Digital Assistant Readiness Mapping From Affective-Cognitive Load
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Solution Overview
Problem
Existing digital assistants, such as advanced driver assistance systems, struggle to accurately assess a user's focus and emotional state, leading to inadequate assistance during complex driving situations due to the inability to determine the combined cognitive and affective load of the driver.
Innovation Solution
A computer-implemented method that computes a user's affective-cognitive load by combining cognitive and emotional state measurements, using sensors to determine biometric values, and maps these to a readiness state to trigger appropriate actions or adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a driver monitoring system uses a camera to determine whether the driver's eyes are focused on the road, then the system can detect basic focus status, but the system cannot determine the amount of focus the driver has when performing driving tasks or account for other objects and stimuli that lessen focus
Solution Approach 1:
The patent combines multiple monitoring components (eye tracking camera, distraction detection sensors, cognitive load analysis) into an integrated driver monitoring system. This merging allows the system to comprehensively assess driver focus by synthesizing data from multiple sources, thereby improving measurement precision without requiring separate complex systems for each function.
Solution Approach 2:
The driver monitoring system is designed to perform multiple functions: detecting eye focus, identifying distracting objects, analyzing cognitive load, and assessing overall driver readiness. This multi-functionality allows a single system to comprehensively measure focus accuracy while avoiding the need for separate specialized devices for each monitoring task.
2Reliability
If an ADAS provides basic driver monitoring capabilities, then the system can detect distracting objects and determine eye focus, but the system cannot effectively assist the driver or enable the driver to modify behavior to properly handle necessary driving tasks
Solution Approach 1:
The system continuously monitors driver focus and cognitive load, then provides real-time feedback through notifications and alerts. This feedback loop enables the driver to awareness of their focus level and modify behavior accordingly, thereby improving assistance effectiveness. The system adapts its feedback based on the severity of focus degradation, creating a responsive assistance mechanism.
Solution Approach 2:
The system performs preliminary assessment of driver cognitive load and focus status before critical situations arise. By proactively identifying when driver focus is deteriorating, the system can prepare appropriate assistance measures in advance, such as pre-configuring notifications or preparing automated interventions, thereby improving reliability without requiring complex real-time decision-making under pressure.
Data Source
AI summary
Embodiments of the present disclosure sets forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.