Multi-device gaze tracking load balancing
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Solution Overview
Problem
Computing devices face inefficiencies and user frustration due to limited capacity for task completion and the need for shared input devices, leading to decreased productivity and inaccurate feedback collection, especially when multiple users are involved.
Innovation Solution
A system that utilizes gaze tracking to identify users and adapt computing device behavior, including load balancing and task assignment across multiple devices based on gaze input data, using a shared computing component to optimize resource allocation and streamline user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple users share a single computing device, then device utilization increases, but task completion efficiency decreases due to limited device capacity and shared input device conflicts
Solution Approach 1:
The system segments the computing environment by creating separate virtual workspaces for different users on the same device. Each user has dedicated input controls and task environments, eliminating conflicts from shared input devices while maintaining high device utilization through multi-user support.
Solution Approach 2:
The system introduces a gaze-tracking intermediary that detects user intent and automatically translates it into appropriate device actions. This mediator eliminates the need for physical input device sharing by directly converting gaze direction into control commands, thereby maintaining productivity while supporting multiple users.
2Device complexity
If computing devices have limited processing capacity, then device complexity is reduced, but task completion speed decreases leading to decreased productivity
Solution Approach 1:
The system performs preliminary actions by using gaze tracking to predict user intent before explicit input is provided. Tasks are initiated and prioritized based on predicted user focus, allowing the device to prepare and execute tasks more efficiently within its limited capacity, thereby improving completion speed without increasing complexity.
Solution Approach 2:
The computing device serves itself by automatically managing task prioritization and resource allocation based on gaze input patterns. The system self-adjusts its processing focus to match user attention, eliminating the need for complex manual task management and improving productivity within existing hardware constraints.
3Ease of operation
If traditional input devices are shared among multiple users, then device accessibility improves, but feedback accuracy decreases due to inability to reliably attribute actions to specific users
Solution Approach 1:
The system implements continuous feedback through gaze tracking that constantly monitors which user is looking at the device. This feedback mechanism maintains high user attribution accuracy by dynamically identifying the active user based on real-time gaze data, while still allowing multiple users to access the device seamlessly.
Solution Approach 2:
The system replaces mechanical input devices with optical gaze tracking to identify user intent. This substitution eliminates the ambiguity of shared physical controls by using non-contact optical sensing to accurately attribute actions to specific users, maintaining accessibility while improving measurement precision.
Data Source
AI summary
Aspects of the present disclosure relate to multi-user, multi-device gaze tracking. In examples, a system includes at least one processor, and memory storing instructions that, when executed by the at least one processor, causes the system to perform a set of operations. The set of operations include identifying a plurality of computing devices, and identifying one or more users. The set of operations may further include receiving gaze input data and load data, from two or more of the plurality of computing devices. The set of operations may further include performing load balancing between the plurality of devices, wherein the load balancing comprises assigning one or more tasks from a first of the plurality of computing devices to a second of the plurality of computing devices based upon the gaze input data.