Digital Assistant Dialect Library for Natural Language Disambiguation
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Solution Overview
Problem
Current digital assistants are limited in their ability to understand natural language commands, require extensive training, and often struggle with variations of tasks, leading to frustration and privacy concerns due to pre-configured patterns and data collection by third parties.
Innovation Solution
A crowd-sourced digital assistant system that learns from users by creating and distributing an ever-growing library of dialects and actionable operations, allowing users to invoke computing events on various devices through intuitive commands, while prioritizing privacy by utilizing existing device applications and minimizing data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital assistants use pre-configured patterns and assumptions to reduce initial training requirements, then ease of operation is improved, but adaptability deteriorates as they cannot easily override or adjust to user-specific needs
Solution Approach 1:
The system performs preliminary actions by pre-configuring default patterns and assumptions that enable immediate operation without training. Meanwhile, it prepares the infrastructure for later user-specific customization through the framework that allows users to add, remove, and modify actions and parameters according to their needs.
Solution Approach 2:
The digital assistant transitions from a static pre-configured state to a dynamic user-adapted state. Users can dynamically add new actions, modify existing parameters, and customize the system behavior through natural language commands, allowing the system to evolve and adapt to specific user workflows and preferences.
2Measurement precision
If digital assistants require extensive training to understand natural language commands, then understanding precision is improved, but loss of time increases due to training requirements
Solution Approach 1:
The system uses templates as reusable copies of command structures. Instead of training from scratch, the digital assistant leverages pre-defined action templates with parameters that can be filled in by users through natural language. This allows the system to understand commands accurately without extensive training by matching user input against the template structure.
Solution Approach 2:
The action templates serve multiple functions: they define the structure for understanding commands, specify the parameters needed for execution, and provide a framework for generating responses. This multi-functionality allows the system to achieve accurate command understanding without requiring separate training processes for each function.
3Productivity
If digital assistants collect contextual data to improve task completion capabilities, then productivity is improved, but harmful factors increase due to privacy concerns and third-party data collection
Solution Approach 1:
The system extracts only the necessary contextual information needed for task completion while leaving sensitive personal data on the user's device. The framework allows users to specify which parameters should be collected and how they should be used, enabling the system to achieve productivity improvements without requiring extensive data collection that would raise privacy concerns.
Solution Approach 2:
The digital assistant acts as an intermediary between the user and third-party services. Instead of directly sharing contextual data with third parties, the system processes information locally and only transmits necessary, anonymized data when required for task execution, thereby maintaining productivity while protecting user privacy and reducing harmful data sharing.
Data Source
AI summary
Embodiments described herein are generally directed towards systems and methods relating to a crowd-sourced digital assistant system and techniques for disambiguating commands based on personalized usage of a digital assistant device, among other things. In various embodiments, the digital assistant device can use personal data, collected device usage data, and other types of collected contextual information, to disambiguate received commands for the proper selection and execution of operations on the digital assistant device. The digital assistant can process and interpret ambiguous commands and even unique user dialects without requiring extensive training to recognize and act on the received commands, even if the particular phraseology of the command has not previously been encountered by the digital assistant.