Crowd-Sourced Digital Assistant Command Interpretation
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Solution Overview
Problem
Conventional digital assistants face issues with privacy concerns, frequent misinterpretations of spoken commands, unavailability due to weak signals, and the requirement for users to structure their commands in uncomfortable dialects.
Innovation Solution
A crowd-sourced digital assistant system that evolves by learning from users through an ever-growing library of dialects, with a framework for receiving feedback and distributing improvements, allowing it to perform any operation on a computing device by interpreting commands and generating relevant action datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital assistants use fixed interpretation algorithms, then system complexity is reduced, but command interpretation accuracy deteriorates due to misinterpretations and limited dialect support
Solution Approach 1:
The patent implements dynamic adaptability by enabling the digital assistant to learn and evolve its command interpretation capabilities over time. The system transitions from static algorithms to dynamic learning models that adapt to user-specific dialects and preferences, thereby improving interpretation accuracy while managing complexity through incremental learning rather than complete system redesign
Solution Approach 2:
The system employs self-service mechanisms through automated feedback loops where user corrections and interactions automatically train and refine the interpretation models. This self-learning capability allows the system to improve accuracy without requiring manual reconfiguration or complex external intervention, balancing enhanced performance with operational simplicity
2Reliability
If digital assistants require structured command dialects, then command processing reliability is improved, but ease of operation deteriorates due to uncomfortable dialect requirements
Solution Approach 1:
The system dynamically adjusts interpretation parameters based on learned user preferences and dialect patterns. By changing the flexibility and strictness of command parsing parameters adaptively, the system maintains reliable processing for critical commands while allowing more natural, comfortable input styles for routine operations, thus balancing reliability with ease of use
Solution Approach 2:
The command processing system transitions from rigid structured requirements to dynamic adaptability, where the system learns and adapts to each user's natural speech patterns and preferences over time. This dynamic approach maintains processing reliability through learned patterns while significantly improving ease of operation by accepting more natural, less structured input
3Adaptability or versatility
If digital assistants operate independently without crowd-sourcing, then system simplicity is maintained, but adaptability deteriorates due to limited learning capabilities
Solution Approach 1:
The patent implements a multi-functional architecture where the digital assistant system serves both individual user devices and a broader crowd-sourced network. The same core learning mechanisms operate at both individual and collective levels, allowing the system to maintain adaptability benefits while sharing the computational burden and complexity management across multiple devices and users
Solution Approach 2:
The system introduces a cloud-based intermediary layer that coordinates crowd-sourced learning data and model updates across multiple devices. This intermediary manages the complexity of distributed learning by providing centralized data aggregation, model training coordination, and selective distribution of improvements, thereby enabling enhanced adaptability while containing system architecture complexity through specialized intermediary components
Data Source
AI summary
Embodiments described herein are generally directed towards systems and methods relating to a crowd-sourced digital assistant system and related methods. In particular, embodiments facilitate techniques to crowdsource the training of a language model of the crowd-sourced digital assistant system. The digital assistant device can generate new action datasets based on manual inputs detected by the digital assistant device. The manual inputs can be recorded as a set of instructions, which can be interpreted by another digital assistant device to reproduce the detected manual inputs based on a command received by the other digital assistant device. The digital assistant server can receive action datasets, maintain action datasets, and distribute action datasets to one or more digital assistant devices. In various embodiments, the digital assistant device or server can also determine whether received action datasets are related. Each digital assistant device in the described system can participate in the training of a language model that establishes relationships between action datasets and/or command templates based on user feedback received via the digital assistant device.