Personalized Notification Summarization for Limited Message Space
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Solution Overview
Problem
Existing eye-tracking technologies using bright/dark pupil effects are brittle and sensitive to occlusions, while event-based corneal glint tracking is prone to noise from background events, limiting robustness and accuracy in eye tracking applications.
Innovation Solution
Implement an event-sensor bright/dark pupil tracking method using beacons to produce alternating pupil responses, combined with frequency filtering to isolate and decode pupil events, enhancing robustness and accuracy by suppressing background noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bright/dark pupil tracking is used, then eye tracking can be implemented, but the system becomes sensitive to occlusions and less reliable
Solution Approach 1:
The patent segments the eye tracking signal into multiple components: corneal glint events (from specular reflections) and pupil events (from bright/dark pupil effects). By processing these separately and combining them, the system achieves more reliable tracking that is less sensitive to occlusions affecting either component alone.
Solution Approach 2:
The patent merges corneal glint tracking with bright/dark pupil tracking into a unified eye tracking system. The combination of these two methods compensates for their individual weaknesses, resulting in improved reliability and reduced sensitivity to occlusions.
2Speed
If event-based corneal glint tracking is used, then tracking speed is improved, but background noise increases and measurement precision decreases
Solution Approach 1:
The patent extracts and isolates pupil-specific events from the overall event stream using frequency filtering. By separating pupil events from background corneal glint events based on their temporal frequency characteristics, the system achieves both high tracking speed and precise pupil location measurement.
Solution Approach 2:
The patent uses frequency filtering as a feedback mechanism to continuously separate and identify pupil events from background noise. The filtering process provides real-time discrimination between signal and noise, maintaining measurement precision at high tracking speeds.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves robust and accurate eye tracking with reduced sensitivity to occlusions and background noise, suitable for low-power, high-speed applications in augmented and virtual reality headsets.
Implementation Method 1
using beacons to produce glints from the cornea
Data Source
AI summary
A method may include (1) identifying, using a machine-learning-based summarization model, interests of a user of a social media network, (2) extracting, using the machine-learning-based summarization model, information from a post to the social media network that aligns with the user's interests, and (3) generating, using the machine-learning-based summarization model, a personalized notification message for the user that is based on the information extracted from the post that aligns with the user's interests. Various other methods and systems are disclosed.