Adaptable Smart Virtual Assistant for Long-Term Care
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Solution Overview
Problem
Current smart virtual assistants lack adaptability and situational awareness, particularly in long-term care settings, as they fail to recognize and respond to behavioral anomalies and cognitive or physical changes in users over time, leading to inadequate support for users with aging or health-related issues.
Innovation Solution
A system and method for a smart virtual assistant that creates a normalized user profile, identifies behavioral anomalies, and adapts its interface to accommodate these changes by modifying interaction modes, such as graphical displays, voice controls, and prompts, using analytics and IoT devices to provide personalized support and alert caregivers when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a smart virtual assistant uses a fixed interface design, then the device complexity is reduced, but the adaptability to user needs deteriorates
Solution Approach 1:
The virtual assistant interface transitions from a static design to a dynamic one that automatically adjusts based on detected user anomalies. The system monitors user interactions, identifies deviations from normal behavior patterns, and modifies interface characteristics (such as simplifying navigation, changing communication modes, or providing additional guidance) in real-time to accommodate the user's current state.
Solution Approach 2:
The system implements a feedback loop where user interactions are continuously monitored and analyzed. When anomalies are detected, the system adjusts the interface accordingly and continues to monitor the effects of these adjustments, creating a closed-loop system that learns and adapts to user needs over time.
2Measurement precision
If the virtual assistant monitors user behavior continuously, then the detection precision of anomalies is improved, but the loss of time for processing data increases
Solution Approach 1:
The system pre-establishes baseline behavior models for each user during normal interactions. These models capture typical patterns of user behavior, preferences, and interaction styles. When monitoring for anomalies, the system compares current behavior against these pre-built models, enabling rapid detection without requiring extensive real-time analysis.
Solution Approach 2:
The patent replaces complex real-time behavioral analysis with simpler pattern-matching algorithms that compare current user actions against pre-defined anomaly indicators and baseline models. This substitution reduces computational complexity and processing time while maintaining detection precision.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a virtual assistant that includes a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of creating a normalized profile for a user of the virtual assistant device; identifying an anomaly deviating from the normalized profile over a threshold time period during use of the virtual assistant device; and changing an interface of the virtual assistant device to accommodate the identified anomaly. Other embodiments are disclosed.