduce privacy concerns by utilizing applications on individual devices, with a framework for distributing improvements and actionable operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional digital assistants use speech recognition technologies with natural language processing algorithms, then they can provide conversational interface and interpret commands, but they suffer from constant misinterpretations of spoken commands and require users to structure commands in specific dialects
Solution Approach 1:
The system creates a universal command interpretation framework that handles multiple dialects and speech patterns through a centralized natural language processing engine. This engine processes various user inputs (spoken commands, text inputs, gestures) and translates them into standardized device commands, making the digital assistant adaptable to different user expressions while maintaining accurate command interpretation.
Solution Approach 2:
The system incorporates feedback mechanisms where the digital assistant provides clarification questions when command interpretation is ambiguous, and learns from user corrections to improve future interpretations. This feedback loop enables the system to adapt to user-specific dialects and preferences over time, resolving the contradiction between maintaining accurate interpretation and accommodating dialect flexibility.
2Adaptability or versatility
If digital assistants collect and process user data to improve command interpretation, then they can learn from interactions and evolve their capabilities, but they raise privacy concerns
Solution Approach 1:
The system implements local processing of sensitive user data on the device itself rather than cloud-based processing. Personalized learning models are trained locally using user interaction data, allowing the digital assistant to adapt to individual user preferences and dialects without transmitting private information to external servers. This localized approach enables learning capabilities while protecting user privacy.
Solution Approach 2:
The system extracts and processes only the minimal necessary data elements required for command interpretation and learning, separating essential functional data from unnecessary personal information. By extracting only the critical features needed for improving command recognition (such as speech patterns and command contexts) while discarding or encrypting sensitive personal data, the system achieves learning capability with reduced privacy risks.
3Reliability
If digital assistants rely on weak or absent network signals, then they become unavailable and cannot process commands, but maintaining constant connectivity increases device complexity
Solution Approach 1:
The system performs preliminary actions by pre-loading command interpretation models, action datasets, and frequently used application interfaces into local memory before network connectivity is needed. This allows the digital assistant to process commands and execute actions offline using stored data, ensuring service availability during network outages. The device automatically synchronizes with the network when connectivity is restored to update its knowledge base.
Data Source
AI summary
Embodiments facilitate the intuitive creation, maintenance, and distribution of action datasets that include computing events or tasks that can be reproduced when a command is received by a digital assistant. The digital assistant can generate new action datasets, on-board new action datasets, and receive new action datasets or updates to existing action datasets locally or via a digital assistant server, among other things. The digital assistant server can also receive action datasets, maintain action datasets, and distribute action datasets to one or more digital assistants, among other things.