Crowd-Sourced Digital Assistant Command Interpretation
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Solution Overview
Problem
Conventional digital assistants face challenges with privacy concerns, misinterpretation of spoken commands, unavailability due to weak signals, and the requirement for structured commands that are uncomfortable for users.
Innovation Solution
A crowd-sourced digital assistant system that allows users to record and contribute desired operations and commands, enabling seamless integration of crowd-sourced data, including action datasets and command templates, to improve command interpretation and execution across digital assistant devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition algorithms are used, then the digital assistant can provide conversational interface, but it causes misinterpretation of spoken commands
Solution Approach 1:
The patent combines multiple speech recognition algorithms and crowd-sourced command variations into a unified recognition system. By merging diverse data sources and processing methods, the system achieves more accurate and reliable command interpretation compared to single-algorithm approaches.
Solution Approach 2:
The system implements feedback mechanisms where unrecognized or misrecognized commands are collected, analyzed, and used to improve future recognition accuracy. Crowd-sourced corrections and alternative phrasings feed back into the recognition system to enhance its performance over time.
2Ease of operation
If structured commands are required, then the digital assistant can execute operations, but it reduces ease of operation for users
Solution Approach 1:
The system dynamically adapts its command recognition patterns based on learned user behaviors and crowd-sourced data. It transitions from requiring strict structured commands to flexibly accepting natural variations in speech patterns, making the system both easier to use and more adaptable.
Solution Approach 2:
The patent changes the parameters of command recognition by accepting multiple phrasings, synonyms, and structural variations of commands. This allows users to speak naturally while the system maintains the ability to execute operations reliably through parameter normalization.
3Measurement precision
If crowd-sourced data is integrated, then the system improves command interpretation, but it increases device complexity
Solution Approach 1:
The patent segments the crowd-sourced data processing into distinct modules: data collection, filtering, validation, and integration. This segmentation manages complexity by handling different aspects of data processing separately while maintaining overall system coherence.
Solution Approach 2:
The system introduces intermediary processing layers that filter and validate crowd-sourced data before integration. These intermediaries manage the complexity by preprocessing data and presenting simplified, validated information to the core recognition system.
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 describe techniques for effectively searching, modifying, and selecting action datasets for distribution to digital assistant devices based on commands received therefrom. Action datasets include computing events or tasks that can be reproduced when a command is received by a digital assistant device and communicated to the server device. The digital assistant server described herein can receive action datasets, maintain action datasets, receive commands from digital assistant devices, and effectively select most relevant action datasets for distribution to the digital assistant devices based on the received commands.