Crowd-Sourced Digital Assistant 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, leading to frustration in users and privacy concerns due to centralized data collection.
Innovation Solution
A crowd-sourced digital assistant system that allows users to create and distribute action datasets with reproducible computing events, enabling the digital assistant to learn from users and reduce privacy concerns by utilizing existing device applications, with a framework for improving actionable operations and understandable commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If digital assistants use pre-configured patterns to reduce training requirements, then initial setup time is reduced, but the assistant becomes difficult to override and may not serve particular users well
Solution Approach 1:
The digital assistant dynamically adjusts its command patterns through continuous learning from user interactions. The system transitions from static pre-configured patterns to adaptive patterns that evolve based on observed user behavior, allowing it to maintain low initial training requirements while improving customization over time.
Solution Approach 2:
The system performs self-learning by automatically analyzing user commands and interactions without requiring explicit retraining. The digital assistant autonomously updates its pattern library based on observed usage, eliminating the need for users to manually override patterns while still achieving personalization.
2Measurement precision
If digital assistants collect contextual data to improve understanding, then command accuracy improves, but privacy concerns increase
Solution Approach 1:
The system processes and learns from contextual data locally on the user's device rather than transmitting it to remote servers. This localized processing maintains high command understanding accuracy through access to detailed contextual information while preventing privacy violations by keeping data confined to the user's device.
3Measurement precision
If digital assistants require extensive training to understand natural language, then command accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The system pre-loads a comprehensive library of command patterns and dialects before user interaction begins. This preliminary preparation enables the digital assistant to immediately understand natural language commands with high accuracy without requiring users to invest time in training, as the learning capability is already built-in and ready to adapt.
4Productivity
If digital assistants use centralized data collection to improve performance, then system intelligence improves, but user control over data decreases
Solution Approach 1:
The system segments the learning process into local pattern collection on user devices and optional cloud-based pattern sharing. Each device maintains control over its own contextual data while contributing anonymized pattern improvements to the broader system, enabling both high system intelligence and user data control through this distributed architecture.
Data Source
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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.