Digital-Assistant Recommendation Updates After User Noncompliance
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Solution Overview
Problem
Existing digital assistants do not proactively monitor user actions or inactions following their recommendations and require additional user input to provide updated recommendations, which can be inconvenient or dangerous for first responders in dynamic situations.
Innovation Solution
An electronic computing device generates and provides digital-assistant recommendations based on contextual data, monitors user compliance, determines correlations between non-compliance and changes in contextual data, and provides updated recommendations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital assistants require additional user input to provide updated recommendations, then recommendation accuracy can be maintained through user feedback, but user convenience deteriorates due to additional interaction requirements
Solution Approach 1:
The system implements automated feedback by monitoring user compliance with recommendations through sensors and device data. When a user does not follow a recommendation, the system automatically detects this non-compliance and uses it as feedback to generate updated recommendations, eliminating the need for explicit user input while maintaining recommendation accuracy
Solution Approach 2:
The digital assistant performs self-updating by automatically monitoring its own recommendation compliance and initiating updates based on observed user behavior and contextual changes. The system serves itself by autonomously detecting when recommendations should be updated without requiring user initiation, thereby improving convenience while maintaining reliability
2Reliability
If digital assistants proactively monitor user actions continuously, then recommendation reliability improves through real-time compliance detection, but device complexity increases due to continuous monitoring requirements
Solution Approach 1:
The system maintains continuous monitoring of user compliance with recommendations by leveraging existing sensors and device data streams that are already active during task execution. This approach enables real-time detection of user actions without requiring separate monitoring systems, thereby improving recommendation reliability while minimizing additional device complexity
Solution Approach 2:
The monitoring system leverages existing multi-functional sensors and device components that serve both their primary functions and compliance monitoring purposes. For example, sensors used for navigation or task execution also detect user compliance with recommendations, eliminating the need for dedicated monitoring hardware and reducing overall system complexity
Data Source
AI summary
A process of updating a digital-assistant recommendation in response to a user not following the recommendation. In operation, an electronic computing device generates and provides a digital-assistant recommendation for a user based on contextual data currently available corresponding to the user and responsively monitors whether the user is following the recommendation. If it is determined that the user is not following the recommendation, the electronic computing device further determines whether there is a correlation between the user not following the recommendation and change in contextual data currently available corresponding to the user. If there is a correlation between the user not following the recommendation and the change in the contextual data currently available corresponding to the user, the electronic computing device generates and provides an updated digital-assistant recommendation for the user based at least in part on the change in the contextual data currently available corresponding to the user.