Crowdsourced Digital Assistant Feedback for Command Interpretation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional digital assistants face issues with privacy concerns, misinterpretations of spoken commands, and the requirement for structured dialects, 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, allowing intuitive command recognition and distribution of actionable operations across devices, while reducing privacy concerns by utilizing existing device applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional digital assistants use speech recognition with natural language processing algorithms, then they can provide conversational interface and interpret commands, but they suffer from constant misinterpretations and require users to structure commands in uncomfortable dialects
Solution Approach 1:
The system implements feedback by recording actual user corrections to command interpretations and using this feedback to improve future interpretations. When users correct misinterpretations, the system learns from these corrections and adjusts its interpretation models, creating a continuous improvement loop that increases accuracy while maintaining ease of use.
Solution Approach 2:
The digital assistant performs self-service by automatically learning and adapting to user dialects and command patterns without requiring manual reconfiguration. The system autonomously improves its interpretation capabilities by processing user interactions and corrections, eliminating the need for users to manually structure commands in formal dialects.
2Reliability
If conventional digital assistants process commands centrally, then they can provide unified service, but they raise privacy concerns and require constant network connectivity
Solution Approach 1:
The system segments command processing between local and cloud components. Sensitive processing occurs locally on the user's device, eliminating the need to transmit personal information over the network. Only non-sensitive data or aggregated statistics are transmitted to cloud services, maintaining service reliability while protecting user privacy through distributed processing architecture.
3Adaptability or versatility
If a digital assistant learns from user interactions to improve command interpretation, then it can adapt to user dialects and improve accuracy, but it increases data processing requirements and potential privacy risks
Solution Approach 1:
The system introduces an intermediary layer that processes learning data locally before any potential cloud transmission. This intermediary architecture allows the device to adapt to user dialects through local machine learning while minimizing data transmission. The intermediary filters and processes information locally, reducing both the complexity of centralized processing and the privacy risks associated with data transmission.
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
Embodiments described herein are generally directed towards systems and methods relating to a crowd-sourced digital assistant and system. In particular, embodiments facilitate the intuitive creation and distribution of action datasets that include computing events or tasks that can be reproduced when an associated command, stored in an action dataset, is determined received by a digital assistant device. The digital assistant device described herein can generate new action datasets, on-board new action datasets, and receive new action datasets or updates to existing action datasets. Each digital assistant device in the described system can participate in the building of action datasets, so as to crowd-source a dialect that can be understood by a digital assistant device.